Evaluation of liquefaction induced lateral displacements using genetic programming |
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| Institution: | 1. Department of Engineering, University of Exeter, Exeter EX4 4QF, Devon, UK;2. Department of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;1. School of Civil Engineering, State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China;2. School of Civil Engineering, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;3. Ertan Hydropower Development Company Limited, Chengdu 610051, China;1. School of Engineering, The University of Warwick, Coventry CV4 7AL, UK;2. Department of Civil Engineering, The University of Nottingham, Nottingham NG7 2RD, UK;3. Computational Geomechanics Division, Norwegian Geotechnical Institute, Oslo No-0806, Norway;1. Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK;2. Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia;1. Professor, Dept. of Civil and Environmental Eng., Univ. of Washington, Seattle, USA;2. Staff Engineer, Shannon & Wilson, Seattle, USA;3. Res. Assistant., Dept. of Civil and Environmental Eng., Univ. of Washington, Seattle, USA |
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| Abstract: | Determination of liquefaction induced lateral displacements during earthquake is a complex geotechnical engineering problem due to the complex and heterogeneous nature of the soils and the participation of a large number of factors involved. In this paper, a new approach is presented, based on genetic programming (GP), for determination of liquefaction induced lateral spreading. The GP models are trained and validated using a database of SPT-based case histories. Separate models are presented to estimate lateral displacements for free face and for gently sloping ground conditions. It is shown that the GP models are able to learn, with a very high accuracy, the complex relationship between lateral spreading and its contributing factors in the form of a function. The attained function can then be used to generalize the learning to predict liquefaction induced lateral spreading for new cases not used in the construction of the model. The results of the developed GP models are compared with those of a commonly used multi linear regression (MLR) model and the advantages of the proposed GP model over the conventional method are highlighted. |
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