Energy Evaluation of Triggering Soil Liquefaction Based on the Response Surface Method

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.