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Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction

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  • Xinhua Xue
  • Xingguo Yang

Abstract

The determination of liquefaction potential of soils induced by earthquake is a major concern and an essential criterion in the design process of the civil engineering structures. A purely empirical interpretation of the filed case histories relating to liquefaction potential is often not well constrained due to the complication associated with this problem. In this study, an integrated fuzzy neural network model, called Adaptive Neuro-Fuzzy Inference System (ANFIS), is developed for the assessment of liquefaction potential. The model is trained with large databases of liquefaction case histories. Nine parameters such as earthquake magnitude, the water table, the total vertical stress, the effective vertical stress, the depth, the peak acceleration at the ground surface, the cyclic stress ratio, the mean grain size, and the measured cone penetration test tip resistance were used as input parameters. The results revealed that the ANFIS model is a fairly promising approach for the prediction of the soil liquefaction potential and capable of representing the complex relationship between seismic properties of soils and their liquefaction potential. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Xinhua Xue & Xingguo Yang, 2013. "Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction," 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. 67(2), pages 901-917, June.
    • Handle: RePEc:spr:nathaz:v:67:y:2013:i:2:p:901-917
    DOI: 10.1007/s11069-013-0615-0
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    Cited by:

    1. V. Kohestani & M. Hassanlourad & A. Ardakani, 2015. "Evaluation of liquefaction potential based on CPT data using random forest," 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. 79(2), pages 1079-1089, November.
    2. Yong-gang Zhang & Junbo Qiu & Yan Zhang & Yongyao Wei, 2021. "The adoption of ELM to the prediction of soil liquefaction based on CPT," 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. 107(1), pages 539-549, May.
    3. Xiwen Zhang & Xiaowei Tang & Ryosuke Uzuoka, 2015. "Numerical simulation of 3D liquefaction disasters using an automatic time stepping method," 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. 77(2), pages 1275-1287, June.
    4. Xuesong Zhang & Biao He & Mohanad Muayad Sabri Sabri & Mohammed Al-Bahrani & Dmitrii Vladimirovich Ulrikh, 2022. "Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    5. 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.
    6. Xinhua Xue & Xingguo Yang, 2014. "Seismic liquefaction potential assessed by fuzzy comprehensive evaluation method," 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. 71(3), pages 2101-2112, April.

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