Predicting rockburst with database using particle swarm optimization and extreme learning machine. (April 2020)

• Record Type: Journal Article
• Authors: Xue, Yiguo; Bai, Chenghao; Qiu, Daohong; Kong, Fanmeng; Li, Zhiqiang
• Is Part Of: Tunnelling and underground space technology. Volume 98(2020)
• Publication Date: 2020-04
• Subjects: Rockburst prediction -- Database -- PSO-ELM -- Machine supervision learning -- K-fold cross-validation -- Confusion matrix; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2020.103287
• Languages: English
• ISSNs: 0886-7798

Abstract

Highlights: ELM optimized by PSO was used to predict rockburst intensity. Rockburst database was established by collecting 344 rockburst cases. Six quantitative rockburst parameters was selected as the inputs of networks. PSO-ELM model was verified by rockburst database and new tunnel engineering. PSO-ELM model was compared with other existing models and empirical criteria. Abstract: Rockburst is a major type of geological hazard that has a very adverse impact on underground engineering in deeply buried areas under high geo-stress. In this study, extreme learning machine (ELM) was used to predict and classify rockburst intensity, and particle swarm optimization (PSO) was used to optimize the input weight matrix and the hidden layer bias in ELM. Six quantitative rockburst parameters were used as input for the PSO-ELM network, including the maximum tangential stress of the surrounding rock σθ, the uniaxial compressive strength of rock σc, the tensile strength of rock σt, the stress ratio σθ / σc, the rock brittleness ratio σc / σt and the elastic energy index Wet . The network was used to learn from a database of 344 collected worldwide rockburst cases, on which the PSO-ELM rockburst prediction model was established and verified using 8-fold cross-validation and independent test set validation. The model was then tested on a new set of fifteen typical rockburst cases from Jiangbian hydropower station in China. The results showed that the PSO-ELM model performed well in rockburst level prediction. In addition, the model showed superior performance compared with previously proposed machine learning models for rockburst prediction and the rockburst empirical criteria, which underscores its utility in future rockburst prediction.