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Application of Soft Computing in Geotechnical Earthquake Engineering

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Latest Developments in Geotechnical Earthquake Engineering and Soil Dynamics

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Abstract

Engineers use various soft computing techniques for solving different problems in geotechnical earthquake engineering. This paper will investigate the application of different soft computing techniques {artificial neural network (ANN), support vector machine (SVM), least square support vector machine (LSSVM), genetic programing (GP), relevance vector machine (RVM), multivariate adaptive regression spline (MARS), extreme learning machine (ELM), adaptive neurofuzzy inference system (ANFIS), minimax probability machine regression (MPMR), Gaussian process regression (GPR), adaptive neurofuzzy inference system (ANFIS)} in different fields of geotechnical earthquake engineering such as liquefaction, lateral spreading, seismic slope stability and reliability. The advantages of different soft computing techniques will be described.

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  1. Department of Civil Engineering, NIT Patna, Patna, Bihar, 800005, India

    Pijush Samui

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Editors and Affiliations

  1. Indian Institute of Technology Guwahati, Guwahati, Assam, India

    T.G. Sitharam

  2. Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Ravi Jakka

  3. Department of Civil Engineering, National Institute of Technology Karnataka, Mangalore, Karnataka, India

    Sreevalsa Kolathayar

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Samui, P. (2021). Application of Soft Computing in Geotechnical Earthquake Engineering. In: Sitharam, T., Jakka, R., Kolathayar, S. (eds) Latest Developments in Geotechnical Earthquake Engineering and Soil Dynamics. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-1468-2_21

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