Multi-Objective Optimization and ML-Driven Sustainability Mechanical Performance Enhancement of Trenchless Spiral Wound Lining Rehabilitation

Addressing safety, environmental, and economic challenges associated with aging urban underground pipeline infrastructure, this study develops an integrated multi-objective optimization framework for sustainable trenchless spiral wound lining (SWL) rehabilitation. The framework integrates machine learning (ML)-driven predictive modeling with structural performance enhancement technologies to advance urban infrastructure management. To enhance the mechanical performance of SWL liners, a multi-objective structural optimization was conducted to systematically examine the impact of strip profile cross-sectional parameters on ring stiffness (Sp), material consumption (V), and total strip profile height (H). ANSYS finite element analysis was employed to conduct numerical simulations of ring stiffness tests for various liner structures, and Sp was calculated based on the resultant loading force (F). Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were evaluated for predicting F and V. The results demonstrated that the SVR model achieved high accuracy in predicting F (R2 = 0.9873), while the XGBoost model exhibited excellent performance in predicting V (R2 = 0.97). Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), multi-objective optimization of the SWL liner was performed, yielding an optimized liner that showed a 24.46% improvement in Sp with only a 1.82% increase in V. The established predictive formula for SWL liner Sp increments (R2 = 0.9874) provides an efficient tool for structural optimization, offering important technical support and a theoretical foundation for sustainable urban pipeline infrastructure management.