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Rock slope stability analysis and charts based on hybrid online sequential extreme learning machine model

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Abstract

The stability of rock slopes is a difficult problem in the field of geotechnical and geological engineering. Less than 20% of all landslides are predictable each year, so a simple, fast, reliable and low-cost method to predict the stability of slopes is urgently needed. This study investigates a new regularized online sequential extreme learning machine, incorporated with the variable forgetting factor (FOS-ELM), based on intelligence computation to predict the factor of safety of a rock slope (F). The Bayesian information criterion (BIC) is applied to establish seven input combinations based on the parameters of the Hoek-Brown criterion and geometrical and mechanical parameters of the slope, such as the geological strength index (GSI), disturbance factor (D), rock material constant (mi), uniaxial compressive strength (σci), unit weight of the rock mass (γ), slope height (H) and slope angle (β). Seven models are established and evaluated to determine the optimal input combination. Various statistical indicators are calculated for the prediction accuracy examination. Compared to the classical extreme learning machine (ELM) model predictions of F, the results of the applied FOS-ELM model demonstrate a better prediction accuracy and are more effective when accounting for an increase in data. The FOS-ELM model with all seven input parameters is used to establish stability charts with the influence coefficient of slope angle change (ηβ), disturbance change (ηD) and slope height change (ηH). Using stability charts with a combination of ηβ, ηD and ηH can be used to quickly and preliminarily analyze rock stability as a guide for engineering practitioners in rock slope design.

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Acknowledgements

This project was supported by the Fundamental Research Funds for the Central Universities of Central South University (2017zzts178; 2018zzts322);.

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Authors and Affiliations

  1. School of Geo-sciences and Info-Physics, Central South University, Changsha, Hu Nan, 410083, People’s Republic of China

    Chao Deng, Huanxiao Hu & Jiale Chen

  2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha, Hu Nan, 410083, People’s Republic of China

    Huanxiao Hu

  3. School of Mathematics and Statistics, Central South University, Changsha, Hu Nan, 410083, People’s Republic of China

    Tianle Zhang

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  1. Chao Deng
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  2. Huanxiao Hu
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  4. Jiale Chen
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Correspondence to Chao Deng.

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Communicated by: H. Babaie

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Deng, C., Hu, H., Zhang, T. et al. Rock slope stability analysis and charts based on hybrid online sequential extreme learning machine model. Earth Sci Inform 13, 729–746 (2020). https://doi.org/10.1007/s12145-020-00458-5

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