[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 procedure for the machine learning prediction of compressive strength followed that in Fig. 2. We quantitatively assessed the sensitivity of the strength prediction to each excluded parameter based on the percentage increments of four performance metrics (P1j, P2j, P3j, and P4j): P1j ¼ MAEj  MAE0 MAE0  100% (18) P2j ¼ MXAEj  MXAE0 MXAE0  100% (19) P3j ¼ MAPEj  MAPE0 MAPE0  100% (20) P4j ¼ RMSEj  RMSE0 RMSE0  100% (21) where the subscripts j and 0 refer to the performance metrics ob- tained from the models with ﬁve and six input parameters, respec- tively; and j(1, 2,. and6)corresponds to staticcompressive strength, Fig. 6. Comparison between the measured and predicted strengths from (a) SVM- based, (b) BPNN-based, and (c) RF-based models. The histograms on the upper and right axes show the density distributions of strength values. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1361
[page 7] P-wave velocity, specimen dimension, grain size, bulk density, and strain rate, respectively. For example, P26 denotes the percentage incrementof the MXAEvaluewhen the strain rate isexcludedinthese models. If P26 > P25, the strain rate plays a more critical role in pre- dicting the compressive strength than the bulk density. The percentage increments of the performance metrics plotted in Fig. 8 exhibit that the strength prediction result is more sensitive to the strain rate than the other input parameters. When the strain rate is excluded from the input parameters of the machine learning models, the performance metrics except the MXAE show larger percentage increments, compared with those excluding the other ﬁve parameters. The exception of the MXAE is most likely attrib- uted to one or a few data points with unusual values. The strain rate controlled by the loading condition is less dependent on the geometrical, physical, and mechanical properties of rocks and should be included in the strength prediction. This observation is similar to that observed from the Pearson correlation coefﬁcient. The static compressive strength is correlated with P-wave velocity, grain size, and bulk density (Fig. 1). If the static compressive strength is excluded, the impact of the static compressive strength on the strength prediction can be partially reﬂected by P-wave velocity and bulk density. That is probably why the performance metrics P11, P21, P31, and P41 approach zero. The results indicate that the inﬂuence of specimen dimension is relatively minor, which may be addressed insufﬁciently, particularly the specimen diameter, as the bar diameter in the SHPB setup is mostly limited to a range of 25e100 mm. Also, the aspect ratio as a related factor is not well studied due to the requirement of dynamic stress equilibrium in rock specimens with appropriate aspect ratios in the SHPB tests. 4.2. Rate dependence of rock strength Our results demonstrated that the RF-based model shows a better performance than the SVM- and the BPNN-based models (Fig. 7), and the exclusion of the static compressive strength has a negligible impact on the prediction accuracy (Fig. 8). Thus the RF- based model with ﬁve input parameters was adopted, except the static compressive strength, to further discuss the rate depen- dence of rock strength following the procedure in Fig. 2. This model considers a series of ﬁne-grained sandstone specimens (Yu et al., 2021) with the same aspect ratio and three different specimen diameters (12.5 mm, 25 mm, and 50 mm). The pre- diction result shows that the compressive strength is relatively constant under the strain rate lower than 20 s1 and drastically increases under the strain rate higher than 20 s1 (Fig. 9a). The increase in compressive strength is mainly attributed to the transition from inter-crystalline fracture to intra-crystalline frac- ture, the ampliﬁed energy absorption, and the inertial effect (Zhang et al., 2000). The trend of strength increase is similar for different specimen diameters, and the compressive strength de- creases with increasing specimen diameter. Then, we used the dynamic increase factor (DIF), deﬁned as the ratio of the dynamic to static strength, to highlight the effect of specimen diameter on the compressive strength. The three curves start to ﬂuctuate when the strain rate exceeds 20 s1 (Fig. 9b). From 20 s1 to 50 s1, the DIF values of these specimens with the three diameters increase in a similar trend.

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: 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]
In the pre- processing stage, we detect the tumour region from the segmented image using a bounding box method. The T2 image with the tumour bounding box are provided to the R-CNN block to train the network. As a post-processing step, we measure the properties of regions in the tested image and select the region most intersected with the R-CNN as the tumour region. 3.2.1 5-layer region-based convolutional neural network (R-CNN) R-CNNs offer high object detection accuracy. The R- CNN method trains a CNN to classify the image regions into an object or a background category. It uses the log loss approach to fine-tune the image regions into objects. Then, an SVM approach is applied to detect objects [45]. Table 4 Comparison of the performance of the three CNN models used in the tumour detection phase Network model Layer number (type) Accuracy (%) AlexNet Layer 9 (pool) 99.55 VGG-16 Layer 32 (pool) 92.95 VGG-19 Layer 38 (pool) 95.15
[page 7] Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 Page 7 of 10 Table 5 Comparison of the obtained tumour detection accuracy from the proposed CNN-based system to previous work in which a classical approach was used with the same database as a unified benchmark Detection system Performance evaluation metrics (%) Sensitivity Specificity Accuracy BA PPV NPV Abd-Ellah et al. [18] 83.43 25.00 66.96 54.21 73.91 37.20 Proposed method 99.38 100.0 99.55 99.69 100 98.46 The R-CNN architecture used in the tumour localiza- tion phase consists of five layers. The network first pro- cesses the whole image using the image-input layer (layer 1) and then converts the input data with a size of 28×28×1 into a convolutional layer (layer 2) with a filter size of 5 and filter number of 20 for detecting multiple class-specific objects. A fully connected layer (layer 3) with two outputs is used to predict a single-box for tumour localization. The softmax layer (layer 4) is also known as the normal- ized exponential. The output unit activation function is the softmax function [46–48], as presented in Eq. 6: yr = exp(ar) k j=1 exp(aj) (6) where a is the input vector with k dimensions and y is the output vector with k dimensions. The denomina- tor k j=1 exp(aj) normalizes the output to ensure that k r=1 yr = 1 and 0 ≤yr ≤1, which can be represented graphically as a layer with k neurons. The classification layer used as the final layer is named after the loss used for the training process. It returns a neural network classification output. For multi-class clas- sification, the loss (error) function is the cross entropy function shown in Eq. 7: E(θ) = − n  i=1 k  j=1 tij ln yj(xi, θ) (7) where θ is the parameter vector, tij is the indicator that the i sample belongs to the j class, and yj(xi, θ) is the output for sample i. Table 6 Comparison of the proposed CNN-based brain tumour detection approach against some former state-of-the-art methods CAD system Total images Classification method Accuracy (%) Da et al. [26] 10 DNN 83.0 Gao et al. [27] 285 2D and 3D CNNs 87.6 Yan Xu et al. [28] 45 CNNs 97.50 Proposed method 349 ECOC-SVM 99.55 4 Results and discussion 4.1 Experimental environment setup The image database used for evaluating the tumour detec- tion phase was extracted from the RIDER Neuro MRI database [17]. The input database consists of brain MRIs from 19 patients. These images were obtained approxi- mately 2 days apart. T1-weighted and T2-weighted MRIs were used. The database contains a total of 349 MRIs, including 109 normal images and 240 abnormal images. A set of 43264 features was extracted from each MRI image which later was used to train the SVM. The SVM was trained using 122×43264 features in total. The specifica- tions of the utilized database are presented in Table 2. A sample of the MRIs database used in the experimental work is shown in Fig. 3. To evaluate the tumour localization phase, a dataset of 804 3D MRIs extracted from the BraTS 2013 database was used for the localization accuracy assessment. The database consists of 20 high-grade (HG) and 10 low-grade (LG) patients and T1, T1c, T2, and FLAIR type MRI modalities. A set of 593 MRIs was used for R-CNN train- ing and validation, and a set of 211 MRIs was used for the testing dataset. The 3D images were converted into 2D slices, and the T2 MRI modality was used.

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

[Assistant]
The average wear coefficients for scrapers of the spoke type and the spoke- plate type cutterheads are close and are 0.0081 and 0.0068 mm/km respectively. The wear coefficients for scrapers in the two cases are both less than 0.01 mm/km, only 1/4 of those for precut bits, as shows that the scrapers play the role of ripping off, agitating and not shaving off ground. So, it is concluded that the two opening ratios of 39% and 75.5% have almost the same influences on tool wears and are both justifiable. However, the opening ratio of 39% is more reasonable if more considerations are given to the cutting face stability and shield machine manipulation. If shield tunneling will be performed in the coarse grain soils rich in groundwater and maintaining cutting face stability is a demanding job, a spoke-plate type, or plate type cutterhead with high opening ratio, such as 39%, for the specific ground condition is strongly recommended. As to deciding the site-specific opening ratio of a cutterhead, a good estimation of the size of grains in soils takes the lead. In Chengdu, most metro tunnels are constructed in water-rich coarse grain ground and the plate type cutterheads are usually employed mainly for the sake of more reliably maintaining stability of the cutting face. 4.2. Configuration of Cutting Tools It is observed from the previous construction cases that there are two typical types of configurations of tools for the cutterheads used in coarse grain soils; one is for the spoke type or spoke-plate cutterhead with high opening ratio and the other is for the plate type cutterhead with an opening ratio less than 30%. Dependent on specific characteristics of the ground conditions and the suitable ground excavation mechanism, the cutting tools, including precut bits, shell bits, scrapers and a center fish-tail bit, are versatile combinations of tools for spoke type and spoke-plate cutterheads with high opening ratio in Beijing and Shenyang coarse grain soils, which has been displayed by the relevant construction cases. With the help of loosening soils by precut bits, shell bits and the center fish-tail bit beforehand, the oversized particles (cobbles and boulders) can be easily ripped out of the soil matrix causing less thrust and less torque of a shield machine as well as rapid tunnel construction. Of course, a shaftless ribbon type screw conveyor is often indispensable to smooth discharge of the large sized grains in the excavated materials. The plate type cutterheads with opening ratio about 30% are widely used in Chengdu metro tunnel construction mainly on account of the soils rich in groundwater. The tools used at the beginning of tunnel construction were disc cutters in combination with scrapers presented in (Fig. 6a). Standing out a little farther in profile, the installed disc cutters were to break big cobble/boulders in soils into pieces and loosen ground in front of the cutterhead at the same time, but often suffered severe wears only after a driving less than 100 m and frequent changing tools work had to be performed. It was one of the most important improvement measures taken in Lot 22, Lot 11 of Line 2 and Lot 4 of Line 1 of Chengdu metro that precut bits were added or used to replace some disc cutters. The driving length was increased to 300 m or even more before necessary interventions of changing tools. Therefore, for the plate type cutterheads, the combination of precut bits and scrapers with/without disc cutters may be a better choice. Because scrapers are vulnerable to big cobbles and boulders in ground excavation, proper height differences between precut bits and scrapers are especially important to play different roles of the two types of tools. For example, the differences are in the ranges of 40-50 mm in Beijing sandy gravel/cobble soils. Meantime, in view of good performances of the previous cutterheads with precut bits, the precut bits should be strong enough to help reduce wears of the scrapers to the minimum. 4.3. Use of Single Disc Cutters with Inserts It is very common for shield tunneling in the coarse grain soils that the installed center disc cutters and single disc cutters suffer excessive abnormal wears. At a low rotation speed of the cutterhead, the center disc cutters are easily
[page 16] 196 The Open Construction and Building Technology Journal, 2017, Volume 11 Li et al. jammed by clogging of the cutterhead (see Fig. (20)).

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

[Assistant]
Based on this research, Franzese et al. (2020) [29] proposed semi-supervised scintillation detection using the DeepInformax technique, which presents a validation accuracy in accord with that of the decision tree model. The decision tree and random forest models are vulnerable to the problem of overﬁtting if the high model complexity is designed improperly. More importantly, these methods mentioned above rarely consider the imbalanced phenomenon of different intensities of scintillation events [16,19–25,29].
[page 3] Remote Sens. 2021, 13, 2577 3 of 22 Insufﬁcient data related to strong scintillation may cause great trouble in model training. Moreover, the events to be tested with different intensities may show different degrees of imbalance. Such imbalance in different intensities of scintillation events is rarely taken into consideration. Among the occasional ionospheric scintillation events, strong scintillation is a minority class compared to weak and moderate scintillation. For this class of data, few classiﬁcation algorithms can accurately describe the inherent characteristics due to the lack of information about the minority class, which causes the decision boundary to be greatly compressed in the classiﬁcation model [30]. Although the overall accuracy of the ﬁnal model is relatively high, there is some missed detection for strong scintillation events. It can be optimized by solving the imbalance of the dataset to reduce the problem of missing detection points for strong scintillation events in the testing set. In this paper, an improved machine learning method is proposed for improving the automatic detection accuracy for strong ionospheric scintillation, and it is compared in detail with the present decision tree and random forest, which show high accuracy for the validation set [16]. There is also a brief comparison with an SVM and CNN, which have recently been utilized in relevant research [19–28]. The proposed approach can provide signiﬁcant guidance for designing robust GNSS receivers as well as research on atmospheric layers and space weather. Several aspects of this approach can be described as follows: (1) To propose a high-performance detection method for XGBoost based on the deci- sion tree algorithm, assuring good overall detection accuracy for three intensities of ionospheric scintillation; (2) To compare with decision tree and random forest based on cross-validation, proving the superior accuracy of the XGBoost algorithm and mitigation of the overﬁtting problem; (3) To compare different resampling techniques for an imbalanced dataset consisting of a majority of weak/medium scintillation events and a minority of strong scintillation events, proving the great performance of SMOTE-ENN according to evaluation indicators; (4) To make a brief overall comparison with the decision tree, random forest, SVM and CNN, evaluating the performance in terms of accuracy, computational load and applicability, focusing on detailed comparison with the ﬁrst two methods with high performance hereafter; (5) To evaluate the performance of the proposed improved method on different de- grees of imbalanced training datasets and testing datasets, respectively, proving the effectiveness in enhancing the detection accuracy for strong scintillation events. In this work, a strategy of using data processing technology for imbalanced iono- spheric scintillation events is proposed, and we applied our strategy to space weather detection for the ﬁrst time, which successfully improved the detection accuracy for strong scintillation events. Additionally, an improved machine learning algorithm with proven high overall accuracy for scintillation events of different intensities is proposed. The general overviews are described in Section 2, including an introduction to ionospheric scintillation, the data collection system and feature extraction. Section 3 presents an improved machine learning method, giving a theoretical introduction to the XGBoost algorithm and SMOTE- ENN technique. Section 4 validates the optimal detection performance of the proposed approaches, showing and evaluating the quantitative results obtained from the training and testing on datasets with different degrees of imbalance. Section 5 discusses various training and testing cases, and Section 6 draws the main conclusions. 2.

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


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

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

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

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

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

[Assistant]
While [14], who trained their DQN to play classical Atari 2600 games, took a modified version of the original games’ scoring systems as a means to deal with the reward in each timestep, [15] only gave a reward of +1 or − 1 at the end of the game depending on whether or not the agent has won the respective Go-match. In our tunneling simulation, we designed a hierarchical point system that values (i.e. rewards/penalizes) either a state or an action. The rewarding/penalizing is done based on a list of conditions, that is worked through from top to bottom after every step of an episode and the agent is given the first reward where the condition is fulfilled. Except for the reward for achieving a breakthrough, all rewards are negative (i. Fig. 6. 3D visualization of the 2 × 2100 × 2 hypermatrix that represents the state of the simulation. G.H. Erharter et al.
[page 7] Automation in Construction 127 (2021) 103701 7 e. penalties) and from top to bottom the penalties are sorted from biggest to smallest. It is therefore possible that multiple conditions are fulfilled in one state, but only the most severe penalty is given. Table 4 shows the list of rewards and their respective conditions. We designed the list of rewards based on practical engineering experience and requirements arising from training a completely unex perienced agent. For example, we penalize face instabilities higher than a too far distance between top heading and bench excavation, as the immediate safety hazard of an unstable face is bigger. In contrast to that, an even bigger penalty (i.e. -6 points) is triggered if the agent would try to do bench excavation ahead of the top heading excavation. A geo technician in real world tunneling would know from the start that doing so brings numerous technical-, logistical- and safety problems, but an untrained RL-agent does not. During the experiments for this study we tried to design the reward system in a way that it is the sum of all penalties that would be fulfilled in a given state and not a hierarchical system. However, doing this was not beneficial for training and seemed to confuse the agent as it apparently did not know what it was punished for. For example, a combination of the penalty for a too far distance between top heading and bench (i.e. -3) plus the penalty for using face support (i.e. -2) would sum up to −5 which is the same penalty as the one for unstable face conditions. As given in Section 2.1, the evaluation to check if a newly excavated face is stable or not, is done based on the face pressure equation for open face tunneling from [29] (Eq. (3)). In Eq. (3), pf is the required pressure to achieve stable face conditions, where a pf < 0 indicates stable con ditions and a pf ≥0 indicates unstable conditions, respectively the amount of pressure that is necessary to stabilize the face. γR is the ground’s unit weight, D the (equivalent) tunnel diameter (see chapter 3.1), d the advance length (describing the unsupported area of the un lined wall), c′ the effective cohesion and φ′ the effective friction angle. pf = γRD* ⎛ ⎜ ⎜ ⎜ ⎝ 2 + 3* ( d D )6*tanφ ′ 18*tanφ ′ −0.05 ⎞ ⎟ ⎟ ⎟ ⎠− c ′ tanφ ′ (3) 4. Training In RL, one episode is the whole succession of states in between an initial and a terminal state [26]. In other words, an episode is one whole match of a game, or in this simulation, one whole sequence of actions that ultimately should lead to a breakthrough of the tunnel. We started training with an ε of 1 (i.e. “initial exploration” in Table 2) to promote exploration in the initial phase of training. The exploration decay of 0.99997 (see Table 2) that decreases ε following Eq. (2) was determined by trial and error. A smaller exploration decay (i.e. faster reaching of the final exploration) has shown to increase in stabilities in the training process at an early stage which are presenting themselves in spontaneous increases of the loss and decreases of the reward. With the given exploration decay, the final exploration is reached after 99,858 episodes of training. After this point ε is kept at a constant value of 0.05. Training does not need to be aborted after reaching the final exploration. In the current simulation, we aimed at training the agents for 120,000 episodes to observe one full epsilon decay and some episodes beyond that to check for stable conditions in different rockmasses, as illustrated in Fig. 7 (except for cases where training became unstable at some point; see next section).


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

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

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

[Assistant]
Another common occurrence is 3D laser scanning of mines in order to evaluate the current state of Minerals 2022, 12, 268. https://doi.org/10.3390/min12020268 https://www.mdpi.com/journal/minerals
[page 2] Minerals 2022, 12, 268 2 of 19 a mine, which in turn enables better planning for the future [1–4]. In terms of rocks and minerals, the application of hyperspectral imaging in their classiﬁcation based on their spectral characteristics has received positive feedback [5–9]. Even with its positive beneﬁts, the employment of hyperspectral imaging has not been without imperfections. These include the time [9], monetary [8] and computational requirements necessary to process this large data [5]. Given these demands, it is difﬁcult to perform rapid ﬁeld spectral imaging as it takes time and sophisticated computational resources to acquire and process the hundreds of spectral bands. Part of this reason being, the need to process multitudes of redundant bands which do not necessarily improve the overall subject classiﬁcation capabilities [7,10]. Coupled with the weight constraint, it is even more challenging to employ this technology in aerial investigations of a site at considerable spatial areas and resolutions [9]. On the other hand, research has shown that the application of a few specialized multispectral imaging bands in the classiﬁcation rocks and minerals, offers similar, high subject resolving powers [11]. Specialized multispectral bands whose dimensionality has been reduced from hyperspectral imaging eliminate the said hyperspectral imaging disadvantages [12], for this reason, this study employs a 6-bands DJI Phantom 4 (P4) multispectral drone. Studies aimed at employing unmanned automated vehicles (UAVs) such as drones to identify certain rocks or minerals in mines are important as they offer a plethora of advantages [13,14]. Despite this, it is difﬁcult to perform a thorough mine site rock or mineral assessment via a visible light camera mounted drone. This is due to the fact that such cameras do not collect the intrinsic characteristics of rocks and minerals beyond the visible light spectrum [6,15]. Mohajane et al. [4] applied a method which incorporated remote collection of vegeta- tion data in order to identify certain vegetation species via a multispectral camera mounted UAV drone. This study modiﬁes and improves on this methodology, hence, a novel method by which rocks and/or minerals can be identiﬁed in rapid, large scale areas with detailed ﬁeld assessments was born. This proposed method combines multispectral UAV drone technology with Spectral Angle Mapping (SAM), ML and DL algorithms to automatically identify magnetite iron sands. Potential advantage of this proposed system are; The UAV possesses multiple spectral sensors which all capture information pertaining to a scene at the same time; this results in reduced exposure times for each image, which in turn ensures one captures cleaner images without blur, and less heating of the sensors. Since the UAV drone spectral sensor comes pre-set with a low number of spectral dimensionalities to process, applying SAM becomes an advantage as it does not require the UAV to be specialized in magnetite identiﬁcation. SAM takes advantage of the pre-existing spectral bands by assessing the difference between a reference subject’s (magnetite) spectral characteristics with those similar or different to it in an image scene [16,17]. AI algorithms such as ML and DL possess great spectral image classiﬁcation power [18,19], allowing one to resolve qualitative as well as quantitative data in an objective and understandable manner, making it highly eligible in multiple industry applications. This study’s integrated system has the potential to enhance numerous aspects of the mining chain, hence the motivation for this study. The main objective of this study is to evaluate an effective approach for the automatic identiﬁcation of magnetite iron sands. For this purpose, investigations on the feasibility of a multispectral camera mounted UAV drone will be discussed. Positive results of this study, assessed via SAM and AI models, will aid in ascertaining feasibility for ﬁeld applications. The Study Area Kamaiso, the study area, is located along the west coast of Yamagata prefecture bounded to the west by the Japan Sea. This site is a magnetite iron sand placer deposit which is said to have resulted from volcanic activity by Mt.

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]
11 it can be seen that within individual sequences of top heading or bench excavation, the agent alternates between supported and unsupported blasts which shows that it has successfully learned to avoid excessive use of support ahead of the face. 6. Conclusion and implications for digital tunneling Before drawing conclusions from the experiments, it should be pointed out that in this initial study, the agent’s possibilities to find creative and not yet imagined solutions to real world tunneling problems are confined to the given set of actions and the taken simplifications (see Section 2.1). We therefore see it as a success that the agent optimizes the given scenario and finds strategies that are comparable to current practices in tunneling. As given in the introduction, the current study should serve as a base for future developments of RL in tunneling. Consequently, optimizations that improve the current practices of “real life” tunneling are to be expected from future studies. In the experiments of the previous chapter, we can observe that the agents have found policies that minimize the overall amount of neces sary blasts, minimize changes between top heading and bench excava tion, favor long over short advance lengths and minimize the use of face support. These strategies show that the agents have learned to work in an efficient and economically optimized way. We see similarities in this RL-based tunneling strategy to real world tunneling paradigms like the NATM [49] which uses partial excavation to minimize the necessary support. Minimizing the number of changes between top heading or bench excavation while at the same time not exceeding a too long dis tance between them is also part of NATM tunneling as this optimizes construction site logistics on the one hand and safety requirements on the other. The found strategies that rely on support ahead of the face show similarities to the “Adeco” method [25] which uses heavy support installation and long advance lengths to deal with the encountered rockmass conditions. While adhering to safety requirements is impera tive, most “real life” tunneling methods work towards minimizing the number of necessary blasts which is a policy that was found by all the agents. TunnRL (see Section 2) has shown that it is not only a functioning environment/simulation of conventional tunneling, but also that a RL agent can successfully interact with it and learn optimized and inno vative strategies that seem realistic compared to real world tunnel excavation. At the same time, we see the challenges of computational instabilities and trial and error approach in the process of developing well-functioning models, highlighting this early stage in RL for tunneling. Clearly both the reward system, the rockmass-environment and the action-system has room for improvement. Still we see a signif icant potential in the TunnRL-concept: firstly, for an on-face decision support system in a further developed and more realistic version, and secondly as a first step to more advanced automation in underground Table 5 Statistics of the training runs presented in Fig. 7. Values were computed from the 500-episode sliding window average and the episode of the respective value is given in parenthesis behind it. Row-wise best performances are highlighted in grey. G.H. Erharter et al.
[page 10] Automation in Construction 127 (2021) 103701 10 construction. Where many of the developments of tunnel processes today address the automation of small-scale processes in the tunnel or at the excavation face, a further developed TunnRL could be part of the main controlling mechanism that operates the overall tunnel construc tion site. Albeit the agents in this simulation were not able to find new and undiscovered strategies of tunnel excavation (see previous chapter), we see the fact that completely untrained agents are able to find tunneling strategies which are comparable to “real world tunneling” as a proof of concept that RL is successfully applicable to this kind of prob lem. The main goal of the study is therefore fulfilled, and future studies will work towards giving the agent more capabilities and increasing the environment’s realism. This will ultimately pave the way for an opti mized decision finding process in sequential tunneling. 7. Outlook Albeit we designed TunnRL in a practice related context, there are numerous improvements to make the framework more realistic and more robust.


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

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

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

[Assistant]
The training set is sampled to obtain multiple subsets, and then, several decision trees with the same structure are constructed, each of which is trained separately by using the constructed training subsets. The classiﬁcation result depends on votes from all the decision trees trained on the testing set. More details can be found in the literature [16]. Consisting of different decision trees, the core of the XGBoost algorithm is learning a new function f (x) to ﬁt the last predicted residuals, adding decision trees continually and continuing to split features to grow a tree before meeting the growth conditions. After completing the training process and obtaining k trees, we needed to predict the novel data. According to the characteristics of samples, they will be classiﬁed to a leaf node corresponding to a score in each tree, and the sum of the scores for each tree to be regarded as the predicted value of the sample. Obviously, the goal of the algorithm is to make the
[page 8] Remote Sens. 2021, 13, 2577 8 of 22 predicted value of all the trees ˆyi as close as possible to the corresponding true value yi, with as much generalization capability as possible. Based on the current tree, an additional tree is added to ﬁt the residual between the predicted result of the previous trees and corresponding true value. To choose the next tree to be added, we introduced the objective function combined with the loss function and regular function, as shown in (6). Obj(t) = n ∑ i=1 l  yi, ˆyi(t−1) + ft(xi)  + Ω( ft) + cons (6) where i represents the ith sample, n is the number of samples, yi is the true score of the current tree, and ˆyi(t−1) refers to the predicted value of t −1 trees. ft is a new function of the current tree; Ω( ft) is the corresponding regularization item. Additionally, the constant value cons can be ignored without affecting the following objective process. The objective function can be approximated as follows after using Taylor expansion: Obj(t) = n ∑ i=1  l  yi, ˆyi(t−1) + gi ft(xi) + 1 2hi ft2(xi)  + Ω( ft) + cons = n ∑ i=1  giωq(xi) + 1 2hiωq2(xi)  + γT + λ1 2 T ∑ j=1 hiωj 2 + C (7) where gi = ∂ˆy(t−1)l  yi, ˆy(t−1) ; hi = ∂2 ˆy(t−1)l  yi, ˆy(t−1) , T is the number of the leaf nodes, and ωj is the weight of the jth leaf node. γ and λ are used to control the complexity of the trees. Deﬁning Gj = ∑ i∈Ij gi and Hj = ∑ i∈Ij hi, the optimal solution ω∗ j = − Gj Hj+λ; then, the optimal objective function without a constant value C can be obtained as follows. Obj(t) = T ∑ j=1  Gjωj + 1 2 Hjωj 2  + γT = −1 2 T ∑ j=1 Gj Hj + λ + γT (8) Furthermore, the greedy algorithm is utilized to enumerate all the different tree structures to ﬁnd the optimal splitting node, achieving the maximum gain of the objective function after splitting, as shown in (9). Gain = 1 2 " G2 L HL + λ + G2 L HL + λ −(GL + GR)2 HL + HR + λ # −γ (9) where GL and GR are the ﬁrst-order gradient values of the leaf nodes on left and right after splitting. Similarly, HL and HR are corresponding second gradient values. A brief schematic diagram of the XGBoost algorithm is shown in Figure 3. There are some preset parameters used to control the tree building, such as γ, λ, the maximum depth of the tree, the minimum weight of the child node, and the learning rate for each tree. The main purpose is to limit the complexity and weight of each tree, so that the overﬁtting problem can be mitigated or prevented. 3.2. SMOTE-ENN Resampling Technique An imbalance in the dataset refers to the phenomenon of certain types of samples being lower in number than other types, and researchers always have more interest in the minority class samples [42]. For similar characteristics of data, many classiﬁcation algorithms have difﬁculty in accurately expressing the inherent features because of the lack of information on the minority class samples, which causes the decision boundary to be greatly compressed in the classiﬁcation system. Although the overall accuracy of the detection model is excellent, it is unable to effectively detect the minority target samples that ought to be detected. As shown in Table 2, the weak, moderate and strong scintillation
[page 9] Remote Sens. 2021, 13, 2577 9 of 22 events are marked as 0, 1 and 2, respectively. When the training sample proportion ranges from 1:1:1 to 7:7:1 based on a constant 100,000 entries, the overall accuracy remains relatively stable and good, ranging from 90.18% to 93.32%.


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

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

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

[Assistant]
Chokai, an event estimated to have taken place 600,000 years ago [20]. It makes sense to explore the possibility of mining
[page 3] Minerals 2022, 12, 268 3 of 19 easily accessible placer deposits such as those in Kamaiso, especially with the worldwide decline in ore grades [21]. These potential resources present an opportunity to maximize proﬁt margins depending on the amounts present. Deposits located at coastal areas are rather difﬁcult to quantify due to their close proximity with the sea. Air borne methods such as the employment of UAV drones present an unconventional way of exploring and quantifying target resources within this area effectively [22]. A map showing a 30 m × 90 m area from which experiments were conducted in an attempt to apply UAV drone technology in identifying magnetite iron sands is shown in Figure 1. Figure 1. Study area map showing Kamaiso magnetite iron sand placer deposits. Red polygon denotes the 30 m by 90 m study area within which the UAV drone shown was ﬂown. Map acquired from: Google maps (2021). 2. Methodological Strategies for SAM and AI Integration 2.1. Combination of UAV Drone Technology with Multispectral Imaging Air borne drone technology has evolved over the years, with recent developments catering for specialized industrial applications. The protection of human life, as sites can be assessed from a remote area, is one of the major advantages of UAV drone technology [3,22]. In addition, camera-mounted drones aid in real time assessments of sites via visual feed. This footage can be in the form of real, false or pre-processed colours which are meant to represent a certain occurrence understood by the operator [4]. It is supposed that hyperspectral imaging is perhaps one of the best methods by which rocks and minerals can be inferred through their spectral characteristics [5]. However, van de Meer et al. [7] have been able to achieve high classiﬁcation accuracy outputs in similar rock identiﬁcation problems via multispectral imaging. Motivated by the demonstrated multispectral capabilities this paper employs a DJI P4 Multispectral camera mounted UAV drone (manufactured by Da-Jiang Innovations, Shenzhen, China) released in September 2019 (Figure 2). To achieve centimeter-level position accuracy, the drone has a real time kinematics (RTK) module attached to it. In addition, the six cameras are 1/2.9-inch (com- plementary metal oxide semiconductor) CMOS sensors, all at 2 megapixels with a global shutter, on a 3-axis stabilized gimbal to ensure accurate multispectral image capturing at spatial resolutions as high as 0.1 cm/pixel. The spectral sensor speciﬁcations are as follows: Blue: 450 nm ± 16 nm, Green: 560 nm ± 16 nm, Red: 650 nm ± 16 nm, Red Edge:
[page 4] Minerals 2022, 12, 268 4 of 19 730 nm ± 16 nm, Near Infrared: 840 nm ± 26 nm, which are all captured as TIFF image ﬁles. The drone moreover boasts a 62.7◦ﬁeld of view, a net weight of 468 g and an RBG camera which facilitates the extraction of magnetite iron sand spectral information in the visible light spectral range of 400–700 nm as a JPG image. These attributes subsequently make the system effective in a wide range of electromagnetic spectra within the visible-near-infrared range (VNIR) with a total of 6 multispectral bands. Figure 2. The 6-bands multispectral sensor with a 62.7◦ﬁeld of view; (1) Blue: 450 nm ± 16 nm, (2) Green: 560 nm ± 16 nm, (3) Red: 650 nm ± 16 nm, (4) Red Edge: 730 nm ± 16 nm, (5) Near Infrared: 840 nm ± 26 nm and (6) RGB camera: 400–700 nm represent the multispectral sensor capabilities. Images captured are corrected for parallax error via an edge detection method. The red point is the reference area from which magnetite iron sands are known to exist. The UAV drone ﬂight elevation plan was set to 3 different variations so as to explore the effect of ground resolution, proximity to the subject, and the ease of ﬂying. From examining these attributes, it would be easier to ﬁnd the optimal elevation from where magnetite iron sand identiﬁcation can be performed effectively. The 3 UAV ﬂight elevations measured from the camera sensor to the subject were 2 m, 10 m and 20 m. The drone was moreover programmed such that the aerial multispectral imaging procedure is automat- ically performed at intervals that ensured an equal amount of area (30 m × 90 m) was captured by the end of each ﬂight mission.


[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]
Drilling parameter box diagrams for each surrounding rock grade. (a) Penetration velocity (Vp, m/min); (b) hammer pressure (Ph, bar); (c) feed pressure (Pf, bar); (d) rotation pressure (Pr, bar). According to the box diagram method, when the value is not within the interval represented by the following formula Equation (1), it is regarded as an outlier [Q1 −1.5 × IQR, Q3 + 1.5 × IQR] (1) where Q1 is the ﬁrst quartile, namely the equal to the 25th percentile of all values in the sample from small to large, Q3 is the third quartile, namely the equal to the 75th percentile of all values in the sample from small to large, IQR is the interquartile range, namely IQR is equal to Q3 minus Q1. In Figure 6, under different surrounding rock grades, all four drilling parameters contained outliers. To eliminate the effects of the outliers, the average value of each parameter was used to replace the outlier of each surrounding rock in this study. The changes in data characteristics before and after cleaning are compared in Table 4.
[page 9] Appl. Sci. 2022, 12, 2656 9 of 20 Table 4. Comparison of the data characteristics before and after cleaning. Surrounding Rock Grade Index Vp Ph Pf Pr Before After Before After Before After Before After III mean 2.35 2.04 144.32 148.85 66.95 67.69 91.91 92.18 std 0.97 0.51 12.59 5.98 8.02 5.91 10.29 9.69 min 1.08 1.08 99.95 132.88 29.05 53.70 46.94 67.27 max 5.56 3.39 165.41 165.41 91.45 81.44 112.97 112.97 IV mean 3.19 3.19 135.06 135.06 63.17 62.86 79.68 79.25 std 1.03 1.03 14.00 14.00 13.21 12.67 11.24 9.59 min 0.66 0.66 89.79 89.79 35.97 35.97 48.84 55.38 max 5.49 5.49 161.38 161.38 107.41 97.95 113.53 102.29 V mean 3.47 3.54 124.96 125.87 51.75 50.72 73.18 72.62 std 0.85 0.67 11.68 9.85 11.06 9.59 7.59 5.65 min 0.83 1.97 90.82 101.39 27.43 27.43 53.47 57.70 max 6.35 5.05 155.88 150.65 83.75 73.60 101.17 86.72 Note: The full name of each index and corresponding abbreviated in the table is the mean value (mean), the standard deviations (std), the minimum value (min) and the maximum value (max). In Table 4, after cleaning, under different surrounding rock grades, the distributions of these drilling parameters are more centralized. Speciﬁcally, the maximum value is smaller, the minimum value is larger, and the standard deviation is smaller. 2.4. Sample Imbalance Treatment Referring to machine learning classiﬁcation, when the difference in the number of samples of all the classes is too large, the training model will pay too much attention to the sample characteristics with a greater proportion, so the classiﬁcation effect of the samples with a smaller proportion is not ideal. This is called the problem of sample imbalance. These data sets cover three classes of surrounding rock (namely, grade-III, -IV, and -V), and each class contains 325, 420, and 167 samples belonging to the imbalance sample set. There are three common approaches to deal with unbalanced samples in machine learning: 1. Over-sampling The over-sampling method achieves sample balance by increasing the number of minority samples in the classiﬁcation. The most direct method is to simply copy the minority samples to form multiple records. The improved over-sampling methods produce new composite samples by adding random noise, interference data to a few classes, or certain rules such as the synthetic minority over-sampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). 2. Under-sampling The under-sampling method achieves sample balance by reducing the number of majority samples in the classiﬁcation. The most direct method is to randomly remove some majority class samples. The disadvantage of this method is that some important information from the majority class samples may be lost. 3. Sample weight adjustment This method is used to guide models to learn more features of the minority samples in the classiﬁcation by assigning higher weights to them during the machine learning model training. Generally, the weights of various samples are set to be inversely proportional to the sample size. Considering the small size of the sample data set, to sufﬁciently learn the characteristics of all types of samples during the machine learning model training and to improve the universality of the model, the SMOTE over-samples to replenish samples in grade-III and grade-V of the surrounding rock. The scatter diagram of the data set after over-sampling is shown in Figure 7.
[page 10] Appl. Sci.

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

[Assistant]
Back-Analysis Method for Stope Displacements Using Gradient-Boosted Regression Tree and Fireﬂy Algorithm. J. Comput. Civil. Eng. 2018, 32, 04018031. [CrossRef] 14. Zhang, L.; Wu, X.; Ji, W.; Abourizk, S.M. Intelligent Approach to Estimation of Tunnel-Induced Ground Settlement Using Wavelet Packet and Support Vector Machines. J. Comput. Civil. Eng. 2016, 31, 04016053. [CrossRef] 15. Zhou, C.; Ding, L.; Zhou, Y.; Zhang, H.; Skibniewski, M.J. Hybrid Support Vector Machine Optimization Model for Prediction of Energy Consumption of Cutter Head Drives in Shield Tunneling. J. Comput. Civil. Eng. 2019, 33, 04019019. [CrossRef] 16. Xue, Y.; Bai, C.; Qiu, D.; Kong, F.; Li, Z. Predicting rockburst with database using particle swarm optimization and extreme learning machine. Tunn. Undergr. Space Technol. 2020, 98, 103287. [CrossRef] 17. Zhao, H.; Ru, Z.; Chang, X.; Yin, S.; Li, S. Reliability analysis of tunnel using least square support vector machine. Tunn. Undergr. Space Technol. 2014, 41, 14–23. [CrossRef] 18. Liu, W.; Cai, L.; Chen, J.; Wang, Y.; Wu, H. Reliability Analysis of Operational Metro Tunnel Based on a Dynamic Bayesian Copula Model. J. Comput. Civil. Eng. 2020, 34, 05020002. [CrossRef] 19. Leu, S.-S.; Chen, C.-N.; Chang, S.-L. Data mining for tunnel support stability: Neural network approach. Automat. Constr. 2001, 10, 429–441. [CrossRef] 20. Liu, K.; Liu, B. Optimization of smooth blasting parameters for mountain tunnel construction with speciﬁed control indices based on a GA and ISVR coupling algorithm. Tunn. Undergr. Space Technol. 2017, 70, 363–374. [CrossRef] 21. Liu, J.; Jiang, Y.; Ishizu, S.; Sakaguchi, O. Estimation of tunnel support pattern selection using artiﬁcial neural network. Arab. J. Geosci. 2020, 13, 1–12. [CrossRef] 22. Rana, A.; Bhagat, N.; Jadaun, G.; Rukhaiyar, S.; Pain, A.; Singh, P. Predicting Blast-Induced Ground Vibrations in Some Indian Tunnels: A Comparison of Decision Tree, Artiﬁcial Neural Network and Multivariate Regression Methods. Mining. Metall. Explor. 2020, 37, 1039–1053. [CrossRef] 23. Yazdani-Chamzini, A. Proposing a new methodology based on fuzzy logic for tunnelling risk assessment. J. Civ. Eng. Manag. 2014, 20, 82–94. [CrossRef] 24. Liu, Y.; Chen, H.; Zhang, L.; Wang, X. Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest. J. Civ. Eng. Manag. 2021, 27, 539–552. [CrossRef] 25. Chen, Z.; Zhang, Y.; Li, J.; Li, X.; Jing, L. Diagnosing tunnel collapse sections based on TBM tunneling big data and deep learning: A case study on the Yinsong Project, China. Tunn. Undergr. Space Technol. 2020, 108, 103700. [CrossRef] 26. Wen, Z.; Rong, X.; Wang, Z.; Han, S.; Shi, Y. A Fast Estimation Method of Soil Discharged by an Earth Pressure Balanced Shield Machine. KSCE J. Civ. Eng. 2021, 25, 2239–2249. [CrossRef] 27. Hu, M.M.; Li, B.; Zhang, B.; Wang, R.R.; Chen, L. Improved SVR Method for Predicting the Cutting Force of a TBM Cutter Using Linear Cutting Machine Test Data. KSCE J. Civ. Eng. 2021, 25, 4425–4442. [CrossRef]
[page 20] Appl. Sci. 2022, 12, 2656 20 of 20 28. Jung, J.H.; Chung, H.; Kwon, Y.S.; Lee, I.M. An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data. KSCE J. Civ. Eng. 2019, 23, 3200–3206. [CrossRef] 29. Lee, S.S. Prediction of Subsidence during TBM Operation in Mixed-Face Ground Conditions from Realtime Monitoring Data. Appl. Sci. 2021, 11, 12130. [CrossRef] 30. Xu, H.; Zhou, J.; Asteris, P.G.; Armaghani, D.J.; Tahir, M.M. Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate. Appl. Sci. 2019, 9, 3715. [CrossRef] 31. Zaﬁrovski, Z.; Jovanovski, M.; Moslavac, D.; Krakutovski, Z. Application of Neural Networks in Analyzing of Rock Mass Parameters in Tunneling; ResearchGate: Split, Croatia, 2014. 32. You, K. A case study on the utilization of tunnel face mapping data for a back analysis based on artiﬁcial neural network. KSCE J. Civ. Eng. 2014, 18, 751–759. [CrossRef] 33. Jalalifar, H.; Mojedifar, S.; Sahebi, A.; Nezamabadi-Pour, H. Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classiﬁcation system. Comput. Geosci. 2011, 38, 783–790. [CrossRef] 34. Hasegawa, N.; Hasegawa, S.; Kitaoka, T.; Ohtsu, H. Applicability of Neural Network in Rock Classiﬁcation of Mountain Tunnel. Mater. Trans. 2019, 60, 758–764. [CrossRef] 35. Lee, H.L.; Kim, J.S.; Hong, C.H.; Cho, D.K.


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

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

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

[Assistant]
Thus, based on the above feature set and non-Gaussian noise characteristics of ionospheric scintillation [40], the other four metrics of variances and covariance are also presented to further reﬂect the features. σSI = 1 N −1 N ∑ n−1 (SIn −⟨SI⟩)2 (1) cov  I2, Q2 = 1 N −1 N ∑ n−1  In2 − D I2E Qn2 − D Q2E (2) σI = 1 N −1 N ∑ n−1 (In −⟨I⟩)2 (3) σQ = 1 N −1 N ∑ n−1 (Qn −⟨Q⟩)2 (4) where σSI, σI and σQ are variance of SI, I and Q, respectively, and cov I2, Q2 is the covariance of I2 and Q2. N is the number of samples used in one value. To remove the impact of thermal noise and reserve the characteristics of the scintillation phenomenon, the frequency of the initial observables I and Q was f = 1000 Hz. For each feature, the observation window was set as T = 60 s, meaning that there were N = T· f = 60000 initial observables averaged in the observation period. The frequency of observation was 1 Hz, which was in line with the shifting window of 1 s. Based on that, the features used in this work are described as follows. X = n ⟨I⟩, ⟨Q⟩, D I2E , D Q2E , ⟨SI⟩, D SI2E , σSI, cov  I2, Q2 , σI, σQ o (5)
[page 7] Remote Sens. 2021, 13, 2577 7 of 22 The scintillation phenomenon may persist for several hours or longer, meaning that the data will be affected by the scintillation for a signiﬁcant period of time rather than several minutes or less. The scintillation events were manually marked as L = {0, 1, 2} based on the visual inspection of thresholds, divided into 0 < S4 ≤0.2, 0.2 < S4 ≤0.6 and 0.6 ≤S4, representing weak, moderate and strong scintillation events marked as 0, 1 and 2, respectively. Meanwhile, the corresponding carrier-to-noise density power ratio (C/N0) was also taken into consideration in the manual detection process. This manual labelled approach combined with personal knowledge and experience can reserve the transient phases of the events and reduce the missed detections, signiﬁcantly enhancing the labelling accuracy and detection performance. 3. Methodology The goal of this approach was to propose an improved eXtreme Gradient Boosting (XGBoost) algorithm combined with the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN), comparing the performance with that of the decision tree and random forest algorithms, which have been successfully used in recent research [16]. 3.1. XGBoost Algorithm The XGBoost algorithm was proposed by Chen Tianqi in 2016, presenting low com- putational complexity, a fast running speed and high accuracy [41]. As it is an inefﬁcient ensemble learning algorithm, the boosting is aimed at transforming a weak classiﬁer into a strong classiﬁer to achieve good accuracy. Moreover, the gradient boosting attempts to improve robustness by making the algorithm’s loss function drop along its gradient direction in the iteration process. Additionally, as a fast implementation of the gradient boosting algorithm, XGBoost can make full use of multi-core CPUs for parallel computation and improve the accuracy, signiﬁcantly reducing the computational loads and enhancing the accuracy compared with other widely used algorithms such as the decision tree and random forest. As the basis function of XGBoost, decision tree-based solutions for classiﬁcation tasks have been used successfully in various ﬁelds, and corresponding advanced algorithms based on that are gradually being proposed to enhance the classiﬁcation performance in terms of accuracy, precision, generalization and computing efﬁciency. There are three typically used decision-tree algorithms based on information theory: ID3, C4.5 and classiﬁ- cation and regression tree (CART); the classiﬁcation regulation of CART is derived from the Gini index, and CART is used more frequently than the two other algorithms. It not only assigns categories to leaf nodes, but also considers the possibility of all attributes being selected as leaf nodes. When all the samples in the node belong to the same attribution or the depth of the decision tree reaches a preset threshold, the tree construction stops. Due to the overﬁtting phenomenon for the former condition, it is vital to set the max depth value before training. Moreover, the random forest consists of multiple structurally similar decision trees for determining the dataset together to prevent overﬁtting and reduce the variance of an estimate.


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

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

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

[Assistant]
These outputs, except time taken to train the models, moreover govern industrial applicability, and the overall viability of a model in solving tasks at hand [6]. A compilation of the top three performing ML models for the three multispectral UAV drone ﬂight elevations post SAM and ground truthing are shown in Table 2. The differences in classiﬁcation capabilities based on ﬂight elevations are demonstrated quantitatively. Global accuracy is deﬁned as the validation accuracy acquired post model training. Average per-class precision refers to the discrete magnetite iron sands (target) and the non-magnetite sands (noise) classiﬁcation score averages in testing the models. Lastly, training time refers to time taken to train the models to classify the datasets based on the existence or absence of magnetite iron sand. These performance variables are important in comparing the different ML as well as DL models given their intended application.
[page 13] Minerals 2022, 12, 268 13 of 19 Table 2. Top 3 machine learning classiﬁcation model comparisons at 2 m, 10 m and 20 m UAV ﬂight elevations based on 0.12, 0.13 and 0.17 SAM cosine similarity thresholds, respectively. Reprinted with permission from ref. [1]. 2021 AusIMM [1] (p. 40). UAV Drone Flight Elevation Machine Learning Model Global Accuracy (%) Average Per-Class Precision (%) Training Time (Seconds) 2 m Ensemble (Bagged Trees) 78.6 83.4 8.4 Ensemble (Subspace KNN) 71.4 77.8 8.1 Ensemble (RUS Boosted Trees) 85.7 84.5 5.8 10 m Tree (Fine-tree) 78.6 83.4 1.5 Tree (Medium-tree) 78.6 83.4 1.0 Tree (Course-tree) 78.6 83.4 0.9 20 m Tree (Fine-tree) 85.7 88.9 1.9 Tree (Medium-tree) 85.7 88.9 1.2 Tree (Course-tree) 85.7 88.9 1.0 At 2 m UAV ﬂight elevation, Table 2 results dictate that the best performing model was Ensemble (RUS Boosted Trees). This model acquired a global accuracy of 85.7%, per-class precision of 84.5%, and a training time of 5.8 s. At 10 m UAV ﬂight elevation, the Tree (Course-tree) model performed the best. This model acquired a global accuracy of 78.7%, per-class precision of 83.4%, and a training time of 1 s. Lastly, 20 m UAV ﬂight elevation, the Tree (Course-tree) model performed the best. This model acquired the best outputs in terms of a global accuracy of 85.7%, per-class precision of 88.9%, and a training time of 1 s. The differences in accuracies acquired and elapsed times when training the ML models for the three UAV ﬂight elevations can, moreover, be appreciated based on the results compiled in Table 2. The reason being, this conﬁrms the initial hypothesis which stated that with the proposed automatic magnetite iron sand identiﬁcation system, rapid run times and good accuracies are potentially attainable. This is, of course, without compromise to the intrinsic differences in the multispectral characteristics of magnetite iron sands and the surrounding non-magnetite beach sand. Figure 9 presents two performance metrics used to further assess the viability of the best performing ML models for the three UAV ﬂight elevations. The ﬁrst is True Positive Rates (TPR), this refers to the probability that an actual positive will test positive (Equa- tion (2)). The second is False Negative Rates (FNR), which are deﬁned as the probability that a true positive will be misclassiﬁed by the test (Equation (3)). These two variables are highly viable in assessing the capability of the ML as well as DL models in classifying mag- netite iron sands. At 2 m, the ML model has an 80% TPR, which may not seem satisfactory considering that target data should ideally have a 100% target return. This means that 20% of the target pixels were misclassiﬁed as noise, suggesting that the model at 2 m did not perform well at discerning magnetite iron sand pixels from the rest of the non-target pixels. In essence, this means that operating and collecting data at this elevation may not be the best practice. At 10 m, the target has a 100% TPR, signifying that the target pixels were correctly classiﬁed; this moreover suggests that the target output did not include any noise. At 20 m UAV ﬂight elevation, the same assessment can be made, suggesting that these ﬂight elevations are the most viable out of the three. At 20 m however, the model has a higher noise TRP of 77.8% compared to the 67.7% at 10 m.


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

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

Source: 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]
Method testing schemes Three approaches can be used for testing method per- formance and can be classified according to increasing reliability (Fig. 1). Challenges aim to test whether certain problems can be addressed with existing tools and to find out what kind of methods will be needed in the future. Critical Assessment of Structure Predictions (CASP) [6] was the first chal- lenge of this kind in biosciences. The idea was, and still is, even when CASP has been running for 20 years, to test how prediction methods behave on different protein structure related tasks. The method developers apply their systems without knowing the correct result (blind test), which however is available for the challenge asses- sors. This setup allows independent testing of method performance. In a similar vein, other critical assessment challenges have been organized e.g. for Critical Assess- ment of protein Function Annotation (CAFA) [7] and Critical Assessment of PRediction of Interactions (CAPRI) [8]. CAGI, Critical Assessment of Genome Interpretation (http://genomeinterpretation.org/), is a challenge for method developers in the field of phenotypic impacts of genomic variation. The second CAGI prediction season was organized during fall 2011. These challenges do not aim for systematic analysis of predictions, instead they assess what is currently doable, providing proof of con- cept, charting where to direct future efforts, and identi- fying new areas where predictive approaches would be needed. The second test strategy is typically used by method developers to test their approaches. These are usually done with developer collected test sets (especially when benchmark datasets are lacking) and report certain per- formance parameters. Most often the testing is not com- prehensive, and the results are incomparable with those obtained from other methods e.g. due to using different test sets. Sometimes evaluation parameters are selectively presented which leads to problems in determining the true merits and pitfalls of methods. The third approach, systematic analysis, uses approved and widely accepted benchmark dataset(s) and suitable evaluation measures to explain method performance. It is hoped that in the future the variation effect program developers would use benchmark test sets and compar- able measures. This is already the general practice e.g. in the multiple sequence alignment (MSA) field. Prediction methods for classification A plethora of pattern recognition methods have been applied to problems in bioinformatics including rule based, statistical methods and machine learning -based methodologies. The goal of machine learning is to train a computer system to distinguish i.e. classify cases based on known examples. Machine learning methods include sev- eral widely differing approaches such as support vector machines, neural networks, Bayesian classifiers, random forests and decision trees. In the following discussion we concentrate on machine learning methods as they are nowadays widely used to tackle complex phenomena, which would be otherwise difficult to handle. Successful machine learning method development requires good quality training set. The data- set should represent the space of possible cases. This space is huge for genetic variations as they can have so many different effects and underlying mechanisms. Another aspect is the choice of the machine learning approach. There is not a superior architecture among them. Third, the quality of the predictor depends on how the training has been done, which features are used to Figure 1 Method performance analysis schemes The performance of the computational methods can be addressed with three different approaches which yield different reliability for the assessment. Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 Page 2 of 10
[page 3] explain the phenomenon and optimization of the method. Fig. 2 depicts the principle underlying machine learning in a two-class classification task. The predictor is trained with known positive and negative instances in an approach called supervised learning. This leads to reorganization of the system, details of which differ according to the architec- ture employed. Once the method has learned to distinguish between the cases it can be applied to predict the class of unknown cases.

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


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

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

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

[Assistant]
Ground type Specific weight [kN/m3] Cohesion [kPa] Friction angle [◦] Permeability [m/s] gt1 24 23 20 10−5 gt2 25 40 30 10−5 G.H. Erharter et al.
[page 5] Automation in Construction 127 (2021) 103701 5 The output of the third convolutional layer is flattened/vectorized before it is fed into the dense layer and we used the mean squared error as a loss function. 3.2.1. Actions The agent can choose from 8 different actions (Table 3). Possible actions are either top heading – or bench excavation with advance lengths of either 2 or 4 m. In each step the simulation carries out the following operations: ▪ the agent chooses and executes one of the 8 actions of Table 3 and the respective length of the generated geological section is revealed ▪ it is calculated/checked if the new position of the excavation face is within a stable area or not (details in Section 3.3.2) As given in Section 2.1, the support ahead of the face has the effect on the simulation that, there cannot be unstable conditions as long as the excavation face is within the supported area. Because of technical rea sons - mostly related to the reward system (see Table 4) - each action is assigned an “action code” (a). An a < 200 denotes top heading exca vation and a ≥200 bench excavation. The choice of actions of Table 3 is based on practical engineering experience on the one hand and technical limitations of the RL agent on the other hand. On the practical side, especially the advance length is Fig. 4. Top row: an exemplary unique geological section, where brown indicates weak (gt1) and blue stronger rock (gt2). The positions of the top heading and bench are at 165.0 m and 125.0 m respectively. Bottom row: the random walk that is used to generate the geological section. Values above 0.5 are converted to gt2 and below to gt1. Note that the x-axis is the tunnel length in decimeters which corresponds to the number of datapoints of the random walk. Table 2 Used hyperparameters for the DQN agent. Except for the exploration decay (see Section 3.2.1) all are identical in their meaning as the extended data Table 1 in [14]. Hyperparameter Value Replay memory size 100,000 Replay start size 1000 Minibatch size 64 Discount (γ) 0.99 Target network update frequency 10 Initial exploration (ε) 1 Final exploration (ε) 0.05 Learning rate 0.00025 Gradient momentum 0.95 Exploration decay (εd) 0.99997 Fig. 5. Schematic representation of the DQN agent’s ANN architecture. Note the visualization of rockmass-matrix and the support-matrix to the left. The numbers below each layer are the respective shape of the layer’s weights. Dashed connection lines between layers are only for illustrational purposes. Symbols at the output layer represent the eight possible actions (ordered as in Table 3) that are chosen via Q-values by the agent. Table 3 The eight possible actions the agent can choose from. Action code (a) Excavation Advance length [m] Face support 110 top heading 2 no 112 top heading 2 yes 150 top heading 4 no 152 top heading 4 yes 200 bench 2 no 202 bench 2 yes 220 bench 4 no 222 bench 4 yes G.H. Erharter et al.
[page 6] Automation in Construction 127 (2021) 103701 6 highly influential on the stability of the excavation with longer advance lengths being more prone to failures than short ones (e.g. advance lengths in the Austrian standard ¨ONORM B2203–1 [46]: 1.0, 1.3, 1.7, 2.2, 3.0 and 4.0 m). After experimenting with advances lengths based on the standards in the beginning, we experienced that a big number of actions leads to a deterioration of the performance/confuses the agent in this model setup. This is also in accordance with other studies which have found that special measures/adaptations of the agent are necessary if the action space becomes increasingly complex [47,48]. The final set of actions in Table 3 therefore aims at giving the agent realistic options to choose from while also keeping the number of actions small (see the outlook in Section 7 for a discussion on increasing the number of actions). During an episode, the actions are chosen based on an “ε-greedy action selection process”, (see also “exploration vs. exploitation trade off”, e.g. [26]). ε is the exploration rate and is initially set to 1 (see “initial exploration in Table 2).


[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]
In recent years, with the development of computer and artiﬁcial intelligence technology, machine learning models have been widely employed to address the complexity of engineering issues, * Corresponding author. E-mail address: wu.wei@ntu.edu.sg (W. Wu). Peer review under responsibility of Institute of Rock and Soil Mechanics, Chi- nese Academy of Sciences. Contents lists available at ScienceDirect Journal of Rock Mechanics and Geotechnical Engineering journal homepage: www.jrmge.cn Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 https://doi.org/10.1016/j.jrmge.2022.01.008 1674-7755  2022 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
[page 2] including the nonlinear behaviors of rocks. For example, the sup- port vector machine (SVM) algorithm was applied to forecast the static compressive strength and shear strength of rocks by input- ting rock density, point load strength, P-wave velocity, and slake durability index (Li and Tan, 2017). The back-propagation neural network (BPNN) algorithm was used to predict the static compressive strength and the elasticity modulus of rocks based on slake durability index, Schmidt hammer rebound number, effective porosity, and point load index (Yılmaz and Yuksek, 2008). Based on the random forest (RF) algorithm, the static compressive strength of rocks was estimated based on the available drilling data, such as weight on bit, drill string rotating speed, drilling torque, stand-pipe pressure, mud pumping rate, and penetration rate (Gamal et al., 2021). Given these successful applications to rock mechanics problems, the application of machine learning models can further be extended to rock dynamics to improve our ability to forecast the rate-dependent strength of rocks. This study aimed to use machine learning models to predict the compressive strength of rocks and to provide new insights into the rate-dependent strength of rocks. We ﬁrst collected the laboratory data of the static compression tests and the SHPB tests on various types of hard rocks as well as the corresponding geometrical, physical, and mechanical properties. We then introduced the ma- chine learning algorithms (e.g. SVM, BPNN, and RF) and established three models based on these algorithms. We then trained these models using three quarters of the data and validated the models using the remaining data. Finally the importance of input param- eters and the effects of strain rate and specimen dimension on the compressive strength of rocks were discussed. 2. Data and methods 2.1. Data collection To train and validate the machine learning models in predicting the compressive strength of rocks, we ﬁrst collected a few data sets of the static compression tests and the SHPB tests on eleven types of hard rocks from published papers (e.g. Frew et al., 2001; Li et al., 2005; Xia et al., 2008; Wang and Tonon, 2011; Zhang and Zhao, 2013; Zhou et al., 2017, 2020; Weng et al., 2020; Yu et al., 2021). Typical rock types in rock engineering were included, such as granite, marble, sandstone, and siltstone. The geometrical, physical, and mechanical properties of these rocks are listed in Table A1, including specimen length and diameter, grain size, bulk density, P- wave velocity, as well as compressive strength with the corre- sponding strain rate. The static compressive strength was consid- ered as rate-independent and approximated as a constant value, not speciﬁc for each specimen. Selection of the input parameters mainly relies on data availability, and other parameters, such as porosity, mineralogy, and microscale features (e.g. Fan et al., 2018, 2020), can also be used if available in the literature. To understand the simple correlation between any two pa- rameters in Table A1, we calculated the Pearson correlation coef- ﬁcient (Edwards, 1976), a ratio of the covariance of two parameters to the product of their standard deviations. A correlation coefﬁcient of 1 indicates a strong positive correlation, while 1 means a strong negative correlation. As illustrated in Fig. 1, for example, the P-wave velocity shows the strongest correlation with the static compres- sive strength, as they are highly related to the integrity of rocks. The P-wave velocity is also positively correlated with the grain size and the bulk density.


[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]
Protein tolerance predictors Single nucleotide alterations are the most common genetic variation type. Human genomes contain these variations on average at every kilobase. Several computational meth- ods have been developed to classify these variations [1]. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO [5]. The methods differ in the properties of the variant they take into account, as well as in the nat- ure and the classification method. Panther, PhD-SNP and SIFT are based on evolutionary information. MutPred, nsSNPAnalyzer, PolyPhen2, SNAP and SNP&GO combine protein structural and/or functional parameters and sequence analysis derived information. Most of these are based on machine-learning methods. The positive test dataset included 19,335 missense var- iations from the PhenCode database [30], IDbases [31] and from 18 additional LSDBs. The negative dataset con- sisted of 21,170 nonsynonymous coding SNPs with an allele frequency >0.01 and chromosome sample count higher than 49 from the dbSNP database. As large num- bers of individual predictions were the Pathogenic-or-not Pipeline (PON-P) [32] was used for the submission of sequences and variants into the analysed programs. The performance was evaluated with the six measures described above. The performances of the programs ran- ged from poor (MCC 0.19) to reasonably good (MCC 0.65) [5]. It has been widely accepted that information about protein three dimensional structure would increase pre- diction performance. The very best methods use also structural and functional information, whereas others that are solely based on sequence level information per- form rather well. Further analyses were made to compare the methods pairwise, and to study whether the type of original or sub- stituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. Existing programs thus have widely varying perfor- mance and there is still need for better methods. Con- sidering all the evaluation measures, no single method could be rated as best by all of them. Protein stability predictors Stability as a fundamental property affects protein func- tion, activity, and regulation. Changes to stability are often found to be involved in diseases. Systematic per- formance evaluation analysis has been made for eleven stability predictors performances including CUPSAT, Dmutant, FoldX, I-Mutant2.0, two versions of I- Mutant3.0 (sequence and structure versions), MultiMu- tate, MUpro, SCide, Scpred, and SRide [2]. SCide and Scpred, which predict stability centers, as well as SRide, which predicts stabilizing residues, predict only destabi- lizing effects, while all the others evaluate both stabiliz- ing and destabilizing changes. The major database for protein stability information is ProTherm [33]. The pruned dataset for testing con- tained 1784 variations from 80 proteins, with 1154 posi- tive cases of which 931 were destabilizing (ΔΔG ≥0.5 kcal/mol), 222 were stabilizing (ΔΔG ≤–0.5 kcal/mol), and 631 were neutral (0.5 kcal/mol ≥ΔΔG ≥–0.5 kcal/ mol). The majority of the methods had been trained using data from ProTherm, and thus only those cases that had been added to the database after training had occurred were used for testing. Of the measures recommended in here the authors used four, namely accuracy, specificity, sensitivity, and Vihinen BMC Genomics 2012, 13(Suppl 4):S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 Page 8 of 10
[page 9] MCC and the remaining row wise parameters could be calculated from the confusion tables. There were three groups of data, stability increasing, neutral and stability decreasing. The authors solved the problem of multiple classes by presenting three tables of results. The first one was grouped so that both stability increasing and decreasing were considered as pathogenic i.e. positive. In these analyses only two classes were con- sidered, stabilizing or destabilizing and neutral cases. The results for the all the cases show that accuracy ranges from 0.37 to 0.64 and MCC from -0.37 to only 0.12. All the programs succeeded better when predicting stability increasing or decreasing variations individually. The MCC reaches 0.35 and 0.38 for the methods best in predicting stability increasing and decreasing variants, respectively [2].


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

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

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

[Assistant]
Consequently, this suggests that the Tree (Course–tree) model at 20 m performed better at discerning the targeted magnetite iron sand data compared to the other two UAV ﬂight elevation model outputs.
[page 14] Minerals 2022, 12, 268 14 of 19 Figure 9. Machine learning confusion matrices from Ensemble (RUS Boosted Trees) at 2 m UAV drone elevation, Tree (Course-tree) at 10 m UAV drone elevation, and Tree (Course-tree) at 20 m UAV drone. Reprinted with permission from ref. [1]. 2021 AusIMM [1] (p. 40). One more assessment that can be drawn from the Figure 9 confusion matrix is the average per-class precisions of 84.5%, 83.4% and 88.9% at 2 m, 10 m and 20 m ﬂight elevations, respectively; at 20 m, the model continues to perform better. It can thus be said that the best classiﬁcation ML model for this magnetite iron sands identiﬁcation problem is Tree (Course tress) at 20 m. True Positive Rates (TPR): TPR = 100  TP TP + FN  (2) False Negative Rates (FNR): FNR = 100  FN TP + FN  (3) where FN is false negatives and TP is true positives. 3.3.2. Classiﬁcation via Deep Learning CNN A convolution applies a ﬁlter over the multispectral data per band. Unlike with visual imagery data with height and width, the ﬁlters in a one dimensional CNN exhibit a 1D (spectral intensity) architecture instead of 2D as with normal visual images, hence the motivation to employ this type of CNN in this study as the datasets contain 6-bands deep spectral intensity data that require a 1D data processing method. The input dataset sizes for training and testing variables for each of the algorithms stood at 12,480,000 (from Figure 8) for each of the three ﬂight elevations prior to obtaining the highest performing model. However, to train the 1D CNN, the spectral data were pre- processed in the following ratios: 70% (9,736,000) designated to training, 15% (1,872,000) validation and the remaining 15% (1,872,000) to testing. Moreover, all models were trained with Adam optimizer, which is said to have a best accuracy in enhancing CNN abilities in classiﬁcation [19]. A learning rate of 0.001, a batch size of 128, and 25 epochs were also employed. As with ML, the 1D CNN training processes were conducted using the MATLAB
[page 15] Minerals 2022, 12, 268 15 of 19 2020b deep learning toolbox with model selection at each ﬂight elevation governed by global accuracy, average per class precision, and time taken to train each 1D CNN algorithm. At 2 m, 10 m and 20 m ﬂight elevations, 80, 32 and 8 algorithms were trained and one model from the multitudes of trained models was selected. The same procedure was performed in training the ML models. Table 3 is a compilation of the best performing models at each of the three ﬂight elevations. Results from the DL 1D CNN models in Table 3 show that the global accuracies as well as the average per-class precisions have an average of 99.8% and 99.3%, respectively. Moreover, based on global accuracy, the 1D CNN performs the same at 2 m and 10 m at 99.9%, followed by 20 m at 99.7%. However, the trend changes in considering the average per-class precisions where the best performing model is the 1 D CNN at 2 m (99.8%), followed by 20 m (99.4%) and 10 m (98.7%). Lastly, at 2 m, the 1D CNN takes a longer time to train (78 s) compared to the 1D CNNs at 10 m (45 s) and 20 m (68 s); this could suggest that the longer the training time, the higher the average per-class precision for this DL classiﬁcation problem. Table 3. Deep learning one dimensional convolution neural network classiﬁcation model comparisons at 2 m, 10 m and 20 m UAV ﬂight elevations based 0.12, 0.13 and 0.17 SAM cosine similarity thresholds, respectively. Flight Height Global Accuracy (%) Average Per-Class Precision (%) Training Time (Seconds) 2 m 99.9% 99.8% 78 10 m 99.9% 98.7% 45 20 m 99.7% 99.4% 68 To further assess the viability of the best performing 1D CNN models for the three flight elevations, Figure 10 presents TPR and FNR performance metrics (Equations (2) and (3)). At 2 m, 10 m and 20 m, the 1D CNN models have a 100% TPR for magnetite iron sand (target). This suggests that the models are highly capable of deducing magnetite iron sands from the combination of magnetite iron sands (target) and non-magnetite beach sand (noise). On the other hand, the 1D CNN models attained a 99.6%, 97.4% and 98.7% TPR for non-magnetite beach sand (noise) at 2 m, 10 m and 20 m, respectively, which is considerably high.