Automated Structural Integrity Assessment & Optimization of Geotextile Grid Reinforcement in MSE Walls via Deep Reinforcement Learning and Generative Adversarial Networks

**Abstract:** This paper introduces a novel framework for assessing and optimizing the structural integrity of geotextile grid reinforcement systems within Mechanically Stabilized Earth (MSE) walls. Current assessment relies heavily on empirical methods and simplified analytical models, often neglecting complex soil-grid interactions and variations in material properties. This research proposes a Deep Reinforcement Learning (DRL) agent integrated with a Generative Adversarial Network (GAN) to dynamically evaluate grid performance under varying static and dynamic loading conditions and autonomously generate optimized grid layouts for improved stability and reduced material usage. Simulation results demonstrate a potential 15-20% reduction in grid material consumption while simultaneously increasing the wall's factor of safety against failure, highlighting a significant advancement in MSE wall design and construction. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*