Probabilistic estimation of sand liquefaction based on neural network model of radial basis function

Based on the 344 liquefaction data of the twenty-five strong earthquakes in the world,through training and testing the neural network model of Radial Basis Function(RBF),the nonlinear relation between corrected blow count N1 of standard penetration test and cyclic resistance ratio CRR of saturated sand was analyzed,and empirical equation CRRcri of liquefaction limit state curve or critical cyclic resistance ratio curve of saturated sand was also constructed.By statistic analysis,probability density functions of liquefaction and non-liquefaction cases as well as empirical equation between safety factor and liquefaction probability of saturated sands were given,then the empirical equation of cyclic resistance ratio CRR of saturated sands with different probability level was educed.When liquefaction probability level was equal to 50%,the present method was consistent to traditional deterministic method of sand liquefaction estimation,and its reliability for liquefaction and non-liquefaction estimation of saturated sands was 90.4% and 81.2%,respectively.The method made the sand liquefaction probabilistic estimation of engineering site as easy and convenient as traditional deterministic method of sand liquefaction estimation.So it was possible that the method of sand liquefaction probability estimation would be applied in the engineering practice and adopted in codes for seismic design.