An Explainable AI and Digital Twin Framework for Predictive Maintenance of Urban Infrastructure

Abstract

The technological change and digital innovation are vital facilitators of the realization of sustainable development in the fast urbanizing context. The maintenance practices are dangerous in the traditional sense as they are mostly reactive and based on high resources due to aging infrastructure, climate changes, and growing demand in the services. The urban infrastructure systems are undergoing a transformation with the emergence of new technologies, including the Internet of Things (IoT), Artificial Intelligence (AI), Explainable AI (XAI), and Digital Twins. In this paper, the XAI-DTMP – a digitally transformative predictive maintenance framework that combines multi-sensor IoT data, explainable machine learning, and Digital Twin technology to provide transparent, intelligent, and sustainable infrastructure management – is proposed. Isolation Forest based anomaly detection is used in this framework to assess condition in real-time and Temporal Convolutional Network (TCN) to predict long-term degradation and Remaining Useful Life (RUL). The explainability is assured with the help of SHAP and LIME methods, and AI-based decisions can be interpreted with confidence by the stakeholders. A Digital Twin layer will be a reflection of physical infrastructure assets and allow simulating degradation scenarios and maintenance strategies before their implementation. The accuracy of prediction is proven to be reliable, better to interpret, and governable through experimental evaluation. The suggested framework resonates with SDG 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities) well, which helps to build resilient and more affordable, but futurist urban infrastructure systems.