Deterministic, Reliability-Based and Risk-Based Design Optimization of Earth Slopes Using ANN-Based Surrogate Modeling
Abstract
Expanding road and railway networks in developing countries is essential for economic growth and reducing regional disparities. However, such projects often face significant geotechnical stability and risk management challenges. This study presents a three-phase optimization framework consisting of: (1) deterministic, probabilistic, and risk analyses; (2) Random Variable (RV)-based optimization; and (3) Random Field (RF)-based refinement. Soil variability is modeled using both RV and RF approaches. Two Artificial Neural Network (ANN) surrogate models are employed to improve computational efficiency by predicting failure probabilities and associated costs. Two application cases are presented to demonstrate the integrated framework for optimizing earth slopes in transportation infrastructure using Deterministic Design Optimization (DDO), Reliability-Based Design Optimization (RBDO), and Risk Optimization (RO). The application cases further demonstrate the model’s potential for early-stage roadway design under limited geotechnical data. Overall, the framework supports more reliable, cost-effective, and sustainable infrastructure development.