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In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil.This paper addresses Emotional Neural Network(ENN), Cultural Algorithm(CA) and biogeography optimized(BBO) based adaptive neuro-fuzzy inference system (ANFIS) for liquefaction study.The performance of neural emotional network and cultural algorithm has been also discussed. BBO-ANFIS combines the biogeography features to optimize the ANFIS parameters to achieve higher prediction accuracy.The model is trained with case history of liquefaction databases.Two parameters are used as input such as the cyclic stress ratio and standard penetration test (SPT) value.The performance of these models was assessed using different indexes i.e. sensitivity, specificity, FNR, FPR and accuracy rate.The performance of all models is compared.Among the models, the BBO-ANFIS model has been outperformed and can be adopted as new reliable technique for liquefaction study.

There are many deterministic and probabilistic liquefaction assessment measures to classify if soil liquefaction will take place or not. Different approaches give dissimilar safety factor and liquefaction probabilities. So, reliability analysis is required to deal with these different uncertainties. This paper describes a reliability technique for predicting the seismic liquefaction potential of soils of some areas at Bihar State. Here a reliability approach has been presented in order to find the probability of liquefaction. The proposed approach is formulated on the basis of the results of reliability analyses of 234 field data. Using a deterministic simplified Idriss and Boulanger method, factor of safety of soil has been accessed. The reliability index as well as corresponding probability of liquefaction has been determined based on a First Order Second Moment (FOSM) method. The developed method can be used as a robust tool for engineers concerned in the estimation of liquefaction potential.