Explainable AI (XAI) Digital Twin Framework for Predictive Failure Detection and Lifecycle Management of U.S. Drinking Water Treatment Infrastructure

Abstract

This study examined the problem of opaque, reactive, and fragmented condition monitoring in U.S. drinking water treatment infrastructure, where pumps, membranes, filtration units, disinfection systems, and distribution assets are frequently maintained on fixed schedules or after failure, and where data-driven predictive models, when used at all, often function as black boxes that operators cannot interpret or trust. The purpose of the study was to assess how an Explainable AI (XAI) Digital Twin Framework, integrating physics-informed digital twin modeling with interpretable machine-learning methods, influences predictive failure detection and lifecycle management performance for drinking water treatment assets. A quantitative, cross-sectional, case-based design was adopted, and data were collected through a structured five-point Likert-scale questionnaire from 138 valid respondents out of 155 distributed questionnaires, representing an 89.0% valid response rate. The sample included water treatment operators, control and instrumentation engineers, data scientists and machine-learning engineers, asset-management and maintenance planners, and utility managers and regulatory staff, with 65.9% directly involved in condition-monitoring or asset-management activities. The key variables were digital twin modeling and integration, XAI model interpretability, sensor data quality and validation, predictive failure detection accuracy, operator and institutional engagement, framework design quality, and lifecycle management performance. The analysis plan included descriptive statistics, reliability testing using Cronbach's alpha, Pearson correlation, regression modeling, a framework maturity index, and an asset risk-control priority matrix. The headline findings showed that all major constructs were rated high, with lifecycle management performance recording the highest mean score of 4.19, followed by digital twin modeling and integration at 4.14 and framework design quality at 4.10. Reliability was acceptable to excellent, with Cronbach's alpha values ranging from 0.80 to 0.93. Correlation results showed significant positive relationships, including r = 0.77 between framework design quality and lifecycle management performance. Regression results confirmed that the model explained 70.2% of the variance in lifecycle management performance, R² = 0.702, adjusted R² = 0.689, F(6,131) = 51.36, p < 0.001. Framework design quality was the strongest predictor, β = 0.33, followed by digital twin modeling and integration, β = 0.26, and XAI model interpretability, β = 0.23. The findings imply that U.S. water utilities should strengthen physics-informed digital twin modeling, model explainability, sensor validation, and operator engagement to improve the reliability and transparency of predictive asset management.