Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network

Liu, Zaobao; Li, Long; Fang, Xingli; Qi, Wenbiao; Shen, Ji-Mei; Zhou, Hongyuan; Zhang, Yulong

doi:10.1016/j.autcon.2021.103647

Cited by 105 publications

(32 citation statements)

References 40 publications

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“…Data pre-processing can have a significant impact on model performance. It is common, even necessary by some authors [12], to apply an input standardization in order to ensure all inputs get equal attention during training [13,14]. Variables should also be scaled in such a way as to be compatible with the limits of the activation functions in the output layer.…”

Section: On Input Pre-processing and Data Balancingmentioning

confidence: 99%

Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection

Morgenroth

Unterlaß

Sapronova

et al. 2022

Geomechanics and Tunnelling

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Research has demonstrated that machine learning algorithms (MLAs) are a powerful addition to the rock engineering toolbox, and yet they remain a largely untapped resource in engineering practice. The reluctance to adopt MLAs as part of standard practice is often attributed to the ‘opaque’ nature of the algorithms, the complexity in developing them, and the difficulty in determining how the algorithms use the datasets. This article presents tools and processes for developing MLAs, input selection, and data balancing for practical underground rock engineering. MLAs for classification and regression – two main machine learning applications – are presented in terms of developing MLA to extract information from the dataset to obtain the desired output. Engineering verification metrics are selected based on their suitability for specific output. Methods for input selection and data balancing are discussed with a focus on selecting appropriate input data for the problem without introducing bias or excess complexity. Each tool and process for algorithm development, data preparation, and input selection is illustrated with a case study. This article demonstrates that geotechnical practitioners can extract additional value by applying MLAs to rock engineering problems. Once an understanding of the functions of MLAs is reached, the building blocks and open‐source code are available to be adapted to suit the rock mass behaviour of interest.

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Section: On Input Pre-processing and Data Balancingmentioning

confidence: 99%

Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection

Morgenroth

Unterlaß

Sapronova

et al. 2022

Geomechanics and Tunnelling

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“…Additionally, time-series data from partial tunnel boring machine (TBM) sensors have been proven to potentially correlate to the stratigraphic and geological conditions ahead of excavation [24,36,55]. Moreover, the relationship between the TBM operational data and the geological condition ahead of the excavation face can be captured by machine learning (ML) and deep learning (DL) models to achieve timely and continuous prediction [57].…”

Section: Introductionmentioning

confidence: 99%

“…It found that the accuracy of AdaBoost-CART was 0.865, which was higher than other classifiers. Liu et al [36] used a long short-term memory (LSTM) network based on a global attention mechanism for geological condition prediction, which outperformed other models in terms of accuracy and F1-score. Wang et al [50] compared shallow ML and DL algorithms to construct a prediction model of ground conditions ahead of the excavation face.…”

Section: Introductionmentioning

confidence: 99%

Prediction of rock mass class ahead of TBM excavation face by ML and DL algorithms with Bayesian TPE optimization and SHAP feature analysis

Chen

Seo

2023

Acta Geotech.

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In this paper, field construction data from the Singapore Metro Line project were used to study the mapping relationship and establish the prediction model between TBM operation data and the ground condition ahead of the excavation face. The study presents a multi-classifier competition mechanism to construct ten separate classifiers, including logistic regression, support vector machine, random forest, extremely randomized trees, adaptive boosting machine, extreme gradient boosting (Xgboost), light gradient boosting (LightGBM), categorical boosting, long short-term memory and convolutional neural network. The acquired data were used to select 28 key TBM operating parameters by a correlation-based feature selection method, and the selected parameters in the stabilization phase after removing the outliers were calculated as the input to the classifier, and a relatively balanced training set was obtained by the synthetic minority oversampling technique. The hyperparameters of each classifier were optimized using tree Parzen estimator Bayesian optimization. The prediction results show that LightGBM presents the best results among ten different machine and deep learning algorithms with an accuracy of 96.22%, precision of 96.94%, recall of 97.33% and F1-score of 97.33%. In addition, the effect of the input parameters of the LightGBM model on the prediction accuracy of the model was analyzed using Shapley additive explanations, and the effect of sample imbalance on the prediction performance was discussed.

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“…Tunnel boring machine (TBM) integrating the functions of excavation, support, slag discharge, and transportation is one of the most advanced types of equipment for a variety of tunnel constructions, e.g., traffic, municipal, water conservancy, water supply, and gas transmission pipelines [1]. TBM has gradually replaced the traditional blasting excavation in various tunnel projects due to its high efficiency, safety, and environmental protection [2].…”

Section: Introductionmentioning

confidence: 99%

Prediction Method of TBM Tunneling Parameters Based on Bi‐GRU‐ATT Model

Zhang

Yang

Zhu

et al. 2022

Advances in Civil Engineering

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With tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavating, the prediction of important TBM operating parameters plays an important role in the research on TBM adaptable adjustment. This paper proposes an intelligent prediction method of TBM tunneling parameters based on bidirectional gate recurrent unit incorporating attention mechanism (Bi-GRU-ATT) and selects a complete tunneling cycle to predict the tunneling parameters of the TBM complete tunneling cycle. Relying on the TBM3 bid section of Jilin Water Supply Project, 21 key parameters of the complete tunneling cycle are selected as the input features of the model to realize the prediction of four tunneling parameters in the complete driving cycle section of TBM. Compared with the Bi-GRU, GRU, and Long Short-Term Memory (LSTM) models, it can be seen that the Bi-GRU-ATT model has a goodness of fit for predicting TBM tunneling parameters above 0.92, and the average absolute percentage error is less than 1.8%. The results show that the prediction method of TBM tunneling parameters based on Bi-GRU-ATT model proposed in this paper has stronger learning and prediction capabilities. This prediction method provides a more feasible auxiliary intelligent decision-making method for TBM aided intelligent construction.

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Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network

Cited by 105 publications

References 40 publications

Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection

Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection

Prediction of rock mass class ahead of TBM excavation face by ML and DL algorithms with Bayesian TPE optimization and SHAP feature analysis

Prediction Method of TBM Tunneling Parameters Based on Bi‐GRU‐ATT Model

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