High-resolution response characteristics of water erosion-induced ground collapse evolution under varying hydraulic conditions in defective stormwater pipelines: A study integrating computational fluid dynamics–discrete element method and intelligent

Urbanization and increasingly extreme weather patterns have significantly heightened the strain on underground drainage systems. The cumulative impacts of prolonged pipeline aging and continuous ground loading further exacerbate leakage issues. These leaks trigger soil erosion and structural degradation under combined hydraulic pressure and surface loads, eventually leading to ground collapse. To address these challenges, this study developed an advanced intelligent calibration model that integrates the Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) with a Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) framework to analyze the complex interactions involved. The research established a comprehensive soil particle volume loss equation that incorporates key hydraulic and loading variables. Following the validation of CFD-DEM parameters, the complete erosion-to-collapse process was simulated, enabling the construction of an HGAPSO-based predictive model. The results demonstrated that increased flow velocity enhances pressure gradient forces and Magnus lift effects, thereby disrupting the soil's natural pressure arch mechanism and accelerating particle displacement and collapse. The proposed model achieved an accuracy of 0.87 and a correlation coefficient of 0.931, confirming its robustness and predictive reliability. These findings provide valuable insights into the mechanisms underlying ground collapse and offer practical implications for urban infrastructure maintenance and risk management strategies.