[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis; DOI: 10.1186/1471-2164-13-s4-s2; license: cc-by.

[Assistant]
[page 1] PROCEEDINGS Open Access How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis Mauno Vihinen1,2,3 From SNP-SIG 2011: Identification and annotation of SNPs in the context of structure, function and disease Vienna, Austria. 15 July 2011 Abstract Background: Prediction methods are increasingly used in biosciences to forecast diverse features and characteristics. Binary two-state classifiers are the most common applications. They are usually based on machine learning approaches. For the end user it is often problematic to evaluate the true performance and applicability of computational tools as some knowledge about computer science and statistics would be needed. Results: Instructions are given on how to interpret and compare method evaluation results. For systematic method performance analysis is needed established benchmark datasets which contain cases with known outcome, and suitable evaluation measures. The criteria for benchmark datasets are discussed along with their implementation in VariBench, benchmark database for variations. There is no single measure that alone could describe all the aspects of method performance. Predictions of genetic variation effects on DNA, RNA and protein level are important as information about variants can be produced much faster than their disease relevance can be experimentally verified. Therefore numerous prediction tools have been developed, however, systematic analyses of their performance and comparison have just started to emerge. Conclusions: The end users of prediction tools should be able to understand how evaluation is done and how to interpret the results. Six main performance evaluation measures are introduced. These include sensitivity, specificity, positive predictive value, negative predictive value, accuracy and Matthews correlation coefficient. Together with receiver operating characteristics (ROC) analysis they provide a good picture about the performance of methods and allow their objective and quantitative comparison. A checklist of items to look at is provided. Comparisons of methods for missense variant tolerance, protein stability changes due to amino acid substitutions, and effects of variations on mRNA splicing are presented. Background Gene and genome sequencing speed is ever increasing and thus lots of genetic variation information is available. The technological development of sequencing methods has led to a situation where the interpretation of the gen- erated data is a severe bottleneck for the use of genetic information. Numerous prediction methods have been developed during the last decade to address the relevance of gene and protein variants to pathogenicity. General tolerance methods predict whether the variants are dis- ease-related or not (or affect protein function or not), and specific methods are used to address variation effect mechanisms [1]. These methods can be useful. However, until recently their true applicability and performance have not been studied systematically [2-5]. When meth- ods are originally published, authors provide some performance measures, which are usually not comparable with other methods due to the use of different training and test datasets, different reported measures etc. The scope of this article is to discuss how the assessment of method performance should be done and interpretation Correspondence: mauno.vihinen@med.lu.se 1Institute of Biomedical Technology, FI-33014 University of Tampere, Finland Full list of author information is available at the end of the article Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 © 2012 Vihinen; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
[page 2] of the results and the choice of the best methods. The text is mainly intended for scientists who are users of predictors without training in statistics or computer science. Method developers are taken into account by providing a checklist of items to be reported with meth- ods. The examples discussed are related to prediction of variant effects, but description of methods and evaluation measures is general and thereby not application domain specific.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics; DOI: 10.1109/access.2020.3040246; license: cc-by-sa.

[Assistant] García, and R. J. Martínez, ‘‘Approximation of con- tinuous media models for granular systems using cellular automata,’’ in Cellular Automata (Lecture Notes in Computer Science), vol. 3305. Berlin, Germany: Springer, 2004, pp. 230–237, doi: 10.1007/978-3-540-30479- 1_24. [23] M. Pla-Castells, I. García-Fernández, and R. J. Martínez, ‘‘Interactive terrain simulation and force distribution models in sand piles,’’ in Cellular Automata, vol. 4173. Berlin, Germany: Springer, 2006, pp. 392–401, doi: 10.1007/11861201_46. [24] D. Holz, T. Beer, and T. Kuhlen, ‘‘Soil deformation models for real- time simulation: A hybrid approach,’’ in Proc. Vriphys 6th Workshop Virtual Real. Interact. Phys. Simulations, Jan. 2009, pp. 21–30, doi: 10.2312/PE/vriphys/vriphys09/021-030. [25] S. Jaiswal, P. Korkealaakso, R. Aman, J. Sopanen, and A. Mikkola, ‘‘Deformable terrain model for the real-time multibody simulation of a tractor with a hydraulically driven front-loader,’’ IEEE Access, vol. 7, pp. 172694–172708, 2019, doi: 10.1109/ACCESS.2019.2956164. [26] P. Hintjens, ‘‘ZeroMQ,’’ O’Reilly Media, Sebastopol, CA, USA, Tech. Rep., 2013, vol. 53, no. 9. [27] Part 2: Kinds of RL Algorithms—Spinning Up documentation. Accessed: May 29, 2020. [Online]. Available: https://spinningup.openai.com/en/ latest/spinningup/rl_intro2.html [28] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, ‘‘Prox- imal policy optimization algorithms,’’ 2017, arXiv:1707.06347. [Online]. Available: http://arxiv.org/abs/1707.06347 ILYA KURINOV received the B.S. degree in mechanical engineering from the Saimaa Univer- sity of Applied Sciences and the M.S. degree in mechatronics from the Lappeenranta University of Technology. Since 2018, he has been working as a Ph.D. Researcher with the Machine Design Labo- ratory, LUT University. His research inter- ests include application of multibody system dynamics and machine learning algorithms in automation of mechatronic machines. GRZEGORZ ORZECHOWSKI received the B.S. and M.S. degrees and the Ph.D. degree in automa- tion and robotics from the Warsaw University of Technology, Warsaw, Poland, in 2007 and 2012. From 2012 to 2017, he was an Assistant Profes- sor with the Division of Theory of Machines and Robots, Warsaw University of Technology. Since 2017, he has been a Postdoctoral Researcher with the Laboratory of Machine Design, LUT Univer- sity, Lappeenranta, Finland. His research interests include the computational methods in mechanics, studies of the deformable models in challenging dynamical excitations, and reinforcement learning techniques in application to machine control. PERTTU HÄMÄLÄINEN received the M.Sc. (Tech.) degree from the Helsinki University of Technology, in 2001, the M.A. degree in new media from the University of Art and Design, Helsinki, in 2002, and the Ph.D. degree in com- puter science from the Helsinki University of Technology, in 2007. He is currently an Associate Professor with Aalto University. He has published widely on human–computer interaction, computer animation and movement synthesis, and game research. AKI MIKKOLA received the Ph.D. degree in the ﬁeld of machine design in 1997. Since 2002, he has been working as a Pro- fessor with the Department of Mechanical Engi- neering, Lappeenranta University of Technology, Lappeenranta, Finland. He is currently leading the Research Team of the Laboratory of Machine Design, Lappeenranta University of Technology. His research interests include machine dynamics and vibration, multibody system dynamics, and bio-mechanics. He has been awarded ﬁve patents and has contributed to more than 90 peer-reviewed journal articles. 214006 VOLUME 8, 2020

Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks; DOI: 10.1186/s13640-018-0332-4; license: cc-by-sa.

[Assistant]
[page 1] EURASIP Journal on Image and Video Processing Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 https://doi.org/10.1186/s13640-018-0332-4 RESEARCH Open Access Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks Mahmoud Khaled Abd-Ellah1, Ali Ismail Awad2,3* , Ashraf A. M. Khalaf4 and Hesham F. A. Hamed4 Abstract Brain tumour is a serious disease, and the number of people who are dying due to brain tumours is increasing. Manual tumour diagnosis from magnetic resonance images (MRIs) is a time consuming process and is insufficient for accurately detecting, localizing, and classifying the tumour type. This research proposes a novel two-phase multi-model automatic diagnosis system for brain tumour detection and localization. In the first phase, the system structure consists of preprocessing, feature extraction using a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) approach. The purpose of the first system phase is to detect brain tumour by classifying the MRIs into normal and abnormal images. The aim of the second system phase is to localize the tumour within the abnormal MRIs using a fully designed five-layer region-based convolutional neural network (R-CNN). The performance of the first phase was assessed using three CNN models, namely, AlexNet, Visual Geometry Group (VGG)-16, and VGG-19, and a maximum detection accuracy of 99.55% was achieved with AlexNet using 349 images extracted from the standard Reference Image Database to Evaluate Response (RIDER) Neuro MRI database. The brain tumour localization phase was evaluated using 804 3D MRIs from the Brain Tumor Segmentation (BraTS) 2013 database, and a DICE score of 0.87 was achieved. The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature. The obtained results also demonstrate the superiority of the proposed system concerning both tumour detection and localization. Keywords: Brain tumour diagnosis, MRI segmentation, Tumour detection and localization, Convolutional neural networks (CNNs) 1 Introduction Brain tumour is a serious disease in which an abnormal growth of tissue inside the the brain can disrupt proper brain function. The National Brain Tumor Foundation (NBTF) reported that the number of people in devel- oped countries who have died due to brain tumours has increased by 300% over the last three decades [1, 2]. Man- ual brain tumour diagnosis by physicians is a less accurate yet still time-consuming procedure [3, 4]. *Correspondence: ali.awad@ltu.se 2Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE-97187 Luleå, Sweden 3Faculty of Engineering, Al-Azhar University, P.O. Box 83513, Qena, Egypt Full list of author information is available at the end of the article Magnetic resonance imaging is an advanced medical imaging technique providing rich information about the human soft tissue anatomy [5]. Automatic brain tumour detection from magnetic resonance images (MRIs) aims to classify MRIs into normal and abnormal according to the absence or presence of a tumour, respectively. Thus, the diagnosis problem can be addressed as an image clas- sification challenge. The pervasiveness of brain tumours has led to the production of a massive amount of MRI data. Therefore, developing an automatic brain tumour diagnosis system that achieves high tumour detection and localization accuracies is a vital need [6]. Machine learn- ing in general and deep learning approaches in particular play core roles in computer-assisted brain image analysis, © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
[page 2] Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 Page 2 of 10 segmentation, registration, and tumour tissue classifica- tion [7, 8].

Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms; DOI: 10.3390/app12052656; license: cc-by-sa.

[Assistant]
2022, 12, 2656 10 of 20 (a) (b) (c) Figure 7. Scatter diagram of the data set after over-sampling (1260). (a) Drilling parameters of surround rock grade-III (420); (b) drilling parameters of surround rock grade-IV (420); (c) drilling parameters of surround rock grade-V (420). 2.5. Sample Feature Analysis Figure 6 shows that all median, mean, and upper and lower quartile values of each drilling parameter in the box diagram monotonically change with the surrounding rock grade, which preliminarily indicates a correlation between the drilling parameters and the surrounding rock grade.
[page 11] Appl. Sci. 2022, 12, 2656 11 of 20 Pearson correlation coefﬁcient [56] was used to analyze the correlations between the surrounding rock grade and the drilling parameters in 1260 samples, and the correlation coefﬁcients are shown in Table 5. Table 5. Correlation coefﬁcients between the surrounding rock grade and each drilling parameter. Index Vp Ph Pf Pr R 0.59 −0.64 −0.52 −0.65 In Table 5, all the absolute value of the correlation coefﬁcients between the four drilling parameters and the surrounding rock grades are above 0.5, which is a weak correlation, and the correlation coefﬁcients are close to each other. Therefore, these four indices were selected for the surrounding rock classiﬁcation. 2.6. Sample Data Splitting According to the above 1260 data samples, the intelligent surrounding rock classiﬁca- tion sample database was established. In order that the selected parameters can represent the majority of samples, the majority samples are selected for training and the minority samples for prediction in the parameter optimization stage (validation process). As to the model evaluation stage (testing process), we selected more samples for prediction than the previous stage so that the performance evaluation of the model is more convincingly. Thus, the sample database after random sequencing was divided into the training set and prediction set using the ﬁve-fold cross veriﬁcation method in the validation process, and as to the testing process, the ratio is three-fold. The speciﬁc distribution is shown in Table 6. Table 6. Intelligent surrounding rock classiﬁcation sample database distribution. Item Surrounding Rock III IV V Total Validation process Training set 336 336 336 1008 Prediction set (Validation set) 84 84 84 252 Testing process Training set 280 280 280 840 Prediction set (Testing set) 140 140 140 420 In the ﬁeld of machine learning, different evaluation indexes (that is, different features in feature vectors are described as different evaluation indexes) often have different di- mensional and dimensional units, which will affect the results of data analysis. In order to eliminate the dimensional inﬂuence between indexes, data normalization is required. Normalization means that the input data are limited to a certain range, and this time the data are limited 0 to 1. The data are normalized using the min–max normalization method, as deﬁned in Equation (2). x′ = x −xmin xmax −xmin (2) where x is the value of the original data, xmax is the max value of the original data, xmin is the min value of the original data, x’ is the normalized value. The normalization needs to be carried out in the training set in Table 6 ﬁrst, and then the normalization of the prediction set is completed by taking the normalization parameters (xmax and xmin) of the training set.
[page 12] Appl. Sci. 2022, 12, 2656 12 of 20 2.7. Model Parameter Optimization and Evaluation Based on the python machine learning toolkit (Scikit-learn), 10 types of supervised ma- chine learning algorithms were used to train the intelligent surrounding rock classiﬁcation model: the support vector machine (SVM), back propagation neural network (BP) [55,56], radial basis function neural network (RBF), K-nearest neighbor (KNN), Gaussian naive Bayes (GNB), decision tree (DT), random forest (RF), extra trees (ET), bootstrap aggregating (Bagging) and gradient boosting (GB) algorithms. The model parameters were optimized and evaluated by the grid search K-ﬂod cross validation method (k = 5), and the model performance was externally unbiased when using the method of K-ﬂod cross validation (k = 3). In the model performance optimization process, the average accuracy was adopted to determine the optimum hyperparameter combinations.

Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Detection of Ionospheric Scintillation Based on XGBoost Model Improved by SMOTE-ENN Technique; DOI: 10.3390/rs13132577; license: cc-by-sa.

[Assistant]
Testing Set 1 2 3 4 5 6 7 Accuracy (%) Decision tree 2.58 1.57 2.57 0.47 1.18 0.44 1.06 Random forest 2.45 2.07 2.34 0.65 1.24 0.36 0.45 XGBoost 6.04 2.44 2.92 0.94 0.62 1.25 0.29 Recall for class 2 (%) Decision tree 4.09 2.58 10.94 9.15 9.24 8.01 8.41 Random forest 5.82 9.23 10.61 4.51 13.90 7.00 12.61 XGBoost 10.59 12.60 17.69 13.98 16.17 19.40 14.06 F1 score for class 2 (%) Decision tree 2.28 1.76 6.53 2.76 5.46 1.06 4.51 Random forest 3.17 5.01 5.62 1.29 7.5 2.11 5.37 XGBoost 6.00 6.64 9.54 7.15 8.04 10.61 6.61 Figure 10. Cont.
[page 17] Remote Sens. 2021, 13, 2577 17 of 22 Figure 10. Testing results for detection algorithms regarding overall accuracy (top row), recall value for class 2 (middle row) and F1 score for class 2 (bottom row), with different ratios of classes 0/1/2 for testing datasets. Relevant information on training dataset and seven testing datasets is listed in Table 7. The green, yellow and orange bars refer to results for decision tree, random forest and XGBoost, respectively. The highlighted bold bar on the right of each group represents the result of the XGBoost method improved by the SMOTE-ENN technique, with better performance. 5. Discussion All the experiments were carried out on real data collected in an equatorial region during solar maximum years, under various ionospheric scintillation intensities. Firstly, different resampling techniques were compared based on the XGBoost algorithm, and the integration of SMOTE and ENN produced better results regarding XGBoost’s performance. As shown in Table 4, based on random oversampling, there was only a 3% increase in the recall value for the minority class 2 in that it just randomly took out samples in class 2 without any rule, and thus added in several invalid data. Meanwhile, there was little contribution to the other two classes. Different from oversampling, the method of random undersampling is aimed at randomly deleting samples of the majority class until the data of these classes are equal to those of the minority classes in number. Thus, much useful information is discarded, reducing the detection accuracy, presenting similar results to random oversampling. Although the resampling rule of SMOTE is based on the nearest neighboring samples and linear interpolation, it still aims at increasing the minority class
[page 18] Remote Sens. 2021, 13, 2577 18 of 22 without signiﬁcant improvement for the other two classes. As for the SMOTE, ENN only showed a 4% improvement on the recall value of the majority class 0, because the samples of the boundary around classes 0 and 1 were deleted. Meanwhile, the overall samples were still imbalanced, resulting in ineffective enhancement for class 2. Differently, the SMOTE-ENN method ﬁrstly deals with the data of the minority class through the SMOTE oversampling technique and then carries out the ENN undersampling for the data of the majority class. It not only increases the data of the minority class but also deletes the multi-class samples on the boundary as much as possible, signiﬁcantly improving the detection performance in various aspects. The results in Table 4 show that there was an apparent improvement in performance for all three classes, with an approximately 3% increase in overall accuracy. As for the value of recall, it showed a 5% enhancement for class 2, corresponding to strong scintillation events, and 4% increase for class 0, corresponding to weak scintillation events, meaning that more strong and weak scintillation events that may be easily missed can be detected correctly. Thereby, the precision for class 1, corresponding to moderate scintillation events, increased by 6%, with more events of classes 0 and 2 being correctly detected. Meanwhile, the F1 scores for the three classes increased by 3%, 3% and 2%, respectively, demonstrating an improvement in both precision and recall. Especially, the integrated technique outperforms ENN by 4.13% in recall for class 2, while showing a 1.37% enhancement in overall accuracy compared with SMOTE. Compared with the other single resampling methods, the SMOTE-ENN proved to be effective in dealing with imbalanced data with insufﬁcient strong scintillation events. Based on that, brief comparisons of ﬁve methods were drawn.

Detection of Ionospheric Scintillation Based on XGBoost Model Improved by SMOTE-ENN Technique


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Reinforcement learning based process optimization and strategy development in conventional tunneling; DOI: 10.1016/j.autcon.2021.103701; license: cc-by.

[Assistant]
Winkler, Machine learning in tunnelling – capabilities and challenges, Geomechanik Tunnelbau 13 (2020) 191–198, https:// doi.org/10.1002/geot.202000001. [2] DAUB, BIM in Tunnelling: Digital Design, Building and Operation of Underground Structures. http://www.daub-ita.de/fileadmin/documents/daub/gtcrec4/gtcre c11v3_BIM_in_Tunnelling_05-2019.pdf, 2019 (accessed 9 October 2020). [3] L. Wagner, A. Kluckner, C.M. Monsberger, P. Wolf, K. Prall, W. Schubert, W. Lienhart, Direct and distributed strain measurements inside a Shotcrete lining: concept and realisation, Rock Mech. Rock. Eng. 53 (2020) 641–652, https://doi. org/10.1007/s00603-019-01923-4. [4] G.H. Erharter, T. Marcher, C. Reinhold, Application of artificial neural networks for underground construction – chances and challenges – insights from the BBT exploratory tunnel Ahrental Pfons, Geomechanik Tunnelbau 12 (2019) 472–477, https://doi.org/10.1002/geot.201900027. [5] G.H. Erharter, T. Marcher, C. Reinhold, Comparison of artificial neural networks for TBM data classification, in: Proceedings of the 14th International Congress on Rock Mechanics and Rock Engineering (ISRM 2019), Foz de Iguassu, Brazil, 2019. [6] G.H. Erharter, T. Marcher, C. Reinhold, Artificial neural network based online rockmass behavior classification of TBM data, in: Information Technology in Geo- Engineering, firstst ed. twentiethtwentieth, Springer, 2020, pp. 178–188. [7] R. Wu, Y. Fujita, K. Soga, Integrating domain knowledge with deep learning models: an interpretable AI system for automatic work progress identification of NATM tunnels, Tunn. Undergr. Space Technol. 105 (2020) 103558, https://doi. org/10.1016/j.tust.2020.103558. [8] S.K. Shreyas, A. Dey, Application of soft computing techniques in tunnelling and underground excavations: state of the art and future prospects, Innov. Infrastruct. Solut. 4 (2019), https://doi.org/10.1007/s41062-019-0234-z. [9] W. Zhang, R. Zhang, C. Wu, A.T.C. Goh, S. Lacasse, Z. Liu, H. Liu, State-of-the-art review of soft computing applications in underground excavations, Geosci. Front. 11 (2020) 1095–1106, https://doi.org/10.1016/j.gsf.2019.12.003. [10] S. Isam, Z. Wengang, Use of soft computing techniques for tunneling optimization of tunnel boring machines, Underground Space (2020), https://doi.org/10.1016/j. undsp.2019.12.001. [11] B.B. Sheil, S.K. Suryasentana, M.A. Mooney, H. Zhu, Machine learning to inform tunnelling operations: recent advances and future trends, Proc. Inst. Civ. Eng. (2020) 1–18, https://doi.org/10.1680/jsmic.20.00011. [12] Q. Zhang, Z. Liu, J. Tan, Prediction of geological conditions for a tunnel boring machine using big operational data, Autom. Constr. 100 (2019) 73–83, https://doi. org/10.1016/j.autcon.2018.12.022. [13] G.H. Erharter, T. Marcher, MSAC: towards data driven system behavior classification for TBM tunneling, Tunn. Undergr. Space Technol. 103 (2020) 103466, https://doi.org/10.1016/j.tust.2020.103466. [14] V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis, Human-level control through deep reinforcement learning, Nature 518 (2015) 529–533, https://doi.org/10.1038/nature14236. [15] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis, Mastering the game of go with deep Fig. 11. Exemplary log of one episode. The x-axis shows the number of blasts/moves the agent needed to achieve breakthrough (i.e. 105): Top row: Time-distance diagram showing the logged position of top heading excavation (solid line), bench excavation (dashed line) and the difference in between (grey area). Middle row: Record of the actions (see Table 3 for the corresponding action codes). Bottom row: cumulative reward throughout the episode. G.H. Erharter et al.
[page 12] Automation in Construction 127 (2021) 103701 12 neural networks and tree search, Nature 529 (2016) 484–489, https://doi.org/ 10.1038/nature16961. [16] J. Shahrabi, M.A. Adibi, M. Mahootchi, A reinforcement learning approach to parameter estimation in dynamic job shop scheduling, Comput. Ind. Eng. 110 (2017) 75–82, https://doi.org/10.1016/j.cie.2017.05.026. [17] M.

Reinforcement learning based process optimization and strategy development in conventional tunneling


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Detection of Ionospheric Scintillation Based on XGBoost Model Improved by SMOTE-ENN Technique; DOI: 10.3390/rs13132577; license: cc-by-sa.

[Assistant]
As shown in Table 5, XGBoost outperformed the CNN by 1.57%, 12.92% and 8.99% in terms of the overall accuracy, recall and F1 score for class 2 when trained and tested on raw data, showing signiﬁcant superiority in strong scintillation detection. Overall, all four detection algorithms were enhanced when improved by SMOTE-ENN. Moreover, for resampled data, XGBoost outperformed the SVM by 0.65% in terms of overall accuracy. The recall for the SVM was 3.46% higher than that for XGBoost, but the F1 score for the SVM was 4.20% lower. This indicates that the SVM missed many strong scintillation events, with a precision of only 83.79% for class 2. More importantly, larger computational loads and longer running times are needed for SVM detection than detection with the three other machine learning methods. Thus, both methods will not be further discussed due to the unavailability of the CNN and large running loads for the SVM. For the different imbalanced training sets, the accuracy of the improved XGBoost was about 4% to 5% higher than that of the decision tree and random forest, which is clear from Figure 9. Although the recall values of the decision tree were about 2% higher than those of XGBoost based on the training sets of 1, 3 and 6, the F1 scores for XGBoost were about 2% higher than those for the decision tree and random forest. As shown in the right panels of Figure 9, there was a 6.39% decline in the recall value, from 90.86% to 84.47%, and 2.82% decline in the F1 score, from 92.81% to 89.99%, implying more missed strong scintillation events. However, the XGBoost model, trained on the SMOTE-ENN resampled datasets, remained relatively stable, with recall values of about 90% to 91% and F1 scores of 92% to 93%, indicating more enhancement for more severely imbalanced training samples. Meanwhile, there was a 6.37% improvement in the recall value, from 84.47% to 90.84%, and 2.78% improvement in the F1 score, from 89.99% to 92.77%, with the resampled training data with a sample ratio of 7:7:1. Additionally, there was also improvement in the other two classes in the precision, recall and F1 score, as well as the overall accuracy, which increased by 3% to over 92% compared to that with the raw data. These illustrate the effectiveness of the proposed XGBoost algorithm improved by the SMOTE-ENN resampling technique for imbalanced training data in improving the detection performance for strong scintillation events. Considering various natural phenomena of different ionospheric scintillation events, seven groups of testing datasets with different degrees of imbalance were established, and each dataset included nine segments of real one-hour data. The results signiﬁcantly illus-
[page 19] Remote Sens. 2021, 13, 2577 19 of 22 trate the excellent performance of the improved XGBoost in strong scintillation detection under different scintillation conditions. It can be intuitively observed from Figure 10 that the accuracy of the improved XGBoost was higher than that of the other methods based on each testing dataset, while almost all the values of recall and F1 score were similar. The overall testing accuracy of the XGBoost model trained on the resampled dataset varied from 90.42% to 96.04%, higher than that of the XGBoost model trained on the raw dataset, ranging from 85.81% to 95.42%. Table 8 illustrates the improvement ratios for the evaluation indicators for the different testing sets with different detection algorithms trained on the resampled dataset, compared with the raw dataset. For these testing datasets, the results show an increase of at least 10% in the recall values. Especially, for the sixth group of the testing dataset, it increased by 19.40%, from 74.61% to 94.01%. Meanwhile, the F1 score signiﬁcantly increased by more than 6.00%, ranging from 77.31% to 94.82%, for these seven testing datasets, while there was a signiﬁcant increase of 10.61% from 84.14% to 94.75% for the sixth testing dataset. The results also present an improvement or maintenance of the corresponding recall, precision and F1 scores for the other two classes. These results indicate that it is valuable to enhance the detection accuracy for strong scintillation events with different degrees of imbalance in the testing data with the method of resampling the imbalanced training data by SMOTE-ENN before training the XGBoost model. 6.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Detection of Ionospheric Scintillation Based on XGBoost Model Improved by SMOTE-ENN Technique; DOI: 10.3390/rs13132577; license: cc-by-sa.

[Assistant]
IEEE Trans. Knowl. Data Eng. 2009, 21, 1263–1284.
[page 22] Remote Sens. 2021, 13, 2577 22 of 22 43. Wilson, D.L. Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. 1972, 2, 408–421. [CrossRef] 44. Tomek, I. Two modiﬁcations of CNN. IEEE Trans. Syst. Man Cybern. 1976, 6, 769–772. 45. Hart, P.E. The condensed nearest neighbor rule. IEEE Trans. Inf. Theory 1967, 14, 515–516. [CrossRef] 46. Laurikkala, J. Improving identiﬁcation of difﬁcult small classes by balancing class distribution. In Proceedings of the 8th Conference on Artiﬁcial Intelligence in Medicine in Europe, Cascais, Portugal, 1–4 July 2001; pp. 63–66. 47. Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P. SMOTE: Synthetic minority over-sampling technique nitesh. J. Artif. Intell. Res. 2002, 16, 321–357. [CrossRef] 48. Han, H.; Wang, W.; Mao, B. Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In Proceedings of the Advances in Intelligent Computing—International Conference on Intelligent Computing, Hefei, China, 23–26 August 2005; pp. 878–887. 49. Batista, G.E.A.P.A.; Prati, R.C.; Monard, M.C. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newsl. 2004, 6, 20–29. [CrossRef]


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Application of machine learning in predicting the rate-dependent compressive strength of rocks; DOI: 10.1016/j.jrmge.2022.01.008; license: cc-by-sa.

[Assistant] [page 1] Full Length Article Application of machine learning in predicting the rate-dependent compressive strength of rocks Mingdong Wei a, Wenzhao Meng a, Feng Dai b, Wei Wu a,* a School of Civil and Environmental Engineering, Nanyang Technological University, Singapore b State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, China a r t i c l e i n f o Article history: Received 26 October 2021 Received in revised form 15 December 2021 Accepted 20 January 2022 Available online 18 March 2022 Keywords: Machine learning Rock dynamics Compressive strength Strain rate a b s t r a c t Accurate prediction of compressive strength of rocks relies on the rate-dependent behaviors of rocks, and correlation among the geometrical, physical, and mechanical properties of rocks. However, these prop- erties may not be easy to control in laboratory experiments, particularly in dynamic compression ex- periments. By training three machine learning models based on the support vector machine (SVM), back- propagation neural network (BPNN), and random forest (RF) algorithms, we isolated different input parameters, such as static compressive strength, P-wave velocity, specimen dimension, grain size, bulk density, and strain rate, to identify their importance in the strength prediction. Our results demonstrated that the RF algorithm shows a better performance than the other two algorithms. The strain rate is a key input parameter inﬂuencing the performance of these models, while the others (e.g. static compressive strength and P-wave velocity) are less important as their roles can be compensated by alternative pa- rameters. The results also revealed that the effect of specimen dimension on the rock strength can be overshadowed at high strain rates, while the effect on the dynamic increase factor (i.e. the ratio of dy- namic to static compressive strength) becomes signiﬁcant. The dynamic increase factors for different specimen dimensions bifurcate when the strain rate reaches a relatively high value, a clue to improve our understanding of the transitional behaviors of rocks from low to high strain rates.  2022 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/ by/4.0/). 1. Introduction Rate dependence of rock strength is a critical issue in rock dy- namics, as rock behaviors vary drastically at different strain rates. Understanding the rate-dependent strengths of rocks is important for rock engineering design and construction (Zhao et al., 1999; Hudson, 2012). Our current approaches in determining the compressive strength of rocks rely basically on laboratory experi- ments, such as the static compression test (ISRM, 1979) and the split Hopkinson pressure bar (SHPB) test (Zhou et al., 2012). These test methods investigate the rate dependence of compressive strength of rocks at low and high strain rates, respectively. A gen- eral understanding is that the compressive strength slightly in- creases with a higher strain rate at a low strain rate (e.g. < 5 s1) and grows rapidly at a high strain rate (e.g. > 500 s1). The test methods are difﬁcult to be used directly to investigate the compressive strength of rocks at intermediate strain rates (5e500 s1), which are of interest to understand the mechanisms of excavation-induced geohazards such as rockburst (Duan et al., 2019a; Zhong et al., 2021). Efforts have been made to modify the test setups to control the intermediate strain rates (e.g. Li et al., 2005; Whittington et al., 2015). However, a few issues remain challenging, such as inertia effects from different test methods, specimen dimensions used in different test methods, inconstant strain rates in different individual tests. Numerical simulations can control these effects and have been used extensively (e.g. Li et al., 2009; Du et al., 2018; Huang et al., 2018; Duan et al., 2019b), but an accurate prediction of rock strength depends on a reasonable constitutive model and a reliable model calibration. Therefore, a simple, fast, and data-driven method is needed to predict the compressive strength of rocks at various strain rates and to pro- mote our understanding of the rate-dependent strength of rocks.

Application of machine learning in predicting the rate-dependent compressive strength of rocks


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Reinforcement learning based process optimization and strategy development in conventional tunneling; DOI: 10.1016/j.autcon.2021.103701; license: cc-by.

[Assistant] Before every move, a number (r) is drawn from a random uniform distribution in the range between 0 and 1. r governs the probability for a move to be a random action (if r ≤ε) or to be based on the agent’s prior experience (if r > ε). Throughout training ε will decay following Eq. (2), where the new ε for the next episode (εi+1) is computed by the previous ε (εi) times a constant – the epsilon decay (εd). εi+1 = εi*εd (2) This process guarantees that the agent can explore the environment in the beginning and shifts towards more exploitation of its knowledge towards the end of training. We set the minimum ε to 0.05 below which there is no more decay and ε will be kept constant (see “final explora tion” in Table 2). 3.3. Environment As the DQN agent is the RL pendant to the real life geotechnician, the environment is the RL representation of the rockmass and of logistical processes of the construction site (state) as well as the feedback that is received for better or worse excavation performance (reward). Based on the agent’s actions, its main tasks are to update and yield the current state of the construction-site and to provide feedback. 3.3.1. States The state that is observed by the DQN agent (see Section 3.2) is a hypermatrix of the shape 2 × 2100 × 2 which represents a geological section and a section that shows where support is already installed. The hypermatrix is structured in the following way (see Fig. 6): 2 rows for top heading and bench respectively; 2100 columns for the total length of the tunnel in decimeters plus an additional area beyond the break through (see below); 2 channels for the geological section and the sec tion with the installed support respectively. The values of the hypermatrix – originally ranging from 0 to 2 (see Section 3.1) - are then scaled between 0 and 1. In the channel of the geological section, 0 therefore translates to “not yet excavated area” and 0.5 and 1 represent gt1 and gt2 respectively (see Table 1 and Section 3.1). In the channel of the already installed support, 0 means “no installed support” and 1 means “installed support”. There are 2 terminal states in the simulation (i.e. states that lead to abortion of the simulation after their occurrence): ▪ A breakthrough is achieved if and only if both posth and posbi are ≥tl. In this case a breakthrough reward is given (see next section) and the simulation is finished. ▪The second terminal state is a timeout which is set to 200 ac tions; i.e. the agent must achieve a breakthrough within less than 200 actions or otherwise the simulation is aborted, and a negative breakthrough reward is given. The environment is designed in a way that once posth or posbi are ≥tl, the position of this part of the excavation is not updated anymore even if it is further excavated. We introduced the “timeout” as we observed in the experimental phase of the study that the agent sometimes reaches a breakthrough with the top heading or the bench but keeps on excavating in the already excavated part of the tunnel. On the one hand this causes the episode to be infinitely long, and on the other hand this leads to excessively large negative penalties which negatively affect the training process. We set the total length of the sections to 210 m (i.e. tunnellength + maximum possible length of face support) to give the agent the freedom to use actions that install face support even for the last few blasts. From a “real world tunneling” perspective this does not make sense and in the simulation, this would lead to additional penalties, however, the goal is that the agent learns things like this and is not forced to do so. 3.3.2. Reward system Rewards have the purpose to tell the agent if its actions are beneficial for the total reward. The ultimate goal of the agent is to maximize the return (i.e. sum of the rewards throughout the episode – here: excavate the whole tunnel). During an episode, a reward (also called penalty in case of a negative reward) is given for each individual move that the agent takes [26]. The action is chosen to maximize the expected return of discounted rewards. Reward systems in RL can vary greatly depending on the given task.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics; DOI: 10.1109/access.2020.3040246; license: cc-by-sa.

[Assistant]
For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020
[page 2] I. Kurinov et al.: Automated Excavator Based on RL and Multibody System Dynamics In view of the complexity of the task and environment, utilization of machine learning can be considered a com- pelling option [3]. Reinforcement learning (RL) is a machine learning method that can be adopted for efﬁcient learning of interactions with a complicated environment. RL uses the concept of an agent – a program capable of performing actions in the environment [4] – to determine actions with the aim of maximizing a pre-deﬁned expected cumulative future reward. The learning process in RL consists of constant exploration of possible actions, acquiring information about the environment from observations, and receiving an evalua- tion of an action by a reward [4]. The observation and reward functions allow programming of the behavior of the model to adapt to changes in the environment: a positive reward is provided for the right behavior and a negative reward given after incorrect behavior. In addition, the agent can be eas- ily retrained for different behavior patterns, which provides opportunities for enhancement of the automated machine. Machine automation can be addressed in multiple ways, as reported in previous studies. In [5], the automation was based on training a fuzzy logic algorithm to mimic the behavior of a skilled human operator. Another study [6] utilized laser rangeﬁnders mounted onto the excavator for automation of digging and loading procedures. In [7], a tele-operated assistant system controlled a real excavator via a wireless local area network. A multibody simulation and behavior-based excavation control approach for application with excavators performing landscaping tasks was introduced in [8]. In recent years, numerous publications have focused on the topic of artiﬁcial intelligence in automation. An example from the area of automation of heavy machinery is a study examining the application of Proximal Policy Optimization to teach an agent to operate an excavator [9]. The main goal of the study was to train the neural network to accomplish a leveling task known as bucket dragging. The research used a Dynasty simulation engine (propriety software of Cater- pillar Inc.) in combination with the Open AI Gym frame- work [9]. Another study applied a machine learning approach to automation of a wheel loader [10]. Experimental data were collected on an expert driver ﬁlling the bucket of a Volvo L110G wheel loader and a regression model was then optimized with a reinforcement learning algorithm in terms of bucket ﬁlling and fuel efﬁciency [10]. It should be noted that utilization of reinforcement learning for heavy machinery is not limited to controlling the movements of the machine. For example, reinforcement learning has been used in develop- ment of a real-time energy management system of a hybrid excavator to control energy ﬂow and optimize efﬁciency [11]. The approach of mimicking the behavior of a human oper- ator can have negative effects on efﬁciency, because the oper- ator might not operate the machine optimally. Methods based on training with a virtual simulation model use simplistic representations of the environment and machine, which may result in difﬁculties when adopted for implementation on real machines. There is increasing evidence that neural network con- trol policies trained in simulation can also work in the real world if the training scheme is designed correctly [12]–[14]. However, conducting real-world tests on heavy machinery such as an excavator is expensive, and simulation experi- ments are ﬁrst needed to lay the groundwork. This article extends the only prior study on neural network excavator control [9] by learning to control a more detailed multibody excavator model in a more complex task, demonstrating the scalability of the approach. We also describe the multibody simulation in detail, whereas [9] treats the simulator as a black box. A multibody model is a computer-based simu- lation model replicating the physical structure and behav- ior of a machine with interconnected rigid or ﬂexible bod- ies. Use of a multibody machine model is beneﬁcial for training, because it eliminates risks of damaging the real machine and objects on the construction site and endangering personnel. This study applies ﬁve methods for achieving train- ing of the autonomous agent.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Wheel Loader Scooping Controller Using Deep Reinforcement Learning; DOI: 10.1109/access.2021.3056625; license: cc-by-sa.

[Assistant]
The DDPG network structure. D. DDPG ARCHITECTURE Our implementation of DDPG includes two neural networks (actor and critic), as shown in Fig. 6. Both networks have the current state as input; i.e., 12-dimensional sensor data. VOLUME 9, 2021 24149
[page 6] O. Azulay, A. Shapiro: Wheel Loader Scooping Controller Using Deep Reinforcement Learning FIGURE 7. Sequence of scooping motions of the Komodo robot with an arm mechanism in simulated and real-life experiments. The actor network output is composed of 3-dimensional deterministic actuator commands that drive the tilt, lift, and the robot’s velocity. The output of the actor network is merged with the ﬁrst layer of output from the critic network, which in turn is used to output the action-value function Qπ (s, a). The action-value function is used to produce temporal- difference errors deriving the learning in both the actor and critic. We used three hidden layers in both the actor and critic networks. Each layer is fully connected that includes layer normalization whose dimensions decrease from 100, 80, and 60 respectively. A hyperbolic tangent function was used as the activation function to constrain the range of the actions. E. REWARD SHAPING To guide the robot to excavate soil during the task, while not obligating the robot to follow a speciﬁc trajectory, we deﬁned a shaped reward: Pt =  1 −tanh2 (∥dend∥) penetrate 1 −tanh2 (∥dt∥) ¬penetrate  (5) Up to the penetration into the earth; i.e., ¬penetrate, the reward increases while the agent minimizes the relative distance dt, thus encouraging the robot to engage with the pile. During penetration; i.e., penetrate, the reward increases while the robot minimizes the distance to the terminal state, dend. To encourage the robot to scoop more soil during the process, we added a positive reward consisting of mul- tiplying the excavated earth indicator, wt, by a constant cw. Hence, we deﬁned the immediate reward as: rt =  Pt + cwwt ∥dend∥≤ϵ Pt ∥dend∥> ϵ  (6) where ϵ is the distance tolerance to the terminal state. The precondition for penetration into the soil; i.e., penetrate, is deﬁned as: penetrate = minpi dB −dpi ≤1 (7) The values dB, dpi are the robot’s bucket tip and particle i positions, respectively. F. POLICY TRAINING In each episode during the simulation training session, the pile was initialized in a speciﬁc location and particle arrangement. Throughout the trial, the robot attempted to complete the bucket-ﬁlling process within 8 seconds using combination of the three DoF. We considered the outcome of an episode a success and terminated if, within the maximum episode time, the tip of the loader bucket reached the termi- nal located outside the pile. We trained the networks with algorithmic parameters using conservative values to achieve stable and reliable convergence. The model was trained from random initial weight values using a single Nvidia Quadro P2000D GPU for several trials of 800 episodes which took approximately three hours each. We employed a simple exploration strategy as described in [16] that includes noise inputted to the actor’s action. FIGURE 8. Normalized learning curve for the DDPG agent over 800 episodes (A). The fill factor is represented by the excavated soil in the bucket normalized by full bucket capacity (B). Cycle time represents the operation cycle time from the beginning of the episode until it reaches the terminal state (C). VI. SIMULATIONS AND EXPERIMENTAL RESULTS A. SIMULATION EVALUATION We assessed the learned scooping cycles in the simulation using both the average episode reward, and standard earth- moving parameters; namely, the ﬁll factor and operation cycle 24150 VOLUME 9, 2021
[page 7] O. Azulay, A. Shapiro: Wheel Loader Scooping Controller Using Deep Reinforcement Learning FIGURE 9. Evaluation of learned scooping cycles in the simulations. Bucket tip trajectory (A), robot velocity (B), lift angle (C) and tilt angle (D). time, as shown in Fig. 8. The scooping policy converged at the end of the training phase with an average loading cycle time of 5[sec] and a ﬁll factor of 65% from full bucket capacity with an overﬁll (approximately 23 particles). The learned scooping policy can be classiﬁed into 3 phases, as indicated in Fig. 9. In the ﬁrst phase, the robot moves toward the pile of particles while choosing the height and penetration angle to approximately zero tilt and minimum lift angles.

Wheel Loader Scooping Controller Using Deep Reinforcement Learning


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics; DOI: 10.1109/access.2020.3040246; license: cc-by-sa.

[Assistant]
MULTIBODY SYSTEM DYNAMICS Semi-recursive formulation is an efﬁcient multibody- based method for simulating complex mechanical sys- tems [16]–[19]. It enables dynamic responses to be solved in real-time and sometimes even faster than real-time [16]. Next, the formulation is brieﬂy introduced. Consider two rigid bodies n and n−1 with reference frames located at their center of mass as depicted in Fig.1. The bodies produce an open-chain system with Nb = 2 bodies connected by a joint. The locations of the joint in body n −1 and n are denoted Q and P respectively. The relative displacement FIGURE 1. Multibody system of two rigid bodies. vector between points Q and P can be described using vector dn−1,n. The position of the joint ﬁxture point P in the body n can be expressed as [16]: rn = Rcm n−1 + An−1 ¯un−1 + dn−1,n (1) where Rcm n−1 is the position vector that describes the center of mass of body n−1, An−1 is the rotation matrix of body n−1, and ¯un−1 is the location of point Q with respect to the body frame of reference n−1. The rotation matrix An of body n can be expressed using a concept of successive rotations as [16]: An = An−1An−1,n (2) where An−1,n is the relative rotation matrix between two neighboring bodies. In this study, the rotation of the bodies is expressed by Euler parameters. Velocity of point P can be expressed as [16]: ˙rn = ˙Rcm n−1 + ωn−1 × An−1 ¯un−1 + ˙dn−1,n (3) where ˙Rcm n−1 is the velocity vector of the center mass of body n−1 and ωn−1 is the angular velocity of body n−1. Angular velocity of body n can be expressed as [16]: ωn = ωn−1 + ωn−1,n (4) where ωn−1,n is the relative angular velocity between body n −1 and body n. By employing the principle of virtual work, equations of motion for a multibody system can be expressed [16]: δW = δ ˙qT (M¨q + C −Q) (5) where δW is the virtual work of multibody system, δ ˙q is the vector of 6Nb generalized virtual velocities, M is the 6Nb × 6Nb mass matrix, ¨q is the vector of acceleration of generalized coordinates, C is the quadratic velocity vector, and Q is the vector of generalized external forces. Vectors ¨q, C and Q have 6Nb dimensions and have the form [16]: ¨q =  ¨qT 1 ¨qT 2 . . . ¨qT Nb T (6) C = h CT 1 CT 2 . . . CT Nb i (7) 214000 VOLUME 8, 2020
[page 4] I. Kurinov et al.: Automated Excavator Based on RL and Multibody System Dynamics Q = h QT 1 QT 2 . . . QT Nb i (8) where ¨qn= h ¨RcmT n ˙ωT n iT . The vector of virtual velocity δ ˙q can be related to virtual joint velocities δ˙z as [16]: δ ˙q = Rδ˙z (9) where R is the velocity transformation matrix. For open-loop systems, the joint coordinates represents a set of minimal coordinates. By taking the derivative of (9), generalized accelerations can be expressed as [16]: ¨q = R¨z + ˙R˙z (10) Using (10) and (11), the equations of motion can be rewritten as [16]: δ˙zT RT  M R¨z + ˙R˙z  + C −Q  = 0 (11) (11) also holds for independent virtual velocities δ˙z; there- fore, virtual velocities can be eliminated [16]: RT MR¨z = RT (Q −C) −RT M ˙R˙z (12) The velocity transformation matrix R plays an important role in the formulation as it directly affects the efﬁciency of the method. One of the most efﬁcient ways to compute the velocity transformation matrix R is application of the element-by-element technique [17]: RNb = [R1 Nb R2 Nb . . . RPb Nb] (13) Closed-loop systems can be computed by expressing the system in the form of an open-loop system and then closing it be employing constraints [16]. Accordingly, the equation of motion takes the form [17]: (RT MR + αφT z φz)¨z = RT (Q −C) −RT M ˙R˙z −αφT z  β2φ + µ ˙φ + ˙φz˙z + ˙φt  (14) where α is a constant penalty factor, β2 and µ are constants representing natural frequency and damping ratio respec- tively, φ is the loop closure constraint equations, φz is a Jacobian matrix of the constraints, and ˙φ and ¨φ are the ﬁrst and second order derivatives of the constraint equations. C. HYDRAULICS In this study, the hydraulic system is modelled using the lumped ﬂuid theory [20]. The approach is based on partition- ing the system into discrete volumes in which the pressure is assumed to be equally distributed. The pressure in one volume can be computed as: ˙ps = Bes Vs Xnc k=1 Qsk (15) where Vs is a volume, Qsk is the sum of the ﬂows going in or out of the volume, nc is the number of hydraulic compo- nents belonging to the volume, and Bes is the effective bulk modulus corresponding to the volume.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Cutterhead and Cutting Tools Configurations in Coarse Grain Soils; DOI: 10.2174/1874836801711010182; license: cc-by.

[Assistant]
[page 1] See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/316320965 Cutterhead and Cutting Tools Conﬁgurations in Coarse Grain Soils Article in The Open Construction and Building Technology Journal · April 2017 DOI: 10.2174/1874836801711010182 CITATIONS 4 READS 2,310 3 authors, including: Xinggao li Beijing Jiaotong University 24 PUBLICATIONS 368 CITATIONS SEE PROFILE All content following this page was uploaded by Xinggao li on 10 August 2017. The user has requested enhancement of the downloaded file.
[page 2] Send Orders for Reprints to reprints@benthamscience.ae 182 The Open Construction and Building Technology Journal, 2017, 11, 182-199 1874-8368/17 2017 Bentham Open The Open Construction and Building Technology Journal Content list available at: www.benthamopen.com/TOBCTJ/ DOI: 10.2174/1874836801711010182 RESEARCH ARTICLE Cutterhead and Cutting Tools Configurations in Coarse Grain Soils Xinggao Li 1,*, Dajun Yuan 1 and Qingfei Huang 2 1 School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China 2 CCCC Highway Consultants CO., Ltd, Beijing 100088, China Received: November 30, 2016 Revised: February 13, 2017 Accepted: February 13, 2017 Abstract: Background: Many subway/metro tunnels were constructed using close face shield machines in coarse grain soils in last decades. Configurations of the shield cutterhead and cutting tools are critical in terms of cost and project duration. There are currently no recognized methodologies available for the configurations due to the various geological and technical influencing factors. Several aspects have been considered in many single case studies. Objective and Method: To aid a better understanding for the involved design and operational aspects in a system way, main physical and mechanical characteristics of the coarse grain soils encountered in China are briefly summarized; advantages and disadvantages of the spoke type and plate type cutterhead equipped with corresponding cutting tools are outlined; performances of the different shield cutterheads used in the typical subway construction cases are introduced. Results: Based on the jobsite experiences, discussed are the options with respect to design of the cutterhead and cutting tools in the coarse grain soils, including opening ratio of the cutterhead, configuration of the cutting tools, use of the single disc cutters with inserts, reasonable movement of shield machine, robust shield machines against wears and interventions for changing tools. Conclusion: According to the discussions, the rule-of-thumb suggestions are presented to provide references for later similar projects. Keywords: Cutterhead, Cutting tools, Coarse grain soils, Opening ratio, Disc cutter, Wear. 1. INTRODUCTION In the largest cities of China, the subway/metro systems have been increased dramatically due to the large population and limited surface spaces in recent years. So far, most of the subway tunnels in the cities have been constructed using the Earth Pressure Balanced (EPB) shield tunneling methods because of the many advantages that they offer over the conventional methods. Due to alignment constraints, many subway tunnels in the cities such as Beijing, Shenyang and Chengdu, are buried in whole or part in the coarse grain ground mainly consisting of fine/medium coarse sand and sandy gravel/cobble stratum with boulders. Various difficulties are encountered in the shield tunneling work undertaken in such ground conditions. Particularly, when tunneling in the cobble and boulder laden ground, the cutting tools suffer excessive wears and tears, and are liable to damage, fail and pull out. And even worse the cutterhead is worn through, which will adversely affect the rapid construction of tunnels. Consequently, it is of great significance to study the reasonable configurations of the cutterhead and cutting tools that is appropriate for the * Address correspondence to this author at the School of Civil Engineering, Beijing Jiaotong University, No.3 of Shangyuancun, Xizhimenwai, Haidian District, Beijing, China; Tel: (86-10) 51688115; Fax: (86-10) 51688022; E-mails: lixg@bjtu.edu.cn, lxg_njtu@163.com
[page 3] Cutterhead and Cutting Tools Configurations The Open Construction and Building Technology Journal, 2017, Volume 11 183 shield tunneling in coarse grain soils.

Cutterhead and Cutting Tools Configurations in Coarse Grain Soils


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Reinforcement learning based process optimization and strategy development in conventional tunneling; DOI: 10.1016/j.autcon.2021.103701; license: cc-by.

[Assistant]
A copy of the agent is saved after every 1000 episodes. To observe how big the differences are between individual training runs, we trained several identical DQN agents in the above described environment. After every episode, 21 parameters are saved to monitor the training progress. Below, the parameters that are mentioned in the paper are given (see the code “A_utilities.py” in Appendix 1 for a list of all recorded parameters): ▪ number of the current episode ▪ cumulative reward of the episode ▪ current value of ε ▪ the number of face instabilities of the whole episode ▪ average loss of the DQN agent throughout the episode ▪ number of moves/blasts that were required to finish the episode/to reach a terminal state ▪ 8 counters for how many times each of the actions of Table 3 were used; the goal of these counters is to see if the agent favors some actions over others and to detect “strategy changes” 5. Experiments In this section we present five training paths of exemplary agents and discuss the different strategies they found to deal with the given task. In Fig. 7, recordings of the agents’ training are given which shows that each agent has found a unique solution, and all training paths are substan tially different from one another. Comparing the five agents to one another, all of them were able to increase the cumulative reward per episode above 200 within 10,000 episodes (Fig. 7 first row). After around 30,000 episodes, first differ ences arise where first the cumulative reward of agent 2 and then of agent 3 started to stagnate. The reward of agent 1 stagnates at around 80,000 episodes. The cumulative rewards of the agents 4 and 5 kept on increasing, whereas agent 5 became instable after around 75,000 epi sodes and agent 4 reached the maximum reward at the end of the 120,000 training episodes. Right from the start, all agents started to use long advance lengths as a means to decrease the number of blasts/moves per episode as this is an effective way to maximize the achievable reward (Fig. 7 second row). Where the agents 1, 4 and 5 all took a similar strategy that aims at continuously minimizing the blasts/moves per episode throughout the whole training, agents 2 and 3 reached a minimum of around 120 blasts/ moves per episode after around 30,000 episodes. This correlates well with the stagnating rewards after 30,000 episodes of agents 2 and 3 as described above. The biggest differences in training paths can be observed with Table 4 Rewards that the agent receives from the environment in response to its actions. Reward (points) Description Condition tl * 3 reward for achieving breakthrough posth ≥tl and posbi ≥tl tl * 3 * -1 penalty for a timeout if number of moves in current episode is >200 −6 penalty for using the wrong excavation sequence, i.e. the bench is driven further ahead than the top heading posbi > posth −5 penalty for unstable tunnel face conditions determined from face pressure pf as evaluated by Eq. (3) (see below) pf ≥0 −4 penalty for changing from top heading to bench excavation or vice versa, as this usually involves a delay of the excavation due to logistics if current a ≥200 and prev. a < 200 or if current a < 200 and prev. a ≥200 −3 penalty for a too far distance between top heading and bench (see Section 3.1) if posth – posbi > distmax (initially set to 50 m) −2 penalty for using face support as this consumes additional time and resources if a = 112; a = 152; a = 202; a = 222 −1 penalty for every other move that does not meet any of the above conditions no other condition is fulfilled G.H. Erharter et al.
[page 8] Automation in Construction 127 (2021) 103701 8 respect to face instabilities, where only the agents 2 and 3 started to actively decrease the number of face instabilities (Fig. 7 third row) early in the training process (within 10,000 episodes). In contrast to that, the number of face instabilities increased within the first 10,000 episodes for the other agents and then only decreased slowly throughout the rest of the training. The best performing agent 4 shows a remarkable trend of stagnating face instabilities until around 80,000 episodes, followed by a decrease towards the end of training. While the goal was to let all agents train for 120,000 episodes, the training process of the agents 2 and 5 became unstable after around 75,000 and 85,000 episodes respectively, which led to the abortion of training after it could be observed that the agent would not recover from this.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks; DOI: 10.1186/s13640-018-0332-4; license: cc-by-sa.

[Assistant]
Deep learning methods provide high efficiency and the ability to process numerous MRIs from databases [35]. This study focuses on CNNs, which have gained popularity among researchers for object recognition and biological image segmentation. The developed diagnosis system has two phases for tumour detection and localization. The first phase con- verts tumour detection into an MRI classification problem and then categorizes MRIs into normal and abnormal images. The second phase focuses on tumour localiza- tion inside the abnormal images. Figure 1 shows a block diagram of the proposed system. MRIs are delivered to the system’s input, and then, the preprocessing tasks are performed. A feature extraction tool is used to select the relevant features, and a classification tool is applied to decide whether these features belong to a normal or an abnormal MRI. To achieve accurate results, the five- layer R-CNN has been trained and evaluated using MRIs from BraTS 2013 as a standard benchmark for tumour localization. 3.1 Brain tumour detection phase 3.1.1 Input image and preprocessing Due to the ability of magnetic resonance imaging to provide information about the soft tissues in the brain, databases of MRIs were used in this study as an input to the developed system. The goal of the preprocessing stage is to ensure the MRI readiness for the next stage. It improves image quality, reduces the noise level, and enhances the overall image resolution by employing clas- sical filtering techniques [36]. The preprocessing stage also adjusts the image size and type to a 227 × 227 RGB image, which is provided to the trainable CNN in the feature extraction stage.
[page 4] Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 Page 4 of 10 Fig. 1 A flowchart of the developed CNN-based system for brain tumour detection and localization. The flowchart shows the two system phases (detection and localization phases) and highlights the two different types of convolutional neural network (CNN) used for each system phase. The AlexNet, VGG-16, and VGG-19 models fit the CNN in the detection phase, and a five-layer fully developed R-CNN was used in the localization phase 3.1.2 Convolutional neural network (CNN) CNN is the deep learning model that has improved over the past two decades. CNNs can learn features automati- cally from input data, especially images, as is the case in this research [37]. The convolutional layers are used to convolve the input image with kernels (weights) to obtain a feature map. The weights of the kernels connect the fea- ture map units to the previous layer. AlexNet and two other CNN models, namely, VGG-16 and VGG-19, were used and evaluated in the tumour detection phase. How- ever, the focus of the theoretical foundation section is on the AlexNet model. The AlexNet model was selected due to its flexibility to be modified, its ability to reduce over- fitting using a dropout layer, and its capability to train faster through using a rectified linear unit (ReLU). The AlexNet model utilized for tumour detection is a pre-trained network from the MatConvNet toolbox [12]. It consists of 23 layers with weights. The first layer defines the input dimensions, including an input image size of 227 × 227 × 3. A series of convolutional layers intervene with ReLU and max-pooling layers. The final layer is a classification layer with 1000 classes. The tumour detection phase employs the CNN network for feature extraction, and there are few layers in the CNN that are suitable for this goal. The CNN depends on nine layers, as shown in Fig. 2: an image input, two convolution, two ReLU, two cross channel normalization (Norm), and two max-pooling layers. Layers 2 and 6 are convolutional layers with 11×11×3 and 5×5×48 convolutions, respec- tively. If we suppose that the input x and the weight w are realized on integer t, the 2D discrete convolution can be defined as in Eq. 1: y(i, j) = x(i, j) ∗w(i, j) =  m  n x(m, n)w(i −m, j −n) (1) where m ∗n is the size of the convolution matrix, w and x are the inputs from the previous layer, and y is the output of the convolutional layer. Table 2 The configurations of the MRIs database in the training and testing datasets used in tumour detection phase Total images Distribution in the training set Distribution in the testing set Normal Abnormal Normal Abnormal 349 45 77 64 163
[page 5] Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 Page 5 of 10 Fig.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Reinforcement learning based process optimization and strategy development in conventional tunneling; DOI: 10.1016/j.autcon.2021.103701; license: cc-by.

[Assistant] Chollet (Ed.), Deep Learning with Python, Manning, Shelter Island, NY, 2018. [44] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man´e, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi´egas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensor flow.org/, 2015. [45] F. Chollet, Others, Keras, 2015. [46] ¨Osterreichisches Normungsinstitut, Untertagebauarbeiten: Teil 1: Zyklischer Vortrieb, Wien 91.010.20; 93.020, 2019. [47] Z. Zhao, Y. Liang, X. Jin, Handling large-scale action space in deep Q network, in: 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), IEEE, Chengdu, 2018, pp. 93–96. [48] T. Zahavy, M. Haroush, N. Merlis, D.J. Mankowitz, S. Mannor, Learn what not to learn: action elimination with deep reinforcement learning, in: Advances in Neural Information Processing Systems 31 (NIPS 2018), Montreal, Canada, 2018, pp. 3566–3577. [49] ¨OGG, Guideline for the Geotechnical Design of Underground Structures with Conventional Excavation: Ground characterization and coherent procedure for the determination of excavation and support during design and construction. Translated from version 2.1, second.first, Salzburg, 2010. [50] H. Lauffer, Gebirgsklassifizierung für den Stollenbau, Geol. Bauwesen 24 (1958) 46–51. [51] P. Zhang, H. Li, Q.P. Ha, Z.-Y. Yin, R.-P. Chen, Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses, Adv. Eng. Inform. 45 (2020) 101097, https://doi.org/10.1016/j.aei.2020.101097. [52] J. van Eldert, H. Schunnesson, D. Johansson, D. Saiang, Application of measurement while drilling technology to predict rock mass quality and rock support for Tunnelling, Rock Mech. Rock. Eng. 53 (2020) 1349–1358, https://doi. org/10.1007/s00603-019-01979-2. [53] A. Radinger, F. Fasching, G. Pack, I. Kreutzer, D. Kostial, Consistent exploration by probe drilling and TSWD through the example of the Koralm tunnel/Konsequente Vorauserkundung mittels Bohrungen und TSWD am Beispiel des Koralmtunnels, Geomechanik Tunnelbau 7 (2014) 540–550, https://doi.org/10.1002/ geot.201400038. G.H. Erharter et al.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Reinforcement learning based process optimization and strategy development in conventional tunneling; DOI: 10.1016/j.autcon.2021.103701; license: cc-by.

[Assistant]
In this paper we present a novel RL based framework for construction process optimization and strategy development for conventional * Corresponding author. E-mail address: erharter@tugraz.at (G.H. Erharter). Contents lists available at ScienceDirect Automation in Construction journal homepage: www.elsevier.com/locate/autcon https://doi.org/10.1016/j.autcon.2021.103701 Received 1 December 2020; Received in revised form 29 March 2021; Accepted 2 April 2021
[page 2] Automation in Construction 127 (2021) 103701 2 tunneling. Such models can act as decision support for the geotechnical engineer, engineering geologist, geotechnician etc. (hereafter “geo technician” is used) (design choices, progress-planning) and in the long run such models work towards full automation in underground con struction. Hence, the model is a first attempt to automate decisions made by the geotechnician on face in underground construction. In the next section (2) we frame the process of conventional tunneling as a RL problem and provide details on how these two disci plines can connect. Section 3 is the main methodological section that presents the geotechnical scenario at the background of this RL simu lation as well as the agent and the environment. In Section 4 we describe the training process and in Section 5 we show experiences gathered during the training and testing of agents. A conclusion and implications for the vision of “digital tunneling” is given in Section 6 and we present an outlook in the last Section 7. A reference to the Python based code for this paper, is given in the appendix. 2. Conventional tunneling as a reinforcement learning problem The process of reinforcement learning (RL) is typically depicted as a closed loop where an agent takes different actions, to influence an environment which responds by sending an updated state as well as a reward signal to the agent (e.g. [26]). To apply RL, the learning problem must undergo the Markov property, i.e. we only need to know the cur rent state of the system, to make a decision [27]. Therefore, the state must include information about all aspects of the past agent–environ ment interaction that make a difference for the future. This can be said to be true for tunnel excavation where we only need to know the state of the rockmass and the excavation-process to decide on how to proceed. Tunnel construction follows several cyclic and sequential processes, some of which can be framed as loops and therefore translated to RL problems. The most outstanding loop in conventional tunneling is the excavation of an underground opening with a sequential construction process of: blasting, mucking and rock support installation [28] (“excavation loop” in Fig. 1). While the components of this excavation loop are themselves often sequential processes (e.g. blasting sequence, support installation etc.), the excavation loop specifically is based on a sequence of geotechnical decisions belonging to a bigger cycle which we refer to as the excavation sequence decision – loop. Looking at the bigger picture, excavation sequence decisions are one part of the whole con struction phase of a tunnel and therefore part of the whole tunnel life cycle (Fig. 1). The focus of this study is to create a simulation of a simplified version of the process that governs the general excavation sequence decisions and frame it as a RL-loop. This process can be translated to a RL-loop/ Markov decision process [27] consisting of the following components: ▪ the decision making geotechnician is the agent ▪ processes like “top heading excavation”, “bench excavation”, “installation of face support” etc. are the actions ▪the rockmass itself and the construction site with all its pro cesses are the environment (here described at each timestep by the state of the environment and the reward-system) ▪the sum of all delays (planned and unplanned) and complica tions throughout the course of the excavation which result from the geotechnician’s actions are the reward ▪ the current state of the excavation including information about the past and recent rockmass conditions as well as the already installed support are the state Fig. 2 is a graphical representation of this process which we refer to as “TunnRL” (Tunnel automation with Reinforcement Learning). The individual components of this loop in the above given list as well as in Fig. 1 and Fig. 2 are only for explanatory purpose and do not claim to be complete.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Cutterhead and Cutting Tools Configurations in Coarse Grain Soils; DOI: 10.2174/1874836801711010182; license: cc-by.

[Assistant]
In view of the high abrasivity of the sandy cobble/gravel soils with boulders, the primary wear on cutting tools and the secondary wear on the support structures such as the cutterhead, if not controlled, are
[page 5] Cutterhead and Cutting Tools Configurations The Open Construction and Building Technology Journal, 2017, Volume 11 185 excessive, and especially the cutting teeth made of hard metal (cemented carbide) is liable to flake off when subject to impacts of the over-sized particles. Being loosely cemented and with less cohesion, most coarse grain soils exposed are typically unstable strata and 3. their mechanical properties are unfavorable for force generation and transmission, and the ground surface is liable to cave in if improper manipulations of the shield machines exist. Fig. (1). Typical sandy gravel/cobble soil with boulders encountered in Beijing area. 3. CUTTERHEADS, CUTTING TOOLS CONFIGURATIONS AND SHIELD CUTTERHEAD PERFORMANCES 3.1. Cutterheads and Cutting Tools Configurations Advance of a shield machine is heavily dependent on its cutterhead as well as the combination and arrangement of the installed cutting tools. Generally, as shown in Figs. (2 & 3), there are basically two types of cutterheads for soft ground shield machines; one is the spoke type and the other is the plate type. The most significant difference between the two types of cutterheads is the discrepancy in opening ratio of the cutterheads, which is the percentage of openings of the total face area of a cutterhead. The advantages and disadvantages related to opening ratio of the two type’s cutterheads in excavating tunnels are displayed in Table 1. Fig. (2). Spoke type cutterhead.
[page 6] 186 The Open Construction and Building Technology Journal, 2017, Volume 11 Li et al. Fig. (3). Plate type cutterhead. Table 1. Advantages and disadvantages of the spoke type and plate type cutterhead. Items Spoke Type Cutterhead Plate Type Cutterhead Pressure control of the cutting face Being almost equal to the earth chamber pressure, the cutting face pressure is easy to be controlled. On account of the indirect transmission between earth chamber pressure and cutting face pressure, it is a little difficult to perform precise pressure control of the cutting face. Entry of muck into the excavation chamber Entry of the excavated materials into cutterhead is smooth on account of the high opening ratio, almost resulting in no clogging of the cutterhead. Affected by the plate, the entry of the excavated materials is not smooth and the cutterhead is liable to be clogged and jammed by the excavated materials. Torque of cutterhead The required torque of the cutterhead is small and the manufacturing price of the equipment is low. Necessitate high torque of the cutterhead and large capacity of the equipment, as leads to a high construction cost. Wear of the cutterhead and cutting tools The high opening ratio of the cutterhead causes the relatively minor wears of the cutterhead and cutting tools. Because of the small opening ratio of cutterhead, the excessive wear of the cutterhead and cutting tools is a hackneyed phenomon. Adaptability to the over- sized particles Over-sized grains or boulders that can’t be extracted from the excavation chamber by the screw conveyor must be handled by manual work. Kept out of the cutterhead, the over-sized grains or boulders can be broken by disc cutters. Safety of changing tools The high opening ratio of the cutterhead to some extent decreases the stability of the cutting face. The plate is helpful to stability of the cutting face, and a higher construction safety results. Requirements for shield machine manipulations The requirements are relatively high. The requirements are relatively low. The prevalent excavation tools used for the spoke type cutterhead include tooth bits (scraper, ripper), shell bits, precut bits (ripper) and a center fish-tail bit (nose cone ripper). The idea of this combination of tools is to loosen ground before cutterhead with the center fish-tail bit and the precut bits, and rip off the over-sized stones out of the soil matrix with rippers, and shave off the weak materials with scrapers.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Cutterhead and Cutting Tools Configurations in Coarse Grain Soils; DOI: 10.2174/1874836801711010182; license: cc-by.

[Assistant]
Bilgin et al. (2012) performed some theoretical estimations to estimate the effect of replacing disc cutters with chisel tools on performance of a tunneling machine in difficult ground conditions based on field measurements [18]. Willis (2012) showed the advances of quality and configuration for the cutting tools developed by Robbins engineers to reduce cutterhead wear at the EPB shield machines [19]. Havekost et al. (2013) introduced a plan to remove the close face shield machine and replace it with a digger shield when constructing the 1829 m long Portsmouth Force Main Tunnel in Portland and the machine was unable to advance through running soils containing cobbles and boulders [20]. Köppl and Thuro (2013) set up a prognosis model for cutter life in soft ground for mixshield machines based on empirical correlations with soil parameters and machine design and advance parameters after the detailed data analysis of the 18 mixshield drives [21]. Duhme and Edelmann (2014) presented the general principles of inspection and monitoring technology developed by Herrenknecht of the shield machine cutting tools as well as discussed examples of their applications [22]. Roby and Willis (2014) analyzed the record-breaking and high-performing projects to identify factors that contribute to fast advance of the EPB shield machines, such as adequate cutting tools and cutterhead design, and an effort was made to form simple, high level guidelines for the optimal shield machine excavation in mixed ground conditions [23]. In-door tests can only be used to deal with limited interaction features and sometimes are not realistic because of the ignorance
[page 4] 184 The Open Construction and Building Technology Journal, 2017, Volume 11 Li et al. of some basic factors. Numerical simulation seems to be a promising way to capture the interaction between tunneling shields and soils, but its application is greatly limited in practical engineering on account of difficulty in determining constitutive relations of the coarse grain soils. Field observations remain the commonly recognized approach for cutterhead performance prediction. The soft ground shield machine cutterhead design has historically been driven by the iterative designs based on empirical data, observations over various projects and industry rules of thumb [3]. Realistic performance predication of the EPB shield is only possible on the basis of the specific project data [17]. Any in-situ study conducted in the lately finished shield tunnel projects in coarse grain soils is valuable to promote the work of the cutterhead and cutting tools configurations. This issue of the cutterhead and cutting tools configurations for the tunneling shields in coarse grain soils has caused widespread concern in the last decade in China with the completion of many subway shield tunnels, and a growing attention and interest is focused on it. Methodologies for shield tunneling in coarse grain soils progress steadily and some experiences of the cutterhead selection and cutting tools configurations have been published.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV; DOI: 10.3390/min12020268; license: cc-by-sa.

[Assistant] [page 1]   Citation: Sinaice, B.B.; Owada, N.; Ikeda, H.; Toriya, H.; Bagai, Z.; Shemang, E.; Adachi, T.; Kawamura, Y. Spectral Angle Mapping and AI Methods Applied in Automatic Identiﬁcation of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV. Minerals 2022, 12, 268. https://doi.org/ 10.3390/min12020268 Academic Editor: Yosoon Choi Received: 21 January 2022 Accepted: 18 February 2022 Published: 20 February 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). minerals Article Spectral Angle Mapping and AI Methods Applied in Automatic Identiﬁcation of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV † Brian Bino Sinaice 1,* , Narihiro Owada 2, Hajime Ikeda 1 , Hisatoshi Toriya 1 , Zibisani Bagai 3, Elisha Shemang 4, Tsuyoshi Adachi 1 and Youhei Kawamura 5 1 Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, Japan; ha2ikeda@gmail.com (H.I.); toriya@gipc.akita-u.ac.jp (H.T.); adachi.t@gipc.akita-u.ac.jp (T.A.) 2 Faculty of International Resource Sciences, Technical Division, Akita University, Akita 010-8502, Japan; owada@gipc.akita-u.ac.jp 3 Department of Geology, University of Botswana, Private Bag UB 0022, Gaborone, Botswana; bagaizb16@gmail.com 4 Department of Earth and Environmental Science, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana; shemange@biust.ac.bw 5 Faculty of Engineering, Division of Sustainable Resources Engineering, Hokkaido University, Hokkaido 060-8628, Japan; kawamura@eng.hokudai.ac.jp * Correspondence: bsinaice@rocketmail.com † This article is an expanded version this conference paper: Sinaice, B.B.; Takanohashi, Y.; Owada, N.; Utsuki, S.; Hyongdoo, J.; Bagai, Z.; Shemang, E.; Kawamura, Y. Automatic magnetite identiﬁcation at Placer deposit using multi-spectral camera mounted on UAV and machine learning. In Proceedings of the 5th International Future Mining Conference 2021—AusIMM 2021, Online, 6–10 December 2021; pp. 33–42; ISBN 978-1-922395-02-3. Abstract: The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artiﬁcial Intelligence (AI). Depth possessing multispectral data were captured at different ﬂight elevations. This was in an attempt to ﬁnd the best elevation where remote identiﬁcation of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/−16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to ﬁnd the best performing model at classifying magnetite iron sand. Considering the post ﬂight logs, the spatial area coverage of 338 m2, a global classiﬁcation accuracy of 99.7%, as well the per- class precision of 99.4%, the 20 m ﬂight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices. Keywords: UAV; remote sensing; hyperspectral imaging; multispectral imaging; spectral angle mapping; artiﬁcial intelligence; machine learning; deep learning 1. Introduction Safety, cost efﬁciency and overall system optimization via artiﬁcially intelligent (AI) machine learning (ML) as well as deep learning (DL) algorithms are goals set by modern day industries that invest heavily in output maximization [1]. To improve safety and ore tracking abilities, the mining industry has been no stranger to this trend as the introduction of machines such as automated haul trucks and conveyer belts has been seen [1,2].

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV