license_audit.csv

item_key,title,doi,license,oa_status,decision,reason,source_url
GU32S3VX,Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests,10.1016/j.jrmge.2019.08.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S167477551930722X
Z8FUWHQI,Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples,10.3389/feart.2024.1337823,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.frontiersin.org/articles/10.3389/feart.2024.1337823/pdf?isPublishedV2=False
QCGVHUIB,Generative Adversarial Networks for Data Augmentation,10.48550/arxiv.2306.02019,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2306.02019
C854XZHK,Solving Geophysical Inversion Problems with Intractable Likelihoods: Linearized Gaussian Approximations Versus the Correlated Pseudo-marginal Method,10.1007/s11004-023-10064-y,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://link.springer.com/content/pdf/10.1007/s11004-023-10064-y.pdf
NJHSNAPD,Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield,10.1155/2021/2565488,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://downloads.hindawi.com/journals/ace/2021/2565488.pdf
84YRA63D,Generative Adversarial Networks,10.48550/arxiv.1406.2661,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1406.2661
KYAMZZ2W,At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition,10.1029/2024jh000164,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2024JH000164
3LEUKYF6,Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,10.1016/j.jcp.2018.10.045,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.sciencedirect.com/science/article/pii/S0021999118307125
I2U934GX,Deep Learning–Based Enhancement of Small Sample Liquefaction Data,10.1061/ijgnai.gmeng-8381,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ascelibrary.org/doi/10.1061/IJGNAI.GMENG-8381
M749KDAT,The Hoek–Brown failure criterion and GSI – 2018 edition,10.1016/j.jrmge.2018.08.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775518303846
BCL8C6WU,"A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering",10.1007/s00521-024-09893-7,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://link.springer.com/content/pdf/10.1007/s00521-024-09893-7.pdf
S69BUPZM,Towards physics-informed neural networks for landslide prediction,10.1029/2024jh000164,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2024JH000164
9JBSAC7S,Using Physics Informed Generative Adversarial Networks to Model 3D porous media,10.48550/arxiv.2409.11541,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2409.11541
9MUL2KBS,A simple method to estimate tensile strength and Hoek-Brown strength parameter mi of brittle rocks,,unknown,,rag_only,No DOI/license evidence; use private RAG only,
KBYTQG86,Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield,10.1155/2021/2565488,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://downloads.hindawi.com/journals/ace/2021/2565488.pdf
7D2INP39,Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,10.1016/j.jcp.2018.10.045,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.sciencedirect.com/science/article/pii/S0021999118307125
K2IYEF6X,Deep Learning–Based Enhancement of Small Sample Liquefaction Data,10.1061/ijgnai.gmeng-8381,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ascelibrary.org/doi/10.1061/IJGNAI.GMENG-8381
IQLL7DYW,"A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering",10.1007/s00521-024-09893-7,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://link.springer.com/content/pdf/10.1007/s00521-024-09893-7.pdf
A52YD2YB,Handbook of geotechnical investigation and design tables,,unknown,,rag_only,No DOI/license evidence; use private RAG only,
YFCBB3UM,Kolmogorov Complexity and Algorithmic Randomness,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://www.ams.org/surv/220
QYVEIT47,This document is licensed under a Creative Commons Attribution-Share Alike 2.5 Switzerland License.,,unknown,,rag_only,No DOI/license evidence; use private RAG only,
DWE7QVZ5,Neural Machine Translation by Jointly Learning to Align and Translate,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1409.0473
7I3VA34E,Neural Message Passing for Quantum Chemistry,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1704.01212
9RBJNCFZ,Multi-Scale Context Aggregation by Dilated Convolutions,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1511.07122
NVNARW4Y,Deep Residual Learning for Image Recognition,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1512.03385
3ANKNJS3,GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1811.06965
6NR6RJXB,Order Matters: Sequence to sequence for sets,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1511.06391
WDQ3LRFT,ImageNet Classification with Deep Convolutional Neural Networks,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://proceedings.neurips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
6AQ6S8DG,Pointer Networks,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1506.03134
J263ZW8S,colt93.pdf,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://www.cs.toronto.edu/~hinton/absps/colt93.pdf
SSKSF3FR,Recurrent Neural Network Regularization,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1409.2329
DEY5NHQ6,The First Law of Complexodynamics,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://scottaaronson.blog/?p=762
2C83MAX6,Earthquake damage detection in the Imperial County Services Building III: Analysis of wave travel times via impulse response functions,10.1016/j.soildyn.2007.07.001,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0267726107000899
PAMGFJ9H,Damage detection in a precast structure subjected to an earthquake: A numerical approach,10.1016/j.engstruct.2016.08.058,cc-by-nc-nd,green,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S0141029616304758
38NNR3AM,THE USE OF THE SPECIFIC DRILLING ENERGY FOR ROCK MASS CHARACTERISATION AND TBM DRIVING DURING TUNNEL CONSTRUCTION [Tunnel Engineering - Mechanized Tunneling] - Geotechpedia,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://geotechpedia.com/Publication/Show/211/THE-USE-OF-THE-SPECIFIC-DRILLING-ENERGY-FOR-ROCK-MASS-CHARACTERISATION-AND-TBM-DRIVING-DURING-TUNNEL-CONSTRUCTION

3R6W3EXF,A Tutorial on Bayesian Optimization,10.48550/arxiv.1807.02811,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1807.02811

5JFX7MM6,Analysis of rock cuttability based on excavation parameters of TBM,10.1007/s40948-023-00628-x,cc-by,gold,allow_public_training,Permissive or explicit reuse license,https://link.springer.com/content/pdf/10.1007/s40948-023-00628-x.pdf

NRH8LWXF,TBM_DRIVING_DURING_TUNNEL.pdf,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://subterra-ing.com/wp-content/uploads/2018/11/TBM_DRIVING_DURING_TUNNEL.pdf

RG2IIXWI,The energy method to predict disc cutter wear extent for hard rock TBMs,10.1016/j.tust.2011.11.001,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779811001404

C6Z96VSM,Validity of the NTNU Prediction Model for D&B Tunnelling,10.1007/s00603-023-03585-9,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://link.springer.com/content/pdf/10.1007/s00603-023-03585-9.pdf

YZDW5Q2I,1E-98e.pdf,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://folk.ntnu.no/pdj/bruland%201998/1E-98e.pdf

U636PUCI,New model for performance production of hard rock TBMs,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

FDKTHZR9,Ozdemir_10781980.pdf,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://repository.mines.edu/bitstream/handle/11124/175919/Ozdemir_10781980.pdf?sequence=1&isAllowed=y

85278JAX,Soft ground tunnel lithology classification using resampling and supervised learning,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

KPD436N8,An Approach Integrating Dimensional Analysis and Field Data for Predicting the Load on Tunneling Machine,10.1007/s12205-019-0266-0,cc-by-nc-nd,closed,exclude,NoDerivatives license; do not use as training data by default,https://doi.org/10.1007/s12205-019-0266-0

FFPFMB4B,Challenges and opportunities of using tunnel boring machines in mining,10.1016/j.tust.2016.01.023,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815303680

I9ZAP9IH,Editorial for <i>Advances and applications of deep learning and soft computing in geotechnical underground engineering</i>,10.1016/j.jrmge.2022.01.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775522000208

SNUMPIT2,A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM,10.1016/j.jrmge.2023.06.010,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775523001968

A8JWVVAH,An improved numerical manifold method with multiple layers of mathematical cover systems for the stability analysis of soil-rock-mixture slopes,10.1016/j.enggeo.2019.105373,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S001379521930955X

6R2CLU96,Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass,10.1016/j.ijrmms.2015.09.019,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1365160915300472

6GCX6BLW,Learning hyperparameter optimization initializations,10.1109/dsaa.2015.7344817,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/document/7344817

Z3U2C6CX,"Evaluation and prediction of earth pressure balance shield performance in complex rock strata: A case study in Dalian, China",10.1016/j.jrmge.2022.09.010,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775522001962

TCGA2NYK,Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance,10.1016/j.jrmge.2023.06.015,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775523002202

D6AGH2W7,A Comparative study of Hyper-Parameter Optimization Tools,10.1109/csde53843.2021.9718485,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/document/9718485

WDT75BNI,Predicting EPBM advance rate performance using support vector regression modeling,10.1016/j.tust.2020.103520,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779820304740

W546BM5R,A support vector regression model for predicting tunnel boring machine penetration rates,10.1016/j.ijrmms.2014.09.012,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1365160914002536

8NIN87WE,Study on support time in double-shield TBM tunnel based on self-compacting concrete backfilling material,10.1016/j.tust.2019.103212,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S088677981930313X

X3XSFWN9,"Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS",10.48550/arxiv.1912.06059,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1912.06059

VJNPM2TM,One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction,10.3390/app12052410,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/12/5/2410/pdf?version=1646036099

PACPHD6Y,Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine,10.1016/j.jrmge.2023.02.013,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775523000720

MXJ8F3XE,Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system,10.1016/j.tust.2010.01.008,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779810000246

686CRK7T,An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data,10.1007/s12205-019-1460-9,cc-by-nc-nd,closed,exclude,NoDerivatives license; do not use as training data by default,https://doi.org/10.1007/s12205-019-1460-9

W9XNHA69,A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm,10.1016/j.jrmge.2021.11.008,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775522000439

DYTATJVA,Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning,10.1016/j.jrmge.2021.05.004,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775521000810

5DZCNZ2C,Development of a rock mass characteristics model for TBM penetration rate prediction,10.1016/j.ijrmms.2008.03.003,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1365160908000634

EJ4MR7UW,An investigation into the forces acting on a TBM during driving – Mining the TBM logged data,10.1016/j.tust.2012.06.006,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779812001162

MY3HBTEW,Study of various models for estimation of penetration rate of hard rock TBMs,10.1016/j.tust.2012.02.012,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779812000442

FBDKARTY,Challenges in Design and Construction of MRTA Tunnel and Station in Recent Bangkok Blue Line Extension Project,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

USBD9GTN,Modelling TBM performance with artificial neural networks,10.1016/j.tust.2004.02.128,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.sciencedirect.com/science/article/pii/S0886779804002081

FEERX39H,Application of several optimization techniques for estimating TBM advance rate in granitic rocks,10.1016/j.jrmge.2019.01.002,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775518303056

B9ALTKGT,Optuna: A Next-generation Hyperparameter Optimization Framework,10.1145/3292500.3330701,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1145/3292500.3330701

VC2E8G7Q,Employing NCA as a Band Reduction Tool in Rock Identification from Hyperspectral Processing,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://dx.doi.org/

2WD8JV6P,Development of Hyperspectral Database and Web Based Classifying System for Rock Type Identification,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://dx.doi.org/

R4HL67JF,Iron ore grade estimation using machine learning and visible and near-infrared hyperspectral imaging,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://confit.atlas.jp/guide/event/mmij2023b/subject/21101-15-15/detail

DAF4F9QY,Application of Deep Learning Approaches in Igneous Rock Hyperspectral Imaging,10.1007/978-3-030-33954-8_29,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/978-3-030-33954-8_29

GGKFCHH4,Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV,10.3390/min12020268,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2075-163X/12/2/268/pdf?version=1645347690

64WD2PCB,Development of Asbestos Containing Serpentinite Identification Method Using Hyperspectral Imaging,10.5188/ijsmer.25.189,unknown,gold,rag_only,License unclear or not permissive enough for public model training,https://www.jstage.jst.go.jp/article/ijsmer/25/2/25_189/_pdf

2BKGAN5A,An intelligent method for TBM surrounding rock classification based on time series segmentation of rock-machine interaction data,10.1016/j.tust.2023.105317,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779823003371

T3IDKKTX,TBM Performance Prediction in Rock Tunneling Using Various Artificial Intelligence Algorithms,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

YZMMRHT4,Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon,10.1016/j.ijrmms.2012.06.005,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1365160912001220

6I9LIH96,A review and case study of Artificial intelligence and Machine learning methods used for ground condition prediction ahead of tunnel boring Machines,10.1016/j.tust.2022.104497,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779822001377

8YM5NKFP,An Early-Warning System to Validate the Soil Profile during TBM Tunnelling,10.3390/geosciences12030113,cc-by,gold,allow_public_training,Permissive or explicit reuse license,https://www.mdpi.com/2076-3263/12/3/113/pdf?version=1646270166

X3X4ZCP3,Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method,10.1016/j.jrmge.2021.07.012,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S167477552100127X

46IDA6MV,Three-Dimensional Seismic Ahead-Prospecting Method and Application in TBM Tunneling,10.1061/(asce)gt.1943-5606.0001785,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ascelibrary.org/doi/10.1061/%28ASCE%29GT.1943-5606.0001785

H9FYQGYH,An overview of ahead geological prospecting in tunneling,10.1016/j.tust.2016.12.011,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815303138

U6AW4N2R,Deep Learning Applications for Hyperspectral Imaging: A Systematic Review,10.33969/jiec.2020.21004,unknown,gold,rag_only,License unclear or not permissive enough for public model training,https://iecscience.org/uploads/jpapers/202002/j9hwj0ImrxD9gj7z5O7BmPz8p7lSaQG4twqjfF07.pdf

69RDF3MS,Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach,10.1016/j.autcon.2022.104386,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S092658052200259X

GKW3NAWX,Learning from explainable data-driven tunneling graphs: A spatio-temporal graph convolutional network for clogging detection,10.1016/j.autcon.2023.104741,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580523000018

E3MZK5Z2,Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning,10.1016/j.tust.2020.103636,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779820305903

P7PS6E5N,A causal-temporal graphic convolutional network (CT-GCN) approach for TBM load prediction in tunnel excavation,10.1016/j.eswa.2023.121977,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S095741742302479X

KDWL44WE,A Unified Approach to Interpreting Model Predictions,10.48550/arxiv.1705.07874,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1705.07874

VPZNUCAU,Human-Oriented Assembly Line Balancing and Sequencing Model in the Industry 4.0 Era,10.1007/978-3-030-43177-8_8,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://doi.org/10.1007/978-3-030-43177-8_8

8WYJDVYA,Smart Working in Industry 4.0: How digital technologies enhance manufacturing workers' activities,10.1016/j.cie.2021.107804,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0360835221007087

DX5VN2PT,Digital twin-based smart production management and control framework for the complex product assembly shop-floor,10.1007/s00170-018-1617-6,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00170-018-1617-6

AEBRWKGY,Human-Oriented Assembly Line Balancing and Sequencing Model in the Industry 4.0 Era,10.1007/978-3-030-43177-8_8,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://doi.org/10.1007/978-3-030-43177-8_8

SAGL6REJ,Predictive Maintenance and Intelligent Sensors in Smart Factory: Review,10.3390/s21041470,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/1424-8220/21/4/1470/pdf?version=1614067678

KY9ZT69X,Smart seru production system for Industry 4.0: a conceptual model based on deep learning for real-time monitoring and controlling,10.1080/0951192x.2022.2078514,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1080/0951192X.2022.2078514

V74XEYUQ,Human-machine interface in smart factory: A systematic literature review,10.1016/j.techfore.2021.121284,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0040162521007186

GNBXAM6N,"Internet of things for smart factories in industry 4.0, a review",10.1016/j.iotcps.2023.04.006,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.sciencedirect.com/science/article/pii/S2667345223000275

MEI6NT5F,"Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory Settings",10.3390/s23218926,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/1424-8220/23/21/8926/pdf?version=1698913642

K7ZZBZXR,"Industry 4.0 in Danish Industry: An empirical investigation of the degree of knowledge, perceived relevance and current practice",,unknown,,rag_only,No DOI/license evidence; use private RAG only,

3GTH2SN3,SHAP for additively modeled features in a boosted trees model,10.48550/arxiv.2207.14490,unknown,,rag_only,License unclear or not permissive enough for public model training,https://arxiv.org/abs/2207.14490

K8R4SM75,SHAP for additively modeled features in a boosted trees model,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://www.semanticscholar.org/paper/SHAP-for-additively-modeled-features-in-a-boosted-Mayer/ea99dd5984555e1245f2ad036a68d7ca16264835

7IHLKAUX,Explanation of Machine Learning Models Using Improved Shapley Additive Explanation,10.1145/3307339.3343255,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://dl.acm.org/doi/10.1145/3307339.3343255

4RIDHFPL,Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset,10.3390/s22218135,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/1424-8220/22/21/8135/pdf?version=1667989423

JT9NKXE2,"The relevance of Industry 4.0 and its relationship with moving manufacturing out, back and staying at home",10.1080/00207543.2019.1660823,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1080/00207543.2019.1660823

PCKV6C6W,Augmented Technology for Safety and Maintenance in Industry 4.0:,10.4018/978-1-7998-3904-0.ch008,unknown,closed,rag_only,License unclear or not permissive enough for public model training,http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-3904-0.ch008

FEKCUVBE,Analyzing the influential factors of industry 4.0 in precision machinery industry,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://www.semanticscholar.org/paper/Analyzing-the-influential-factors-of-industry-4.0-Lai-Chen/00d68f9ba8e6d8c5256361b9dc5b2469adab9df0

FHHZ9YAS,"Industry 4.0 for sustainable manufacturing: Opportunities at the product, process, and system levels",10.1016/j.resconrec.2020.105362,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://linkinghub.elsevier.com/retrieve/pii/S0921344920306777

ERRVHZXI,Industry 4.0 and its associated technologies,10.57040/jet.v1i1.24,cc-by-nc-sa,gold,rag_only,NonCommercial license; keep out of broad public reusable model by default,https://journals.jozacpublishers.com/jet/article/download/24/25

YWQKJGN8,Industry 4.0 for Inclusive Development,10.18356/9789210014441,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.un-ilibrary.org/content/books/9789210014441

B6RUJWVM,Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions,10.3390/app11125725,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/11/12/5725/pdf?version=1624418544

DRK2ILMT,Industry 4.0 technology implementation in SMEs – A survey in the Danish-German border region,10.1016/j.ijis.2020.05.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://linkinghub.elsevier.com/retrieve/pii/S2096248720300229

TQQXZEDY,Perspectives on the future of manufacturing within the Industry 4.0 era,10.1080/09537287.2020.1810762,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.tandfonline.com/doi/full/10.1080/09537287.2020.1810762

GSICQWSW,Assessment of Industry 4.0 for Modern Manufacturing Ecosystem: A Systematic Survey of Surveys,10.3390/machines10090746,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2075-1702/10/9/746/pdf?version=1661765186

XRS88YQY,A Tutorial on Bayesian Optimization,10.48550/arxiv.1807.02811,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1807.02811

SVPGIE62,"Outlier Detection: Methods, Models, and Classification",10.1145/3381028,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1145/3381028

QHXN66UP,Effect of data standardization on neural network training,10.1016/0305-0483(96)00010-2,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/0305048396000102

HH7EX2ZT,Design_Guide.pdf,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://www.pipejacking.org/assets/pj/static/Design_Guide.pdf

6CXRLNH7,Physics-based machine learning method and the application to energy consumption prediction in tunneling construction,10.1016/j.aei.2022.101642,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1474034622001069

9PZ9UVW9,K-means-based heterogeneous tunneling data analysis method for evaluating rock mass parameters along a TBM tunnel,10.1038/s41598-023-49033-0,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.nature.com/articles/s41598-023-49033-0.pdf

GKB3QF2U,Reinforcement learning-based optimizer to improve the steering of shield tunneling machine,10.1007/s11440-023-02136-4,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s11440-023-02136-4

375P2MLG,An empirical method for estimating TBM penetration rate using tunnelling specific energy,10.1016/j.tust.2023.105525,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S088677982300545X

B7D2LMVD,Characterizing the as-encountered ground condition with tunnel boring machine data using semi-supervised learning,10.1016/j.compgeo.2022.105159,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0266352X22004967

ZHECDZ8X,Breaking new ground: Opportunities and challenges in tunnel boring machine operations with integrated management systems and artificial intelligence,10.1016/j.autcon.2023.105199,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://www.sciencedirect.com/science/article/pii/S0926580523004594

WGUAST3V,QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency,10.1007/s11709-022-0908-z,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s11709-022-0908-z

GWC8A49U,Towards explainable deep neural networks (xDNN),10.1016/j.neunet.2020.07.010,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://arxiv.org/pdf/1912.02523

A5KPPJGZ,A spatial estimation model for continuous rock mass characterization from the specific energy of a TBM,10.1007/s00603-007-0160-9,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-007-0160-9

8XTZED2X,"TBM performance prediction using LSTM-based hybrid neural network model: Case study of Baimang River tunnel project in Shenzhen, China",10.1016/j.undsp.2022.11.002,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2467967423000260

2MYI5YFD,Use of soft computing techniques for tunneling optimization of tunnel boring machines,10.1016/j.undsp.2019.12.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2467967419300972

YFBZIF2W,From advance exploration to real time steering of TBMs: A review on pertinent research in the Collaborative Research Center “Interaction Modeling in Mechanized Tunneling”,10.1016/j.undsp.2018.01.002,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2467967417300739

5GB2EV46,Significance and methodology: Preprocessing the big data for machine learning on TBM performance,10.1016/j.undsp.2021.12.003,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S246796742200006X

YBPNCQ9U,Prediction of the geological indicators in TBM tunnel based on optimized proportion of surrounding rock grades,10.1016/j.undsp.2023.01.004,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2467967423000405

LUTAVGPE,Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction,10.1016/j.undsp.2021.04.003,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2467967421000374

C5PJ57PK,TBM penetration rate prediction based on the long short-term memory neural network,10.1016/j.undsp.2020.01.003,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S246796741930100X

PH5J3HHJ,Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization,10.1016/j.undsp.2020.05.008,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2467967420300507

3LM6TFZB,Significance and methodology: Preprocessing the big data for machine learning on TBM performance,10.1016/j.undsp.2021.12.003,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S246796742200006X

GYRUYGK6,A new model for TBM performance prediction in blocky rock conditions,10.1016/j.tust.2014.06.004,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.sciencedirect.com/science/article/pii/S0886779814000960

M4T8WLTF,Prediction of optimum TBM penetration strategy with minimum energy consumption in hard rocks,10.1016/j.compgeo.2022.104844,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0266352X2200194X

8J8NI7TS,Performance prediction of tunnel boring machine through developing high accuracy equations: A case study in adverse geological condition,10.1016/j.measurement.2019.107244,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0263224119311091

GKWC7IIK,Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization,10.1016/j.jrmge.2023.02.014,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775523000732

IUTH3PSK,A Wear Rule and Cutter Life Prediction Model of a 20-in. TBM Cutter for Granite: A Case Study of a Water Conveyance Tunnel in China,10.1007/s00603-017-1176-4,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-017-1176-4

T2YN6C6Y,A Data-Driven Framework for Tunnel Geological-Type Prediction Based on TBM Operating Data,10.1109/access.2019.2917756,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://ieeexplore.ieee.org/ielx7/6287639/8600701/08718274.pdf

SMA3MQ69,Prediction of tunnel boring machine operating parameters using various machine learning algorithms,10.1016/j.tust.2020.103699,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779820306532

GB8JS3S6,Soil Classification by Machine Learning Using a Tunnel Boring Machine’s Operating Parameters,10.3390/app122211480,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/12/22/11480/pdf?version=1668740320

SGWN3Z4E,A case study on TBM cutterhead temperature monitoring and mud cake formation discrimination method,10.1038/s41598-021-99439-x,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.nature.com/articles/s41598-021-99439-x.pdf

CC2P4VSM,Rock fragmentation indexes reflecting rock mass quality based on real-time data of TBM tunnelling,10.1038/s41598-023-37306-7,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.nature.com/articles/s41598-023-37306-7.pdf

FQQA39PV,Challenges and opportunities of using tunnel boring machines in mining,10.1016/j.tust.2016.01.023,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815303680

LS8985VF,Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From an Explainable AI Competition,10.1162/99608f92.5a8a3a3d,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://hdsr.mitpress.mit.edu/pub/f9kuryi8/download/pdf

9XSE5IEJ,Performance prediction of tunnel boring machine through developing a gene expression programming equation,10.1007/s00366-017-0526-x,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00366-017-0526-x

QIN2D9NL,Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance,10.1007/s10706-017-0238-4,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s10706-017-0238-4

YBP2TKXA,Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions,10.1007/s11771-015-2520-z,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s11771-015-2520-z

56WY3DYM,Application of artificial neural networks to the prediction of tunnel boring machine penetration rate,10.1016/s1674-5264(09)60271-4,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1674526409602714

K8VFSXVI,Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms,10.3390/app12052656,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/12/5/2656/pdf?version=1646383280

8UXCJRLG,Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms,10.1016/j.gr.2022.10.004,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1342937X22002738

BTGZRFJ4,Prediction of geological conditions for a tunnel boring machine using big operational data,10.1016/j.autcon.2018.12.022,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580518308628

QBFHWVMA,Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization,10.1016/j.gsf.2020.03.007,cc-by-nc-nd,hybrid,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674987120300669

4FWY22KY,Applications of NTNU/SINTEF Drillability Indices in Hard Rock Tunneling,10.1007/s00603-012-0253-y,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-012-0253-y

XUCEJY9H,Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines,10.1007/s00603-021-02723-5,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-021-02723-5

7AY29X24,Evaluation of the geological condition ahead of the tunnel face by geostatistical techniques using TBM driving data,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

LGPEYEPA,Application of two non-linear prediction tools to the estimation of tunnel boring machine performance,10.1016/j.engappai.2009.03.007,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0952197609000670

ZUACKBQX,Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection,10.1016/j.jrmge.2022.05.009,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775522001202

QXBT5R8L,A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data,10.1016/j.ins.2021.02.056,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0020025521001985

8CSDG7AM,A model of tunnel boring machine performance,10.1007/bf00881969,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/BF00881969

PJRJ9RLE,SUPPORT DETERMINATIONS BASED ON GEOLOGIC PREDICTIONS,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://trid.trb.org/view/125914

X3ANCXB5,Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP,10.1016/j.aap.2021.106261,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S000145752100292X

A395F7CV,Efficient reliability analysis of earth dam slope stability using extreme gradient boosting method,10.1007/s11440-020-00962-4,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s11440-020-00962-4

3APXPDTH,How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis,10.1186/1471-2164-13-s4-s2,cc-by,gold,allow_public_training,Permissive or explicit reuse license,https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-13-S4-S2

FWU2J86H,Modified rock mass classification system by continuous rating,10.1016/s0013-7952(02)00185-0,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0013795202001850

D85KLSPN,Towards TBM Automation: On-The-Fly Characterization and Classification of Ground Conditions Ahead of a TBM Using Data-Driven Approach,10.3390/app11031060,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/11/3/1060/pdf

QYW2X8G9,Configurable Fast Block Partitioning for VVC Intra Coding Using Light Gradient Boosting Machine,10.1109/tcsvt.2021.3108671,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://ieeexplore.ieee.org/abstract/document/9524713

RDWRWSQZ,Rock excavation by disc cutter,10.1016/0148-9062(75)90547-1,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/0148906275905471

35EZX9UG,Predicting anomalous zone ahead of tunnel face utilizing electrical resistivity: I. Algorithm and measuring system development,10.1016/j.tust.2016.08.007,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815302388

CTKMKGXU,An empirical method for design of grouted bolts in rock tunnels based on the Geological Strength Index (GSI),10.1016/j.enggeo.2009.05.003,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0013795209001161

37CHUXU2,Outline of the Comprehensive Soil Classification System of Japan – First Approximation,10.6090/jarq.49.217,unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.jstage.jst.go.jp/article/jarq/49/3/49_217/_pdf

EGESK88B,Mixture of Activation Functions With Extended Min-Max Normalization for Forex Market Prediction,10.1109/access.2019.2959789,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://ieeexplore.ieee.org/ielx7/6287639/8600701/08933074.pdf

CXN9FNDP,Multicollinearity's Effect on Regression Prediction Accuracy with Real Data Structures,10.31523/glmj.044001.004,unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://doi.org/10.31523/glmj.044001.004

TWJWZW4F,New Rock Abrasivity Test Method for Tool Life Assessments on Hard Rock Tunnel Boring: The Rolling Indentation Abrasion Test (RIAT),10.1007/s00603-015-0854-3,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-015-0854-3

GEKT2SVT,Clustering Method of Raw Meal Composition Based on PCA and Kmeans,10.23919/chicc.2018.8482823,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/8482823

C99GC75D,Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets,10.1016/j.jrmge.2021.08.018,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.sciencedirect.com/science/article/pii/S167477552100175X

9SSNXFMD,Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network,10.1016/j.autcon.2021.103647,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580521000984

MERZWRZW,Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data,10.1016/j.tust.2020.103595,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779820305496

BMY45GWZ,Improved support vector regression models for predicting rock mass parameters using tunnel boring machine driving data,10.1016/j.tust.2019.04.014,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779818310575

T3MQMBMZ,Forward modelling and imaging of ground‐penetrating radar in tunnel ahead geological prospecting,10.1111/1365-2478.12613,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.earthdoc.org/content/journals/10.1111/1365-2478.12613

6PM9WAHI,A Novel Method of Multitype Hybrid Rock Lithology Classification Based on Convolutional Neural Networks,10.3390/s22041574,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/1424-8220/22/4/1574/pdf?version=1645438462

92BHWK9J,The Practice of Forward Prospecting of Adverse Geology Applied to Hard Rock TBM Tunnel Construction: The Case of the Songhua River Water Conveyance Project in the Middle of Jilin Province,10.1016/j.eng.2017.12.010,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2095809917308147

V95XZTMA,"SMOTE-Out, SMOTE-Cosine, and Selected-SMOTE: An enhancement strategy to handle imbalance in data level",10.1109/icacsis.2014.7065849,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/7065849

UMBW4FBU,Effect of Rock Abrasiveness on Wear of Shield Tunnelling in Bukit Timah Granite,10.3390/app10093231,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/10/9/3231/pdf?version=1588840119

MHKP6VNM,LightGBM: A Highly Efficient Gradient Boosting Decision Tree,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html

HPAVHEGB,A NEW MODEL FOR PERFORMANCE PREDICTION OF HARD ROCK TBMS.,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://cir.nii.ac.jp/crid/1570854174221536512

YF8XK6XU,Historical aspects of soil classification in Japan,10.1080/00380768.2004.10408519,unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.tandfonline.com/doi/pdf/10.1080/00380768.2004.10408519?needAccess=true

B9ZEWE7L,Learning from Imbalanced Data,10.1109/tkde.2008.239,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/5128907

PREYEB2B,"Introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks; a case history of Karaj water conveyance tunnel, Iran",10.1016/j.tust.2014.05.007,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779814000765

UNQKZH4D,Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic,10.1007/s10064-013-0497-0,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s10064-013-0497-0

MSM9MHJA,Low-code AutoML-augmented Data Pipeline – A Review and Experiments,10.1088/1742-6596/1828/1/012015,cc-by,gold,allow_public_training,Permissive or explicit reuse license,https://dx.doi.org/10.1088/1742-6596/1828/1/012015

ZBNZ8DKY,Primary and secondary tools’ life evaluation for soft ground TBMs,10.1007/s10064-021-02223-4,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s10064-021-02223-4

RUCRNPUT,Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE,10.1016/j.ins.2018.06.056,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://arxiv.org/pdf/1711.00837

VB4DKY8V,Automatic Classification of Lithofacies with Highly Imbalanced Dataset Using Multistage SVM Classifier,10.1109/iecon48115.2021.9589254,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/9589254

UV79PDFU,"Comparison of geoelectrical imaging and tunnel documentation at the Hallandsås Tunnel, Sweden",10.1016/j.enggeo.2009.05.005,custom-or-unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.sciencedirect.com/science/article/pii/S0013795209001173

JPZV3N2H,A Machine Learning Application for Predicting and Alerting Missed Approaches for Airport Management,10.1109/dasc52595.2021.9594418,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/9594418

JSPZJ9UW,The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,10.1186/s12864-019-6413-7,cc-by,gold,allow_public_training,Permissive or explicit reuse license,https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-019-6413-7

GCWQXRVH,Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning,10.1016/j.ijmst.2021.08.004,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2095268621000896

Y6UZ797B,Experimental and analytical studies of the parameters influencing the action of TBM disc tools in tunnelling,10.1007/s11440-016-0453-9,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://iris.polito.it/bitstream/11583/2640244/6/Experimental_Fig_1-10.pdf

FQ3H8FJX,Hard Rock Tunnel Boring,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/231256

UVKNLIFS,Real-time hard-rock tunnel prediction model for rock mass classification using CatBoost integrated with Sequential Model-Based Optimization,10.1016/j.tust.2022.104448,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779822000888

XJ34EQ6I,"Engineering Rock Mass Classifications: A Complete Manual for Engineers and Geologists in Mining, Civil, and Petroleum Engineering",,unknown,,rag_only,No DOI/license evidence; use private RAG only,

78HDWCC8,Engineering classification of rock masses for the design of tunnel support,10.1007/bf01239496,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/BF01239496

QIXREL5A,TBM Tunnelling in Jointed and Faulted Rock,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

88FI58EW,Multi-tier method using infrared photography and GPR to detect and locate water leaks,10.1016/j.autcon.2015.10.006,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580515002113

4TBFAA3C,Neural network model for imprecise regression with interval dependent variables,10.1016/j.neunet.2023.02.005,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,http://arxiv.org/abs/2206.02467

J4I6NCQX,Deep learning technologies for shield tunneling: Challenges and opportunities,10.1016/j.autcon.2023.104982,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S092658052300242X

M6HKG59Z,A new method for selecting hard rock TBM tunnelling parameters using optimum energy: A case study,10.1016/j.tust.2018.03.030,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779817304911

HKGC6UID,Towards end-to-end pulsed eddy current classification and regression with CNN,10.1109/i2mtc.2019.8826858,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/8826858

V49Y5QGN,"Convolutional neural network: a review of models, methodologies and applications to object detection",10.1007/s13748-019-00203-0,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s13748-019-00203-0

YX6ZUSVL,A review on deep convolutional neural networks,10.1109/iccsp.2017.8286426,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/8286426

EN34PFEU,Understanding of a convolutional neural network,10.1109/icengtechnol.2017.8308186,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/8308186

J2WGBMHF,Suggestion of an empirical prognosis model for cutting tool wear of Hydroshield TBM,10.1016/j.tust.2015.04.017,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815000826

V95SUS9C,Challenges of methods and approaches for estimating soil abrasivity in soft ground TBM tunnelling,10.1016/j.wear.2013.06.022,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0043164813004237

67Y6XHTD,A Wear Rule and Cutter Life Prediction Model of a 20-in. TBM Cutter for Granite: A Case Study of a Water Conveyance Tunnel in China,10.1007/s00603-017-1176-4,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-017-1176-4

8VUELIDI,"A Guide to Planning, Constructing, and Supervising Earth Pressure Balance TBM Tunneling",,unknown,,rag_only,No DOI/license evidence; use private RAG only,

WCNAU9AL,TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran,10.1002/geot.201500005,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://onlinelibrary.wiley.com/doi/abs/10.1002/geot.201500005

NW4U2USJ,Mechanical Excavation in Mining and Civil Industries,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

8B8PQI8A,"Evaluation of tool wear in EPB tunneling of Tehran Metro, Line 7 Expansion",10.1016/j.tust.2016.11.001,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815301231

UYFC5A2J,Prediction of Disc Cutter Life During Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network,10.1016/j.eng.2020.02.016,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2095809920302332

RSCLZLKK,A field parameters-based method for real-time wear estimation of disc cutter on TBM cutterhead,10.1016/j.autcon.2021.103603,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580521000546

27UI48SJ,Application of Soft Computing Techniques to Estimate Cutter Life Index Using Mechanical Properties of Rocks,10.3390/app12031446,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/12/3/1446/pdf?version=1643393740

JULM7CZE,Machine learning forecasting models of disc cutters life of tunnel boring machine,10.1016/j.autcon.2021.103779,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580521002302

R2TMS99N,Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm,10.3390/app9040780,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/9/4/780/pdf?version=1551091190

PCGSL6S4,Advanced prediction of tunnel boring machine performance based on big data,10.1016/j.gsf.2020.02.011,cc-by-nc-nd,hybrid,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674987120300530

ABFVJ24G,A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance,10.1007/s00366-020-01217-2,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00366-020-01217-2

BYG5W23B,Application of deep neural networks in predicting the penetration rate of tunnel boring machines,10.1007/s10064-019-01538-7,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s10064-019-01538-7

VZYYDAKA,Predicting tunnel boring machine performance through a new model based on the group method of data handling,10.1007/s10064-018-1349-8,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s10064-018-1349-8

LASHJWWT,Performance Prediction of Hard Rock TBM Based on Extreme Learning Machine,10.1007/978-3-642-40849-6_40,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/978-3-642-40849-6_40

BEZB8ZHG,Hard Rock Tunnel Boring,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/231256

D3WRS5LY,A NEW MODEL FOR PERFORMANCE PREDICTION OF HARD ROCK TBMS.,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://cir.nii.ac.jp/crid/1570854174221536512

C8IUW8FS,"Analysis and prediction of TBM disc cutter wear when tunneling in hard rock strata: A case study of a metro tunnel excavation in Shenzhen, China",10.1016/j.wear.2020.203190,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0043164819314231

B6V6CZXH,Cutter wear evaluation from operational parameters in EPB tunneling of Chengdu Metro,10.1016/j.tust.2019.103043,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779818304619

2UEC8YXA,A TBM Cutter Life Prediction Method Based on Rock Mass Classification,10.1007/s12205-020-1511-2,cc-by-nc-nd,closed,exclude,NoDerivatives license; do not use as training data by default,https://doi.org/10.1007/s12205-020-1511-2

HBIIVZYD,Evaluation of TBM Cutter Wear in Naghadeh Water Conveyance Tunnel and Developing a New Prediction Model,10.1007/s00603-021-02640-7,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-021-02640-7

VPUSMHWK,Prediction Model of TBM Disc Cutter Wear During Tunnelling in Heterogeneous Ground,10.1007/s00603-018-1549-3,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-018-1549-3

CSHUXWS2,"Experimental study on wear behaviors of TBM disc cutter ring under drying, water and seawater conditions",10.1016/j.wear.2017.09.020,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0043164817313698

7HFJ3MZL,Experimental study on artificially induced crack patterns and their consequences on mechanical excavation processes,10.1016/j.ijrmms.2017.10.024,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1365160917301399

6XB578PR,Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning,10.1016/j.tust.2020.103636,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779820305903

YJN92NTT,Improving neural networks by preventing co-adaptation of feature detectors,10.48550/arxiv.1207.0580,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1207.0580

3IIGDZIK,Towards end-to-end pulsed eddy current classification and regression with CNN,10.1109/i2mtc.2019.8826858,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/8826858

EBT7MBEB,A review on deep convolutional neural networks,10.1109/iccsp.2017.8286426,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/abstract/document/8286426

89DLHA22,"Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS",10.48550/arxiv.1912.06059,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1912.06059

LLA25YK8,Learning hyperparameter optimization initializations,10.1109/dsaa.2015.7344817,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/dsaa.2015.7344817

5YYCXSDB,Comparative study of random search hyper-parameter tuning for software effort estimation,10.1145/3475960.3475986,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1145/3475960.3475986

7GXUUZ8W,Studies on the key parameters in segmental lining design,10.1016/j.jrmge.2015.08.008,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775515001195

3VJYKT22,"Evaluation and prediction of earth pressure balance shield performance in complex rock strata: A case study in Dalian, China",10.1016/j.jrmge.2022.09.010,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775522001962

FIRBDITS,An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data,10.1007/s12205-019-1460-9,cc-by-nc-nd,closed,exclude,NoDerivatives license; do not use as training data by default,https://doi.org/10.1007/s12205-019-1460-9

C367TBWJ,Application of several optimization techniques for estimating TBM advance rate in granitic rocks,10.1016/j.jrmge.2019.01.002,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775518303056

E3QT7VM5,Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM),10.1016/j.jrmge.2015.06.008,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775515000839

JA84CBD6,Editorial for Advances and applications of deep learning and soft computing in geotechnical underground engineering,10.1016/j.jrmge.2022.01.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775522000208

4KMK46BV,An Approach Integrating Dimensional Analysis and Field Data for Predicting the Load on Tunneling Machine,10.1007/s12205-019-0266-0,cc-by-nc-nd,closed,exclude,NoDerivatives license; do not use as training data by default,https://doi.org/10.1007/s12205-019-0266-0

MI3FQRSP,An improved numerical manifold method with multiple layers of mathematical cover systems for the stability analysis of soil-rock-mixture slopes,10.1016/j.enggeo.2019.105373,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S001379521930955X

KV888577,Time series analysis and long short-term memory neural network to predict landslide displacement,10.1007/s10346-018-01127-x,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://doi.org/10.1007/s10346-018-01127-x

VCEEHXEV,Development of a rock mass characteristics model for TBM penetration rate prediction,10.1016/j.ijrmms.2008.03.003,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1365160908000634

9DS9T7SC,Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system,10.1016/j.tust.2010.01.008,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779810000246

NTFWW94Z,Utilizing rock mass properties for predicting TBM performance in hard rock condition,10.1016/j.tust.2007.04.011,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779807000508

Q9AL2PEH,North American Tunneling 2018 Proceedings,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

MNTBC8LN,Experience acquisition simulator for operating microtuneling boring machines,10.1016/j.autcon.2011.12.002,custom-or-unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.sciencedirect.com/science/article/pii/S0926580511002275

Q5TCD8JM,Challenges in Design and Construction of MRTA Tunnel and Station in Recent Bangkok Blue Line Extension Project,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

N22XFUZY,Editorial for Advances and applications of deep learning and soft computing in geotechnical underground engineering,10.1016/j.jrmge.2022.01.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775522000208

A47JWCXU,Challenges and opportunities of using tunnel boring machines in mining,10.1016/j.tust.2016.01.023,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815303680

H2LXGZIY,Experience acquisition simulator for operating microtuneling boring machines,10.1016/j.autcon.2011.12.002,custom-or-unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.sciencedirect.com/science/article/pii/S0926580511002275

G5XPG3N2,Machine learning-based automatic control of tunneling posture of shield machine,10.1016/j.jrmge.2022.06.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775522001263

UQQYBYCD,Hybrid Artificial Neural Networks for TBM performance prediction in complex underground conditions,10.1109/sii.2011.6147611,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/sii.2011.6147611

7YKRQC4J,An Operating Model for an EPB Shield TBM Simulator by the Correlation Analysis of Operational Actions and Mechanical Responses,10.3390/app112311443,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/11/23/11443/pdf?version=1638613985

WK73F5UC,"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation",10.7717/peerj-cs.623,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://peerj.com/articles/cs-623

L8R2RU5I,Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach,10.1016/j.autcon.2022.104386,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S092658052200259X

TUWSQAQR,Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost),10.1007/s10064-022-02708-w,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s10064-022-02708-w

9H5F92MW,Transition of the pipe jacking technology in Japan and investigation of its application status,10.1016/j.tust.2023.105212,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779823002328

3YSUYGUP,Full-Scale Linear Cutting Tests to Propose Some Empirical Formulas for TBM Disc Cutter Performance Prediction,10.1007/s00603-019-01865-x,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-019-01865-x

6C8834HI,Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model,10.1016/j.tust.2014.01.006,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779814000108

6D8UUCWH,Evaluation of cutting efficiency during TBM disc cutter excavation within a Korean granitic rock using linear-cutting-machine testing and photogrammetric measurement,10.1016/j.tust.2012.08.006,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779812001496

9MNEKNSL,Investigation into the effects of different rocks on rock cuttability by a V-type disc cutter,10.1016/j.tust.2012.02.018,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779812000508

3CTLSF4I,The energy method to predict disc cutter wear extent for hard rock TBMs,10.1016/j.tust.2011.11.001,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779811001404

YYILZKBL,Modeling Specific Energy for Shield Machine by Non-linear Multiple Regression Method and Mechanical Analysis,10.1007/978-3-642-28314-7_10,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/978-3-642-28314-7_10

GL2M65JD,Correlation of specific energy of cutting saws and drilling bits with rock brittleness and destruction energy,10.1016/j.jmatprotec.2008.06.004,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0924013608005050

IJH4R7X5,Rock Mass Excavability (RME) indicator: New way to selecting the optimum tunnel construction method,10.1016/j.tust.2005.12.016,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1016/j.tust.2005.12.016

LQXVKDTE,A study of disc cutting in selected British rocks,10.1016/0148-9062(82)91151-2,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/0148906282911512

CREN8U7U,Prediction of the performance of disc cutters in anisotropic rock,10.1016/0148-9062(85)93229-2,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/0148906285932292

KGZU6RN8,A COMPARISON OF LABORATORY CUTTING RESULTS AND ACTUAL TUNNEL BORING PERFORMANCE,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://trid.trb.org/view/125977

YH5L7IUX,"Correlation of rock cutting tests with field performance of a TBM in a highly fractured rock formation: A case study in Kozyatagi-Kadikoy metro tunnel, Turkey",10.1016/j.tust.2008.12.001,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779808001168

GASCJUES,A fuzzy logic model to predict specific energy requirement for TBM performance prediction,10.1016/j.tust.2007.11.003,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779807001137

XV8RAEVW,Correlation of specific energy with rock brittleness concepts on rock cutting,10.10520/aja0038223x_2948,unknown,,rag_only,License unclear or not permissive enough for public model training,https://journals.co.za/doi/abs/10.10520/AJA0038223X_2948

SSQ9HHZY,The concept of specific energy in rock drilling,10.1016/0148-9062(65)90022-7,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/0148906265900227

KMIXMMZH,"Application of the Cohesion Softening–Friction Softening and the Cohesion Softening–Friction Hardening Models of Rock Mass Behavior to Estimate the Specific Energy of TBM, Case Study: Amir–Kabir Water Conveyance Tunnel in Iran",10.1007/s10706-018-0617-5,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s10706-018-0617-5

LS8SZWQK,Application of deep neural networks in predicting the penetration rate of tunnel boring machines,10.1007/s10064-019-01538-7,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s10064-019-01538-7

FANVU69S,A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance,10.1007/s00366-020-01217-2,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00366-020-01217-2

UNGIHSZE,Application of deep neural networks in predicting the penetration rate of tunnel boring machines | SpringerLink,10.1007/s10064-019-01538-7,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://link.springer.com/article/10.1007/s10064-019-01538-7

64YSCC42,Performance Prediction of Hard Rock TBM Based on Extreme Learning Machine,10.1007/978-3-642-40849-6_40,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/978-3-642-40849-6_40

VPMW5BS8,Introducing Tree-Based-Regression Models for Prediction of Hard Rock TBM Performance with Consideration of Rock Type,10.1007/s00603-022-02868-x,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://link.springer.com/content/pdf/10.1007/s00603-022-02868-x.pdf

ENIK3EI9,Early warning of tunnel collapse based on Adam-optimised long short-term memory network and TBM operation parameters,10.1016/j.engappai.2022.104842,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0952197622001002

ISRBTZ72,New model for performance production of hard rock TBMs,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

TRM2QN6V,Mechanics of disc cutter penetration,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://www.osti.gov/etdeweb/biblio/6448939

IANM9U4F,A spatial estimation model for continuous rock mass characterization from the specific energy of a TBM,10.1007/s00603-007-0160-9,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-007-0160-9

IETDLJPF,A fuzzy logic model to predict specific energy requirement for TBM performance prediction,10.1016/j.tust.2007.11.003,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779807001137

A8ZQ5JA8,"Experimental study on wear behaviors of TBM disc cutter ring under drying, water and seawater conditions",10.1016/j.wear.2017.09.020,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0043164817313698

V24BF4UR,Experimental study on artificially induced crack patterns and their consequences on mechanical excavation processes,10.1016/j.ijrmms.2017.10.024,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1365160917301399

JGGJBEIX,Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning,10.1016/j.tust.2020.103636,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779820305903

DKWXSZAZ,Study on Tunnelling Performance of Dual-Mode Shield TBM by Cutterhead Working Performance and Tunnelling Difference Comparison: A Case in Shenzhen Metro Line 12,10.1007/s00603-023-03328-w,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s00603-023-03328-w

GUKB3D74,A review of physics-based machine learning in civil engineering,10.1016/j.rineng.2021.100316,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2590123021001171

KEE7WUTK,Automl: Building An Classfication Model With Pycaret,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

4QJWFQMW,Convolutional neural networks for classification and regression analysis of one-dimensional spectral data,10.48550/arxiv.2005.07530,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2005.07530

VI7LWZL5,Inference in High-dimensional Linear Regression,10.48550/arxiv.2106.12001,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2106.12001

BK8AV78W,All of Linear Regression,10.48550/arxiv.1910.06386,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1910.06386

PWWLFHPM,A model to predict daily advance rates of EPB-TBMs in a complex geology in Istanbul,10.1016/j.tust.2016.11.008,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815300328

3THQFKLJ,A fuzzy logic model to predict specific energy requirement for TBM performance prediction,10.1016/j.tust.2007.11.003,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779807001137

KQK9AKWI,A new method for selecting hard rock TBM tunnelling parameters using optimum energy: A case study,10.1016/j.tust.2018.03.030,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779817304911

63T25ST7,http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S1794-61902011000100001&lng=en&nrm=iso&tlng=en,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S1794-61902011000100001&lng=en&nrm=iso&tlng=en

BTICVXXU,Estimation of the specific energy of tunnel boring machine using post-failure behaviour of rock mass.Case study: Karaj-tehran water conveyance tunnel in Iran,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

2QGBCTXL,The effect of EPB face pressure on TBM performance parameters in different geological formations of Istanbul,10.1016/j.tust.2023.105184,cc-by-nc-nd,hybrid,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S0886779823002043

476T9Z3B,An analysis of timber sections and deep learning for wood species classification,10.1007/s11042-020-09212-x,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s11042-020-09212-x

WEHQSJQI,Training Stochastic Model Recognition Algorithms as Networks can Lead to Maximum Mutual Information Estimation of Parameters,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://proceedings.neurips.cc/paper/1989/hash/0336dcbab05b9d5ad24f4333c7658a0e-Abstract.html

SAKG8M3M,Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels,10.48550/arxiv.1805.07836,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1805.07836

KBKSXY8D,"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation",10.48550/arxiv.2010.16061,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2010.16061

R3ZGIWJU,An Introduction to Convolutional Neural Networks,10.48550/arxiv.1511.08458,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1511.08458

WHN7MF4Z,Drill bit wear monitoring and failure prediction for mining automation,10.1016/j.ijmst.2022.10.006,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2095268622001483

DQ58GQ5P,Estimating locations of soil–rock interfaces based on vibration data during shield tunnelling,10.1016/j.autcon.2023.104813,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://www.sciencedirect.com/science/article/pii/S0926580523000730

RQJXP7WK,Tunnel boring machine vibration-based deep learning for the ground identification of working faces,10.1016/j.jrmge.2021.09.004,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775521001323

DYULKVNZ,Investigation of parameters affecting vibration patterns generated during excavation by EPB TBMs,10.1016/j.tust.2023.105185,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779823002055

VAMG9X2K,TBM cutting performance in Istanbul,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

M22MG2FC,"Barton and Bilgin, 2016. TBM fast and slow. Cappadocia, Eurock",,unknown,,rag_only,No DOI/license evidence; use private RAG only,

TVDN6XJP,"Explainable artificial intelligence (XAI): Precepts, models, and opportunities for research in construction",10.1016/j.aei.2023.102024,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1474034623001520

JUILEQKP,Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review,10.3390/eng4020087,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2673-4117/4/2/87/pdf?version=1685447373

L62PRX9Z,Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach,10.1016/j.autcon.2022.104386,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S092658052200259X

ZFDRNPCQ,Introduction to Tunnel Construction,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

RWHKMEH5,TBM Tunneling in deep underground excavation in hard rock with spalling behaviour,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

FP9SQI7M,Integrated parameter optimization approach: Just-in-time (JIT) operational control strategy for TBM tunnelling,10.1016/j.tust.2023.105040,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779823000603

TSJTCIW9,Torque fluctuation analysis and penetration prediction of EPB TBM in rock–soil interface mixed ground,10.1016/j.tust.2019.103002,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779818308307

CAM46BHB,"A geochemical approach for source apportionment and environmental impact assessment of heavy metals in a Cu–Ni mining region, Botswana",10.1007/s12665-021-10158-y,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s12665-021-10158-y

Z4AHQGGI,"Geochemical Investigation of Metals and Trace Elements around the Abandoned Cu-Ni Mine Site in Selibe Phikwe, Botswana",10.4236/gep.2019.75020,cc-by,gold,allow_public_training,Permissive or explicit reuse license,http://www.scirp.org/journal/PaperDownload.aspx?paperID=93040

VNPDHT3Y,Goldschmidt Abstracts: Abstract Details,10.46427/gold2020.1856,unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://goldschmidtabstracts.info/2020/1856.pdf

M92B2MKR,An empirical method for design of grouted bolts in rock tunnels based on the Geological Strength Index (GSI),10.1016/j.enggeo.2009.05.003,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0013795209001161

595K68AF,Multicollinearity's Effect on Regression Prediction Accuracy with Real Data Structures,10.31523/glmj.044001.004,unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://doi.org/10.31523/glmj.044001.004

ARNS95AI,Parametric study of soil abrasivity for predicting wear issue in TBM tunneling projects,10.1016/j.tust.2014.10.010,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779815000292

YNL5FKU9,How to Use Quantile Transforms for Machine Learning,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://machinelearningmastery.com/quantile-transforms-for-machine-learning/

2VJBBXGP,Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms,10.3390/app12052656,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/12/5/2656/pdf?version=1646383280

FMY96Q39,Evaluation of the geological condition ahead of the tunnel face by geostatistical techniques using TBM driving data,10.1016/s0886-7798(03)00030-0,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779803000300

ETSPNUZ3,A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data,10.1016/j.ins.2021.02.056,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0020025521001985

JWUCS92G,How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis,10.1186/1471-2164-13-s4-s2,cc-by,gold,allow_public_training,Permissive or explicit reuse license,https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-13-S4-S2

WEIEU9F2,Proceedings of the international workshop on rock mass classification in underground mining.,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://www.cdc.gov/niosh/mining/works/coversheet1093.html

KYFZC7IK,Proceedings of the international workshop on rock mass classification in underground mining.,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://www.cdc.gov/niosh/mining/works/coversheet1093.html

G7E97XTP,Clustering Method of Raw Meal Composition Based on PCA and Kmeans,10.23919/chicc.2018.8482823,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.23919/chicc.2018.8482823

XTEHHZB3,Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network,10.1016/j.autcon.2021.103647,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580521000984

285C7ZU7,Forward modelling and imaging of ground-penetrating radar in tunnel ahead geological prospecting,10.1111/1365-2478.12613,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2478.12613

Z45BRH7R,Improved support vector regression models for predicting rock mass parameters using tunnel boring machine driving data,10.1016/j.tust.2019.04.014,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779818310575

3594CQ5M,A Novel Method of Multitype Hybrid Rock Lithology Classification Based on Convolutional Neural Networks,10.3390/s22041574,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/1424-8220/22/4/1574/pdf?version=1645438462

QUDJ5NK5,LightGBM: A Highly Efficient Gradient Boosting Decision Tree,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html

YLCQPXZ7,Learning from Imbalanced Data,10.1109/tkde.2008.239,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/tkde.2008.239

2N87XUQX,Low-code AutoML-augmented Data Pipeline – A Review and Experiments,10.1088/1742-6596/1828/1/012015,cc-by,gold,allow_public_training,Permissive or explicit reuse license,https://dx.doi.org/10.1088/1742-6596/1828/1/012015

S53L9N45,Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE,10.1016/j.ins.2018.06.056,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://arxiv.org/pdf/1711.00837

B4ZS9NSE,A Machine Learning Application for Predicting and Alerting Missed Approaches for Airport Management,10.1109/dasc52595.2021.9594418,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/dasc52595.2021.9594418

ZYQVV4Z4,Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning,10.1016/j.ijmst.2021.08.004,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2095268621000896

AQRQLB7U,Experimental and analytical studies of the parameters influencing the action of TBM disc tools in tunnelling | SpringerLink,10.1007/s11440-016-0453-9,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://iris.polito.it/bitstream/11583/2640244/6/Experimental_Fig_1-10.pdf

DDQ9XDAA,Engineering classification of rock masses for the design of tunnel support | SpringerLink,10.1007/bf01239496,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://link.springer.com/article/10.1007/BF01239496

8TWAMCM5,Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP),10.3390/su141911843,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2071-1050/14/19/11843/pdf?version=1663752259

29BEBVJS,Deep Reinforcement Learning With Adversarial Training for Automated Excavation Using Depth Images,10.1109/access.2022.3140781,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://ieeexplore.ieee.org/ielx7/6287639/9668973/09672107.pdf

YG8AJJJQ,Towards autonomous and optimal excavation of shield machine: a deep reinforcement learning-based approach,10.1631/jzus.a2100325,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://link.springer.com/10.1631/jzus.A2100325

59AJZE2D,Experimental study of specific matching characteristics of tunnel boring machine cutter ring properties and rock,10.1016/j.wear.2017.01.072,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S004316481730193X

V4U8XGVS,Dynamic analysis and experimental study of a Tunnel boring Machine testbed under multiple conditions,10.1016/j.engfailanal.2021.105557,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1350630721004180

B3FBJPVA,19970034695.pdf,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://ntrs.nasa.gov/api/citations/19970034695/downloads/19970034695.pdf

ZYI7ER4N,Tunnel boring machine vibration-based deep learning for the ground identification of working faces,10.1016/j.jrmge.2021.09.004,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674775521001323

83RRPI4G,On the Effect of Shield Friction in Hard Rock TBM Excavation,10.1007/s00603-022-03211-0,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://link.springer.com/content/pdf/10.1007/s00603-022-03211-0.pdf

4723I5MT,AWAC: Accelerating Online Reinforcement Learning with Offline Datasets,10.48550/arxiv.2006.09359,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2006.09359

QFWFPSG6,Offline Reinforcement Learning with Implicit Q-Learning,10.48550/arxiv.2110.06169,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2110.06169

39SBYAMI,Conservative Q-Learning for Offline Reinforcement Learning,10.48550/arxiv.2006.04779,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2006.04779

URNWAPZU,COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning,10.48550/arxiv.2010.14500,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2010.14500

7TPG8QY5,A Workflow for Offline Model-Free Robotic Reinforcement Learning,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

VZPP5RT8,Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization,10.1016/j.autcon.2022.104457,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580522003302

PHG2WID9,delivery.pdf,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://deliverypdf.ssrn.com/delivery.php?ID=530112070066077001090009075103071022041056033020093009075112062039097087103081124011053111100106008103091065079125003112042111074012065096071103001019068070077124078002094019100018025107032008041023027040057106006107081113112095015119123093088122030089101083105077089092115008073005070&EXT=pdf&INDEX=TRUE

J2KALP6C,Learning to Throw with a Handful of Samples using Decision Transformers,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

GYWI4K3U,Cutterhead mud-caking detection method and application based on cutter wear and temperature measurement,10.1299/jamdsm.2019jamdsm0088,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.jstage.jst.go.jp/article/jamdsm/13/4/13_2019jamdsm0088/_pdf

YSGSZA99,Dispersant for Reducing Mud Cakes of Slurry Shield Tunnel Boring Machine in Sticky Ground,10.1155/2021/5524489,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://downloads.hindawi.com/journals/amse/2021/5524489.pdf

MA6DVTY9,Haptic-Based and $SE(3)$-Aware Object Insertion Using Compliant Hands,10.1109/lra.2022.3224670,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://ieeexplore.ieee.org/document/9963587/

QBXNHCIZ,An Improved Ensemble Learning for Imbalanced Data Classification,10.1109/itaic.2019.8785887,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/itaic.2019.8785887

WSVUMU7Q,Learning from Imbalanced Data,10.1109/tkde.2008.239,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/tkde.2008.239

4DZRN82C,The relationship between Recall and Precision,10.1002/(sici)1097-4571(199401)45:1<12::aid-asi2>3.0.co;2-l,cc-by-nc-nd,green,exclude,NoDerivatives license; do not use as training data by default,https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-4571%28199401%2945%3A1%3C12%3A%3AAID-ASI2%3E3.0.CO%3B2-L

T99JAN7G,Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE,10.1016/j.ins.2018.06.056,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://arxiv.org/pdf/1711.00837

XUC8KMIS,An improvement of the convergence proof of the ADAM-Optimizer,10.48550/arxiv.1804.10587,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/1804.10587

JYS9UVU7,Convolutional Neural Network Hyperparameter Tuning with Adam Optimizer for ECG Classification,10.1109/asyu50717.2020.9259896,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/asyu50717.2020.9259896

ISXTQHRU,A novel enhanced softmax loss function for brain tumour detection using deep learning,10.1016/j.jneumeth.2019.108520,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0165027019303772

2ZZSU54R,Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks,10.1186/s13640-018-0332-4,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://jivp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13640-018-0332-4

INVS422C,A Review of Convolutional Neural Networks,10.1109/ic-etite47903.2020.049,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/ic-etite47903.2020.049

GEYZDXSU,"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation",10.48550/arxiv.2010.16061,unknown,,rag_only,License unclear or not permissive enough for public model training,http://arxiv.org/abs/2010.16061

SHHDH7YB,2010.16061.pdf,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://arxiv.org/ftp/arxiv/papers/2010/2010.16061.pdf

37AQ2EDE,Supervised Linear Discriminant Analysis for Dimension Reduction and Hyperspectral Image Classification Method Based on 2D-3D CNN,10.1109/acmi53878.2021.9528191,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/acmi53878.2021.9528191

VPIE7856,Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV,10.3390/min12020268,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2075-163X/12/2/268/pdf?version=1645347690

MB8AUKZ3,CONTRIBUTION AND COMBINATION OF DIFFERENT WOOD SECTIONS IN SPECIES RECOGNITION USING IMAGE TEXTURE ANALYSIS METHODS,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

FA2LVDPA,Writing a scientific article: A step-by-step guide for beginners,10.1016/j.eurger.2015.08.005,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://linkinghub.elsevier.com/retrieve/pii/S1878764915001606

5KQXG9WS,Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression,10.1016/j.undsp.2020.03.001,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S246796742030009X

XW6FLPI7,Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization,10.1016/j.gsf.2020.03.007,cc-by-nc-nd,hybrid,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S1674987120300669

HS6NPPFM,Vibration response and parameter influence of TBM cutterhead system under extreme conditions,10.1007/s12206-018-0944-8,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,http://link.springer.com/10.1007/s12206-018-0944-8

C8TKKZZR,Development and in-situ application of a real-time monitoring system for the interaction between TBM and surrounding rock,10.1016/j.tust.2018.07.018,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779817308933

LHMZAPCG,Development and application of cutterhead vibration monitoring system for TBM tunnelling,10.1016/j.ijrmms.2021.104887,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S1365160921002719

7KDIA2X2,Cutterhead and Cutting Tools Configurations in Coarse Grain Soils,10.2174/1874836801711010182,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://openconstructionbuildingtechnologyjournal.com/VOLUME/11/PAGE/182/PDF/

R6W5XAZ3,"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems",,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/2005.01643

WRTJCKWI,Mitigating tunnel-induced damages using deep neural networks,10.1016/j.autcon.2022.104219,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580522000929

N5Q33JID,Reinforcement learning based process optimization and strategy development in conventional tunneling,10.1016/j.autcon.2021.103701,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://www.sciencedirect.com/science/article/pii/S0926580521001527

CIT4A8F8,AWAC: Accelerating Online Reinforcement Learning with Offline Datasets,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/2006.09359

ZY55E7UF,Simulation-based Reinforcement Learning Approach towards Construction Machine Automation,10.22260/isarc2020/0064,unknown,closed,rag_only,License unclear or not permissive enough for public model training,http://www.iaarc.org/publications/2020_proceedings_of_the_37th_isarc/simulation_based_reinforcement_learning_approach_towards_construction_machine_automation.html

5X4DB52M,TaskNet: A Neural Task Planner for Autonomous Excavator,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://ieeexplore-ieee-org.ezproxy.biblio.polito.it/document/9561629

CTE97B52,Soil-Adaptive Excavation Using Reinforcement Learning,10.1109/lra.2022.3189834,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://ieeexplore.ieee.org/document/9826363/

KNCWQQJD,Application of machine learning in predicting the rate-dependent compressive strength of rocks,10.1016/j.jrmge.2022.01.008,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.sciencedirect.com/science/article/pii/S167477552200049X

EC5ADN93,Introducing Reinforcement Learning to Tunneling,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

QB6IGPY7,Towards optimized TBM cutter changing policies with reinforcement learning,10.1002/geot.202200032,custom-or-unknown,bronze,rag_only,Free-to-read/OA copy found but no clear reuse license,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/geot.202200032

RXHUVV3X,Reinforcement learning based process optimization and strategy development in conventional tunneling,10.1016/j.autcon.2021.103701,cc-by,hybrid,allow_public_training,Permissive or explicit reuse license,https://www.sciencedirect.com/science/article/pii/S0926580521001527

TS8NQX9K,TaskNet: A Neural Task Planner for Autonomous Excavator,10.1109/icra48506.2021.9561629,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/icra48506.2021.9561629

4HVCQBUS,On-line trajectory planning for autonomous robotic excavation based on force/torque sensor measurements,10.1109/mfi.1994.398430,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/mfi.1994.398430

Q5DBMVX6,Robotic excavation in construction automation,10.1109/100.993151,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/100.993151

X63A2EW6,Planning and Control for Autonomous Excavation,10.1109/lra.2017.2721551,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/lra.2017.2721551

JPSRPGC7,Development of autonomous excavation technology for hydraulic excavators,,unknown,,rag_only,No DOI/license evidence; use private RAG only,

IDZBIHT7,Wheel Loader Scooping Controller Using Deep Reinforcement Learning,10.1109/access.2021.3056625,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://ieeexplore.ieee.org/ielx7/6287639/9312710/09344588.pdf

KDTVCGSA,Excavation Reinforcement Learning Using Geometric Representation,10.1109/lra.2022.3150511,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/lra.2022.3150511

5YV5IZ2W,Surface settlement predictions for Istanbul Metro tunnels excavated by EPB-TBM,10.1007/s12665-010-0530-6,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s12665-010-0530-6

7NX5KU7R,Möglichkeiten der Prognose von Oberflächensetzungen beim Tunnelvortrieb im Lockergestein – Teil 1: Empirisches Prognoseverfahren,10.1002/gete.201200019,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://onlinelibrary.wiley.com/doi/abs/10.1002/gete.201200019

ACFQJXBM,Design of a Neural Controller Using Reinforcement Learning to Control a Rotational Inverted Pendulum,10.1109/rem49740.2020.9313887,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/rem49740.2020.9313887

7F2IQNFD,Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics,10.1109/access.2020.3040246,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://ieeexplore.ieee.org/ielx7/6287639/6514899/09268069.pdf

ZRV8WX5K,Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems,10.1109/ai4i46381.2019.00015,custom-or-unknown,green,rag_only,Free-to-read/OA copy found but no clear reuse license,https://arxiv.org/pdf/2004.09263

2WEIRF8T,Seamless shifting of a two-speed dual clutch transmission for electric vehicles using machine learning,10.1299/mel.21-00301,unknown,gold,rag_only,License unclear or not permissive enough for public model training,https://www.jstage.jst.go.jp/article/mel/7/0/7_21-00301/_pdf

UGAERXYU,Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms,10.3390/app12052656,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2076-3417/12/5/2656/pdf?version=1646383280

I4KTSMXT,Clustering Centroid Selection using a K-means and Rapid Density Peak Search Fusion Algorithm,10.1109/icsess49938.2020.9237746,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/icsess49938.2020.9237746

LKJJ9WPZ,How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis,10.1186/1471-2164-13-s4-s2,cc-by,gold,allow_public_training,Permissive or explicit reuse license,https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-13-S4-S2

KTGRCUKB,Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results,10.1109/icics49469.2020.239556,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/icics49469.2020.239556

X7YJYU83,Forward modelling and imaging of ground-penetrating radar in tunnel ahead geological prospecting,10.1111/1365-2478.12613,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2478.12613

4YEIWMK3,Detection of Ionospheric Scintillation Based on XGBoost Model Improved by SMOTE-ENN Technique,10.3390/rs13132577,cc-by-sa,gold,allow_public_training,"Allowed, but ShareAlike obligations must be considered in dataset/model card",https://www.mdpi.com/2072-4292/13/13/2577/pdf?version=1625212202

WQ2GLLPR,ADASYN: Adaptive synthetic sampling approach for imbalanced learning,10.1109/ijcnn.2008.4633969,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/ijcnn.2008.4633969

MM27L7HU,Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data,10.1016/j.agwat.2019.105758,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0378377419302768

H564RS42,Automatic Classification of Lithofacies with Highly Imbalanced Dataset Using Multistage SVM Classifier,10.1109/iecon48115.2021.9589254,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/iecon48115.2021.9589254

XULW67P5,Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning,10.1016/j.ijmst.2021.08.004,cc-by-nc-nd,gold,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2095268621000896

F52F8YYT,Multi-tier method using infrared photography and GPR to detect and locate water leaks,10.1016/j.autcon.2015.10.006,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0926580515002113

ZG9SQDXI,Modeling tunnel boring machine performance by neuro-fuzzy methods,10.1016/s0886-7798(00)00055-9,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0886779800000559

A59K8B7E,ADASYN: Adaptive synthetic sampling approach for imbalanced learning,10.1109/ijcnn.2008.4633969,unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/ijcnn.2008.4633969

B83JYMTP,Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results,10.1109/icics49469.2020.239556,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1109/icics49469.2020.239556

JWPE2UCC,"Advances in information retrieval: 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain, March 21-23, 2005 ; proceedings",,unknown,,rag_only,No DOI/license evidence; use private RAG only,

LV5P92W7,Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data,10.1016/j.agwat.2019.105758,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://www.sciencedirect.com/science/article/pii/S0378377419302768

KN5LNXP4,LightGBM: A Highly Efficient Gradient Boosting Decision Tree,,unknown,,rag_only,No DOI/license evidence; use private RAG only,https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html

2WNCJZYE,Autonomous Martian rock image classification based on transfer deep learning methods,10.1007/s12145-019-00433-9,custom-or-unknown,closed,rag_only,License unclear or not permissive enough for public model training,https://doi.org/10.1007/s12145-019-00433-9

DBGDTUAX,Very Deep Convolutional Networks for Large-Scale Image Recognition,,unknown,,rag_only,No DOI/license evidence; use private RAG only,http://arxiv.org/abs/1409.1556

Y4XUWLNM,Deep learning in the construction industry: A review of present status and future innovations,10.1016/j.jobe.2020.101827,cc-by-nc-nd,hybrid,exclude,NoDerivatives license; do not use as training data by default,https://www.sciencedirect.com/science/article/pii/S2352710220334604