Simulating the Deterioration Behavior of Tunnel Elements Using Amalgamation of Regression Trees and State-of-the-Art Metaheuristics

Tunnel infrastructures worldwide face escalating deterioration challenges due to aging materials, increasing load demands, and exposure to harsh environmental conditions. Accurately predicting the onset and progression of deterioration is paramount for ensuring structural safety, optimizing maintenance interventions, and prolonging service life. However, the complex interplay of environmental, material, and operational factors poses significant challenges to current predictive deterioration models. Additionally, they are constrained by small datasets and a narrow range of tunnel elements that limit their generalizability. This paper presents a novel hybrid metaheuristic-based regression tree (REGT) model designed to enhance the accuracy and robustness of tunnel deterioration predictions. Leveraging metaheuristic algorithms’ strengths, the developed method jointly optimizes critical regression tree hyperparameters and identifies the most relevant features for deterioration prediction. A comprehensive dataset encompassing material properties, environmental stressors, traffic loads, and historical condition assessments was compiled for model development. Comparative analyses against conventional regression trees, artificial neural networks, and support vector machines demonstrated that the hybrid model consistently outperformed baseline techniques regarding predictive accuracy and generalizability. While metaheuristic-based regression trees outperformed classic machine learning models, no single metaheuristic variant dominated all tunnel elements. Furthermore, the metaheuristic optimization framework mitigated overfitting and provided interpretable insights into the primary factors driving tunnel deterioration. Finally, the findings of this research highlight the potential of hybrid metaheuristic models as powerful tools for infrastructure management, offering actionable predictions that enable proactive maintenance strategies and resource optimization. This study contributes to advancing the field of predictive modeling in civil engineering, with significant implications for the sustainable management of tunnel infrastructure.