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Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model

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Abstract

Rockburst is a major instability issue faced by underground excavation projects, which is induced by the instantaneous release of a large amount of strain energy stored in rock mass. Because of its disastrous damage to infrastructures and facilities, more and more studies have been focused on rockburst prediction. However, due to highly nonlinear relationships between the occurrence of rockburst and potential triggering factors, traditional mechanism-based prediction methods have great difficulties in providing the reliable results. In this study, a multivariate adaptive regression splines (MARS) model and a novel deep forest algorithm were applied to predict and classify rockburst intensity of a database including 344 rockburst cases collected worldwide. The t-distributed stochastic neighbor embedding method (t-SNE) was utilized for nonlinear dimensionality reduction and visualization of the original input features. After that, the Gaussian mixture model was adopted to relabel original data to determine relative intensity of these rockburst cases. Then, the MARS model and deep forest model were constructed with these newly labeled data. Their performances were compared with some widely used machine learning methods, such as random forest, extreme gradient boost, and ANN model. The results clearly proved the capability of the proposed models to assess and forecast rockburst risk. It also proved that these approaches should be used as cross-validation against each other. The Shapley additive explanations method was adopted to investigate the relative importance of input features of the developed MARS model. The result shows that σθ and σc are the most important features for rockburst intensity prediction, where σθ is the tangential stress around underground opening and σc refers to uniaxial compressive strength of the rock.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 51778092), Natural Science Foundation (cstc2017jcyjAX0073), Chongqing.

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Authors and Affiliations

  1. Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, Chongqing, 400045, China

    Hemao Chen, Libin Tang & Zhixiong Chen

  2. School of Civil Engineering, Chongqing University, Chongqing, 400045, China

    Hemao Chen, Libin Tang & Zhixiong Chen

  3. National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, Chongqing, 400045, China

    Hemao Chen, Libin Tang & Zhixiong Chen

  4. Sichuan Railway Investment Group Co., Ltd, Tianfu 1st St., High-Tech Zone of ChengduSichuan Province, Chengdu, 100036, China

    Deping Guo

  5. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

    Pijush Samui

  6. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

    Pijush Samui

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  1. Deping Guo
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  2. Hemao Chen
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Correspondence to Zhixiong Chen.

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Guo, D., Chen, H., Tang, L. et al. Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model. Acta Geotech. 17, 1183–1205 (2022). https://doi.org/10.1007/s11440-021-01299-2

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