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 ( S p ), 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 S p 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 (R 2 = 0.9873), while the XGBoost model exhibited excellent performance in predicting V (R 2 = 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 S p with only a 1.82% increase in V . The established predictive formula for SWL liner S p increments (R 2 = 0.9874) provides an efficient tool for structural optimization, offering important technical support and a theoretical foundation for sustainable urban pipeline infrastructure management.