Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City, Bangladesh

Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the loss of life and property, hazard analysis a prerequisite seismic risk management. Liquefaction potential index (LPI) widely used to determine severity quantitatively spatially. LPI estimated from factor safety that ratio cyclic (CRR) stress calculated applying simplified procedure. Artificial neural network (ANN) algorithm has been in present study predict CRR directly normalized standard penetration test blow count (SPT-N) near-surface shear wave velocity (Vs) data Dhaka City. It observed ANN models have generated accurate data. Three zones are identified City on basis cumulative frequency (CF) distribution each geological unit. The maps prepared city using its zone. CF SPT-N based indicates 15%, 53%, 69% areas, whereas Vs 11%, 48%, 62% areas Zone 1, 2, 3, respectively, show surface manifestation earthquake moment magnitude, Mw 7.5 with peak horizontal ground acceleration 0.15 g.