Physics-informed neural networks for predicting ultimate bearing capacity of layered soils under complex loading conditions

Towards physics-consistent machine learning models: A geomechanics-based artificial neural network for high-cyclic soil response

It is proposed to design a Geomechanics-based Artificial Neural Network (GANN) that bypasses the need for calibration parameters and instead uses common soil descriptors, ensuring that the predicted strain evolution remains consistent with soil mechanics principles.

Bayesian-Optimized Ensemble Machine Learning for Predicting Settlement of Cohesionless Soil Under Loads

Geotechnical Evaluation of Gradient-Based Neural Networks for Factor of Safety Prediction in Homogeneous Soil Slopes Under Hydraulic Variability

Slope stability assessment remains a fundamental challenge in geotechnical engineering because of the complex nonlinear interactions among soil properties, slope geometry, and hydraulic conditions, particularly variations in pore-water pressure. This study investigates the reliability of Artificial Neural Network–Multi...

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

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 inte...

Integrating deep neural network with elastoplastic analysis: a hybrid approach for slope stability analysis

Conventional data-driven methods for slope stability analysis often exhibit an over-reliance on data while neglecting underlying physical principles. To address this limitation, this study proposes a physics-informed neural network (PINN) framework that integrates a neural network surrogate with the elastoplastic defor...

A Physics-informed Neural Network Approach for Robust Buckling Load Prediction and Reliability-Based Design of Thin Truncated Conical Shells

Thin-walled truncated conical shells are widely used in aerospace, marine, offshore, and lightweight infrastructure systems due to their high strength-to-weight ratio and geometric efficiency. Their buckling resistance under axial compression, however, is highly sensitive to geometric imperfections, manufacturing toler...