A case study of seismic forward prospecting based on the tunnel seismic while drilling and active seismic methods

Xu, Xinji; Zhang, Panlong; Guo, Xu; Liu, Bin; Chen, Lei; Zhang, Qingsong; Zhang, Yi

doi:10.1007/s10064-020-02088-z

Cited by 15 publications

(4 citation statements)

References 24 publications

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“…The performance was evaluated based on accuracy, and the differences between the training-set and test-set accuracies were used to evaluate the generalization performance of the model. Table 9 shows the following: (1) The BILSTM model had the highest accuracy values in both the training and test sets, thereby proving its high performance. (2) The difference values of the BILSTM, MLP, and SVM models were 0.054, 0.116, and 0.094, respectively.…”

Section: Modelmentioning

confidence: 90%

“…Tunnel engineering is a crucial part of transportation engineering. Mountainous areas are characterized by significant terrain-height differences, strong tectonic activities, and complex and changeable hydrogeological environments, which can result in great safety hazards during tunnel construction [1,2]. Therefore, it is necessary to evaluate the surrounding rock conditions in front of the tunnel face during tunnel constructions; this can be done through advanced geological forecasting and can prevent potential geological disasters [3][4][5], such as large surrounding rock deformation, landslides, karst collapse, water and/or mud inflow [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Kadkhodaie-Ilkhchi et al based on the fuzzy logic (FL) algorithm [23], Fang et [24][25][26], applied machine learning methods in practical engineering with a large amount of noisy data. Although current machine-learning algorithms can achieve good prediction results, several shortcomings persist: (1) Drilling is a continuous process; however, these studies only use the drilling data corresponding to the position to classify the rock class at a certain depth during the drilling process. Gupta et al demonstrated through their research that considering the information contained in the sequence can improve the accuracy of results [27].…”

Section: Introductionmentioning

confidence: 99%

“…To address the aforementioned issues, this study proposes a machine-learning method with BILSTM as the core, using depth-sequence MWD data to classify rock-mass classes in real-time. (1) The BILSTM was used to fully consider the influence of depth-sequence MWD data on rock-classification results. Information related to the surrounding rock class were extracted in both the forward and reverse directions.…”

Section: Introductionmentioning

confidence: 99%

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BILSTM-Based Deep Neural Network for Rock-Mass Classification Prediction Using Depth-Sequence MWD Data: A Case Study of a Tunnel in Yunnan, China

Tang

et al. 2023

Applied Sciences

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Measurement while drilling (MWD) data reflect the drilling rig–rock mass interaction; they are crucial for accurately classifying the rock mass ahead of the tunnel face. Although machine-learning methods can learn the relationship between MWD data and rock mechanics parameters to support rock classification, most current models do not consider the impact of the continuous drilling-sequence process, thereby leading to rock-classification errors, while small and unbalanced field datasets result in poor model performance. We propose a novel deep neural network model based on Bi-directional Long Short-Term Memory (BILSTM) to extract information-related sequences in MWD data and improve the accuracy of the rock-mass classification. Two optimization modules were designed to improve the model’s generalization performance. Stratified K-fold cross-validation was used for model optimization in small and unbalanced datasets. Model validation is based on the MWD dataset of a highway tunnel in Yunnan, China. Multiple metrics show that the prediction ability of the network is significantly better than those of a multilayer perceptron (MLP) and a support-vector machine (SVM), while the model exhibits an improved generalization performance. The accuracy of the network can reach 90%, which is 13% and 15% higher than the MLP and SVM, respectively.

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Section: Modelmentioning

confidence: 90%