A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

Abstract

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.

Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm

“…The use of ANN-based techniques has been widely adopted. Hybridization with different nature-based optimizations has also been practiced, resulting in optimal solutions (Ghani & Kumari 2022; Umar et al, 2021) .Based on the literature, several optimization frameworks, including metaheuristics algorithms, have been employed in regression-based problems. They are effective in estimating with a standalone approach, but the literature indicates they are highly e cient when used with ensemble or ANN models.…”