Earthquake induced liquefaction hazard analysis for Chittagong City using machine learning

Abstract

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 ([Formula: see text]) 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 [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

Keywords

• Liquefaction potential index (LPI)
• factor of safety (FS)
• artificial neural network (ANN)
• limit state function (LSF)
• surface geology
• Chittagong City