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Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

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Abstract

Liquefaction has caused many catastrophes during earthquakes in the past. When an earthquake is occurring, saturated granular soils may be subjected to the liquefaction phenomenon that can result in significant hazards. Therefore, a valid and reliable prediction of soil liquefaction potential is of high importance, especially when designing civil engineering projects. This study developed the least squares support vector machine (LSSVM) and radial basis function neural network (RBFNN) in combination with the optimization algorithms, i.e., the grey wolves optimization (GWO), differential evolution (DE), and genetic algorithm (GA) to predict the soil liquefaction potential. Afterwards, statistical scores such as root mean square error were applied to evaluate the developed models. The computational results showed that the proposed RBFNN-GWO and LSSVM-GWO, with Coefficient of Determination (R2) = 1 and Root Mean Square Error (RMSE) = 0, produced better results than other models proposed previously in the literature for the prediction of the soil liquefaction potential. It is an efficient and effective alternative for the soil liquefaction potential prediction. Furthermore, the results of this study confirmed the effectiveness of the GWO algorithm in training the RBFNN and LSSVM models. According to sensitivity analysis results, the cyclic stress ratio was also found as the most effective parameter on the soil liquefaction in the studied case.

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Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Mingxiang Cai & Xiaoling Chen

  2. Laboratoire Génie Physique des Hydrocarbures, Faculté des Hydrocarbures et de la Chimie, Université M’Hamed Bougara de Boumerdes, Avenue de l’Indépendance, 35000, Boumerdes, Algeria

    Ouaer Hocine

  3. Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah, Kurdistan Region, Iraq

    Ahmed Salih Mohammed

  4. Département Études Thermodynamiques, Division Laboratoires, Sonatrach, Boumerdes, Algeria

    Menad Nait Amar

  5. Department of Mining Engineering, University of Kashan, Kashan, Iran

    Mahdi Hasanipanah

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  1. Mingxiang Cai
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  2. Ouaer Hocine
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  4. Xiaoling Chen
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Cai, M., Hocine, O., Mohammed, A.S. et al. Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential. Engineering with Computers 38, 3611–3623 (2022). https://doi.org/10.1007/s00366-021-01392-w

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