A Physics-Data Hybrid Model for Predicting Earth Pressure Evolution in Buried Horizontal Cylindrical Tanks

Abstract Buried horizontal cylindrical tanks are susceptible to stress instabilities, such as shell buckling and weld fatigue, under nonuniform ground settlement. Classical Terzaghi-based earth pressure theories simplify key parameters into static constants, rendering them inadequate for capturing the dynamic soil-tank interaction and parameter evolution induced by settlement. To address this limitation, a physics-data hybrid model (PDHM) is developed by embedding a genetic programming (GP) module into a three-dimensional (3D) analytical earth pressure framework for medium-dense sand conditions. This approach leverages the symbolic regression capability of GP to derive explicit nonlinear expressions for the dynamic load-bearing width B and lateral pressure coefficient K , thereby unifying data-driven adaptability with physical constraints. To ensure transparency and reproducibility, a standalone GP model is constructed as a purely data-driven baseline, utilizing identical feature inputs, preprocessing procedures, and training-testing protocols. Results demonstrate that the PDHM reduces prediction errors by 82.1% to 96.1% compared to the baseline, with decreases in RMSE and MAPE of 51.89% and 92.34%, respectively. Consequently, the PDHM offers an interpretable, generalizable, and computationally efficient tool for analyzing stress evolution and supporting the safety assessment of buried horizontal cylindrical tanks in medium-dense sand conditions.