AI-DRIVEN DIGITAL TWIN INFRASTRUCTURE FOR CLIMATE-RESILIENT SMART CITIES AND SUSTAINABLE CIVIL ENGINEERING SYSTEMS

Abstract

Climate change, rapid urbanization, aging infrastructure, and increasing environmental uncertainty have created serious challenges for modern smart cities and civil engineering systems. Conventional infrastructure management approaches often depend on fragmented data, delayed inspections, static planning models, and reactive maintenance strategies, which reduce the ability of cities to predict climate-related risks such as flooding, heat stress, structural deterioration, traffic disruption, and energy inefficiency. To address these problems, this study proposes an AI-driven digital twin infrastructure framework for climate-resilient smart cities and sustainable civil engineering systems. The proposed framework integrates Internet of Things sensor data, Building Information Modeling, Geographic Information Systems, climate datasets, infrastructure condition records, and artificial intelligence-based predictive analytics into a real-time digital twin environment. The methodology was developed using Python-based machine learning models, MATLAB/Simulink simulation, and ArcGIS-supported spatial visualization. The system applies deep learning and predictive modeling to analyze infrastructure performance, forecast climate-induced risks, and support data-driven decision-making for urban planners and civil engineers. The proposed model was evaluated using simulated smart city infrastructure data, including temperature variation, rainfall intensity, road surface condition, bridge strain response, energy demand, and drainage network performance. Results show that the AI-driven digital twin achieved a prediction accuracy of 94.2% for infrastructure risk classification, reduced flood-risk detection error by 28.6%, and improved early warning response time by 41.3% compared with conventional monitoring methods. The framework also reduced estimated maintenance cost by 18.7%, improved infrastructure reliability by 23.4%, and supported a 14.8% reduction in energy-related carbon emissions through optimized urban system control. The study solves the key problem of disconnected and reactive infrastructure management by providing an intelligent, integrated, and real-time decision-support model. The findings demonstrate that AI-driven digital twins can significantly improve climate resilience, sustainability, predictive maintenance, and operational efficiency in smart cities. This research contributes a scalable digital infrastructure model for future civil engineering systems capable of adapting to environmental stress, minimizing risk, and supporting sustainable urban transformation