Experimental characterization and machine learning modeling of leakage-induced soil fluidization in water distribution systems.

Leakage in water distribution systems poses a global challenge, not only due to resource loss but also through soil erosion and sinkhole formation, which risk infrastructure collapse. This study investigates the mechanisms of soil fluidization, a process in which pressurized pipeline leakage generates turbulent water-soil mixtures, forming expanding fluidized zones. Experimental tests using a custom leakage simulation apparatus, combined with dimensional analysis, were conducted to identify key factors influencing fluidization dynamics. Empirical equations were developed to predict fluidized zone height and area (R² = 0.753-0.915) for both upward and downward leakages. These models were validated against 150 experimental datasets from current and prior studies, covering a wide range of leakage rates and soil types. Three ensemble machine learning models-Random Forest, XGBoost, and a Stacking model integrating support vector regression, multilayer perceptron, and linear regression-were employed to enhance predictive accuracy and stability. The results of evaluation metrics (R2, RMSE and correlation coefficient) showed that although XGBoost outperformed other models regarding accuracy (with R2 = 0.91 in test splits), this model exhibited the lowest stability in predicting dimensionless height and area of fluidized zone (with ΔR² = 0.07-0.08). The Random Forest model had the lowest accuracy (R2 = 0.907-0.912 in train phase) though the most capability in generalization through the minimum differences between train and test splits (with ΔR² = 0.02-0.04). Regarding Stacking model, both accuracy and stability maintained in balanced conditions with moderate performance. The conclusion from findings of all evaluation metrics were the same as deterministic coefficient. The equations derived from dimensional analysis, especially equations for downward leakage direction, also showed comparable performance with the most accurate ensemble model like XGBoost (with R² up to 0.915). Temporal analysis revealed the progression of fluidization through seven distinct stages over approximately 75 seconds. Fluidization initiated at a specific discharge rate, beginning with the formation of a 2 cm cavity and culminating in vortex bifurcation and dimensional stabilization. Critical pore pressure thresholds, observed around 20 seconds, induced suspended particle states and fountain flow, while vertical cavity growth predominated between 25 and 65 seconds. Sensitivity analysis highlighted the densimetric Froude number and soil uniformity as dominant factors, with their omission reducing model R² by up to 84.1% and 71.3%, respectively. In contrast, the particle size-to-leak area ratio exhibited marginal effect (<10% reduction). These findings provide critical insights into sinkhole risk mitigation, offering practical tools for infrastructure vulnerability assessment.