Sunil Thakur

Machine learning prediction of strain dependent soil damping improves seismic analysis of bridge foundations

Conventional empirical soil damping models often neglect strain-dependent, site-specific variability in heterogeneous alluvial deposits (sands, silts, gravels), leading to uncertainty in seismic bridge foundation design. This study develops a Random Forest (RF) model to predict strain-dependent damping from routine geo...

Integrating sugarcane bagasse ash into geopolymer concrete: strength, durability, and predictive modelling with ANN