Abstract
Liquefaction is one of the most destructive phenomena caused by earthquakes, and it has been studied regarding the issues of risk assessment and hazard analysis. The strain energy approach is a common method to evaluate liquefaction triggering. In this study, the response surface method (RSM) is applied as a novel way to develop six new strain energy models in order to estimate the capacity energy required for triggering liquefaction (W), based on laboratory test results collected from the literature. Three well-known design of experiments (DOEs) are used to build these models and evaluate their influence on the developed equations. Furthermore, two groups of artificial neural network (ANN) and RSM models are derived to investigate the complicated influence of fine content (FC). The first group of models is based on a database without limitation on the range of input parameters, and the second group is based on a database with FC lower than the critical value of 28%. The capability and accuracy of the six presented models are compared with four existing models in the literature by using additional new laboratory test results (i.e., 20 samples). The results indicate the superior performance of the presented RSM models and particularly the second group of the models based on a limited value of FC.
Highlights
Liquefaction is one of the most destructive effects of earthquakes
To demonstrate the capability and accuracy of the response surface method (RSM) equations presented in this study, their prediction values are compared with the genetic programming (GP), linear genetic programming (LGP), multi expression programming (MEP) [28], and multivariate adaptive regression splines (MARS) [29] models, which are presented in the Appendix A
Industry, to medicine, science, energy according to liquefaction the literature it has to equations estimate and the capacity of soil (W).review, While the RSMnot hasbeen been used used in BB28 shows the highest accuracy in predicting W
Summary
Liquefaction is one of the most destructive effects of earthquakes. This phenomenon, which has occurred several times during recent earthquakes, is caused by seismic shear waves that propagate upward to the surface layers, increasing pore water pressure in saturated, relatively loose or loose sandy deposits. A number of approaches and models have been presented to assess the liquefaction potential of soils Some of these approaches follow a stress-based procedure, on the basis of the equation presented by Seed and Idriss [1]. Baziar et al [26] collected a large number of datasets with a wide range of test results, including six parameters, and they divided them randomly into a testing and training phase in order to present artificial neural networks (ANNs). To increase the accuracy and capability of the ANN models, the dataset is divided into three groups by considering the statistical aspects of parameters with similar mean as well as mean coefficient of variation (COV) values, instead of random division.
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