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

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

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

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


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

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

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

[Assistant]
When the plate type cutterhead is employed for shield machines in the coarse grain ground, disc cutters in combination with soft ground cutting tools (scraper, ripper and pick) are usually mounted on the cutterhead, and the installed disc cutters are tools to break big cobble/boulders into pieces, and the remaining work is fulfilled by the soft ground tools. 3.2. Performances of Shield Cutterheads The two types of cutterheads are both adopted in China subway/metro construction to dig tunnels in the coarse grain soils, but received different responses. Effects of the cutterheads equipped with the corresponding cutting tools are revealed and illustrated by the following construction cases. 3.2.1. Cases of Using the Plate Type Cutterhead (1) The plate type cutterhead EPB shield was used in some project of Beijing subway Line 5. After driving 346 m, the machine slowed down and a sharp increase in the torque of cutterhead took place. Through looking over the cutterhead with access to the plenum (excavation chamber), it was observed that the four pairs of scrapers at the outer side of the cutterhead were totally worn out and the face scrapers were also seriously abraded (see Fig. (4)) and this was (a) Cutterhead with disc cutters. (b) Cutterhead without disc cutters.
[page 7] Cutterhead and Cutting Tools Configurations The Open Construction and Building Technology Journal, 2017, Volume 11 187 also true of the main body of the cutterhead. After restoring the cutterhead with repair welding, reinstalling all side scrapers and replacing 68 face scrapers, the shield machine started to work again. The main reasons for the excessive wears are as follows: the small opening ratio less than 30%; the four-spoke design and not enough scrapers; large forces acting on the scrapers on account of not installing precut bits and center fish-tail bit. Fig. (4). Excessive wears of the cutterhead used for some project of Beijing subway Line 5. (2) A running tunnel on Beijing subway Line 4 was bored by an EPB shield machine equipped with disc cutters. The construction work was completed after many times of replacements of disc cutters. It was observed from Fig. (5) that the cutterhead and the cutting tools were seriously damaged, all top layers of the breasting plate located out of the central area of the cutterhead fell off, and the space between the remaining middle and bottom layers of the plate was full of muck. The causes for the damages can be summarized as: the small opening ratio of about 25% of the cutterhead; no enough reaction forces on disc cutters resulting in abnormal wears of disc cutters; not installing precut bits including nose cone bits and excessive secondary wears by rolling of the over-sized grains between the breasting plates and the cutting face. Fig. (5). Damaged cutterhead of some project of Beijing subway Line 4. (3) Two EPB shield machines with plate type cutterheads were adopted in building running tunnels of some project of Chengdu metro line 2. The opening ratio of cutterhead is about 20% and not equipped with precut bits, as shown in Fig. (6). Due to the large content of over-sized particles, the abnormal wears of the disc cutters were serious and frequent replacements of tools were performed per 50-100 m. After changing types of the alloys and workmanship for the scrapers, installing new scrapers in some disc cutter boxes, and welding the reinforced-type precut bits to the cutterhead, the boring length before the next replacing tools was increased to more than 300 m. The reasons for the tough driving work are as follows: small opening ratio of about 20%; unreasonable configurations of cutting tools and cutterhead. The driving distance was only prolonged by the improved cutting tools because of the unmodifiable structure of the cutterhead.
[page 8] 188 The Open Construction and Building Technology Journal, 2017, Volume 11 Li et al. Fig. (6). Cutterhead and damaged disc cutters of some project of Chengdu metro Line 2. 3.2.2. Cases of Using the Spoke Type Cutterhead The spoke type cutterhead was ever widely used in Beijing shield tunneling. In the following construction cases the shield machines of 6.14 m diameter are employed, and the cutterheads are given in (Fig. 7). The six cylindrical spoke type cutterhead with opening ratio of 75.5% is equipped with 82 scrapers, 28 precut bits (ripper teeth), 12 shell bits, one center fish-tail bit and two copy cutters, as presented in (Figs. 8-11). Fig. (7).

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: 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]
Hamming distance and quadratic distance (also called for Euclidean distance), which are the same for binary data, and relative entropy and mutual information [27]. ROC analysis Receiver operating characteristics (ROC) analysis is a visualization of prediction performance, that can be used to select suitable classifier (for review see [28,29]). It indi- cates the tradeoffs between sensitivity and specificity. ROC curves can be drawn with specific programs when the pre- dictor is of probabilistic type and provides a score for the classification. The score is usually not a real p value, but a value usable for ranking the predictions. ROC curve (Fig. 7a) is drawn by first ranking the data based on the prediction score. Then the data are divided to intervals of equal size. The upper limit for the partitions is the number of cases in the dataset. ROC curve has on x-axis 1-specificity also called FPR and on the y-axis sensi- tivity (TPR). Computer program establishes cut offs at intervals, calculates contingency table for data in the interval, and determines the values for sensitivity and 1-specificity, which is plotted to the graph. The procedure is repeated for each partition. If cross validation has been used, then the ROC curve can be used to show the average and variance of the results. In an ideal case all the true positive cases are on the first half of the ranked list and the plot rises to (0,1) and then continues straight to the right with all the true negative cases. A random classification would be on the diagonal i.e. mixed correct and wrong cases. The faster the curve rises and the higher it reaches in the beginning the better the method is. Methods can be compared with ROC ana- lysis when the same test dataset (benchmark) is used (Fig 7b). The curve that runs higher is for a better method. If the curves cross (Fig 7c) the comparison is no more meaningful. Area under the ROC curve (AUC) has been used as a measure of goodness for predictions (Fig. 7a). It approxi- mates the probability of ranking a randomly chosen posi- tive instance higher than a randomly chosen negative one. A value of 0.5 indicates random and useless classification while 1 would indicate perfect classifier. Note that AUC Figure 7 ROC analysis and AUC a) Principle of ROC analysis. b) Comparison of predictors based on the ROC curves when the methods are tested with the same dataset (benchmark). c) If the curves cross the comparison is no more meaningful. Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 Page 7 of 10
[page 8] can be even smaller than 0.5. One should bear in mind that the ROC curve does not directly indicate the perfor- mance of a method. It shows the method’s ranking poten- tial, which is related to overall performance, further strengthening the fact that a single measure cannot fully describe the predictive performance even if it produces a graph. What if the data is classified to more than two classes? If there are more than two classes the measures described above cannot be applied. The data can still be presented in an N x N contingency table. One approach is to divide the data into several partitions of two categories. If parameters are needed for all the classes there are some options available, however, single measures are more problematic. It is possible to calculate row and col- umn wise ratios in the same way as in Fig. 4. MCC is in fact a special case for binary data of linear correlation coefficient, which can be used for several classes in its general format. Mutual information analysis can be used in these cases, as well. Applicable measures have been discussed e.g. in [27]. Examples of performance comparisons This section discusses examples of variation effect pre- diction method evaluations. These include methods for amino acid substitution (missense variation) tolerance, point variation effects on protein stability and variations related to mRNA splicing. The discussion concentrates on the comparison principles, especially in the light of the discussion on requirements mentioned above. The actual comparisons are not presented as it would have required publication of substantial parts of the reports. As a single parameter is insufficient for ranking meth- ods, the readers are directed to the original articles to find all the details. Here a summary to the methodology and use of the evaluation parameters is provided.

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

[Assistant]
As shown in Fig. (15), the scrapers were seriously collided and part tip of the center fish-tail bit was broken off and the two sides of the ripper tooth were damaged though it remained good shape and could still work. Fig. (15). Damages of Fish-rail and precut bit caused by collisions. 3.2.3. Cases of Using the Spoke-plate Type Cutterhead (1) The running tunnels of Lot 4 of Beijing subway Line 4 were bored using two shield machines equipped with the spoke-plate type cutterhead of 39% opening ratio, as shown in (Fig. 16). The ground is very much alike that encountered at Lot 14 of Beijing subway Line 4 mentioned above. The combination of cutting tools is very similar to that used in Lot 14 of Beijing subway Line 4, consisting of 94 scrapers, 40 precut bits, 12 shell bits, one center fish-tail bit and two copy cutters. The running tunnels were finished without changing tools. The cutterhead and some tools after driving are presented in (Fig. 17). (a) Damaged fish-tail bit. (b) Damaged precut bit.
[page 13] Cutterhead and Cutting Tools Configurations The Open Construction and Building Technology Journal, 2017, Volume 11 193 Fig. (16). Spoke-plate type cutterhead. Fig. (17). Used cutterhead and the damaged cutting tools. (a) Used cutterhead. (b) Damaged precut bits. (c) Damaged scrapers.
[page 14] 194 The Open Construction and Building Technology Journal, 2017, Volume 11 Li et al. (2) As shown in Fig. (18), the spoke-plate type cutterhead with opening ratio of 27% was used in building running tunnels of Lot 9 of Shenyang metro Line 1. The tunnels mainly passed through the sandy gravel/cobble soils and the maximum size of particle was about 200 mm, and the initial cutting tools were face scrapers and circumferential bucket scrapers. After driving the No. 193 ring, the cutterhead and cutting tools were seriously destroyed. Main retrofitting jobs included wear resistant plating for cutterhead, installing 28 precut bits as well as repairing damaged scrapers and tooth bits (see Fig. (19)). Using the improved cutterhead and cutting tools, the remaining part of the tunnel was bored without interruption. Fig. (18). Initial cutterhead and the cutting tools. Fig. (19). Retrofitted cutterhead and cutting tools. 4. DISCUSSIONS AND SUGGESTIONS Though configurations of the cutterhead and tools of a shield machine must be customized for specific ground conditions, some options with respect to key parameters of the configurations can be deduced from the above- mentioned construction cases. 4.1. Opening Ratio of Cutterhead Although the opening ratio of a cutterhead is the result of a compromise involving a large number of aspects, a high opening ratio of the shield cutterhead used in sandy gravel/cobble soils with boulders is a better choice, as has been proved by the many above cases of using the spoke type and spoke-plate type cutterheads. In these cases, the tunnels were bored without change cutting tools. Whereas in the cases of using the plate type cutterheads with low opening             !"
[page 15] Cutterhead and Cutting Tools Configurations The Open Construction and Building Technology Journal, 2017, Volume 11 195 ratios of 20%-30%, the interventions for replacing cutting tools are indispensable to realizing driving of the whole tunnel at the expenses of additional time and money. The spoke type and spoke-plate type cutterheads with high opening ratios of 39-75.5% are favorable to passing of the ripped-off big stones into the rear mixing chamber without the need of a substantial pressure drop, thus mitigating wears and damages of cutting tools and other components as well as reducing interventions of changing tools. This is one of the main reasons why the spoke type cutterhead is widely adopted in Beijing to construct subway tunnels. The optimal opening ratio of a cutterhead exists, as can be deduced from the calculated wear coefficients of cutting tools in the projects of Lot 4 and 14 of Beijing subway Line 4. The wear coefficient is defined as the wear amount dividing by the tool path length. The involved strata are almost identical and configurations of the cutting tools are very alike, as previously mentioned. The average wear coefficients for precut bits, including shell bits at the periphery, of the two cutterheads are about 0.035 mm/km.


[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]
The average accuracy was calculated by ﬁrst averaging the accuracy of each validation set over the three surrounding rock grades, and then averaging the averages over the ﬁve validation sets. In the ﬁnal evaluation of the model performance, the precision, recall, F1 score, and accuracy were adopted for a comprehensive evaluation. And these indexes are the average of the results of the three test sets. The process is shown in Figure 8. Figure 8. Model parameter optimization and evaluation process. The performance of the models after training is shown in Figure 9 and Table 7.
[page 13] Appl. Sci. 2022, 12, 2656 13 of 20 (a) (b) (c) (d) Figure 9. Model parameter optimization and evaluation process: (a) precision; (b) recall; (c) F1; (d) accuracy. Table 7. Performance of the intelligent surrounding rock models. Performance Index Rock Grade Supervised Learning Algorithm Average SVM BP RBF KNN GNB DT RT ET Bag-ging GB Precision III 0.81 0.79 0.73 0.77 0.75 0.75 0.81 0.80 0.80 0.81 0.78 IV 0.90 0.87 0.76 0.84 0.75 0.59 0.85 0.88 0.83 0.87 0.81 V 0.86 0.88 0.87 0.90 0.82 0.84 0.90 0.92 0.91 0.88 0.88 Recall III 0.96 0.97 0.94 0.93 0.91 0.73 0.92 0.94 0.93 0.96 0.92 IV 0.62 0.61 0.52 0.63 0.50 0.60 0.67 0.67 0.67 0.65 0.61 V 0.97 0.94 0.89 0.94 0.92 0.86 0.96 0.96 0.93 0.94 0.93 F1 score III 0.88 0.87 0.82 0.84 0.82 0.74 0.86 0.87 0.86 0.88 0.84 IV 0.73 0.72 0.62 0.72 0.60 0.59 0.75 0.76 0.74 0.75 0.70 V 0.91 0.91 0.88 0.92 0.87 0.85 0.93 0.94 0.92 0.91 0.90 Accuracy - 0.85 0.84 0.78 0.84 0.78 0.73 0.85 0.86 0.84 0.85 0.82 The following conclusions can be drawn from Figure 9 and Table 7. Among 10 machine learning algorithm models, the average precision, recall, F1 score and accuracy were above 0.7, except for the recall of grade-IV surrounding rock. In particular, the average recalls of grade-III and -V were greater than 0.90. Thus, these machine learning models established by the drilling parameters are feasible and reliable in the intelligent classiﬁcation of surrounding rocks.
[page 14] Appl. Sci. 2022, 12, 2656 14 of 20 2.8. Model Selection and Recommendation When comprehensively considering the precision, recall, F1 score, and accuracy, three types of models (SVM, RT, and ET) had better performance among the 10 machine learning algorithm models. Their average precision, recall, F1 score, and accuracy were above 0.8, except for the recall of grade-IV surrounding rock. In particular, the recall of the grade-V surrounding rock, which has a great inﬂuence on the safety of the tunnel, was above 0.95. Thus, these machine learning models based on SVM, RT, and ET have some degree of safety and high practical value. 3. Case Study 3.1. Intelligent Surrounding Rock Classiﬁcation System Software The tunnel surrounding rock intelligent classiﬁcation system by the drill and blast method was established based on the intelligent surrounding rock classiﬁcation models based on SVM, RT, and ET. It can automatically record and transmit the drilling parameters and intelligently classify the surrounding rock with carriers of the intelligent drill jumbo. Speciﬁcally, the surrounding rock grade of the tunnel face is determined by the votes of three models (SVM, RT, and ET). When all types of the surrounding rock levels are not superior, the higher grade is taken as the surrounding rock grade of the tunnel face in consideration of safety. In addition, these samples for the models do not require consideration of the modiﬁca- tion conditions (i.e., low crustal stress, without groundwater or drip outﬂow of ground- water, and without a main weak structure surface). Therefore, in consideration of safety and popularization, the grade determined from these models is the basic surrounding rock grade; the grade amended by the groundwater state, major weak structural surface, and crustal stress state is the ﬁnal surrounding rock grade. The speciﬁc process is shown in Figure 10. Figure 10. Classiﬁcation process of the intelligent surrounding rock classiﬁcation system. The system interface is shown in Figure 11.
[page 15] Appl. Sci. 2022, 12, 2656 15 of 20 grade-VI grade-V grade-IV grade-III grade-II grade-I Detials Figure 11. Interface of the ‘tunnel surrounding rock intelligent classiﬁcation system by the drill and blast method’. 3.2. Project Overview of Test Tunnel The Gaojiaping Tunnel of the Zhengzhou–Wanzhou high-speed railway is located in Nanzhang county, Xiangyang city, Hubei province, China.

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

[Assistant]
A solution would be to take a version of the agent that was saved before instability occurred and continue training with that, but we left Fig. 7. Different training paths for five exemplary agents over 120,000 episodes. The first row shows cumulative rewards per episode; the second row the average number of blasts/moves that were required to complete each episode; the third row the average number of face instabilities per episode; the fourth row shows the average loss per episode. Transparent colors in the background show the raw data records and the solid lines in the foreground a 500-episode sliding window average. Fig. 8. Histogram for the performance of an agent who plays 10,000 episodes with completely random moves (i.e. ε = 1). G.H. Erharter et al.
[page 9] Automation in Construction 127 (2021) 103701 9 the agents 2 and 5 as they are for explanatory purposes. Before discussing the performance of the agents, we first establish a baseline for the minimum performance that an agent must reach by letting an agent play with completely random moves (i.e. ε = 1) for 10,000 episodes. In these 10,000 random episodes the agent reached a ▪ minimum/maximum/median reward of −544/255/-13 points, ▪ minimum/maximum/median number of 0/27/8 instabilities per episode, ▪ minimum/maximum/median number of 124/197/145 blasts/ moves per episode. Histograms for the 10,000 random episodes are given in Fig. 8. The maximum rewards, minimum number of face instabilities and minimum number of blasts/moves that the five agents of Fig. 7 needed are given in Table 5. We computed these numbers based on sliding window averages of 500 episodes as to avoid individual episodes that performed extraordinarily well. Albeit substantial differences can be seen in these statistics, it can generally be observed that all maximum rewards of the agents are above the maximum rewards of the randomly played episodes and therefore training was generally a success. In these five agents, two groups can be observed, where the agents 2 and 3 reached their maximum performance between episodes 40,000 and 60,000 and the agents 1, 4 and 5 reached their maximum perfor mance between episodes 80,000 and 120,000. Although the first group has reached their peak performance sooner, the achieved reward is generally lower than that of the second group (see description of training paths above). We conclude this section by presenting the strategy that the best performing agent 4 has found after 119,000 episodes – at its peak per formance (in terms of highest reward and low instabilities). We tested the saved agent’s checkpoint for 10,000 episodes with a fixed ε of 0.05 which corresponds to the ε at that stage of training and is in accordance to [14] who recommend an ε > 0 also for testing, as this helps the agent to deal with unexpected situations. The histograms of Fig. 9 show the same test statistics as given for the random moves in Fig. 8. The strategy that the agent adopted is focused on long advance lengths without face support, in alternation with long advance lengths with face support. By doing so the agent avoids face instabilities, while also minimizing the required support (see Table 4 for the respective rewards/penalties). The boxplot of Fig. 10 illustrates this, as it can be observed that the majority of actions is 4 m long advance lengths. Furthermore, the agent focuses on long advance lengths without face support in both excavation types (top heading and bench) which shows that it tries to avoid excessive use of support measures, thus showing a tendency towards economical optimization. It can also be seen that the agent still uses small advance lengths sometimes and, in this case, favors the actions without face support as it has realized that the small advance lengths do not lead to face instabilities in the given conditions. In Fig. 11 an example of one episode for the agent 4 is visualized. The time-distance diagram in the top row of this figure shows that the agent has learned to optimize the excavation process by minimizing changes between top heading and bench excavation which would be associated with unwanted delays in “real life” tunneling (e.g. building and removing of access ramps). As given in Section 2.1, installed support ahead of the current face always covers 10 m. With the maximum advance length being set to 4 m it would be unnecessary and uneco nomical to install face support in two consecutive rounds. In the second row of Fig.


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

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

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

[Assistant] Rock strength prediction in real-time while drilling employing random forest and functional network techniques. J. Energy Resour. Technol. 143 (9), 093004. Hochreiter, S., Younger, A.S., Conwell, P.R., 2001. In: Learning to Learn Using Gradient Descent. International Conference on Artiﬁcial Neural Networks. Springer, Berlin, Heidelberg. Huang, X., Qi, S., Xia, K., Shi, X., 2018. Particle crushing of a ﬁlled fracture during compression and its effect on stress wave propagation. J. Geophys. Res. Solid Earth 123 (7), 5559e5587. Hudson, J.A., 2012. Design methodology for the safety of underground rock engi- neering. J. Rock Mech. Geotech. Eng. 4 (3), 205e214. ISRM, 1979. Suggested methods for determining the uniaxial compressive strength and deformability of rock materials. Int. J. Rock Mech. Min. Sci. 16 (2), 135e140. Krauthammer, T., Elfahal, M.M., Lim, J., Ohno, T., Beppu, M., Markeset, G., 2003. Size effect for high strength concrete cylinders subjected to axial impact. Int. J. Impact Eng. 28 (9), 1001e1016. Li, Q.M., Lu, Y.B., Meng, H., 2009. Further investigation on the dynamic compressive strength enhancement of concrete-like materials based on split Hopkinson pressure bar tests. Part II: numerical simulations. Int. J. Impact Eng. 36 (12), 1335e1345. Li, W., Tan, Z., 2017. Research on rock strength prediction based on least squares support vector machine. Geotech. Geol. Eng. 35, 385e393. Li, X.B., Lok, T.S., Zhao, J., 2005. Dynamic characteristics of granite subjected to intermediate loading rate. Rock Mech. Rock Eng. 38 (1), 21e39. Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J.C.W., van den Bossche, P., Mierlo, J.V., Omar, N., 2018. Random forest regression for online capacity esti- mation of lithium-ion batteries. Appl. Energy 232, 197e210. Masoumi, H., Saydam, S., Hagan, P.C., 2015. Uniﬁed size-effect law for intact rock. Int. J. GeoMech. 16 (2), 04015059. Munoz, H., Taheri, A., 2017. Specimen aspect ratio and progressive ﬁeld strain development of sandstone under uniaxial compression by three-dimensional digital image correlation. J. Rock Mech. Geotech. Eng. 9 (4), 599e610. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by back propagating errors. Nature 323, 533e536. Samui, P., 2008. Support vector machine applied to settlement of shallow founda- tions on cohesionless soils. Comput. Geotech. 35, 419e427. Smola, A.J., Scholkopf, B., 2004. A tutorial on support vector regression. Stat. Comput. 14, 199e222. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014. Dropout: a simple way to prevent neural networks from overﬁtting. J. Mach. Learn. Res. 15 (1), 1929e1958. Wang, Y., Tonon, F., 2011. Dynamic validation of a discrete element code in modeling rock fragmentation. Int. J. Rock Mech. Min. Sci. 48 (4), 535e545. Weng, L., Wu, Z., Liu, Q., 2020. Dynamic mechanical properties of dry and water- saturated siltstones under sub-zero temperatures. Rock Mech. Rock Eng. 53, 4381e4401. Whittington, W.R., Oppedal, A.L., Francis, D.K., Horstemeyer, M.F., 2015. A novel intermediate strain rate testing device: the serpentine transmitted bar. Int. J. Impact Eng. 81, 1e7. Xia, K., Nasseri, M.H.B., Mohanty, B., Lu, F., Chen, R., Luo, S.N., 2008. Effects of mi- crostructures on dynamic compression of Barre granite. Int. J. Rock Mech. Min. Sci. 45 (6), 879e887. Yılmaz, I., Yuksek, A., 2008. An example of artiﬁcial neural network (ANN) appli- cation for indirect estimation of rock parameters. Rock Mech. Rock Eng. 41, 781e795. Yu, M., Li, S., Sun, Q., Wang, S., 2021. Inﬂuence of grain size on the strain-rate- dependent dynamic response of sandstones. Geomech. Geophys. Geo. 7, 74. Zhang, Q.B., Zhao, J., 2013. Determination of mechanical properties and full-ﬁeld strain measurements of rock material under dynamic loads. Int. J. Rock Mech. Min. Sci. 60, 423e439. Zhang, Z.X., Kou, S.Q., Jiang, L.G., Lindqvist, P.A., 2000. Effects of loading rate on rock fracture: fracture characteristics and energy partitioning. Int. J. Rock Mech. Min. Sci. 37, 745e762. Zhao, J., Zhou, Y.X., Hefny, A.M., Cai, J.G., Chen, S.G., Li, H.B., Liu, J.F., Jain, M., Foo, S.T., Seah, C.C., 1999. Rock dynamics research related to cavern development for ammunition storage. Tunn. Undergr. Space Technol. 14 (4), 513e526. Zhong, K., Zhao, W., Qin, C., Gao, H., Chen, W., 2021. Mechanical properties of roof rocks under superimposed static and dynamic loads with medium strain rates in coal mines.

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


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

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

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

[Assistant]
The strain rate is poorly correlated with the other parameters, as it is controlled by the loading condition. The spec- imen length and diameter are strongly correlated with each other but poorly correlated with the other parameters due to a length-to- diameter ratio of 0.5e1 suggested by the SHPB test standard (Zhou et al., 2012) and widely adopted in many studies. 2.2. Machine learning algorithms Three well-proved machine learning algorithms, i.e. SVM, BPNN, and RF, in rock mechanics were used. However, their applicability to rock dynamics has rarely been reported yet. Fig. 2 illustrates the speciﬁc steps of applying the three algorithms to predict the rate- dependent compressive strength of rocks. The basic features of these algorithms are summarized in Figs. 3e5. 2.2.1. SVM algorithm The SVM algorithm was originally proposed by Boser et al. (1992) for data classiﬁcation and subsequently developed by Drucker et al. (1996) for regression and prediction analysis. The SVM based on the principle of structural risk minimization can minimize empirical risk and conﬁdence range, as well as obtain excellent statistical laws even for small sample sizes (Samui, 2008). Fig. 3 shows a schematic diagram of the basic principle of the SVM to solve the regression and prediction problems. The goal of the SVM training is to ﬁnd the best hyperplane closest to all data points. The training process is to continuously adjust the hyperplane pa- rameters to minimize the sum of the distances of all data points from the hyperplane plane. In this study, the SVM is used to construct the mapping relationship between the compressive strength and the other parameters in Table A1. Thus, the feature vector of the compressive strength can be written as {xi, yi} (i ¼ 1, 2, ., m; xi ˛ Rd; yi ˛ R), where x represents the input, y denotes the output, m is the number of data points, R and Rd are the one- dimensional and d-dimensional vector space, respectively, and d is the number of input variables. The value of y (i.e. compressive strength) can be solved as y ¼ w,4ðxÞ þ b (1) where w is a vector of weight coefﬁcient, 4(x) is a nonlinear mapping function, and b is a bias constant. The parameters w and b can be determined by minimizing 1 2kwk2 under the following premise (Samui 2008): Fig. 1. Correlations between input parameters based on Pearson correlation co- efﬁcients. Values 1 and -1 mean strong positive and negative correlations, respectively. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1357
[page 3] jyi  ½w , 4ðxiÞ þ bj  ε (2) where ε is a free parameter considered as a threshold. Two relaxation variables, i.e. xi and x* i , are introduced to penalize the points outside the ε-insensitive band (Smola and Scholkopf, 2004). The best regression line is deﬁned by minimizing 1 2kwk2 þ c Pd i ¼ 1 xi þ x* i , where c is the penalty parameter determining the trade-off between the model complexity and the training error, under the following precondition (Samui 2008): yi  ½w,4ðxiÞ þ b   ε þ xi ½w,4ðxiÞ þ b   yi  ε þ x* i ) (3) To facilitate the solution, the Lagrangian multipliers (a* i , ai, a* j and aj) are introduced, and the above quadratic programming problem (Eq. (3)) is transformed into a dual problem of maximizing Pd i ¼ 1yiða* i aiÞ  Pd i ¼ 1εða* i þaiÞ  1 2 Pd i ¼ 1 Pd j ¼ 1ða* i aiÞða* j ajÞxT i xj under the following premise: X d i ¼ 1 a* i  X d i ¼ 1 ai ¼ 0 0  ai  c 0  a* i  c 9 > > > > > = > > > > > ; (4) Fig. 2. A ﬂowchart summarizing how to apply SVM, BPNN, and RF algorithms to establish machine learning models. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1358
[page 4] By solving Eq. (4), the function model for support vector regression can be determined as y ¼ X d i ¼ 1 a* i  ai Kðxi; xÞ þ b (5) where K (xi,x) is the kernel function, adopting the radial basis function to address nonlinear problems (Samui, 2008): Kðxi; xÞ ¼ eðgkxixk2Þ (6) As summarized in Fig. 2, the two parameters (i.e. c and g) in the radial basis function should be optimized. The SVM-based model aims to ﬁnd the optimum c and g values through the grid search method (Cheung et al., 1997) and to achieve good generalization capacity and high calculation speed. 2.2.2. BPNN algorithm The BPNN algorithm developed by Rumelhart et al. (1986) is a multi-layer feed-forward algorithm.


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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]
In this study, hydraulic valves are modelled by semi-empirical approach [20]. In this approach, ﬂow through a throttle valve can be described as: Q =    Cv √|dp|, dp > 0 0, dp = 0 −Cv √|dp|, dp < 0 (16) where Cv is the ﬂow rate constant of the valve, and dp is pressure difference between the input and output. Flow through a directional valve can be described as [21]: Q =    CvU√|dp|, dp > 0 0, dp = 0 −CvU√|dp|, dp < 0 (17) where U is the relative spool position, which is described [21]: ˙U = Uref −U τ (18) where Uref is a control signal and τ is a time constant describ- ing the spool dynamics of the valve. D. DEFORMABLE GROUND Together with the multibody approach and hydraulics sim- ulation, the deformable ground forms the basis for devel- opment of the autonomous agents. The deformable ground provides a representation of soil behavior and allows sim- ulation of working scenarios of the machine. In this study, the environment of the excavator is described by employing a combination of the cellular automata-based [22], [23] and particle-based methods, as in [24], [25]. This combination of techniques deﬁnes an accurate deformable ground and enables ground material to be moved from one location to another as shown in Fig.2. FIGURE 2. Deformable ground working principle [25]. In the cellular automata-based method [22], [23], the soil is described as cells in a grid (heightﬁeld) which allows the formation of the landscape, as shown in Fig. 2. The cells are vertically divided into piles of blocks. The pressure over the base of one cell is obtained as a combination of the pressure received from their own block and the pressure from a ﬁnite number of nearby blocks above the base cell. In this model, the displacement of the soil can occur for two reasons: 1) A large difference in the heightﬁeld of two neigh- boring cells, resulting in avalanches; and 2) A large difference between the vertical forces applied to the neighboring cells, resulting in soil compression and displacement. In this study, the deformation of the heightﬁeld depends on the interaction between the ground and external objects, such as an excavator VOLUME 8, 2020 214001
[page 5] I. Kurinov et al.: Automated Excavator Based on RL and Multibody System Dynamics bucket or tracks. Depending on the amount of force induced on the soil, the cellular automata redeﬁne the heightﬁeld of the cells, i.e. adding the vertical deformation. The particle-based method allows one to simulate the behavior of the soil when it is sheared with an external force [25]. In this method, particles are generated when the horizontal component of the applied force exceeds the shear impulse limit. During this event, the portion of the cor- responding heightﬁeld is substituted with spherical shaped particles, which have six degrees of freedom. The dynam- ics of the particles can be expressed by methods discussed in the multibody system dynamics subchapter [25]. Particle swarm has a void factor, which is responsible for the space between generated particles, i.e. soil compaction [24]. The soil compaction allows accurate dynamics representation of the soil compared with nature [24]. When the particles reach an equilibrium state, they merge back to the heightﬁeld with a volume update [24]. E. DESCRIPTION OF APLICATION PROGRAMMING INTERFACE RL training and simulation are performed by separate pro- grams. Therefore, a connection between the simulation mod- els and the reinforcement learning algorithm was developed using a custom Application Programming Interface (API). In this study, the API was implemented using the ZeroMQ asynchronous messaging library to send data between the applications. The client-server messaging pattern, which allows multiple clients to be connected to the server [26], was used as the base of the API. The RL side was used as a server and the simulation side as a client. The server side is shown in Algorithm 1 and the simulation side is shown in Algorithm 2. This structure allows simultaneous use of multiple simulations for training as depicted in Fig.3. FIGURE 3. Structure of the API based on Gym, State and Parameters classes. The API consists of the three main classes: Gym, Parame- ters and State. The Gym class is used to control the simulation and handle the connection to the algorithm. The class consists of the make, step and reset methods. The make method is used for initialization of the variables in the simulation.

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] Cotton RG, Al Aqeel AI, Al-Mulla F, Carrera P, Claustres M, Ekong R, Hyland VJ, Macrae FA, Marafie MJ, Paalman MH, et al: Capturing all disease-causing mutations for clinical and research use: toward an effortless system for the Human Variome Project. Genet Med 2009, 11:843-849. 26. Kohonen-Corish MR, Al-Aama JY, Auerbach AD, Axton M, Barash CI, Bernstein I, Beroud C, Burn J, Cunningham F, Cutting GR, et al: How to catch all those mutations–the report of the third Human Variome Project Meeting, UNESCO Paris, May 2010. Hum Mutat 2010, 31:1374-1381. 27. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16:412-424. 28. Fawcett T: An introduction to ROC analysis. Pattern Recognition Letters 2006, 27:861-874. 29. Sonego P, Kocsor A, Pongor S: ROC analysis: applications to the classification of biological sequences and 3D structures. Brief Bioinform 2008, 9:198-209. 30. Giardine B, Riemer C, Hefferon T, Thomas D, Hsu F, Zielenski J, Sang Y, Elnitski L, Cutting G, Trumbower H, et al: PhenCode: connecting ENCODE data with mutations and phenotype. Hum Mutat 2007, 28:554-562. 31. Piirilä H, Väliaho J, Vihinen M: Immunodeficiency mutation databases (IDbases). Hum Mutat 2006, 27:1200-1208. 32. Olatubosun A, et al: PON-P: Integrated predictor for pathogenicity of missense variants. Human Mutation [http://onlinelibrary.wiley.com/doi/ 10.1002/humu.22102/pdf]. 33. Kumar MD, Bava KA, Gromiha MM, Prabakaran P, Kitajima K, Uedaira H, Sarai A: ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. Nucleic Acids Res 2006, 34:D204-206. 34. Lonquety M, Lacroix Z, Chomilier J: Pattern recognition in bioinformatics. Heidelberg: Springer; 2008. 35. Tastan O, Yu E, Ganapathiraju M, Aref A, Rader AJ, Klein-Seetharaman J: Comparison of stability predictions and simulated unfolding of rhodopsin structures. Photochem Photobiol 2007, 83:351-362. doi:10.1186/1471-2164-13-S4-S2 Cite this article as: Vihinen: How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis. BMC Genomics 2012 13(Suppl 4):S2. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color ﬁgure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 Page 10 of 10


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

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

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

[Assistant]
The process of constructing the BPNN includes signal forward propagation and error back- propagation. The structure of the BPNN consists of an input layer, one or more hidden layers, and an output layer (Fig. 4). In this study, the compressive strength is set as the output neuron, and the others (e.g. strain rate, P-wave velocity) are the inputs. To accelerate the convergence of the neural network, the raw data (X) are normalized using Eq. (7) and mapped into a range of 0e1: Xi ¼ X  Xmin Xmax  Xmin (7) where Xi is the normalized data; and Xmax and Xmin are the maximum and minimum values, respectively. The number of hidden-layer nodes has a signiﬁcant inﬂuence on the BPNN training and is determined as h ¼ ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ d þ k p þ a (8) where h and k are the number of nodes in the hidden and output layers, respectively, and a is a constant with a value of 0e10. In our model, d ¼ 7 and k ¼ 1. By taking a ¼ 8, h can be determined as 10. Information transfer between the layers relies on the matrix operation: y g ¼ x g  w g þ b g (9) where x g is the eigenvector of the previous layer, y g is the vector output to the next layer, and w g and b g are the weight and bias matrixes used to convert the eigenvector to the vector output, respectively. In the error back-propagation algorithm, the Sigmoid function is used to introduce nonlinearity to the BPNN-based model to better solve complex nonlinear problems: fsigðzÞ ¼ 1 1þez (10) In the initial BPNN-based model, the weights and biases are randomly initialized. After being fed with the input data, the model compares the output data using the loss function: floss ¼ ðby  yÞ2 (11) where floss is the prediction error, and by and y are the measured and the predicted strength values, respectively. Because of the random parameter setting, a learning mechanism should be implemented to optimize the model. The BPNN-based model has the two parameters (i.e. w g and b g ) to be adjusted during the training process (Fig. 2). Based on the pre- diction error, the error term of each hidden layer is calculated in reverse order through the error back-propagation algorithm, and the gradient descent method is applied to adjust the two param- eters of each hidden layer (Hochreiter et al., 2001). The adjustment of the weight and bias values is determined by the gradient of each parameter calculated using the loss function. The dropout regula- rization method is used to prevent the model from overﬁtting (Srivastava et al., 2014). An unacceptable error is propagated backwards again, and the weight and bias values are adjusted accordingly. This process is repeated until ﬁnding an acceptable error or reaching the maximum cycle number. 2.2.3. RF algorithm The RF algorithm proposed by Breiman (2001) can integrate multiple decision trees into an ensemble algorithm and average the results of these decision trees as the output (Fig. 5). The RF has a fast convergence speed and can prevent data from overﬁtting to improve the prediction accuracy. The main features of the RF include (i) bootstrap sampling, (ii) random feature selection, (iii) Fig. 3. A schematic diagram of the principle of SVM algorithm. Fig. 4. A schematic diagram of the principle of BPNN algorithm. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1359
[page 5] out-of-bag (OOB) error estimation, and (iv) full-depth decision tree growth (Li et al., 2018). The training data set (Sm) containing m observations can be written as Sm ¼ fðx1; y1Þ; ðx2; y2Þ; :::; ðxm; ymÞg ðxi ˛ Rd; yi ˛ RÞ (12) where m is the number of training data points. Also shown in Fig. 2, the bootstrap sampling is performed to generate t sets of training data from the original data set. Each new data set corresponds to a decision tree that is fully grown and not pruned by the classiﬁcation and regression trees (CART) method- ology (Duda et al., 2000). The RF can ﬁnd the best split among the randomly selected features at each node. It is common to artiﬁcially control the number of branches in the decision tree or let it grow freely for a limited number of times and subsequently to seek the best single decision tree. After the training process, a global pre- diction function y ¼ hðx; SmÞ is constructed over Sm. The RF pro- duces the prediction result by averaging the output of these decision trees (i.e. ~y1 ¼ ~hðx; ~S1Þ, ~y2 ¼ ~hðx; ~S2Þ, ., ~yt ¼ ~hðx; ~StÞ).


[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]
Further improve- ments have been realized to classify an abnormal image as benign (noncancerous) or malignant (cancerous) by applying a double classifier Abd-Ellah et al. [19]. Devasena and Hemalatha [20] proposed a CAD system for the detec- tion of abnormal parts in MRIs using a hybrid abnormality detection algorithm (HADA). In [21], Patil and Udupi used preprocessing, segmenta- tion, feature extraction, and a probabilistic neural network (PNN) method to identify brain tumours. Arakeri and Reddy [22] applied three different classifiers: an SVM, an artificial neural network (ANN), and a k-nearest neigh- bour (k-NN) algorithm to distinguish between different types of tumours. Dandıl et al. [23] proposed a system for brain tumour classification where spatial-fuzzy C-means (FCM) was applied for brain tumour segmentation, and an SVM was used in the classification stage. Goswami and Bhaiya [24] presented a new MRI brain tumour classification based on an ANN. Histogram equalization, noise filtering, and edge detection were applied in the preprocessing stage. The features were extracted by independent component analysis (ICA). A self-organizing map neural network (SOMNN) was
[page 3] Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 Page 3 of 10 applied in the classification stage. Deepa and Devi [25] proposed a system consisting of feature extraction, clas- sification, and tumour segmentation. Optimal texture features were extracted from tested images using statis- tical features. A back propagation neural network (BPN) and a radial basis function neural network (RBFN) were used in the classification and segmentation stages, respec- tively. A short comparison of the aforementioned classical learning-based diagnosis methods is provided in Table 1. Few deep learning-based methods for brain tumour diagnosis were found in the state-of-the-art in this field. In [26], brain CT image classification based on a deep neural network (DNN) was presented. A gray-level co- occurrence matrix (GLCM) was used for feature extrac- tion, and a DNN was applied for classification. The aver- age accuracy of the reported method was 83%. Gao et al. [27] introduced a CT brain image classification that inte- grated both 2D and 3D CNNs. The average classification accuracy rate was 87.6%, while each network consisted of seven layers. Yan Xu et al. proposed a method using deep convolutional activation features for brain tumour classification and segmentation. The system classification accuracy was 97.5% [28]. Several deep learning methods for brain tumour local- ization have been presented in the literature. Havaei et al. [29] demonstrated automatic brain tumour localization using a DNN based on the CNNs with an average DICE score of 0.88. In [30], a fully convolutional residual neural network (FCR-NN) combined with a fully convolutional architec- ture with optimization gains from residual identity was developed. The network consists of a 22 complex layers, and the achieved DICE score was 0.87. Casamitjana et al. Table 1 Comparison of the available traditional classification approaches used in brain tumour detection CAD system Total images Classification method Accuracy (%) El-Dahshan et al. [2] 101 FFBPNN 99.00 Devasena and Hemalatha [20] 250 HADA 98.8 Arakeri and Reddy [22] 550 SVM, ANN and k-NN 99.09 Dandıl et al. [23] 376 KSVM 91.49 Goswami and Bhaiya [24] 70 SOMNN 98.60 Deepa and Devi [25] 42 BPN and RBFN 85.71 Abd-Ellah et al. [18] 80 KSVM 100 Abd-Ellah et al. [19] 120 KSVM 100 [31] proposed the use of 3D CNNs for the segmenta- tion of abnormal parts in MRIs based on a network using multi-resolution features and reported a DICE score of 0.84. Zhao et al. [32] used a fully convolutional neural net- work (FCNN) and conditional random fields (CRFs) as a post-processing step to segment brain tumours with an obtained DICE score of 0.87. Pereira et al. in [33] presented a preliminary study on brain tumour segmen- tation based on fully convolutional network (FCN) with standardization of MRI histograms as a preprocessing stage, which demonstrated a DICE score of 0.85. More recently, Pereira et al. [34] examined a complete seg- mentation system that includes preprocessing, CNN, and post-processing stages. The new system could achieve an improved DICE score of 0.88 at the expense of increased system complexity. 3 Methods Currently, deep learning models are of considerable research importance.

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

[Assistant]
We then established three machine learning models based on SVM, the BPNN, and the RF algorithms. In the machine learning models, the compressive strength was the output, and the other properties (i.e. static compressive strength, P- wave velocity, specimen dimension, grain size, bulk density, and strain rate) were the inputs. After training and validating the models, we discussed the importance of each input parameter in the strength prediction and the transitional behaviors of rocks with different specimen dimensions. Our results indicated that the machine learning models can achieve reliable prediction for the rate-dependent strength of rocks. The RF algorithm manifests slight superiority to SVM and BPNN in predicting the rock strength. The strain rate as input is more important than the other parameters to ensure the excel- lent performance of the models. The dynamic increase factor is independent of specimen dimension at low strain rates but bifurcate for the strain rate over a relatively high level. This observation can be used to study the transitional behaviors of rocks from low to high strain rates, which may yield more in- sights into the specimen dimension effect on rock strength and further highlight the strain rate effect. This study introduced a new avenue to study the complex mechanical behaviors of rocks at various strain rates. Fig. 9. Effect of strain rate on (a) Compressive strength, and (b) Dynamic increase factor for different specimen diameters (12.5mm, 25mm, and 50 mm), predicted by the RF-based model. Fig. 10. Effect of strain rate on (a) Compressive strength, and (b) Dynamic increase factor for different aspect ratios (0.5, 0.75, and 1), predicted by the RF-based model. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1364
[page 10] 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. Acknowledgments This research is supported by National Research Foundation, Singapore under its Virtual Singapore R&D Programme (Award No. NRF2019VSG-GMS-001). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jrmge.2022.01.008. References Boser, B.E., Guyon, I.M., Vapnik, V.N., 1992. A training algorithm for optimal margin classiﬁers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144e152. Pittsburgh Pennsylvania, USA. Breiman, L., 2001. Random forests. Mach. Learn. 45 (1), 5e32. Chen, W.W., Song, B., 2011. Split Hopkinson (Kolsky) Bar: Design, Testing and Ap- plications. Springer, New York. Cheung, B.K.S., Langevin, A., Delmaire, H., 1997. Coupling genetic algorithm with a grid search method to solve mixed integer nonlinear programming problems. Comput. Math. Appl. 34 (12), 13e23. Drucker, H., Burges, C.C., Kaufman, L., Smola, A.J., Vapnik, V.N., 1996. Support vector regression machines. Adv. Neural Inf. Process. Syst. 28 (7), 779e784. Du, H.B., Dai, F., Xu, Y., Liu, Y., Xu, H.N., 2018. Numerical investigation on the dy- namic strength and failure behavior of rocks under hydrostatic conﬁnement in SHPB testing. Int. J. Rock Mech. Min. Sci. 108, 43e57. Duan, K., Ji, Y.L., Wu, W., Kwok, C.Y., 2019a. Unloading-induced failure of brittle rocks and implications for excavation-induced strain burst. Tunn. Undergr. Space Technol. 84, 495e506. Duan, K., Li, Y.C., Wang, L., Zhao, G.F., Wu, W., 2019b. Dynamic responses and failure modes of stratiﬁed sedimentary rocks. Int. J. Rock Mech. Min. Sci. 122, 104060. Duda, R.O., Hart, P.E., Stork, D.G., 2000. In: Pattern Classiﬁcation, third ed. John Wiley & Sons, New York, USA. Edwards, A.L., 1976. An Introduction to Linear Regression and Correlation. W. H. Freeman. Fan, L.F., Gao, J.W., Wu, Z.J., Yang, S.Q., Ma, G.W., 2018. An investigation of thermal effects on micro-properties of granite by X-ray CT technique. Appl. Therm. Eng. 140, 505e519. Fan, L.F., Gao, J.W., Du, X.L., Wu, Z.J., 2020. Spatial gradient distributions of thermal shock-induced damage to granite. J. Rock Mech. Geotech. Eng. 12 (5), 917e926. Frew, D.J., Forrestal, M.J., Chen, W., 2001. A split Hopkinson pressure bar technique to determine compressive stress-strain data for rock materials. Exp. Mech. 41 (1), 40e46. Gamal, H., Alsaihati, A., Elkatatny, S., Haidary, S., Abdulraheem, A., 2021.


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

[Assistant]
2021, 13, 2577. https://doi.org/10.3390/rs13132577 https://www.mdpi.com/journal/remotesensing
[page 2] Remote Sens. 2021, 13, 2577 2 of 22 be related to the magnetic local time, resulting in the fast movement of large-scale iono- spheric plasma structures and phase ﬂuctuations [8–10]. In low-latitude areas, equatorial scintillation activities are stronger and frequent during years with solar maxima, which are associated with the solar activities [11]. The GNSS receiver is affected by amplitude and phase ﬂuctuation, carrier Doppler jitter, cycle slips, and the serious loss of lock and even navigation interruption, which have adverse effects on both ionospheric research and navigation and positioning services [12,13]. Evidence suggests that ionospheric scintillation plays a vital role in receiver perfor- mance in positioning and navigation services. To improve the quality and reliability of GNSS observations, the implementation of the automatic detection and monitoring of ionospheric scintillation is vital, especially during strong scintillation. Relevant ionospheric scintillation monitoring receivers (ISMRs) have recently been designed for use in commerce and research, which are used by presetting thresholds for amplitude and phase indices [14]. However, detection methods such as traditional thresholding technologies are inﬂuenced by various factors. To be speciﬁc, this approach requires detrending and ﬁltering algo- rithms for observations and overlooks higher-order moment information of the GNSS signals [15]. Moreover, it is sensitive to false alarms due to factors such as multipath, and has been proved to present a lower detection accuracy of 81%, compared to the approach of manual visual inspection [15,16]. Some wavelet decomposition and transform-based techniques using Butterworth ﬁlters with non-indices are proposed as alternatives to over- come the problem of detrending, but they rely on expensive computation and complex implementation [17,18]. In recent years, researchers have investigated a variety of machine learning approaches to achieve automatic scintillation detection. Jiao et al. (2017) [19,20] ﬁrstly exploited the support vector machine (SVM) for amplitude scintillation detection on two classes. They used a mass of real data and manual labels in the training process and achieved a detection accuracy of 91–96%, outperforming other traditional triggering approaches and non-index techniques. A similar detection method for phase scintillation presents an accuracy of around 92% [21,22]. Lin et al. (2020) [23] analyzed the effects of a binary classiﬁcation SVM model on hyperparameters and achieved excellent performance in testing. However, based on features such as scintillation indices and relevant maximum and average values, these methods require a ﬁltering process for the scintillation indices S4/SigmaPhi and predeter- mined elevation mask of 30◦. Ludwig-Barbosa, V. et al. (2021) [24] trained SVM models with features combining amplitude/phase scintillation indices along with corresponding maximum and mean values, as well as intensity power spectral density (PSD), showing about 91% accuracy in the detection of ionospheric scintillation. Besides the detection of ionospheric scintillation by an SVM, similar research such as that on jamming detection in GNSS bands with an SVM was conducted, with 94.4% accuracy [25]. An accuracy of 91.36% was found when performing a similar task using convolutional neural networks [26], which are widely researched for various classiﬁcation tasks, showing great performance [26–28]. Linty et al. (2019) [16] proposed a decision tree algorithm relying on the in-phase and quadrature correlator outputs of the receiver tracking loop, which are considered as sample features after simple computation. The results of 10-fold cross-validation show that the accuracy of amplitude scintillation detection reached 96.7% for features consisting of S4, the carrier-to-noise rate (C/N0) and satellite elevation. Furthermore, for features used by correlator outputs and corresponding combinations, the cross-validation accuracy in- creased from 98.0% to 99.7% when using a random forest algorithm. The overall F1 score reached a high value of 90%, compared with the value of 80.1% achieved with the semi- hard rule [16].

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


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

[Assistant]
General Overviews This section presents brief descriptions of GNSS ionospheric scintillation, the data collection system and the feature extraction process.
[page 4] Remote Sens. 2021, 13, 2577 4 of 22 2.1. GNSS Ionospheric Scintillation GNSS satellites transmit low-rate navigation messages modulated by unique spread- ing sequences or codes, which are carried by radio frequency (RF) signals and received by GNSS receivers on the Earth. Corresponding computations on position, velocity and time can be utilized in land applications such as autonomous vehicle navigation and vehicle tracking monitoring, in marine applications such as ocean transportation and inland river shipping, and in aviation applications such as route navigation, airport scene monitoring and precision approach. In the propagation of RF signals from satellites to receivers, there are natural distur- bances in the GNSS signals when they pass through the ionospheric irregularity, resulting in temporal delay and ﬂuctuations, degrading the accuracy, integrity and reliability of the system’s performance. Figure 1 shows the effects of ionospheric scintillation on the process of transiting satellite signals to GNSS receivers. Ionospheric scintillation, a form of major interference, is difﬁcult to model due to its quasi-random nature. With the increased de- mand for and dependence on navigation systems in various ﬁelds, research on improving the accuracy of scintillation detection is signiﬁcant. Ionospheric irregularity Rotaion polarization Multipath effect Figure 1. Representative effects of ionospheric scintillation on GNSS receiver. The green blocks refer to the ionospheric irregularities on the area of ionospheric layer, and the red lines represent the main disturbances on the ground receiver. Ionospheric scintillation is classiﬁed into amplitude scintillation and phase scintilla- tion; the former is more frequent in low-latitude areas. It is extremely important for a better detection method to be developed for the Beidou navigation system (BDS), which has been built in recent years with an immature scintillation detection technique [31]. Based on existing GPS data consisting of strong scintillation that were collected in the latest peak year for solar activity, relevant research on scintillation detection can be carried out to develop corresponding technology for the BDS. The performance can also be evaluated in the coming peak year for solar activity (2023–2024). S4, commonly used as an indicator, ranges from 0 to 1, for which a large value represents a stronger intensity of ionospheric scintillation. Values greater than 1 represent the occurrence of extremely severe ionospheric scintillation. The scintillation levels are normally classiﬁed into three categories [32–34]: strong if 0.6 ≤S4, moderate if 0.2 < S4 ≤0.6, and weak if 0 < S4 ≤0.2. Due to the occasional contingency, irregularity and certain seasonal characteristics of ionospheric scin- tillation, there are far fewer strong scintillation events than weak and moderate scintillation events [35]. Moreover, most of the scintillation activity over the course of a day occurs from sunset to dawn [36]. In other words, the proportion of strong scintillation events in with respect to scintillation events of all intensities is relatively low, causing imbalance [16,20,23].
[page 5] Remote Sens. 2021, 13, 2577 5 of 22 Thus, the imbalance exists naturally in ionospheric scintillation detection and monitoring. However, such a phenomenon has not been taken into consideration for the detection of interference in space weather monitoring. 2.2. Data Collection System The data used in this paper were collected at the low-latitude site of São José dos Cam- pos, Brazil (23.2S, 45.9W), from 2013 to 2015 during the last peak of the solar cycle, where the phenomenon of ionospheric scintillation was extremely active due to its geographical location close to both the South Atlantic Magnetic Anomaly (SAMA) and the EIA [37]. Months of GPS data from 2013 to 2015 were recorded, with strong ionospheric scintillation detected during each hour of monitoring. Aiming at studying the natural, occurrent and unpredictable phenomenon, a Septentrio PolaRx ionospheric scintillation monitoring (ISM) receiver was utilized to monitor the scintillation activity. Figure 2 shows the framework of the data collection system used to monitor ionospheric scintillation events.


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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]
In principle, this meant that more multispectral images were to be captured at 2 m with a smaller spatial area per image, followed by 10 m, and the least number of images being captured at 20 m with a relatively larger spatial area. The drone was ﬂown over the 30 m × 90 m area in order to pre-program the ﬂight plan. This ﬂight setting procedure ensures the drone ﬂies within the desired area, direction and elevation before allowing it to perform all these maneuvers autonomously without human intervention. 2.2. Parallax Error Correction Since there is a small distance between the 6 spectral sensors of the drone, parallax error always needs to be corrected for. This ensures that all sensors are aimed at the same image scene depending on the UAV ﬂight height. At low altitudes, the parallax error is signiﬁcantly higher relative to higher ﬂight altitudes [23]. There are several methods
[page 5] Minerals 2022, 12, 268 5 of 19 such as Hough transform and least-squares techniques amongst others which can be applied to perform this task aimed at interpolating the images into perfectly overlapping images [23,24]. However, according to Laurence [24], the edge detection method performs well as it takes into account the individual pixels within each of the 6 multispectral images to be overlapped. It calculates the centers of gravity, and automatically nulliﬁes the differences in position, thereby creating a perfect overlay of images, hence the application of this method in this study [24]. It should be noted however, that in order to assume a perfect overlay, each of the 6 images loses a small part of their edges. The lower the UAV elevation, the more data are lost relative to higher ﬂight elevations. The data loss depends on the distance from the drone sensor to the subject, as well as the distance between sensors within the drone, and so, parallax error always needs to be corrected. Though a small percentage of data is lost per image during parallax correction, this is not the case when considering the study area as a whole. The reason for this is that the UAV ﬂight missions were set up such that each captured image slightly overlaps those around it. This, in principle, ensures zero data loss within the 30 m × 90 m study area, with only the image regions outside the study area experiencing data loss. 2.3. A Scrutiny of SAM Analysis Based on a number of vegetation criteria, Mohajane et al. [4] have employed several indexes in an attempt to perform rapid ﬁeld vegetation classiﬁcation. These included the intensity of certain vegetation colours and moisture content via SAM. Zhang and Li [8] have used the same principle in employing SAM as an index to classify lithology. Weyermann et al. [11] deﬁne SAM as a physically based spectral classiﬁcation that uses an n-dimensional angle to match pixels to reference spectra (Figure 3). The algorithm determines the spectral similarity between spectra by deducing the angle between them, treating them as vectors in a space with dimensionality equal to the number of bands [17], which in this study is 6 bands. This technique, when used on calibrated reﬂectance data as done in this study, is relatively insensitive to illumination and albedo effects [16]. For this reason, SAM is highly applicable in multitudes of real life ﬁeld observations, hence the motivation to employ this technique in this study. In short, SAM compares the angle between the endmember spectrum vector and each pixel vector in the n-dimensional space. Narrow angles represent closer matches to the reference spectrum [8]. Pixel data further away from the speciﬁed maximum angle threshold in radians are not classiﬁed as the reference spectrum of what is sought after, which in this study is magnetite iron sand. Figure 3 illustrates the principle of SAM analysis via cosine similarity. Figure 3. Schematic diagram showing the principle of spectral angle mapping via cosine similarity. Reference spectrum is the sought after target. Angle a is the cosine similarity threshold between a reference and a true spectrum.
[page 6] Minerals 2022, 12, 268 6 of 19 The drone is linked to a tablet held by the operator on the ground, it receives captured data allowing for on-site SAM analysis, aiding in the identiﬁcation of magnetite iron sands. Though best performed in the ﬁeld, this operation can be performed in laboratory analytics. To perform SAM, each captured multispectral image is imported into a SAM operation algorithm such as python.

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] Deep Q-learning is a deep reinforcement learning technique that extends the capabilities of clas sical Q-learning [34,35] by replacing the value iteration in the Q-table with the function approximator of deep artificial neural networks (ANN). Although applications of ANNs for geotechnical purposes are still often seen as complementary to conventional computational models [36,37], in this case the use of ANNs allows for applications in complex and continuous states spaces while classical Q-learning is confined to discrete states. DQN algorithms are off-policy, model free RL techniques following the Bellman equation (Eq. (1) after [14]) where the optimal action-value function Q*(s,a) is based on a state s and after having taken an action a. The best action is chosen by maximizing the expected value of r + γQ*(s′,a′) where r is the reward, γ is the discount factor that determines how important the future reward is to the algorithm (see also Table 2), s′ is the state at the next time step and a′ are all possible actions [14]. Q*(s, a) = Es′ [ r + γmax a′ Q*(s ′, a ′) |s, a ] (1) [14] have shown that the DQN algorithm can be used for a wide range of different RL problems and albeit there are improvements to deep Q- learning we chose the original implementation as we see it as well suited to establish a baseline for further developments in geotechnical RL. Our implementation is based on the DQN after [14] and the custom DQN implementation from [38]. The main deviations of the network archi tecture in comparison to [14,38] are due to the shape of the input and output data with the input being an 2 × 2100 × 2 array and the output a vector of length 8. The number of hidden layers and the decreasing kernel size from the top to the bottom convolutional layers is in accor dance with [14] as is the size of the kernel’s stride which is half the size of the kernel itself. While [14] used 32, 64 and 64 filters for each of the three hidden layers respectively, we used 32, 64 and 32 filters for these layers, as we observed that the agent’s performance did not suffer from this decrease, while the computational speed increased. In accordance with these authors we used rectified linear units (ReLU) [39] activation functions. ReLU activation functions have been widely adopted for ANNs within the past decade as they have shown to achieve a better performance than previously used activation functions like the sigmoid (see [40]). As given in [14] the general DQN’s ANN architecture is that of a deep convolutional neural network [41] whose hierarchical struc ture mimics the effect of receptive fields and is inspired by [42]. Like [14] we did not perform systematic hyperparameter tuning by random search or similar techniques (see e.g. [43]) due to the big computational effort of the simulation. Hyperparameters were thus optimized manually throughout the course of the development of the RL-simulation. We nevertheless point out that the given DQN architecture as well as the used hyperparameters still have room for improvement (see outlook in Section 7). Table 2 lists all hyperparameters for our DQN implementation. As we use the same terminology as [14] the reader is referred to this paper for more information on the individual parameters. We implemented the DQN using the tensorflow [44] based Python library Keras [45]. Training was done on a NVIDIA GeForce RTX 2080 Ti. From input to output, the agent’s architecture goes as follows and a graphical representation is given in Fig. 5 (with adaptions of architec ture from [14] as described in the section ahead): ▪ The input consists of an array with the shape 2 × 2100 × 2 (see Section 3.3.1). ▪ One convolutional layer, with 32 filters, a kernel size of 1 × 16 and a stride of 1 and 8 applying a ReLU activation function [39] ▪ One convolutional layer, with 64 filters, a kernel size of 1 × 8 and a stride of 1 and 4 applying a ReLU activation function ▪ One convolutional layer, with 32 filters, a kernel size of 1 × 4 and a stride of 1 and 2 applying a ReLU activation function ▪ One fully connected layer with 256 neurons applying a ReLU activation function ▪ One fully connected layer with 8 neurons (one per action) as the output layer which applies a linear activation Table 1 The mechanical parameters and permeability of the two ground types, where gt1 represents weak rock and gt2 stronger rock.


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[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]
In the column wise parameters, which are for the ratios of either positive or negative cases (sensitivity and specificity), are not affected whereas there is a significant difference in NPV and PPV, which are row wise ratios based on numbers of both posi- tive and negative cases. In all the examples, 75 % of both positive and negative cases are correctly predicted and therefore sensitivity and specificity remain the same. It is thus apparent that imbalance in class sizes grossly affects the NPV and PPV evaluation criteria. To overcome the class imbalance problem different approaches can be taken. One is to prune the size of the bigger class to be that of the smaller one. It is also possible to normalize in the contingency table the values of either positive or negative cases to have the total of the other class. Quite often in bioinformatics limited amount of data are available and therefore one would be reluctant to delete part of the datasets. When normalizing the data be sure that the existing dataset is representative otherwise bias in the set may further be increased. Accuracy and MCC Specificity, sensitivity, PPV and NPV are calculated by using only half of the information in the contingency table and thus cannot represent all aspects of the performance. Accuracy (Fig. 4) and Matthews correlation coefficient (MMC) take benefit of all the four numbers and as such are more balanced, representative and comprehensive than the line or column wise measures. The MCC is calculated as follows: MCC TP TN FP FN TP FN TN FP TP FP TN FN         ( )( )( )( ) . For all the measures discussed in here applies that higher the value the better. Except for MCC, the values range from 0 to 1. MCC ranges from -1 to 1. -1 indi- cates perfect negative correlation, 0 random distribution and 1 perfect correlation. Accuracy and MCC are affected by class imbalance only in extreme cases. The effect of the correctly predicted cases on the para- meters in equally distributed dataset is shown in Fig. 6. The value for MCC grows slower than the others reach- ing 0.5 when 75 % of cases are correctly predicted. Ran- dom results (50 % of both negative and positive correctly predicted) gives a value of 0, while the other parameters - sensitivity, specificity, PPV, NPV, and accuracy are 0.5. Fig. 6. can be used to check the performance of equally Table 1 Evaluation measures for test data -50 % -25 % Equal +25 % +50 % tp 750 750 750 750 750 fn 250 250 250 250 250 tn 375 563 750 938 1125 fp 125 187 250 312 375 sensitivity 0.75 0.75 0.75 0.75 0.75 specificity 0.75 0.75 0.75 0.75 0.75 PPV 0.86 0.80 0.75 0.71 0.67 NPV 0.60 0.69 0.75 0.79 0.82 accuracy 0.75 0.75 0.75 0.75 0.75 MCC 0.48 0.50 0.50 0.50 0.49 Example of a situation when both positive and negative cases have the same rate of correct predictions (75 %) and when the amount of negative cases is either equal or 25 or 50 % lower or higher than that for positive cases. Figure 6 The growth of the performance measures along increasing reliability Graphs for quality measures for equally distributed data (same amount positive and negative cases) when the performance increases equally in both classes. The solid curve indicates the growth of sensitivity, specificity, PPV, NPV, and accuracy. The dotted line is for MCC. Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 Page 6 of 10
[page 7] distributed dataset if e.g. some parameters in an article are not provided. Biases can easily be seen as deviations from the relationships in the figure. To obtain full picture of the predictor performance it is important to evaluate all the six measures together. Other parameters Several other parameters can be derived from the contin- gency matrix. These are not discussed further as they are not widely used in literature and can be easily calculated from the six previously presented parameters. These include false positive rate (FPR) which equals 1-specificity and false negative rate (FNR) which is 1-sensitivity. False discovery rate (FDR) is 1-PPV. Positive and negative likelihood ratios are calculated as follows: LR LR      sensitivity specificity sensitivity FPR and sensi 1 1 tivity specificity FNR specificity  . F measure is another one that uses all the data. It is calculated as: F     2 PPV sensitivity PPV sensitivity . Other measures include e.g.


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

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

[Assistant]
However, when the strain rate ex- ceeds 50 s1, the DIF value of the specimen with 50 mm diameter increases faster than those of the other specimens. The effect of specimen diameter can be ascribed to an ampliﬁed inertia effect (Chen and Song, 2011). The results additionally reveal that the compressive strength and the DIF value exhibit different re- sponses to an increase in strain rate. The difference among the three curves for compressive strength decreases with a higher strain rate, while the three curves for the DIF values bifurcate when the strain rate is greater than 50 s1. This is because the static compressive strength decreases with a larger specimen diameter (Masoumi et al., 2015). Fig. 7. Percentage metrics, including (a) MAE, (b) MXAE, (c) MAPE, and (d) RMSE, to evaluate the performance of the SVM-based, BPNN-based, and RF-based models, respectively. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1362
[page 8] The RF-based model also considers similar ﬁne-grained sandstone specimens with a ﬁxed diameter (50 mm) and three different aspect ratios (0.5, 0.75, and 1). The results exhibit that the compressive strength slightly decreases with increasing aspect ratio at a given strain rate, probably due to more defects in a longer specimen and weaker end friction effect(Fig.10a). The negative correlation between the compressive strength and specimen length is also observed in other rocks and solid materials (like concrete) (Krauthammer et al., 2003; Munoz and Taheri, 2017). Fig. 10b shows that the aspect ratio has a minor effect on the DIF value for a strain rate lower than 50 s1, but obviously inﬂuences the DIF value beyond the strain rate. The ﬂuctuation of thecurvesislikely due tothediscrepancies of input data points, which can be related to the system errors from the test setup and the data acquisition unit. Combining Figs. 9b and 10b, it suggests that the inﬂuence of specimen dimension on the strength enhance- ment can beoverlookedindetermination of thecompressive strength at low strain rates (e.g. < 50 s1), but should be examined at inter- mediate and high strain rates. Both the ﬁgures also illustrate that the DIF values for different specimen dimensions increase at the same increment rate at low strain rates. However, the increment rate be- comes different at higher strain rates. This observation provides new insights into the transitional behaviors of rocks from the low to high strain rates. The strength enhancement at the beginning of the tran- sitional process is less dependent of the specimen dimension, and the dependence of the specimen dimension becomes obvious with increasing strain rate. Fig. 8. Percentage increments of performance metrics: (a) MAE, (b) MXAE, (c) MAPE, and (d) RMSE, obtained from excluding each input parameters in the SVM- (red solid line), BPNN- (black dotted line), and RF-based (blue dashed line) models, respectively. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1363
[page 9] Our study highlights that the correlations among various geometrical, physical, and mechanical properties of rocks are nonlinear and very different from the simple correlations based on the Pearson correlation coefﬁcient (Fig. 1). Additionally, the nonlinear correlations can be even more complex if the three models can be trained by the data points obtained from laboratory and ﬁeld experiments on rocks with larger sizes under higher strain rates. In other words, this study is limited with the data points in small ranges of specimen dimensions and strain rates available in the literature. The established models are applicable to forecast the compressive strength of hard rocks at a strain rate ranging from 5  106 s1 to 223 s1 (Table A1). For other strain rates, additional training with the corresponding data is needed to extend the applicability of these models. The study demonstrates that machine learning models could be an effective method to explore the complexity of rock dynamics, particularly those difﬁcult to control in laboratory experiments. 5. Conclusions This study addressed the feasibility of applying machine learning models to predict the rate-dependent strength of rocks. We ﬁrst collected the strength data of various types of hard rocks as well as the corresponding geometrical, physical, and mechanical properties in the literature.