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Purpose – In this paper, the peak kinetic energy density (KED) of soil particles during earthquake excitation is used as an intensity measure for the evaluation of liquefaction potential under field conditions. The paper seeks to discuss this measure. Design/methodology/approach – Using centrifuge tests data, it is shown that seismic pore water pressure buildup is proportional to cumulative KED at a particular soil depth. Linear relationships are found between cumulative kinetic energy and corresponding cumulative strain energy. To consider the effect of soil amplification, several equivalent linear ground response analyses are performed and the results are used to derive an equation for depth reduction factor of peak kinetic energy density. Two separate databases of liquefaction case histories are used in order to validate the proposed model. The performance of the proposed model is compared with a number of commonly used shear stress-based liquefaction assessment methods. Finally, the logistic regression method is employed to obtain probabilistic boundary curves based on the present model. Parametric study of the proposed probabilistic model is carried out to verify its agreement with the previous methods. Findings – It has been shown that the kinetic energy model works satisfactorily in classifying liquefied and non-liquefied cases compared with the existing recommendations of shear stress-based criterion. The results of the probabilistic kinetic energy model are in good agreement with those of previous studies and show a reasonable trend with respect to the variations of fines content and effective overburden pressure. The proposed model can be as used an alternative approach for assessment of liquefaction potential. Originality/value – These findings make a sound basis for the development of a kinetic energy-based method for assessment of liquefaction potential. [ABSTRACT FROM AUTHOR]

Abstract: Prediction of liquefaction and the resulting lateral displacement is a complex engineering problem due to heterogeneous nature of soils and participation of a large number of factors involved. In this paper new models are developed, based on evolutionary polynomial regression (EPR), for assessment of liquefaction potential and lateral spreading. The models developed for liquefaction and lateral spreading are compared to those obtained from neural network and linear regression based techniques. It is shown that the developed models are able to learn the complex relationship between either of these problems and their contributing factors in the form of a function with high level of accuracy (mostly in excess of 90%). The results of the EPR model developed for the liquefaction determination are used to find a novel 3-D boundary surface that discriminates between the cases of occurrence and non-occurrence of liquefaction. The developed boundary surface is employed to calculate the factor of safety against liquefaction occurrence. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a new approach is presented, based on evolutionary polynomial regression (EPR), for determination of liquefaction potential of sands. EPR models are developed and validated using a database of 170 liquefaction and non-liquefaction field case histories for sandy soils based on CPT results. Three models are presented to relate liquefaction potential to soil geometric and geotechnical parameters as well as earthquake characteristics. It is shown that the EPR model is able to learn, with a very high accuracy, the complex relationship between liquefaction and its contributing factors in the form of a function. The attained function can then be used to generalize the learning to predict liquefaction potential for new cases not used in the construction of the model. The results of the developed EPR models are compared with a conventional model as well as a number of neural network-based models. It is shown that the proposed EPR model provides more accurate results than the conventional model and the accuracy of the EPR results is better than or at least comparable to that of the neural network-based models proposed in the literature. The advantages of the proposed EPR model over the conventional and neural network-based models are highlighted. [Copyright &y& Elsevier]