Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

This research harnessed the potential of artificial neural networks (ANNs) to anticipate the characteristics of bricks derived from recycled soil. The study encompassed the production of bricks employing varying proportions of recycled soil, spanning from 0 to 50% with incremental steps of 10%. Subsequently, these bricks underwent exposure to both controlled and uncontrolled temperature conditions. Post-production, a curing process was initiated, followed by subjecting the bricks to comprehensive testing to evaluate their water absorption and compressive strength, a week after curing. Two distinct ANN models were accurately constructed and employed to predict the attributes of bricks post-burning under controlled and uncontrolled temperature settings. To gauge the accuracy and efficacy, the trained ANN model were assessed by analysing statistically, examining training graphs, and applying k-fold cross-validation techniques. The results showcased the capability of the ANN models in generating precise forecasts for water absorption and compressive strength values. Impressively, the ANN model exhibited high regression values of 0.99621 for bricks subjected to controlled temperatures and 0.99874 for those exposed to uncontrolled temperatures, underscoring the robustness and accuracy of the predictions.

Earthquake damage in the twenty-first century has piqued the interest of numerous scholars and engineers working on enhancing the seismic safety of heavily populated regions. Prayagraj is one of India's fastest-growing cities, is located on the banks of the Ganga and Yamuna rivers. The river Ganga transports maximum part of alluvial soil, which is an essential factor in determining soil liquefaction potential. Some of the other factors which also affects the liquefaction potential are local site conditions, and water table. The current study focuses on liquefaction potential of soil as determined by semi-empirical approaches suggested by Modified Seed method. The developed soft computing models' assessment were compared with evaluated Liquefaction Potential which significantly matches with output of models. Therefore ANN & ANFIS models can be used for predicting Liquefaction potential of soils. The Seed's and Idriss approach are utilized for evaluating soil liquefaction potential since it has a higher estimating capacity than other standard methods. Bore log data from SPT tests done at locations were used to evaluate the liquefaction potential. For training ANN and ANFIS models, 100 datasets from thirty-three bore wells up to a depth of ten meter were gathered, while 26 datasets were retained for verifying the models. The projected findings of ANN and ANFIS models when compared to the Seed's and Idriss technique suggest that training ANN and ANFIS models were capable of accurately forecasting liquefaction potential.