AI-driven prediction of the impermeable boundary in karst rock mass for optimized anti-seepage curtain design

Introduction Accurately determining the bottom boundary of anti-seepage curtains is critical for ensuring the integrity and performance of this key engineered composite structure in karst reservoirs. This study leverages artificial intelligence (AI) to address this materials design challenge. Methods We developed hybrid models by integrating a Genetic Algorithm (GA) with Backpropagation (BP), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) algorithms. These models were trained and validated using a comprehensive dataset from the Dehou Reservoir, incorporating critical material and hydrogeological properties of the karst rock mass. A comparative analysis with Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) was also conducted. Results The results demonstrated that GA optimization significantly enhanced predictive performance. The GA-BP model achieved superior accuracy (R 2 = 0.98, MSE = 7.58). Furthermore, from an engineering safety perspective, the GA-SVM model provided the most reliable recommendations, frequently yielding conservative depth estimates. The comparative analysis validated the competitive advantage of the proposed hybrid models over other benchmark algorithms. Discussion This research underscores the potential of AI-driven approaches for the performance prediction and rational design of engineered geomaterial systems. The findings offer a powerful tool for infrastructure projects in complex geological settings, balancing predictive accuracy with critical engineering safety considerations.