Multi-Modal Data Fusion in Digital Twin Environments for Structural Health Monitoring

Structural Health Monitoring (SHM) is increasingly essential for maintaining the safety and longevity of critical infrastructure. Traditional SHM systems, while effective in isolated applications, often lack the capacity to integrate heterogeneous sensor data or provide real-time predictive insights. Digital Twin (DT) technology offers a promising solution by enabling virtual representations of physical structures that evolve with incoming data. This study proposes a novel multi-modal data fusion framework within a DT environment to enhance the detection, localization, and quantification of structural damage. The framework integrates diverse sensor modalities—including vibration, acoustic emission, thermal imaging, and visual inspection—using advanced fusion algorithms to improve situational awareness and decision-making. A pilot implementation on a reinforced concrete bridge validates the model's ability to process real-time sensor inputs and simulate damage progression. Comparative analysis demonstrates that the multi-modal DT approach outperforms single-modality systems in accuracy, robustness, and response time. This research highlights the transformative potential of multi-modal fusion in DT-based SHM, offering new pathways for predictive maintenance, lifecycle optimization, and disaster resilience in civil engineering.

Keywords:

Digital Twin , Structural Health Monitoring (SHM) , Multi-Modal Data Fusion , Sensor Integration , Predictive Maintenance , Infrastructure Resilience , Real-Time Monitoring , Civil Engineering