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Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network

Author

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  • Sufyan Ghani

    (National Institute of Technology Patna)

  • Sunita Kumari

    (National Institute of Technology Patna)

Abstract

The present research aims to co-relate the plasticity and liquefaction response of soil as well as its significance in defining liquefaction probability. To accomplish this, metaheuristic hybrid models (ANN-PSO, ANN-GA, ANN-GWO, ANN-CA, ANN-FA, and ANN-GBO) were used to forecast the liquefaction probability (PL) of alluvium soil deposits belonging to the Indo-Gangetic plain (Bihar region, India). The first-time application of ANN-based techniques hybridized with the Gradient-Based Optimizer (GBO) algorithm for predicting the PL of fine-grained soils brings a certain degree of novelty. The main advantage of hybrid computational models is that they can subjectively analyze an unlimited amount of data and give reliable outcomes and assessments. The use of the plasticity index (PI) to measure the liquefaction behavior of fine-grained soil has a considerable impact in defining the liquefaction susceptibility for soil with moderate to high plasticity, and it seeks to make a significant contribution to the liquefaction studies. The ANN-GBO model has the best prediction ability, according to performance metrics. The overall analysis suggests that the application PI along with the proposed ANN-GBO model can be thought of as a novel tool to help geotechnical engineers estimate the occurrence of liquefaction during the early design stage of any engineering project. It was observed that fine-grained soil with PI > 14%, shows a lower PL of about less than 35% and falls under safe to moderately safe zones. Soil with 9

Suggested Citation

  • Sufyan Ghani & Sunita Kumari, 2022. "Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(3), pages 2995-3029, April.
    • Handle: RePEc:spr:nathaz:v:111:y:2022:i:3:d:10.1007_s11069-021-05165-y
    DOI: 10.1007/s11069-021-05165-y
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    References listed on IDEAS

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    1. Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
    2. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
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    Cited by:

    1. Abhijit Chakraborty & V. A. Sawant, 2023. "Earthquake response of embankment resting on liquefiable soil with different mitigation models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3093-3117, April.
    2. Abdullah Hulusi Kökçam & Caner Erden & Alparslan Serhat Demir & Talas Fikret Kurnaz, 2024. "Bibliometric analysis of artificial intelligence techniques for predicting soil liquefaction: insights and MCDM evaluation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 11153-11181, September.

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