[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]
As the present paper should be the first introduction of RL into tunneling we refrained from over-complicating the simulation and rather give an impetus for future studies that build upon this work. The below given improvements are a non-exhaustive list of ideas that we think are worth to be further explored. Ever improving processing power will alleviate problems related to computational cost over time. Nevertheless, all improvements must consider that each step in the framework will be done millions of times throughout the training and state of the art RL is by itself already computationally heavy. Improvements address either the agent or the environment: ▪ On the environment’s side, improvements could work towards making the geotechnical scenario more realistic by involving more ground types and other phenomena like groundwater and in-situ stress conditions. The excavation geometry could become more complex so that also other excavation shapes and sequences such as full-face excavation, or a further division of the top heading and bench excavation are possible. ▪ Closely related to the excavation geometry – and in our view one of the main points that should be improved – is the stability evaluation of the simulation. The reason why we used Eq. (3) after [29] as a substitute for more sophisticated means of sta bility assessment (e.g. tunnel cross sectional analyses such as analytical convergence confinement methods or 2D and 3D finite element analysis) is that this analytical solutions is computationally very efficient and does not prolongate the training process too much. For example, 100,000 episodes of the current framework with around 120 moves per episode would require ~1.2e+7 FEM based stability assessments. However, this reduction of the stability assessment is only us able for the ground conditions described in [29] and also ne glects other phenomena like the 3D stress state at the tunnel face. Improvements could be to do the stability assessment based on stand-up time concepts that also involve the rockmass quality [50] or ideally 3D finite element analysis as given above. ▪ Improvements for the agent on the one hand address the agent’s performance by modifying the agent’s architecture itself. Sys tematic hyperparameter tuning was not conducted yet and may Fig. 9. Histograms showing the performance of the agent 4 that was tested for 10,000 episodes in Fig. 7. Note the different scales of the x-axes in comparison to the histograms of Fig. 8. Fig. 10. Boxplot that shows how many times the agent 4 uses each action throughout 10,000 test episodes. Bold black lines in the boxes represent median values; the boxes confine the upper and lower quartiles and the whiskers show min.-max. Values; “al” in the x-labels refers to “advance length”. G.H. Erharter et al.
[page 11] Automation in Construction 127 (2021) 103701 11 help to further improve the agent’s performance (e.g. grid−/ random search or even RL based hyperparameter optimization [51]). From a geotechnical point of view though, interesting improvements mainly concern an extension of the agent’s ca pabilities by introducing more possible actions. Whereas direct improvements to the given framework would be more advance lengths and types of tunnel support, other ideas are to involve exploration ahead of the face by simulated measurement while drilling [52] or geophysical exploration [53] to give the agent an idea what might be in front of the current excavation face. An idea in this regard is also to extend the agent to a multi- agent framework as it was used successfully before [15] where different agents have different tasks to fulfill. Future studies will work towards a more realistic environment and more complex agents in the TunnRL framework. Where TunnRL fits well in the line of the current development of automation, the greatest po tential lies in the possibility to develop new and not yet considered tunneling strategies for sequential tunnel excavation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix Appendix 1: Link to the GitHub repository where this paper’s code can be found: https://github.com/geograz/Tunnel-automation-with-Re inforcement-Learning-TunnRL- References [1] T. Marcher, G.H. Erharter, 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: 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. https://doi.org/10.3390/app12052656 https://www.mdpi.com/journal/applsci
[page 2] Appl. Sci. 2022, 12, 2656 2 of 20 results. With the development of information technology, artiﬁcial intelligence, the Internet of Things, big data, and other technologies, the trend of tunnel construction automation and unmanned is becoming more and more obvious, and the intelligent classiﬁcation of surrounding rock is the most important part. Thus, an automatic, rapid and accurate intelligent classiﬁcation technology of surrounding rock is required for the construction of tunnel with high quality and high efﬁciency. Machine learning is an important method of artiﬁcial intelligence research [7–9] that has been applied in many types of tunnel analyses, such as deformation prediction [10–14], prediction of energy consumption of cutter head drives [15], rock burst prediction [16], reliability analysis [17,18], stability analysis [19], optimization of blasting parameters [20], support pattern selection [21], the prediction of blast-induced ground vibrations [22], tunneling risk prediction and assessment [23,24], diagnosing tunnel collapse sections [25], and TBM tunneling construction and management [26–30]. Machine learning is also an important method in the intelligent classiﬁcation of surrounding rock. The physical and mechanical parameters of rock mass have been applied to the RMR value prediction using a neural network [31,32]. These parameters include the bulk density, compressive strength, ingress of water, rock quality designation (RQD), average distance between leak, and seismic velocity. The prediction of RMR has also been realized by using a neuro-fuzzy inference system based on the uniaxial compressive strength, RQD, joint or discontinuity spacing, joint condition, and groundwater condition [33]. In addition, the geophysical parameters, such as the seismic velocity and resistivity [34,35], have been used to classify the surrounding rocks [36–38]. The machine learning algorithms used in these studies include a variety of single basic algorithms, optimization algorithms, and integrated algorithms. These previous studies all show that the appropriate machine learning algorithms have excellent performance in different ﬁelds when there is an internal connection between input index and output index and the number of samples is sufﬁcient. Especially for the highly nonlinear problems, the machine learning method often has better performance and higher computational efﬁciency than traditional statistical analysis methods. More importantly, the machine learning methods have the intelligent characteristics of automatic analysis and continuous learning, which provides effective help for this study. However, such methods generally require manual ﬁeld testing of classiﬁcation indices, such as rock strength and rock mass integrity, followed by manual input into the system. None of these parameters applied to the intelligent classiﬁcation of surrounding rocks can achieve real-time automatic collection in the tunneling process. The emergence of measure-while-drilling (MWD) technology provides a good solution to this problem. The correlation between the drilling parameters and the surrounding rock quality parameters was studied and explored by scholars as early as the 1960s and 1970s [39–43]. Using ﬁeld experiments with statistical analysis, the correlations between drilling parameters and surrounding rock quality parameters, such as the uniaxial com- pressive strength [44–46], shear strength [47], Schmidt rebound hardness [48], cutting performance (Kerf angle d and speciﬁc energy) [49,50], RQD [51], and zones of volcanic weathering and decomposition grades [52] have been studied. In recent years, drilling parameters have been used for surrounding rock classiﬁcation based on the Q method [53] or RMR method [54]. Although these previous studies have conﬁrmed the existence of a relationship be- tween drilling parameters and the quality of surrounding rock, the correlation based on current research of the drilling parameters and the surrounding rock quality parameters mostly refers to a certain lithology. Furthermore, the samples do not cover common rock lithology and are not universal.

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

[Assistant] This evaluation underscores the importance of the expert knowledge required for excavation. By contrast, most previous solutions to the automation of the scooping task (1) do not generalize to different machines and pile environments (2) rely on prior knowledge of an expert oper- ator and (3) require accurate models of the machine [10]. We presented a method that outperforms the above using the power of RL. Our learned controller acts based on measured sensors and implements the transitioning between phases with ﬁxed predetermined criteria, which were developed solely through the training in the simulation. Future work should consider testing the proposed controller on a full-size skid-steer loader, as well as tackling the full problem of earthmoving including navigating to the pile, attempting a scooping cycle, navigating to another site and dumping. ACKNOWLEDGMENT The authors would like to thank RoboTiCan for lending them the robot used in the study presented in this work. REFERENCES [1] S. Dadhich, U. Bodin, and U. Andersson, ‘‘Key challenges in automation of earth-moving machines,’’ Autom. Construct., vol. 68, pp. 212–222, Aug. 2016. [2] S. Dadhich, ‘‘A survey in automation of earth-moving machines,’’ Lulea Univ. Technol., Luleå, Sweden, Res. Rep., 2015. [3] S. Singh, ‘‘State of the art in automation of earthmoving,’’ J. Aerosp. Eng., vol. 10, no. 4, pp. 179–188, Oct. 1997. [4] R. Filla, M. Obermayr, and B. Frank, ‘‘A study to compare trajectory gener- ation algorithms for automatic bucket ﬁlling in wheel loaders,’’ in Proc. 3rd Commercial Vehicle Technol. Symp. Commercial Vehicle Technol. (CVT), 2014, pp. 588–605. [5] T. Tomatsu, K. Nonaka, K. Sekiguchi, and K. Suzuki, ‘‘Model predictive trajectory tracking control for hydraulic excavator on digging operation,’’ in Proc. IEEE Conf. Control Appl. (CCA), Sep. 2015, pp. 1136–1141. [6] O. Luengo, S. Singh, and H. Cannon, ‘‘Modeling and identiﬁcation of soil-tool interaction in automated excavation,’’ in Proc. IEEE/RSJ Int. Conf. Intell. Robots Systems. Innov. Theory, Pract. Appl., Oct. 1998, pp. 1900–1906. [7] K. Althoefer, C. P. Tan, Y. H. Zweiri, and L. D. Seneviratne, ‘‘Hybrid soil parameter measurement and estimation scheme for excavation automa- tion,’’ IEEE Trans. Instrum. Meas., vol. 58, no. 10, pp. 3633–3641, Oct. 2009. [8] D. Schmidt, M. Proetzsch, and K. Berns, ‘‘Simulation and control of an autonomous bucket excavator for landscaping tasks,’’ in Proc. IEEE Int. Conf. Robot. Autom., May 2010, pp. 5108–5113. [9] S. G. Olsen and G. M. Bone, ‘‘Modelling of robotic bulldozing operations for autonomous control,’’ in Proc. 24th Can. Conf. Elect. Comput. Eng. (CCECE), 2011, pp. 001188–001193. [10] S. Dadhich, F. Sandin, U. Bodin, U. Andersson, and T. Martinsson, ‘‘Field test of neural-network based automatic bucket-ﬁlling algorithm for wheel- loaders,’’ Autom. Construct., vol. 97, pp. 1–12, Jan. 2019. [11] I. Popov, N. Heess, T. Lillicrap, R. Hafner, G. Barth-Maron, M. Vecerik, T. Lampe, Y. Tassa, T. Erez, and M. Riedmiller, ‘‘Data-efﬁcient deep rein- forcement learning for dexterous manipulation,’’ 2017, arXiv:1704.03073. [Online]. Available: http://arxiv.org/abs/1704.03073 [12] L. Tai, G. Paolo, and M. Liu, ‘‘Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,’’ in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Sep. 2017, pp. 31–36. [13] J. Hwangbo, I. Sa, R. Siegwart, and M. Hutter, ‘‘Control of a quadrotor with reinforcement learning,’’ IEEE Robot. Autom. Lett., vol. 2, no. 4, pp. 2096–2103, Oct. 2017. [14] S. Gu, E. Holly, T. Lillicrap, and S. Levine, ‘‘Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,’’ in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), May 2017, pp. 3389–3396. [15] J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, ‘‘Sim-to-real: Learning agile locomotion for quadruped robots,’’ 2018, arXiv:1804.10332. [Online]. Available: http://arxiv. org/abs/1804.10332 [16] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, ‘‘Continuous control with deep reinforce- ment learning,’’ 2015, arXiv:1509.02971. [Online]. Available: http://arxiv. org/abs/1509.02971 [17] O. Azulay. Package for Simulating Wheel Loader in Gazebo Engine for Reinforcement Learning Purposes. Accessed: Jan. 12, 2021. [Online]. Available: https://github.com/osheraz/komodo [18] P. J. A. Lever and F.-Y.

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

[Assistant]
Overall, the accuracy of XGBoost remained higher than that of the decision tree and random forest, which presented similar accuracy at different tree depths. Meanwhile, the high accuracy of XGBoost started to stabilize when the tree depth reached 6, while there was a signiﬁcant advantage in accuracy compared with the other methods. The higher accuracy of XGBoost with a lower tree depth means a simpler trained model and less running time, compared with the random forest, which relies on a larger tree depth and is more computationally expensive. Even though the decision tree model shows a high training efﬁciency, the accuracy was signiﬁcantly lower than that of XGBoost, even when the tree depth reached 20 or higher, causing overﬁtting on the validation dataset and affecting the detection performance for novel data. The XGBoost algorithm proved to be appropriate, with high accuracy and low computational expense. Other, more vital parameters were set to default because there were no huge training data or large feature classes. Considering the running time and detection accuracy, the tree depth of XGBoost was set to 3 to reduce overﬁtting and improve the efﬁciency. The number of samples is also important for dealing with overﬁtting and underﬁtting problems. Based on the overall 349,559 points of the training set, 30% of the data were randomly reserved as an additional validation set, while 70% of the data were the training set, divided into 10, and the training size ranged from 0.1 to 1. As shown in Figure 7, the training accuracy across ten cross-validations and testing accuracy was compared at 10 different ratios for the training set. With an increase in training samples, the validation accuracy and testing accuracy gradually approached the same value and presented lower differences, which means that the model showed low bias and variance. Moreover, when the ratio of the training samples was set to 0.4 or higher, the accuracy variation decreased slightly and presented little ﬂuctuation. To prevent overﬁtting or underﬁtting, the ratio was set to 0.4, meaning that approximately 100,000 points would be trained in following experiments. 4.3. Performance with Different Resampling Algorithms Aiming at mitigating the impact of data imbalance on the detection of strong scintil- lation, a combination of oversampling and undersampling algorithms was proposed to achieve resampling. As shown in Table 4, comparisons were made between the SMOTE- ENN algorithm and other several single oversampling or undersampling methods, namely, the random oversampling, random undersampling, SMOTE, and ENN algorithms, on the basis of the raw data used in the training process. All of the comparison experiments were
[page 13] Remote Sens. 2021, 13, 2577 13 of 22 based on 100,000 points of data randomly selected from the overall training dataset, and the ratio of weak, moderate and strong scintillation events was 4.05:3.54:1, in accord with those in the overall training dataset. The training process was achieved with the XGBoost algorithm with a tree depth of 3. The raw data were trained directly on the XGBoost model, while the other methods required resampling on the raw data and then training on the XGBoost model. All the trained models were tested on the 38,841 points of the dataset mentioned in Table 1 with a ratio of weak, moderate and strong scintillation events equal to 3.30:2.56:1, including 11 segments of one-hour data that were not involved in the training phase. Accuracy (%) Figure 7. Training accuracy and testing accuracy of XGBoost versus the sample number used in the training set. In the overall training dataset mentioned in Table 1, 30% of the samples were randomly selected as the validation set, and the reserved samples were randomly selected as the training datasets according to different training sizes. Table 4. Performance comparison for XGBoost algorithm improved by different resampling techniques based on testing results.

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: 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] Recently, deep learning paradigms have received con- siderable research attention. Deep learning models have multiple layers that can be trained using either supervised or unsupervised training approaches [9, 10]. In recent studies, deep learning models have achieved competi- tive results compared to the classical learning models in brain tumour diagnosis and tumour image classification [11, 12]. In addition, deep learning models like convolu- tional neural networks (CNNs) have improved the field of object detection and classification in different applications [13, 14]. As a deep learning paradigm, CNNs [15] have been used to extract high-level features from raw image data [16]. Although several studies have targeted the application of deep learning methods to brain tumour diagnosis, a complete system for automatic tumour detection and localization is not yet available in the literature. In addi- tion, integrating tumour detection and localization accu- racy within a single diagnosis system is still an open chal- lenge. Having brain tumour detection phase preceding the tumour localization in a single system leads to discarding the normal images from being processed in the localiza- tion phase. This opens doors to the real-time deployment of such automatic tumour diagnosis systems that save time and computing power from locating tumours in normal images. This research addresses the aforementioned challenges by developing a novel and complete computer-aided diag- nosis (CAD) system for tumour detection and localization from MRIs. In the tumour detection phase, the system combines a CNN, which is used for feature extraction due to its ability to learn features from raw data, with an error-correcting output codes support vector machine (ECOC-SVM), which is used for feature classification. The system is considered a two-phase multi-model arte- fact due to its detection and localization abilities using different CNN models. Three CNN models, namely, AlexNet, Visual Geome- try Group (VGG)-16, and VGG-19, have been deployed and evaluated for tumour detection. A five-layer fully developed region-based convolutional neural network (R- CNN) has been used in the second phase for tumour localization. The accuracy of the developed two-phase multi-model system has been evaluated using MRIs extracted from two different databases: the Reference Image Database to Evaluate Response (RIDER) Neuro MRI database [17] and the Brain Tumor Segmentation (BraTS) 2013 database. The key contributions of this study exist in several dimensions. First, a complete two-phase multi-model sys- tem for brain tumour detection and localization that combines a CNN and an ECOC-SVM is implemented and evaluated. Although CNNs and SVMs are not new approaches, the state-of-the-art in this field does not yet include systems similar to the one developed here, i.e., with these specific phases and CNN learning models. Sec- ond, integrating tumour detection and localization in one artefact is a good step towards real-time deployment of the research findings. Third, 349 MRIs from the RIDER Neuro MRI dataset are extracted and manually annotated, and this dataset will be available as a standard benchmark for other researchers who conduct research on tumour detection. Fourth, the conducted experimental works have proven the superiority of the developed system in terms of tumour detection and tumour localization. The remainder of this article is organized as follows. Section 2 is devoted to reporting related work on both the detection and localization of brain tumours. The description of the two-phase multi-model tumour diag- nosis system with respect to design and implementation is provided in Section 3. The experimental studies and the simulations for evaluating the whole diagnosis system in terms of tumour detection and localization accuracy are documented in Section 4. Finally, concluding remarks are outlined in Section 5. 2 Related work Brain tumour detection has received substantial research attention, and several detection methods have been pre- sented over the past two decades. In [18], a combina- tion of morphological filters, discrete wavelet transforms (DWTs), principal component analysis (PCA), and ker- nel support vector machines (KSVM) were applied to classify MRIs as normal and abnormal.

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

[Assistant]
Conclusions Severe ionospheric scintillation is an adverse factor inﬂuencing the amplitude and carrier phase of a GNSS signal; its detection is a prerequisite in the design of an advanced re- ceiver with greater accuracy, reliability and efﬁciency. Nevertheless, the natural appearance of strong ionospheric scintillation occurs incidentally compared to that of weak/moderate scintillation. The imbalance may prove a challenge in achieving higher detection accuracy for strong scintillation events. As a strategy for detecting the severe ionospheric scintilla- tion events, the eXtreme Gradient Boosting (XGBoost) algorithm improved by the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) resampling technique was developed as follows: (1) The machine learning method of XGBoost was proposed to improve the overall detection accuracy. According to 10 cross-validations, the accuracy was better than that of the decision tree and random forest. Meanwhile, XGBoost demonstrated sufﬁcient validation accuracy when the tree depth was set to a small value, which not only signiﬁcantly simpliﬁed the model complexity, but also effectively alleviated the overﬁtting problem. (2) Aiming at dealing with imbalance, different resampling techniques were compared based on the XGBoost detection model. SMOTE-ENN outperformed the other tech- niques on the whole. Moreover, similar improvements were observed for the decision tree and random forest detection models, after the SMOTE-ENN resampling tech- nique, while the improved XGBoost performed better than the other methods. (3) As for training datasets with different degrees of imbalance in classes 0/1/2, with ra- tios ranging from 1:1:1 to 7:7:1, different detection models and corresponding models improved by SMOTE-ENN were trained and then tested on the same novel dataset. The results showed overall enhancements for the improved detection methods com- pared to the corresponding raw methods, among which the improved XGBoost method showed the best performance. (4) The improved methods were tested with different degrees of imbalance in real data to evaluate the performance of the improved XGBoost. The results show distinct enhancements in overall accuracy, recall for class 2 and F1 scores for class 2, proving signiﬁcant improvements in detecting severe scintillation events as well as reducing the problem of missing important events. Consequently, the performance of XGBoost improved by SMOTE-ENN was examined in comparative tests and under various conditions. The signiﬁcance of these results lies in dealing with the problems of the natural and incidental appearance of strong scintillation
[page 20] Remote Sens. 2021, 13, 2577 20 of 22 events, which may cause imbalance and especially affect the detection accuracy for strong scintillation events. This work would be of general interest for researchers in the ﬁelds of detecting interference in satellite signals (e.g., ionospheric scintillation, solar radio burst, and spooﬁng); the design of advanced receivers with greater accuracy, reliability and efﬁciency; and the atmospheric layer and space weather. Author Contributions: Conceptualization, M.L.; methodology, M.L.; software, X.Z.; validation, X.T. and G.T.; formal analysis, M.L.; writing—original draft preparation, M.L.; writing—review and editing, T.H.; supervision, X.C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Key Research and Development Plan of China, grant number 2018YFB0505103, and the National Natural Science Foundation of China, grant number 61873064. Data Availability Statement: Relevant data are available on request from the corresponding author. Please contact Xuefen Zhu (zhuxuefen@seu.edu.cn). Acknowledgments: The authors would like to thank three anonymous reviewers for their valuable and insightful comments. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Cai, X.; Burns, A.G.; Wang, W.; Qian, L.; Liu, J.; Solomon, S.C.; Eastes, R.W.; Daniell, R.E.; Martinis, C.R.; McClintock, W.E. Observation of Postsunset OI 135.6 nm radiance enhancement over South America by the GOLD mission. J. Geophys. Res. Space Phys. 2020, 126, 2020JA028108. 2. Karan, D.K.; Daniell, R.E.; England, S.L.; Martinis, C.R.; Eastes, R.W.; Burns, A.G.; McClintock, W.E. Early morning equatorial ionization anomaly from GOLD observations. J. Geophys. Res. Space Phys.


[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]
This is mainly because the use of an intelligent drill jumbo (which refers to a drill jumbo that can automatically collect and transmit drilling param- eters) for tunnel construction is low, which makes it more difﬁcult to collect sufﬁcient drilling parameter samples for the surrounding rock classiﬁcation. Thus, the classiﬁcation method with more objective, intelligent, and efﬁcient evaluation requires further study.
[page 3] Appl. Sci. 2022, 12, 2656 3 of 20 The purpose of this research is to introduce 10 machine learning algorithms to predict the quality of surrounding rock using MWD data (drilling parameters) obtained from ﬁve tunnels of the Zhengzhou–Wanzhou line of the high-speed railway project in China. Through comparative analysis, three machine learning models with better comprehensive performance among them were selected to establish the tunnel surrounding rock intelligent classiﬁcation system by the drill and blast method. The results of this study lay a solid foundation for the dynamic design and intelligent construction of tunnels. 2. Materials and Methods 2.1. Proposed Methods and Procedures In this study, we ﬁrstly collected sample data, and then conducted a range of data processing, including sample data cleaning, sample imbalance treatment, sample feature analysis, and sample data splitting. Finally, we trained some intelligent classiﬁcation models of the surrounding rock, and selected the better of them. The research ﬂow chart about main procedures and proposed methods of this study is shown in Figure 1. Figure 1. The research ﬂow chart about main procedures and proposed methods. 2.2. Sample Collection The sample of this study was obtained from some tunnels of the Zhengzhou–Wanzhou line of the high-speed railway project in China. This railway line runs from Zhengzhou East Railway Station to Wanzhou North Railway Station, with a total length of 818 km. It has 18 stations and a designed speed of 350 km/h, which connects Henan province, Hubei province, and Chongqing province. There are 32.5 tunnels in the Hubei province section, with a total length of 167.6 km, and the lithologies of stratum exposed by these tunnels are mainly dolomite, sandstone, limestone, shale, and mudstone. As shown in Figure 2, the
[page 4] Appl. Sci. 2022, 12, 2656 4 of 20 sample collection was carried out in ﬁve tunnels in the Hubei province section. The New Austrian Tunneling Method was employed in these tunnels. Figure 2. Locations of Zhengzhou–Wanzhou high-speed railway line and the tunnels where the samples were collected. The sample of intelligent surrounding rock classiﬁcation is composed of the drilling parameters and the surrounding rock grade of the tunnel face. In this study, the drilling parameters were collected by using the intelligent drill jumbo (Figure 3), which was made by China Railway Construction Heavy Industry Corpora- tion Limited. Figure 3. Intelligent drill jumbo (made by China Railway Construction Heavy Industry Corpora- tion Limited). The intelligent drill jumbo has the functions of automatic positioning, automatic drilling and automatic recording of log. It can complete the drilling operation of pipe roof, anchor bolt and blast hole. In the process, the integrated sensors of the intelligent drill jumbo are used to automatically collect the drilling parameters (penetration velocity, hammer pressure, rotation pressure, and feed pressure) during the drilling of the borehole in the tunnel face. The drilling parameters are described below: 1. Penetration velocity (Vp, m/min): the rate of penetration of the drill bit through the rock mass.
[page 5] Appl. Sci. 2022, 12, 2656 5 of 20 2. Hammer pressure (Ph, bar): the measurement of the impact pressure of the bit against the rock mass. 3. Rotation pressure (Pr, bar): the pressure of the bit against the rock to maintain the required rotation. 4. Feed pressure (Pf, bar): the hydraulic pressure inside the cylinders required to keep the bit in contact with the bottom of the hole. All of these parameters are recorded at equal depth intervals of 20 mm by the help of displacement transducer. The drilling parameters data in this study were collected through the process of blasting hole drilling in the tunnel face (Figure 4a), and the bit used was a cemented carbide bit with a diamond content of 7%, spherical shape, and nine teeth (Figure 4b). (a) (b) Figure 4.


[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]
In particular, the recall of grade-V surrounding rock, which has great inﬂuence on the safety of the tunnel, was above 0.95. Thus, these three machine learning models have a degree of safety and high practi- cal value. 4. The classiﬁcation accuracy of the surrounding rock intelligent classiﬁcation system on-site is 86.7%, which indicates that these models have good generalization. This study found that the drilling parameters Vp, Ph, Pf, and Pr can be used to grade the surrounding rock directly by training the machine learning model. Compared with the traditional manual method, the proposed method is faster and has fewer indices, higher classiﬁcation accuracy, and better stability. When carried on an intelligent jumbo, it can realize automatic recording and transmission of drilling parameters and intelligent classiﬁ- cation of surrounding rock grade by using the intelligent surrounding rock classiﬁcation system established in this study. This is what most previous studies have failed to do. However, there is still room to improve the classiﬁcation accuracy of this system. In this study, three models have been used to determine the most unfavorable results to ensure the safety of site construction, and its essence is a management means. Therefore, the research on data preprocessing and algorithm optimization should be strengthened to further improve the reliability of the technology. In addition, the samples collected in this study only covered ﬁve lithologies and three surrounding rock grades. Thus, the sample types and numbers should be further expanded in the future to improve the application range of the technique. Author Contributions: Conceptualization, S.Z. and J.T.; methodology, S.Z. and J.T.; software, W.Y.; data curation, D.Y.; writing—original draft preparation, S.Z. and J.T.; writing—review and editing, M.W. and J.T.; visualization, D.Y.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript. Funding: The authors are grateful for the support from the National Natural Science Foundation of China (51878567, 51878568) and China Railway Corporation Science and Technology Research and Development Program (K2018G014, K2020G035).
[page 19] Appl. Sci. 2022, 12, 2656 19 of 20 Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Zhang, X.; Wang, M.; Li, J.; Wang, Z.; Tong, J.; Liu, D. Safety factor analysis of a tunnel face with an unsupported span in cohesive-frictional soils. Comput. Geosci. 2020, 117, 103–221. [CrossRef] 2. Dla, D.; Yl, B.; Jc, C.; Hm, A. An analytical model for axial performance of rock bolts under constant conﬁning pressure based on continuously yielding criterion. Tunn. Undergr. Space Technol. 2021, 113, 103955. 3. Li, D.; Cai, M.; Masoumi, H. A constitutive model for modiﬁed cable bolts exhibiting cone shaped failure mode. Int. J. Rock Mech. Min. 2021, 145, 104855. [CrossRef] 4. Barton, N.; Lien, R.; Lunde, J. Engineering classiﬁcation of rock masses for the design of tunnel support. Rock Mech. 1974, 6, 189–236. [CrossRef] 5. Bieniawski, Z.T. The geomechanics classiﬁcation in rock engineering applications. In Proceedings of the 4th ISRM Congress, Montreux, Switzerland, 2–8 September 1979. 6. Yunmei, L. Study of BQ formula in national standard of qunatitative classﬁcation for basic quality of rock mass. Chin. J. Geotech. Eng. 1999, 4, 481–485. 7. Reich, Y. Machine learning techniques for civil engineering problems. Comput.-Aided Civ. Infrastruct. Eng. 1997, 12, 295–310. [CrossRef] 8. Elshaboury, N.; Abdelkader, E.M.; Al-Sakkaf, A.; Alfalah, G. Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction. Water 2021, 13, 3546. [CrossRef] 9. Elshaboury, N.; Mohammed Abdelkader, E.; Al-Sakkaf, A.; Alfalah, G. Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model. Processes 2021, 9, 2045. [CrossRef] 10. Xue, X.; Xiao, M. Deformation evaluation on surrounding rocks of underground caverns based on PSO-LSSVM. Tunn. Undergr. Space Technol. 2017, 69, 171–181. [CrossRef] 11. Mahdevari, S.; Torabi, S.R. Prediction of tunnel convergence using artiﬁcial neural networks. Tunn. Undergr. Space Technol. 2012, 28, 218–228. [CrossRef] 12. Torabi-Kaveh, M.; Sarshari, B. Predicting Convergence Rate of Namaklan Twin Tunnels Using Machine Learning Methods. Arab. J. Sci. Eng. 2019, 45, 3761–3780. [CrossRef] 13. Qi, C.; Fourie, A.; Zhao, X.


[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]
The tunnel’s cross section has a total area of 91.31m2 and a height of 10 m with 58.56m2 and 32.75m2 being the areas of the top heading - and the bench and invert excavation respec tively (see Fig. 3). From these areas, equivalent diameters (D, i.e. the diameter of a circle with that area) of 8.41 m and 6.46 m can be computed for both parts of the excavation. There is a penalty if the distance between the top heading’s- and the bench’s tunnel face is too big. This distance (distmax) is set to be 50 m and the idea behind this is, that in many real projects – especially in soil conditions and long tunnels -, the top heading cannot be driven indefi nitely long ahead of the bench, because of safety reasons, necessities to Fig. 2. Simplified schematic plot of components of drill and blast tunneling (grey) as a reinforcement learning process - TunnRL. Fig. 3. Tunnel cross section of the given simulation. G.H. Erharter et al.
[page 4] Automation in Construction 127 (2021) 103701 4 have a fast final ring closure, excavate cross cuts or general construction logistics. We defined two ground types (gt) for the simulation and the relevant ground properties are given in Table 1. Both are considered to be of homogeneous, isotropic and continuous nature and one can imagine them as a type of hard soil/soft rock (HSSR) material [30,31]. With the chosen parameters, gt1 represents “unfavorable” ground conditions and gt2 represents “favorable” ground conditions. The condition for a gt to be favorable/unfavorable is based on the ground properties in combi nation with the given tunnel geometry (see above) as evaluated by the chosen stability assessment criterion (see Section 3.3.2). The simulated tunnel is situated above the groundwater table and the permeability is set to 10−5 m/s for both gt. Choosing the given properties to define the gt, is closely connected to the way the stability assessment of the excavation is done (see Eq. (3) in Section 3.3.2). The permeability of 10−5 justifies the use of Eq. (3) for a stability assessment as according to [32] conditions are considered to be drained when the permeability is above 10−7 to 10−6 m/s. Nevertheless, if ground types with lower permeability/undrained conditions are to be used for the simulation, appropriate solutions for stability assessments must be chosen. Implementing more sophisticated stability assessments that take phenomena like ground water conditions into account is desirable but must be done with care as this heavily influences the overall performance of the tunneling simulation and the RL itself. The outlook and discussion of stability assessments in Section 7 goes into more detail on this topic. Using random walks with barriers [33], we created unique, 210 m long geological sections with a decimeter resolution (see Section 3.3.1 for why the sections are 210 and not 200 m long). We created a ground type-vector (gt-vector) of 2100 datapoints by scaling the random walk between 0 and 1, rounding to full numbers and using 0 as gt1 and 1 as gt2 (see Fig. 4 bottom row). To transform the gt-vector to a full geological section with one row for the top heading and one for the bench excavation, the vector is horizontally duplicated to an array of 2 × 2100 datapoints. + 1 is then added to the array so that the number 1 represents gt1, number 2 gt2 and number 0 represents the unexcavated part of the tunnel. The top row of Fig. 4 shows a visualization of such an array, where posth and posbi are at 165 and 125 m respectively. Given the complexity of simulating the process of conventional tunneling (see Section 2), we chose not to complicate the scenario by introducing more gt. However, increasing the number of gt can easily be done with the above described random walk based approach. For example, if it was necessary to simulate four gt then the values of the random walk must be split into four within the boundaries: gt1 < 0.25, 0.25 ≤gt2 < 0.5, 0.5 ≤gt3 < 0.75 and 0.75 ≥gt4. 3.2. Agent Translating the above described geotechnical scenario to RL, the geotechnician who observes the state of the construction and rockmass behavior and makes decisions based on this information, now becomes the “RL agent”. Due to the state-complexity of the problem, we chose a deep Q-network (DQN) as the RL 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: Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics; DOI: 10.1109/access.2020.3040246; license: cc-by-sa.

[Assistant]
At the start of the training, during the loading stage, the excavator used a slightly higher angle of attack to the soil compared to the middle of the training, which resulted in a small inclination of the undercarriage when applying of the dipper arm force. Within the scope of this work, this behavior of the model is not dangerous, but in real-life applications, it can damage the machine. Therefore, in subsequent work, the reward function should penalize changes in the undercarriage rotation using data from an incli- nometer. Interesting behavior of the agent was also noticed prior to the addition of collision sensor data. Without the col- lision penalty, the agent tends to push with force on the hopper to achieve a higher reward. Complications with the reward for soil in the bucket are also worth mentioning. The agent had a tendency to abuse the reward for soil in the bucket and stop moving after ﬁlling the bucket. Therefore, the coefﬁcient k with value of 0.2 was introduced to reduce the effect of the soil-based reward. An agent with a reward function, presented in (24), was able to grab soil and move it to the hopper, as shown in the plot of the average mass in the hopper in Fig.8. It can be clearly observed that the agent started moving ground at around 2 × 104 steps. The bucket is able to hold around 1200 kg of soil. As can be seen from Fig.9, on average the agent learnt to move 67% of the initial mass to the hopper. The maximum value of soil in the hopper was 1168 kg. Therefore, at maximum, the agent was able to deliver 97% of the initial mass in the bucket. In the Fig.8 and Fig.9 can be observed high standard deviation caused by the soil transfer FIGURE 8. Average and standard deviation of episode mass in the hopper. 214004 VOLUME 8, 2020
[page 8] I. Kurinov et al.: Automated Excavator Based on RL and Multibody System Dynamics FIGURE 9. Average and standard deviation of episode mass in the bucket. FIGURE 10. Average and standard deviation of episode return. during simulation cycles, such as loading and unloading of the bucket. As shown in Fig.10, the average episode return increased over the course of the training, indicating that the agent was learning the policy correctly. Nevertheless, the high ampli- tude of the average reward shows that the agent did not reached the convergence. V. CONCLUSION This article introduced a method for development of autonomous machines based on reinforcement learning and a semi-recursive multibody method. The approach was tested on an excavator model with hydraulics and a deformable ground. The research concentrated on the learning agent used to operate the excavator model and move ground from a pre- deﬁned point to the unloading point. The study used the PPO- CMA model-free algorithm. The algorithm and environment were connected via an API built based on the ZeroMQ library. The agent was able to learn a policy for working with the simulation models. The agent of the excavator was trained to load and unload the excavator with satisfactory accuracy. The agent was able to move 67-97% of the maximum mass of the bucket. Nevertheless, as was seen from the learning curves, the reward function still ﬂuctuated. Thus, it can be concluded that increasing the number of episodes will likely result in enhanced excavator performance. The setup presented extends opportunities for development of automated machines using reinforcement learning. The multibody system dynamics allows the creation of machinery model and its environment with a little effort. This makes possible training of agents on the wide variety of machines. Therefore, the method allows highly customized simulation models to be created which can be used for training machine learning algorithms or neural networks. Hence, it will be possible to generate agents for existing machines effectively without endangering the machine or personnel. Furthermore, the approach provides a solid foundation for extension of RL learning to new types of machines, which can be easily reprogrammed by downloading agents to the machine model. REFERENCES [1] S. Dadhich, U. Bodin, F. Sandin, and U. Andersson, ‘‘From tele-remote operation to semi-automated wheel-loader,’’ Int. J. Electr. Electron. Eng. Telecommun., vol. 7, no. 4, pp. 178–182, 2018, doi: 10.18178/ijeetc.7.4. 178-182. [2] S. Dadhich, U. Bodin, and U. Andersson, ‘‘Key challenges in automation of Earth-moving machines,’’ Autom. Construct., vol. 68, pp. 212–222, Aug.

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: 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]
VariBench is a database for variation-related benchmark datasets that can be used for developing, optimizing, com- paring and evaluating the performance of computational tools that predict the effects of variations (Nair and Vihi- nen, submitted). VariBench datasets provide multilevel mapping of the variation position to DNA, RNA and pro- tein as well as to protein structure entries in PDB [24] (when possible). Method developers are requested to sub- mit their datasets to VariBench to be distributed to the community. VariBench datasets have been collected from literature as well as with data mining approaches from diverse sources. Locus specific databases (LSDBs) are the most reliable source for disease-related data. Although lots of variation data are listed in LSDBs, it would be necessary to capture to databases all the cases from clinical and research laboratories [25,26]. An integral part of databases is the annotation of the entries. For variation information collection it would be extremely important to describe the cases in a systema- tic and unambiguous way. Variation Ontology (VariO, http://variationontology.org/ ) has been developed for systematic description and anno- tation of variation effects and consequences on DNA, RNA and/or protein including variation type, structure, function, interactions, properties and other features (Vihi- nen, in preparation). VariO annotated data would allow easy collection of novel dedicated benchmarks. Evaluation measures The outcome of binary (pathogenic/benign) style predic- tors are often presented in a 2x2 contingency table (Fig. 4). The number of correctly predicted pathogenic (non- functional) and neutral (functional) cases are indicated by TP (true positives) and TN (true negatives), and the num- ber of incorrectly predicted pathogenic and neutral cases are FN (false negatives) and FP (false positives), respectively. The goal of two-class prediction methods is to separate positive cases from negative ones. Because the predictions for the two classes usually overlap a cut off distinguishing the categories has to be optimized (Fig. 5). By moving the cut off different amounts of misclassified cases FN and FP appear. By using well behaved representative data and well trained classifier the misclassifications can be minimized. Figure 4 Contingency matrix and measures calculated based on it 2x2 contigency table for displaying the outcome of predictions. Based on the table it is possible to calculate row and column wise parameters, PPV and NVP, and sensitivity and specificity, respectively. These parameters are useful, but are not based on all the information in the table. Accuracy is a measure that is calculated based on all the four figures in the table. Figure 5 Separation of classes In most classification problems the two classes are overlapping. By moving the cut off position the amount of the overlap of the classes can be adjusted. FN and FP are misclassified cases. The prediction methods aim at optimizing the cut off and thereby adjusting the numbers in the contingency table. Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 Page 5 of 10
[page 6] Based on the four descriptors several further measures can be calculated (Fig. 4). Sensitivity, also called true posi- tive rate (TPR) or recall, and specificity (true negative rate, TNR) show the ratio of the pathogenic and neutral cases correctly identified by the programs. Positive predictive value (PPV) (also called precision) and negative predictive value (NPV) is the conditional probability that a patho- genic or neutral variant is predicted as pathogenic or neu- tral, respectively. The mathematical basis of these and other parameters have been discussed in detail [27]. A single parameter cannot capture all the information of the contingency matrix. Unless representative numbers of positive and negative cases are used, the values of NPV and PPV may be biased, even meaningless. The usual requirement is that the numbers be equal. Sometimes in literature the datasets are very skewed. Table 1 indicates the effect of the class imbalance. Results are shown in addition to equally distributed dataset also for analyses when there is ± 25 % or ±50 % difference in the total num- ber of negative and positive cases.

How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis


[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]
Layout of the cutting tools on the spoke type cutterhead. (a) Initial cutterhead. (b) damaged disc cutters.
[page 9] Cutterhead and Cutting Tools Configurations The Open Construction and Building Technology Journal, 2017, Volume 11 189 Fig. (8). Structure of the scraper. Fig. (9). Structure of the precut bits.
[page 10] 190 The Open Construction and Building Technology Journal, 2017, Volume 11 Li et al. Fig. (10). Shell bit. Fig. (11). Center fish-tail bit. The rake and clearance angles of the scrapers with the attachment chip of 55 × 20 mm size are 15° and 10° respectively. The Type I precut bits are installed at the outer area of the cutterhead whereas the Type II precut bit mounted at the inner area of the cutterhead. The precut bits are 50 mm higher than the scrapers. The shell bits, which can be taken as precut bits, are mounted at the periphery of the cutterhead aiming at protecting the cutterhead and reducing frictions between the cutterhead and the soils. The fish-tail bit at the center of the cutterhead is 440 mm higher than the scrapers. (1) The Lot 14 of Beijing subway Line 4 is constituted of three twin running tunnels sectors and two underground
[page 11] Cutterhead and Cutting Tools Configurations The Open Construction and Building Technology Journal, 2017, Volume 11 191 stations. The three twin running tunnels, mainly buried in sandy gravel and cobble ground with the maximum grain size of more than 200 mm, are 0.82 km, 1.47 km and 1.33 km in length respectively. The first running tunnel of 0.82 km long was finished with minor wears (see Fig. (12a)). Subsequent to repairing some cutter bits with surface welding, the second running tunnel, 1.47 km in length, was bored and after completing the second tunnel it was found that the scrapers were seriously damaged but the blades of the ripper teeth were basically remained (see Fig. (12b)). Before driving the third running tunnel of 1.33 km long, extensive repairs were performed for cutting tools, which later guaranteed the smooth completion of the third tunnel construction without changing tools. Fig. (12). Cutterhead used for Lot 14 of Beijing subway Line 4 and worn cutting tools. (2) The Lot 11 of Beijing subway Line 10 comprises two twin running tunnels 2.1 km and 0.7 km in length respectively, located in sandy gravel\cobble ground. According to the geological investigation, the cobble with grain size more than 15 mm accounts for as much as 45% by volume and the SPT values are 60-65. After finishing the first running tunnel of 2.1 km long, it was observed that the wear amounts of the cutting tools were significantly less than the theoretical values. Subsequent to repairs of cutting tools with surface welding, the second running tunnel was also completed without interruption. (3) The same type shield machine was adopted in constructing a 0.82 km long tunnel in sandy gravel/cobble soils, of Lot 18 of Beijing subway Line 5. The smooth driving without replacing cutting tools was achieved, and the scrapers and the ripper teeth were still in good shape (see Fig. (13)). Fig. (13). Used scraper and ripper tooth in good shape. (a) Minor wears of cutting tools. (b) Worn scraper and ripper tooth.
[page 12] 192 The Open Construction and Building Technology Journal, 2017, Volume 11 Li et al. (4) The same type shield machine was also utilized in digging a 1.02 km long running tunnel of Lot 9 of Shenyang metro Line 1. The running tunnel mainly passed through the sandy gravel/cobble soils with the maximum grain size of 100 mm. The tunnel was finished without changing cutting tools. Only a few wears of the cutterhead and most cutting tools were found except some seriously abraded tooth bits (see Fig. (14)). Fig. (14). Shield machine arrived and the used cutterhead. (5) The same type shield machine was also adopted in driving a 1.17 km long running tunnel of Lot 13 of Shenyang metro Line 1. The gravel/cobble with grain size more than 20 mm accounted for 20-30% of the total weight, and the maximum grain size discovered was 110 mm. The tunnel was completed without replacing cutting tools. After arrival of the shield machine, it was seen that most damages of the cutting tools were caused by collisions.

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: 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]
Thereafter, regions within each image known to be occupied by magnetite iron sand and non-magnetite beach sand are pinpointed so as to act as target and noise ground truths, respectively (Figure 7). Using the quantitative labelled segmentation maps as demonstrated in Figures 6 and 7, input datasets containing within them magnetite and non-magnetite six bands deep ground truths are ready to be employed in the training and testing of supervised ML and DL algorithms. The output variables are predicted or classiﬁed from the training database [10]. This means that algorithms use the ground truths and labelled segmentation maps to try and learn some patterns in the data during training, so as to implement these learnt patterns to the testing dataset [19]. This therefore provides results in relation to the learnt patterns (Figure 7). At 2 m ﬂight elevation, the total dataset size was 998,400,000, derived from the product of image sizes (1300 × 1600), the number of multispectral bands (6), and the number of captured images (80). At 10 m ﬂight elevation, the total dataset size was 399,360,000, derived from the product of image sizes (1300 × 1600), the number of multispectral bands (6), and the number of captured images (32). Lastly, at 20 m ﬂight elevation, the total dataset size was 99,840,000, derived from the product of image sizes (1300 × 1600), the number of multispectral bands (6), and the number of captured images (8).
[page 11] Minerals 2022, 12, 268 11 of 19 Figure 6. Extraction of 6-bands deep segmentation maps (binazized for easier visual representation) used to create a magnetite iron sand database. Noise refers to non-magnetite pixels. Segmentation maps at each UAV ﬂight elevation are based on the 0.12, 0.13 and 0.17 cosine similarity thresholds at 2 m, 10 m and 20 m. This process is summarized in step 2⃝of Figure 7, as well as Appendix A. Figure 7. Proposed process overview with emphasis on how data extracted from spectral angle mapping cosine similarities is segmented, assigned ground truths, and used in training artiﬁcially intelligent algorithms in the classiﬁcation of magnetite iron sand. Appendix A has extended explana- tions on this procedure.
[page 12] Minerals 2022, 12, 268 12 of 19 These dataset sizes were, however, too large to run using the computational resources employed in this study (32 GB memory, Intel i7-8750H CPU @ 2.2 GHz processor, NVIDIA GeForce GTX 1060 graphics, Windows 10, 64 bits operating system). To counter this, Figure 8 demonstrates our AI training approach. At 2 m ﬂight elevation, the input dataset size used to train each algorithm was 12,480,000, derived from the product of image sizes (1300 × 1600), and the number of multispectral bands (6). Since 80 of such datasets exist, 80 algorithms were trained, and the best performing model from the rest was chosen as the most viable. The same approach was applied at 10 m and 20 m ﬂight elevations, the difference being the number of times (32 times and 8 times, respectively) training was performed, this number corresponding to the number of images captured. With this approach, all image datasets can still participate in the training of the various algorithms, the training times are quicker, and it requires less computational resources. Figure 8. Input dataset sizes used to train the machine learning and deep learning algorithms as well as selection of the best performing model at each UAV ﬂight elevation. 3.3.1. Classiﬁcation via Machine Learning Models The input dataset sizes for training and testing variables for each of the ML algorithms stood at 12,480,000 (from Figure 8) for each of the three ﬂight elevation heights. Moreover, a 5-fold-cross-validation was used at all times in order to ensure that every observation (2,496,000 × 5) from the original dataset (12,480,000) has the chance of appearing in training and test sets. Therefore, this method generally results in a less biased model compared to other validation methods [10]. By using the MATLAB R2020b (manufactured by MathWorks, Natick, MA, USA) classiﬁcation learner Machine Learning toolbox, multiple ML algorithms were evaluated. Classiﬁcation performances were presented in terms of global accuracy, average per-class precision, and time taken to train the algorithm. These features are said to be amongst the most signiﬁcant classiﬁcation evaluation criteria [15].

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


[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.


[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]
This suggests that the models are all highly effective at separating magnetite iron sand spectra from non-magnetite beach sand spectra from within the multispectral image scenes. Figure 10. Deep learning one dimensional convolution neural network confusion matrices at 2 m, 10 m and 20 m UAV drone elevation.
[page 16] Minerals 2022, 12, 268 16 of 19 4. Discussion In a study by Gholami et al. [28], they applied independent component analysis (ICA) in remote sensing data processing based on satellite data to map an area and attained good results. However, differences between their method and ours are as follows; with the ICA method, they reduced the dimensionality of spectral bands from six bands to two bands. However, researchers mention that higher dimensionalities are relatively superior in deducing materials as they retain the original data structure [5–9]. Hence, our study applied SAM, which retains the full six bands data structure. In addition, our methodology employs a UAV drone, which allows for collection of variables, as well as higher spatial resolution data. For example, at 2 m ﬂight elevation, our spatial resolution is 0.4 cm/pixel, as opposed to their ﬁxed satellite resolution of 30 m/pixel. Considering our 30 m × 90 m study area, their spatial resolution is rather inadequate. Lastly, we employ supervised DL 1D CNN classiﬁcation algorithms which are known to be state of the art in imagery data classiﬁcation problems. As pointed out by Sharma and Sharma [10], supervised learning algorithms are superior to unsupervised ML methods such as ICA, especially in cases where the output variables (magnetite or non-magnetite) are known, as is the case in this study. Based on this study’s ﬁndings, it was realised that the SAM cosine similarity thresholds require slight adjustments relevant for each UAV ﬂight elevation. With increase in elevation from 2 m, 10 m to 20 m, these thresholds vary from 0.12, 0.13 to 0.17, respectively. Though these SAM cosine similarities vary, the ability of the SAM in differentiating the targeted magnetite iron sand from non-magnetite beach sand remains relatively unchanged from visual analysis. This is demonstrated by the masks at each ﬂight elevation which seem to ﬂawlessly overlap the sought after magnetite iron sands (Figure 5), as well as the high ground truths-supported AI classiﬁcation capabilities. With respect to the UAV drone post ﬂight logs and the typical size of surface mines, it would be in the best interest of engineers to perform mineral identiﬁcation practices at higher elevations such as 20 m. The reason being, the UAV is not as challenged in terms of power consumption, elapsed capturing time, number of images to capture, and area coverage, as at lower elevations (Table 1). Figure 11 is a summarized comparison between the ML and DL CNN model classi- ﬁcation capabilities as well as the length of time required to train each model. Based on global accuracies, the CNN models perform better than the ML models across all three ﬂight elevations. With ML, the attained global accuracies were 85.7%, 78.6% and 85.7% at 2 m, 10 m and 20 m ﬂight elevations, whilst with the CNN, attained global accuracies were 99.9%, 99.9%, 99.7%, respectively. A look into the attained per-class precisions shows that with the ML models, 84.5%, 83.4%, and 89. A total of 9% classiﬁcation capabilities were attained at 2 m, 10 m and 20 m ﬂight elevations. These model classiﬁcation capabilities are different from the 99.8%, 98.7% and 99.4% attained via the CNN at 2 m, 10 m and 20 m, respectively. Therefore, it can be said that based on the global accuracies as well as the per-class precisions (Figure 11a), model capabilities show that the 1D CNNs employed in this study are far superior in classifying the magnetite iron sands, and non-magnetite beach sands, to the ML models. From Figure 11b, one is able to appreciate the differences in times required to train the AI models. With ML models, training times are shorter (5.8, 0.9 and 1.0 s at 2 m, 10 m and 20 m, respectively) compared to those of the CNN models (78, 45 and 68 s at 2 m, 10 m and 20 m, respectively). Though the ML model training times seem relatively more efﬁcient, the time taken to train a model is not what governs its industrial applicability. Training duration merely communicates the time it took to turn an algorithm into a model via training.
[page 17] Minerals 2022, 12, 268 17 of 19 (a) (b) Figure 11.


[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]
Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 Page 3 of 10
[page 4] data should however never be used for training and test- ing as that would only indicate the capacity of the method to memorize examples, not its generalization potential – how well it performs on instances outside the training set. High quality benchmark datasets require meticulous data collection often from diverse sources and careful checking of the correctness of the data. Numerous measures have been developed to describe predictor performance, but no single measure captures all aspects of predictor performance. The measures mainly used, and how to interpret them will be discussed. Typi- cally prediction methods are used as classifiers to define whether a case has the investigated feature or not. Results of this kind of binary predictor can be presented in a 2x2 confusion table also called contingency table or matrix. This, at first glance may appear simple to interpret, but the contrary is the case, as various composite aspects have to be jointly taken into account. Benchmark criteria Benchmark can be defined as a standard or reference for evaluation, in this case prediction method performance. Benchmarks are widely used in computer science and technology. For example computer processor performance is tested with standardized benchmark methods. In bioin- formatics there are benchmarks e.g. for multiple sequence alignment methods already 1990’s [9]. Novel MSA con- struction methods are routinely tested with alignment benchmarks such as BAliBASE [10] HOMSTRAD [11], OxBench suite [12], PREFAB [13], and SABmark [14] . Other bioinformatic benchmarks include protein 3D structure prediction [15-17], protein structure and func- tion prediction [18], protein-protein docking [19] and gene expression analysis [20,21] benchmarks etc. Benchmark usage varies between different communities. For variation effect predictions, benchmarks have not been available and thus authors have used different datasets. The situation has changed only recently with the release of VariBench (http://bioinf.uta.fi/VariBench/) (Nair and Vihinen, submitted). To be useful a benchmark should fulfill certain criteria. These criteria vary somewhat between the domains, but there are also some common features (Fig. 3). The criteria laid by Gray originally for database systems and transac- tion processing systems are still valid [22]. Criteria for MSA [23] and variation data (Nair and Vihinen, sub- mitted) benchmarks have been defined. These include relevance, which means that the data have to capture the characteristics of the problem domain. Portability allows testing of different systems. Scaleability of the benchmark allows testing systems of different sizes, and simplicity means that the benchmark has to be understandable and thereby credible. Accessibility means that the benchmark has to be publicly available, solvability to set the level of the task on suitable level (not too difficult, not hoo hard), independence to guarantee that the benchmark has not Figure 3 Benchmark criteria Criteria for benchmarks in three different studies. VariBench is the database specifically designed for variation benchmark datasets. Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 Page 4 of 10
[page 5] been developed with tools to be tested, and evolution to keep the benchmark up-to-date during time. When considering the variation benchmarks, datasets should be large enough to cover variations related to a cer- tain feature or mechanism. For example in the case of mis- sense variations this means very large numbers of instances as there are altogether 150 single nucleotide changes which cause amino acid substitution. To have sta- tistical power several cases are needed. The required num- bers of cases increase exponentially as features are combined. Datasets have to be non-redundant and devoid of similar or greatly overlapping entries. This criterion relates to independence requirement of [23]. Datasets have to contain both positive (showing the investigated feature) and negative (not having effect) cases so that the capability of methods to distinguish effects can be tested. This may cause problems in data collection as some phenomena are very rare and only a few known cases may exist.


[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]
Meanwhile, Figure 4 shows that the recall value for the majority class 0 remains above 93%, while that for class 1 keeps increasing and reaches over 98%. However, there is a distinct decline for class 2 from 87% to 74%, with an approximate 13% decline, which means that an increasing number of strong scintillation events go undetected. This indicates the great importance of balancing the minority class data in improving the accuracy of strong scintillation detection. ( ) 1f X (1) y (0) y 1( ) Tree X (1) y 2( ) Tree X ( ) 2f X (2) y ( ) kf X ( ) k y ( 1) k y − ( ) Treek X ( ) if X  Training dataset X   Node splitting Obj(1) Node splitting Obj(2) Node splitting Obj(k-1) Iterative boosting Figure 3. The schematic diagram of XGBoost algorithm. Table 2. The overall accuracy values for different sample proportions for classes 0/1/2. Sample Proportion (Class 0/1/2) 1:1:1 2:2:1 3:3:1 4:4:1 5:5:1 6:6:1 7:7:1 Overall Accuracy (%) 90.18 91.47 93.32 93.13 93.31 92.65 92.61 1:1:1 2:2:1 3:3:1 4:4:1 5:5:1 6:6:1 7:7:1 Sample proportion for class 0/1/2 70 75 80 85 90 95 100 class 0 class 1 class 2 13% decline Figure 4. Inﬂuence of imbalanced datasets on performance of XGBoost model. These seven training datasets with 100,000 samples were selected randomly in the overall training dataset listed in Table 1, according to different sample ratios for classes 0/1/2. The testing dataset for each detection model trained by the above training dataset is the same as that mentioned in Table 1. The purpose of resampling in this study was to balance the ionospheric scintillation events of different intensities, enhancing the detection accuracy for strong scintillation events. The normally used undersampling methods include random undersampling of the majority class, edited nearest neighbor (ENN) [43], Tomeklink [44], condensed nearest
[page 10] Remote Sens. 2021, 13, 2577 10 of 22 neighbor (CNN) [45], and neighborhood cleaning (NCL) [46], while the typical oversam- pling methods include random oversampling of the minority class, the synthetic minority oversampling technique (SMOTE) [47], and Borderline-SMOTE (BSM) [48]. However, the former methods miss sample information through reducing the majority samples, and the latter methods add minority samples, causing a constant increase in useless information and an overﬁtting problem. The combinational algorithm SMOTE-ENN preserves the features of majority samples and increases the characteristics of minority samples, resulting in excellent classiﬁcation performance for imbalanced datasets [49]. The basic idea of the SMOTE method is to carry out linear interpolation between neighboring minority class samples to synthesize new minority class samples, solving the problem of signiﬁcant data overlap compared with random oversampling [47]. Further details can be described as follows: for each minority class sample xm ∈X (m = 1, . . . , M), ﬁnd the k nearest neighboring samples of the same class K, and then, choose n samples in K according to the sampling rate n, mark them as ym,i ∈Ym (i = 1, . . . , n, m = 1, . . . , M), and ﬁnally achieve random linear interpolation on the lines between xm and ym,1, . . . , ym,n, respectively. The new built sample can be described as (10). Xnew,m = xm + r ∗(Ym −xm), m = 1, . . . , M (10) where r is the random coefﬁcient ranging from 0 to 1, and Xnew,m is the vector including n new samples built by xm. In total, there are M ∗n new samples. Focusing on majority class samples, the ENN algorithm deletes the sample if there are two or more in the nearest three neighboring samples different from it [43]. However, the majority samples are near each other, which causes limited sample removal. The SMOTE-ENN method achieves oversampling on the minority class samples, ﬁrstly using SMOTE, and then ﬁnishes undersampling on the majority class samples to coalesce their advantages. Thus, the combination of SMOTE and ENN is utilized to balance data with few strong ionospheric scintillation events; its performance was evaluated and compared with that of other methods, such as SMOTE and ENN. Figure 5 shows the process of resampling, training and predicting, using the XGBoost algorithm improved by the SMOTE-ENN resampling 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: Cutterhead and Cutting Tools Configurations in Coarse Grain Soils; DOI: 10.2174/1874836801711010182; license: cc-by.

[Assistant]
Nishitake, "Earth pressure balanced shield machine to cope with boulders", In: Rapid Excavation and Tunneling Conference Proceedings, 1987, pp. 552-572. [11] P.B. Dowden, and D.T. Cass, "Shielded TBM’s. Matching the machine to the job", In: Rapid Excavation and Tunneling Conference Proceedings, 1991, pp. 787-805. [12] W. Burger, "Design principles for soft ground cutterheads", In: Rapid Excavation and Tunneling Conference Proceedings, 2007, pp. 784-792. [13] M. Herrenknecht, and K. Bӓpler, "The latest technology in mechanized tunneling-the design of the World’s largest EPB and slurry shield TBMs", In: Underground Space-The 4 th Dimension of Metropolises, Proceedings of the World Tunnel Congress 2007 and 33 rd ITA/AITES Annual General Assembly, 2007, pp. 1169-1173. [14] W. Burger, and G. Wehrmeyer, "The making of the Mixshield-Part 1", In: Tunnels & Tunnelling International, 2008a, pp. 35-40. [15] W. Burger, and G. Wehrmeyer, "Lifting the lid on Mixshield performance-Part 2", In: Tunnels & Tunnelling International, 2008b, pp. 31-36. [16] C. Metzger, G. Colzani, and G. Irwin, "Selection, design, and procurement of North America’s largest mixshield TBM for Portland, Oregon’s East side CSO tunnel", In: Papid Excavation and Tunneling Conference Proceedings, 2009, pp. 1022-1036. [17] U. Maidl, and M. Comulada, "Prediction of EPB shield performance in soils", In: Rapid Excavation and Tunneling Conference Proceedings, 2011, pp. 1083-1091. [18] N. Bilgin, H. Copur, and C. Balci, "Effect of replacing disc cutters with chisel tools on performance of a TBM in difficult ground conditions", Tunn. Undergr. Space Technol., vol. 27, pp. 41-51, 2012. [http://dx.doi.org/10.1016/j.tust.2011.06.006] [19] D. Willis, "Reducing the cutterhead wear at EPB tunnel boring machines", Tunnel, vol. 2012, no. 7, pp. 17-25, 2012. [Online] Available from: http://www.tunnel-online.info/en/artikel/tunnel_Reducing_the_Cutterhead_Wear_at_EPB_Tunnel_Boring_Machines_1552388.html [Accessed: 1 st Aug. 2016]. [20] M. Havekost, C. Overby, J. Kabat, and P. Nelson, "Use of a digger shield to successfully complete tunnel after ground conditions proved too adverse for a TBM", In: Rapid Excavation and Tunneling Conference Proceedings, 2013, pp. 264-275. [21] F. Köppl, and K. Thuro, "Cutting tool wear prognosis and management of wear-related risks for Mix-ShieldTBM in soft ground", In: Proceedings of the 18 th International Conference on Soil Mechanics and Geotechnical Engineering, 2013, pp. 1739-1742. [22] R. Duhme, and T. Edelmann, "Monitoring and Intervention-Free Inspection of TBM Excavation Tools", Underground Singnapore 2014, 25 th–26 th September, 2014. [23] J. Roby, and D. Willis, "Achieving fast EPB advance in mixed ground: A study of contributing factors", In: North American Tunneling Proceedings, 2014, pp. 182-194. [24] G.P. Le, "Selection of shield machines for Beijing Metro construction", Mod. Tunneling Technol., vol. 40, no. 3, pp. 14-30, 2003. [In Chinese]. [http://dx.doi.org/10.3969/j.issn.1009-6582.2003.03.004] [25] L. Wang, "Application of shield tunneling method in Beijing metro line 5", Urban Rapid Rail Transit, vol. 17, no. 5, pp. 33-37, 2005. (In Chinese). [26] G.F. Liu, and T.T. Song, "Analysis and study on wearing of cutting tools of shield machines used in Chengdu metro construction", Tunnel Constr., vol. 27, no. 6, pp. 89-93, 2007. [In Chinese]. [http://dx.doi.org/10.3969/j.issn.1672-741X.2007.06.025] [27] G.L. Huang, "Study on shield tunneling in Shenyang water-rich sandy gravel ground", Railway Constr., no. 4, pp. 77-78, 2008. (In Chinese). [28] M.F. Zhang, D.J. Yuan, Q.F. Huang, C.B. Huang, and F. Zhang, "Analysis of dynamic abrasion of shield cutters in sandy cobble stratum", Chin. J. Rock Mech. Eng., vol. 27, no. 2, pp. 397-402, 2008. [In Chinese]. [http://dx.doi.org/10.3321/j.issn:1000-6915.2008.02.024] [29] G.Y. Wang, "Shield equipment disposition for Chengdu metro", Tunnel Constr., vol. 28, no. 4, pp. 501-504, 2008. [In Chinese].
[page 19] Cutterhead and Cutting Tools Configurations The Open Construction and Building Technology Journal, 2017, Volume 11 199 [30] Q.Z. Wang, and S.K. Zhang, "The analysis of the arrangement of cutters and the structure of the cutter carrier of the shield for projects of the Shenyang metro in the medium-coarse sand stratum", Traffic Eng. Techno. National Defense, vol. 9, no. 4, pp. 41-43, 2011. [In Chinese]. [31] Q.S.


[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] The predictors can be classified as discrete or probabilistic depending on whether they provide a score, not necessarily a p value, for predictions. In the case of methods with discrete output, more or less ad hoc thresh- olds have been used to detect the most reliable events. Many machine learning based predictors are binary classi- fiers, however, it is possible to have more than two outputs e.g. by using multi-tier two-class prediction system. Features describe the characteristics of the investigated phenomenon. If several features are available it is impor- tant to choose those, which best capture the phenomenon. This is partly due to the curse of dimensionality, which means that much more data are needed when the number of features increases. The volume of the feature space grows exponentially with the dimensionality such that the data become sparse and insufficient to adequately describe the pattern in the feature space. Another problem is over- fitting, which means that the learner, due to sparse data, complex model or excessive learning procedure, describes noise or random features in the training dataset, instead of the real phenomenon. It is crucial to avoid overfitting as it leads to decreased performance on real cases. Many predictors provide a measure for the probability of prediction, in this domain a measure of how likely the var- iation is pathogenic. This information can be used for ranking the investigated cases. A more advanced version is to obtain e.g. by bootstrapping an estimate of the standard error of the prediction indicative of the prediction reliability. Many types of biological data are limited in quantity. The same data cannot be used both for method training and testing. The trick is to partition the dataset. This can be done in different ways, with cross-validation probably being the most popular of these. The dataset is divided into k disjoint partitions, one of which is used for testing and the others for training. This is repeated k times until all the partitions have been used as test set. Ten parti- tions i.e. ten times cross validation is the most common partitioning scheme. The average performance measures computed from the splits are used to describe the overall prediction performance. Random sampling is another approach, however, a problem is that the same cases may appear more than once in the test set and others not at all. Another computationally intensive validation approach is leave one out validation, an extreme case of cross validation with partitioning to the total number of instances. As the name implies, one case at time is left for validation while the remaining cases are used for training. The computational requirements may be prohi- bitive with large datasets. A problem especially for the last scheme is if there are some very similar cases in the dataset. Typically the training set should contain about equal amount of cases in each class. Imbalance in the numbers of cases in the classes can cause problems during perfor- mance evaluation as discussed below. There are some ways to handle class imbalance. Principles of method evaluation To test and compare predictors two requirements have to be met. There has to be available test dataset with known outcome and there has to be in place suitable pre- diction performance evaluation measures. Benchmark is a gold standard dataset - cases with experimentally vali- dated known effects which represent the real world. These can be used for training machine learning methods as well as for testing the developed methods. The same Figure 2 Principles of machine learning Machine learning is a form of supervised learning in which a computer system learns from given positive and negative instances to distinguish between cases belonging to the two classes. During training, positive and negative cases (black and white balls) are provided for the system, which leads to organization of the predictor (indicate by the arrangement of the black and white squares inside the predictor) such that it learns to separate the cases and thus can classify unknown cases (balls with question marks). Depending on the classifier, whether it yields in addition to the classification also a score for the prediction, the results can be called as discrete or probabilistic.


[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.

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