Digital Twin Framework for Real-Time Structural Health Monitoring and Flood Resilience in Urban Smart Infrastructure
Anahtar Kelimeler:
digital twin- flood resilience- smart city- urban infrastructureÖz
Urban infrastructure systems face compounding threats from structural deterioration and climate-driven flood events. This paper proposes an integrated Digital Twin (DT) framework that couples real-time Structural Health Monitoring (SHM) with urban flood resilience management for smart city environments. The framework leverages a heterogeneous Internet of Things (IoT) sensor network — comprising accelerometers, strain gauges, piezometers, and water-level sensors — feeding continuously into a cloud-hosted digital replica of urban built assets. A hybrid machine learning pipeline combining Long Short-Term Memory (LSTM) autoencoders for anomaly detection with ensemble Kalman filtering for real-time model state updating is presented. The flood resilience module integrates hydrodynamic simulation with sensor-driven early-warning logic, enabling proactive emergency response. Architecture components, data flows, computational requirements, and implementation challenges are systematically described. The proposed framework provides a replicable, scalable foundation for data-driven urban resilience, directly applicable to bridges, drainage networks, and critical urban assets. Key contributions include a unified ontology linking structural and hydrological digital twin layers, a latency-optimised edge-cloud processing pipeline, and a multi-criteria decision support module for infrastructure managers.
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