Digital twin implementation by multisensors data for smart evaluation of transport infrastructure

Transport assets are progressively more exposed to several issues, including climate change, and vulnerability to natural hazards such as subsidence, landslides, and seismic phenomena, to mention a few. They can affect the structural integrity of infrastructures causing damages and deteriorations. They support the prioritization of the most effective countermeasures. We propose a method based on Data-Fusion approach, merging multi-source and multi-scale information acquired by multi-sensors systems. It enhances the interpretation of fused data under a holistic framework. For this purpose, processing methods of spaceborne data, such as Multi-temporal SAR Interferometry, are used to detect displacements of transport assets, with millimetre accuracy. This technique is complemented by more detailed data detected by UAVs and Ground Based Non-Destructive Testing Methods, including laser scanner surveys and by prospecting data collected by Ground Penetrating Radar. All these data are simultaneously fused and digitally integrated into a comprehensive and high-resolution Digital Twin, providing a useful tool to support operators and public authorities to identify and prioritize maintenance and rehabilitation actions. According to this holistic framework, the introduction of the Digital Twin, which replicates a real asset in a virtual or augmented reality environment, represents a relevant and strategic tool or facility to tackle effectively and efficiently the maintenance issues, enhancing resilience and operating life of the infrastructures. This study shows an experimental application of the proposed methodology by fusing spatial, aerial, ground-based, and geophysical surveys using also augmented reality tools, to have the best interface for visualizing the twin in the real environment. This allows smart evaluation and monitoring which has resoluted, efficient, holistic and up-to-date information.