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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

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Abstract

This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy (OA), precision, recall, F-measure, and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

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Acknowledgements

The work presented in this paper was part of research sponsored by the National Key Research & Development Plan of China (Nos. 2018YFC1505305 and 2016YFE0200100) and the Key Program of the National Natural Science Foundation of China (No. 51639002).

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Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China

    Mahmood Ahmad & Xiao-Wei Tang

  2. Department of Civil Engineering, University of Engineering and Technology, Peshawar (Bannu Campus), Bannu, 28100, Pakistan

    Mahmood Ahmad

  3. School of Economics & Management, Dalian University of Technology, Dalian, 116024, China

    Jiang-Nan Qiu & Wen-Jing Gu

  4. Department of Civil Engineering, Abasyn University, Peshawar, 25000, Pakistan

    Feezan Ahmad

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  1. Mahmood Ahmad
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  2. Xiao-Wei Tang
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  3. Jiang-Nan Qiu
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  4. Feezan Ahmad
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  5. Wen-Jing Gu
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Corresponding author

Correspondence to Jiang-Nan Qiu.

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Ahmad, M., Tang, XW., Qiu, JN. et al. Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential. Front. Struct. Civ. Eng. 15, 490–505 (2021). https://doi.org/10.1007/s11709-020-0669-5

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  • DOI: https://doi.org/10.1007/s11709-020-0669-5

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