Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Civil steel structures and infrastructures, such as truss railway bridges, are often subject to potential damage, mainly due to fatigue phenomena and corrosion. Therefore, damage detection algorithms should be designed and appropriately implemented to increase their structural health. Today, the vast amount of information provided by data processing techniques and measurements coming from a monitoring system constitutes a possible tool for damage identification in terms of both detection and description. For this reason, the research activity aims to develop a methodology for a preliminary description of the damage in steel railway bridges induced by fatigue phenomena. The proposed approach is developed through an integration of global and local procedures. At the global scale, vibration-based procedures will be applied to improve a forecast numerical model and, subsequently, to identify the zones most involved in fatigue problems. At the local scale, careful and refined local identification will be pursued via image processing techniques whose evidence will be analyzed and described through nonlinear numerical models. A case study of a historical railway bridge in Spain will illustrate the methodology’s performance, potentiality, and critical issue.

Every structure undergoes a maintenance lifecycle, with increasing emphasis on structural health monitoring, defect diagnosis, and repair. As the demand for timely, effective, practical, and cost-efficient solutions grows, new techniques and processes are continually being developed. The traditionally paper-based methods of bridge inspection, defect diagnosis, and repair are evolving into digital processes. The overall efficiency targeted by this digital transformation process can be increased by 35–50 percent when criteria such as time, cost, accuracy and applicability are evaluated. This paper presents an automated data collection and analysis procedure related to structural integrity evaluation. The study focuses on optimizing human-operated inspection procedures by incorporating advanced technologies, such as image processing techniques for defect identification in a digital environment, and integrating defect information into Building Information Modeling (BIM) systems. These focal points and processes, aiming to identify defect identification, and defect information management in BIM environment, are further explored and demonstrated through a case study involving a steel bridge. At the beginning of the study, approximately 160 defect images were collected, examined and some of them were selected for visualization purposes. The authors table a procedure that can be followed, starting with storing defect images and ending with defect visualization in the BIM environment, and present a discussion on the advantages and limits of the process by examining concepts such as automated data collection by drones, machine vision-based damage identification by color detection technique, BIM and Industry Foundation Classes standardisation applied to inspection data.

This paper presents a comprehensive approach to dynamic identification and environmental effect filtering for the Exedra Hall at the Capitoline Museums in Rome. Measurements collected continuously over 1 year were analyzed using the Stochastic Subspace Identification (SSI) method to extract the modal parameters. A k-means clustering algorithm was employed to identify and track the first three natural frequencies. To address temperature-induced frequency variations, a feedforward Artificial Neural Network (ANN) was trained on 8 months of frequency and temperature data, enabling the isolation and removal of temperature effects from the identified frequencies. The filtered frequencies were extrapolated and validated against the identified data for the entire observation period. To improve robustness, physical plausibility, and extrapolation capability under seasonal transitions, a physics-informed neural network (PINN) constrained by finite-element-derived thermomechanical priors was additionally introduced. Finally, a model updating of the Finite Element Model (FEM), has been carried out to describe the temperature effect. This work contributes to Structural Health Monitoring (SHM) practices by integrating advanced identification techniques, machine learning, and model-based simulations.