Artificial Intelligence Based Predictive Modeling for Structural Health Monitoring and Failure Risk Assessment in Water and Sanitation Infrastructure

Abstract

Artificial intelligence–based predictive modeling has increasingly been adopted to enhance structural health monitoring and failure risk assessment in water and sanitation infrastructure systems characterized by aging assets, heterogeneous environmental exposure, and rare-event failure dynamics. This study developed and evaluated an integrated quantitative framework incorporating asset registry attributes, historical failure indicators, telemetry-derived operational metrics, inspection-based condition severity measures, and environmental exposure covariates. The analytical dataset included 1,248 infrastructure assets observed over a 10-year period across multiple operational districts. Hierarchical regression modeling demonstrated that baseline registry variables explained 28.6% of the variance in failure likelihood, while the inclusion of historical break frequency increased explanatory power to 52.4%. The addition of telemetry variability metrics and inspection-based severity indices further improved model performance, resulting in a final integrated model explaining 68.2% of outcome variance. Asset age and material class showed significant standardized effects of 0.31 and 0.27, respectively, while historical recurrence exhibited the strongest influence with a coefficient of 0.39. Telemetry-derived pressure variability improved predictive discrimination by 11.5% in monitored zones. Composite risk prioritization based on integrated modeling captured 74% of observed failures within the top 20% of ranked assets, representing a 31.4% improvement over age-based heuristics. Reliability analysis confirmed internal consistency of multi-item constructs, with Cronbach’s alpha values exceeding 0.80 for telemetry and inspection indices. The findings demonstrate that integrated AI-driven predictive modeling substantially enhances structural condition estimation accuracy and failure risk prioritization efficiency within complex water and sanitation infrastructure systems.