A Multisensor-Based Measurement Procedure for Seismic Damage Identification in Buildings

Effectively and precisely identifying damages after a seismic event is fundamental to ensure timely intervention and optimize building life cycle. The combination of multi-sensor inspection and artificial intelligence technologies is powerful in this context. This work presents the results of the development of a multi-sensor measurement procedure relying on drone-embedded visible and thermal cameras and a terrestrial laser scanner to acquire data on a masonry church damaged by an earthquake. These data were carefully aligned into a unified reference system and enabled the reconstruction of high-resolution 3D models of the building, from which multichannel orthophotos were extracted, with visible, thermal, and depth data, to identify surface lesions. The analysis procedure involved three main stages: (i) pre-processing using principal component analysis (PCA) to improve feature separability and reduce redundancy between channels; (ii) lesion detection using a deep learning U-Net segmentation model trained to identify surface cracks; and (iii) morphological post-processing to refine the predicted masks and eliminate (or reduce) false positives predictions. The combination of multimodal data improves lesion detection, highlighting cracks that are not immediately visible, particularly when thermal gradients or geometric discontinuities provided complementary evidence. The results are promising and demonstrate both the feasibility of lesion identification and the validity of the proposed approach in the context of structural health monitoring. The use of sensors that can acquire data remotely and without contact offers significant advantages in terms of safety in a post-earthquake context.