- author = "Pradyut K. Muduli and Sarat K. Das",
- title = "Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model",
- journal = "Acta Geophysica",
- year = "2014",
- volume = "62",
- pages = "529--543",
- keywords = "genetic algorithms, genetic programming, liquefaction index,, standard penetration test, limits state function, artificial intelligence, multi-gene genetic programming, factor of safety",
-
DOI = "
doi:10.2478/s11600-013-0181-6",
- abstract = "This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19percent) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37percent) and at par with the available support vector machine (SVM) model (94.19percent) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84percent by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s ) against the liquefaction occurrence.",
-
notes = "Department of Civil Engineering, National Institute of
Technology, Rourkela,
India
https://www.springer.com/journal/11600/updates Official Journal of The Institute of Geophysics, PAS and Polish Academy of Sciences",
