In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson’s correlation coefficient > 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE < 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment.

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials.

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.

Real-time, spatially resolved moisture measurement is essential for electrification of industrial drying, yet the gravimetric gold standard is destructive, discontinuous, and blind. This study evaluates short-wave infrared (SWIR) multispectral imaging as a non-contact moisture sensor for microwave (MW)-assisted drying, using an 11-band camera (730–1650 nm) and unified processing pipeline. Two optically contrasting materials were studied: green ceramic tiles, where moisture determines product quality, and calcium carbonate (CaCO3) powder, where moisture is a process-control variable. Nine tiles were dried at 750, 1000, and 1500 W per generator, while CaCO3 was dried at 2000 W, with gravimetric dry-basis moisture content (MC) as reference. The pipeline located analysis regions using segmentation for tiles and template cross-correlation for powder (ρ ≥ 0.999); four-band measurements required ≈0.21 s. Seven per-pixel indices were ranked by MC correlation, cross-sample stability, and relative and moisture-normalized sensitivity. SWIR detected MW-induced drying in all cases, with correct index orientation across all tested cells. The optimal index was material-dependent: the 1450/1050 nm ratio performed best for tiles (R² = 0.82, cross-tile CoV = 0.58%, sensitivity = 0.011 index units per percentage point MC, +5.5% contrast), while the 1550/1050 nm ratio was optimal for CaCO3 (R² = 0.92 longest session, 0.80 pooled, sensitivity = 0.033 index pp⁻¹, +42.0% contrast). White-panel calibration removed camera and illumination responses. Spatial CoV indicated uniform MW heating. Fine CaCO3 trays met the 10% requirement, while coarse trays reached 13%. The sensor saturates below ≈0.5% MC, making it suitable for in-line screening rather than precise ceramic endpoint control.