Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach

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@Article{Baziar2011,

• author = "Mohammad H. Baziar and Yaser Jafarian and Habib Shahnazari and Vahid Movahed and Mohammad Amin Tutunchian",
• title = "Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach",
• journal = "Computer \& Geosciences",
• volume = "37",
• number = "11",
• pages = "1883--1893",
• year = "2011",
• ISSN = "0098-3004",
• DOI = "doi:10.1016/j.cageo.2011.04.008",
• URL = "http://www.sciencedirect.com/science/article/B6V7D-52R9DF5-2/2/08fa46566f649fc2348af34aa83ebbb2",
• keywords = "genetic algorithms, genetic programming, Liquefaction, Capacity energy, Sand, Silt, Wildlife",
• abstract = "Liquefaction is a catastrophic type of ground failure, which usually occurs in loose saturated soil deposits under earthquake excitations. A new predictive model is presented in this study to estimate the amount of strain energy density, which is required for the liquefaction triggering of sand-silt mixtures. A wide-ranging database containing the results of cyclic tests on sand-silt mixtures was first gathered from previously published studies. Input variables of the model were chosen from the available understandings evolved from the previous studies on the strain energy-based liquefaction potential assessment. In order to avoid over training, two sets of validation data were employed and a particular monitoring was made on the behaviour of the evolved models. Results of a comprehensive parametric study on the proposed model are in accord with the previously published experimental observations. Accordingly, the amount of strain energy required for liquefaction onset increases with increase in initial effective overburden pressure, relative density, and mean grain size. The effect of nonplastic fines on strain energy-based liquefaction resistance shows a more complicated behavior. Accordingly, liquefaction resistance increases with increase in fines up to about 10-15percent and then starts to decline for a higher increase in fines content. Further verifications of the model were carried out using the valuable results of some down hole array data as well as centrifuge model tests. These verifications confirm that the proposed model, which was derived from laboratory data, can be successfully used under field conditions.",

}

Genetic Programming entries for Mohammad Hassan Baziar Yaser Jafarian Habib Shahnazari Vahid Movahed Mohammad Amin Tutunchian

Citations