Guided wave array-based structural health monitoring (SHM) is a promising solution for diagnosing damage in metal-connected structures. In this field, the reconstruction algorithm for probabilistic inspection (RAPID) is one of the most widely used algorithms for performing damage localization. In this paper, a density clustering RAPID based on an array-compensated damage index is proposed. A new probability distribution function was constructed based on a new damage index, which is adaptive to different elements in the sensor array to compensate for performance variation. Then, the imaging matrix of the RAPID algorithm was density-clustered to obtain the location and degree of damage. Finally, the method was verified by experiments on a stiffened aluminum plate. The experimental results demonstrate that the method achieves damage localization and enables quantitative damage diagnosis.

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module\'s structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module\'s structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module\'s structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module\'s structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations.

In the context of hydroelectric plants, this article emphasizes the imperative of robust monitoring strategies. The utilization of fiber-optic sensors (FOSs) emerges as a promising approach due to their efficient optical transmission, minimal signal attenuation, and resistance to electromagnetic interference. These optical sensors have demonstrated success in diverse structures, including bridges and nuclear plants, especially in challenging environments. This article culminates with the depiction of the development of an array of sensors featuring Fiber Bragg Gratings (FBGs). This array is designed to measure deformation and temperature in protective grids surrounding the turbines at the Santo Antônio Hydroelectric Plant. Implemented in a real-world scenario, the device identifies deformation peaks, indicative of water flow obstructions, thereby contributing significantly to the safety and operational efficiency of the plant.

Ultrasonic guided waves, which are often generated and detected by piezoelectric transducers, are well established to monitor engineering structures. Wireless solutions are sought to eliminate cumbersome wire installation. This work proposes a method for remote ultrasonic-based structural health monitoring (SHM) using mechanoluminescence (ML). Propagating guided waves transmitted by a piezoelectric transducer attached to a structure induce elastic deformation that can be captured by elastico-ML. An ML coating composed of copper-doped zinc sulfide (ZnS:Cu) particles embedded in PVDF on a thin aluminium plate can be used to achieve the elastico-ML for the remote sensing of propagating guided waves. The simulation and experimental results indicated that a very high voltage would be required to reach the threshold pressure applied to the ML particles, which is about 1.5 MPa for ZnS particles. The high voltage was estimated to be 214 Vpp for surface waves and 750 Vpp for Lamb waves for the studied configuration. Several possible technical solutions are suggested for achieving ultrasonic-induced ML for future remote SHM systems.

During the operation of fabricated small box girder bridges, which face safety issues such as structural degradation and failure, there is an urgent need to propose a safety evaluation method to cope with the possible risks. This article quantitatively evaluates the safety state of a fabricated small box girder bridge in Wuhan City based on Fuzzy Analytic Hierarchy Process (FAHP) and structural health monitoring (SHM) data. Firstly, the FAHP model is established, and stress, deformation, and temperature are selected as evaluation factors. The safety thresholds of stress and deformation are determined by combining the industry specifications and the historical statistical patterns of the massive SHM data. The temperature field of the bridge is simulated and analyzed by combining ANSYS, HYPERMESH, and TAITHREM, and the most unfavorable temperature gradient is determined as a threshold for the safety evaluation. Finally, the scores of indexes of the bridge are determined based on the measured SHM data, which in turn provides a quantitative description of the safety state. The results show that the thresholds determined by the joint industry specifications and the massive SHM data are reasonable; the temperature field simulation model established in this article is consistent with the measured results, and can accurately determine the temperature gradient of the bridge. The safety evaluation result from the FAHP model is the same as the field test results, which verifies the effectiveness and applicability of the proposed method to actual bridge projects.

In this paper, we present a bolt preload monitoring system, including the system architecture and algorithms. We show how Finite Element Method (FEM) simulations aided the design and how we processed signals to achieve experimental validation. The preload is measured using a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) in pulse-echo mode, by detecting the Change in Time-of-Flight (CTOF) of the acoustic wave generated by the PMUT, between no-load and load conditions. We performed FEM simulations to analyze the wave propagation inside the bolt and understand the effect of different configurations and parameters, such as transducer bandwidth, transducer position (head/tip), presence or absence of threads, as well as the frequency of the acoustic waves. In order to couple the PMUT to the bolt, a novel assembly process involving the deposition of an elastomeric acoustic impedance matching layer was developed. We achieved, for the first time with PMUTs, an experimental measure of bolt preload from the CTOF, with a good signal-to-noise ratio. Due to its low cost and small size, this system has great potential for use in the field for continuous monitoring throughout the operative life of the bolt.

Health assessment and preventive maintenance of structures are mandatory to predict injuries and to schedule required interventions, especially in seismic areas. Structural health monitoring aims to provide a robust and effective approach to obtaining valuable information on structural conditions of buildings and civil infrastructures, in conjunction with methodologies for the identification and, sometimes, localization of potential risks. In this paper a low-cost solution for structural health monitoring is proposed, exploiting a customized embedded system for the acquisition and storing of measurement signals. Experimental surveys for the assessment of the sensing node have also been performed. The obtained results confirmed the expected performances, especially in terms of resolution in acceleration and tilt measurement, which are 0.55 mg and 0.020°, respectively. Moreover, we used a dedicated algorithm for the classification of recorded signals in the following three classes: noise floor (being mainly related to intrinsic noise of the sensing system), exogenous sources (not correlated to the dynamic behavior of the structure), and structural responses (the response of the structure to external stimuli, such as seismic events, artificially forced and/or environmental solicitations). The latter is of main interest for the investigation of structures\' health, while other signals need to be recognized and filtered out. The algorithm, which has been tested against real data, demonstrates relevant features in performing the above-mentioned classification task.

Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge\'s natural frequency over time to look for abnormal shifts in frequency that might indicate a change in stiffness. However, bridge frequencies change naturally as the structure\'s temperature changes. Data models can be used to overcome this problem by predicting normal changes to a structure\'s natural frequency and comparing it to the historical normal behaviour of the bridge and, therefore, identifying abnormal behaviour. Most of the proposed data modelling work has been from long-span bridges where you generally have large datasets to work with. A more limited body of research has been conducted where there is a sparse amount of data, but even this has only been demonstrated on single bridges. Therefore, the novelty of this work is that it expands on previous work using sparse instrumentation across a network of bridges. The data collected from four in-operation bridges were used to validate data models and test the capabilities of the data models across a range of bridge types/sizes. The MID approach was found to be able to detect an average frequency shift of 0.021 Hz across all of the data models. The significance of this demonstration across different bridge types is the practical utility of these data models to be used across entire bridge networks, enabling accurate and informed decision making in bridge maintenance and management.

Flexible strain sensors have a wide range of applications in the field of health monitoring of seismic isolation bearings. However, the nonmonotonic response with shoulder peaks limits their application in practical engineering. Here we eliminate the shoulder peak phenomenon during the resistive-strain response by adjusting the dispersion of conductive nanofillers. In this paper, carbon black (CB)/methyl vinyl silicone rubber (VMQ) composites were modified by adding a silane coupling agent (KH550). The results show that the addition of KH550 eliminates the shoulder peak phenomenon in the resistive response signal of the composites. The reason for the disappearance of the shoulder peak phenomenon was explained, and at the same time, the mechanical properties of the composites were enhanced, the percolation threshold was reduced, and they had excellent strain-sensing properties. It also exhibited excellent stability and repeatability during 18,000 cycles of loading-unloading. The resistance-strain response mechanism was explained by the tunneling effect theoretical model analysis. It was shown that the sensor has a promising application in the health monitoring of seismic isolation bearings.

Orthotropic steel decks (OSDs) are commonly used in the construction of bridges due to their load-bearing capabilities. However, they are prone to fatigue damage over time due to the cyclic loads from vehicles. Therefore, the early structural health monitoring of fatigue damage in OSDs is crucial for ensuring bridge safety. Moreover, Lamb waves, as elastic waves propagating in OSD plate-like structures, are characterized by their long propagation distances and minimal attenuation. This paper introduces a method of emitting high-energy ultrasonic waves onto the OSD surface to capture the nonlinear Lamb waves formed, thereby calculating the nonlinear parameters. These parameters are then correlated with the fatigue damage endured, forming a damage index (DI) for monitoring the fatigue life of OSDs. Experimental results indicate that as fatigue damage increases, the nonlinear parameters exhibit a significant initial increase followed by a decrease. The behavior is distinct from the characteristic parameters of linear ultrasound (velocity and energy), which also exhibit changes but to a relatively smaller extent. The proposed DI and fatigue life based on nonlinear parameters can be fitted with a Gaussian curve, with the R-squared value of the fitting curve being close to 1. Additionally, this paper discusses the influence of rib welds within the OSDs on the DI, whereby as fatigue damage increases, it enlarges the value of the nonlinear parameters without altering their trend. The proposed method provides a more effective approach for monitoring early fatigue damage in OSDs.