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Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential

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Abstract

Among the research hotspots in geological/geotechnical engineering, research on the prediction of soil liquefaction potential is still limited. In this research, several machine-learning methods were developed to evaluate the liquefaction potential of soil using random forest (RF) as the base model. The parameters of the RF model were optimized using two optimization algorithms, namely, the grey wolf optimizer (GWO) and genetic algorithm (GA). In the experiment, three in situ databases based on the standard penetration test (SPT), shear wave velocity test (SWVT) and cone penetration test (CPT) were considered and used to investigate the applicability of GA-RF and GWO-RF models. For comparison purposes, a single RF model was also constructed to predict soil liquefaction. The developed models in this study were evaluated using four metrics, i.e., accuracy, recall, precision and F1-score (F1). Furthermore, receiver operating characteristic and precision-recall curves were also proposed for evaluation purposes. The results showed that the developed GA-RF and GWO-RF models can improve the performance of the original classifier. By comparing the two hybrid models, it was found that the GWO-RF performs better on two databases, i.e., CPT and SPT, while in the case of the SWVT database, the GA-RF has better performance. Considering a variety of metrics, the two hybrid models can be employed as powerful techniques to estimate soil liquefaction potential and may be feasible tools to assist technicians in making correct decisions. By implementing sensitivity analysis, the impact of each model predictor on soil liquefaction was evaluated, and the most influential parameters were identified.

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Acknowledgements

This research was funded by the Innovation‐Driven Project of Central South University (2020CX040), the National Natural Science Foundation of China (Nos. 52004161 and 42177164), and the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (No. 2019ZT08G315).

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  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou, Shuai Huang & Yingui Qiu

  2. Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization, Institute of Deep Earth Sciences and Green Energy, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China

    Tao Zhou

  3. Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, Chelyabinsk, 454080, Russia

    Danial Jahed Armaghani

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Zhou, J., Huang, S., Zhou, T. et al. Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential. Artif Intell Rev 55, 5673–5705 (2022). https://doi.org/10.1007/s10462-022-10140-5

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