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This paper introduces a framework to perform operational modal analysis (OMA) for structural health monitoring (SHM) by presenting the development and validation of a low-power, solar-powered wireless sensor network (WSN) tailored for bridge structures. The system integrates accelerometers and temperature sensors for dynamic structural assessment, all interconnected through the energy-efficient message queuing telemetry transport (MQTT) messaging protocol. Moreover, it delves into the details of sensor selection, calibration, and the design considerations necessary to address the unique challenges associated with bridge structures. Special attention is given to the solar-powered aspect, allowing for extended deployment periods without the need for frequent maintenance or battery replacements. To validate the proposed system, a comprehensive field deployment was conducted on an actual bridge structure. The collected data were transmitted through MQTT messages and analyzed by means of OMA. Comparative studies with traditional wired systems underscore the advantages of the solar-powered wireless solution in terms of sustainability, scalability, and ease of deployment. Results from the validation phase demonstrate the system's capability to provide accurate and real-time data needed to assess the health state of the monitored asset. This paper concludes with insights into the practical implications of adopting such a solar-powered WSN, emphasizing its potential to revolutionize bridge health monitoring by offering a cost-effective and energy-efficient solution for long-term infrastructure resilience. [ABSTRACT FROM AUTHOR]

Simple Summary: The transition period is a crucial phase in the life of dairy cows, and the administration of live yeasts could have positive effects on health and milk yield. Live yeast activity in the rumen can increase fiber digestibility and feed intake. In this study, the impact of live yeast supplementation on milk yield and composition, feed intake, rumination time, and metabolic profiles was evaluated. The administration of live yeast resulted in a decreased rumination time paired with an increased milk yield, with a reduced level of reactive oxygen metabolites. Dairy cows face several challenges during the transition period, and the administration of live yeast might be useful to mitigate this stressful condition. In the current study, the effects of live yeast administration on milk production, feed intake, and metabolic and inflammatory conditions were evaluated. Multiparous Holstein cows were enrolled in this randomized controlled trial and received either a control diet (CTR, n = 14) or the control diet plus 4 g/d of live Saccharomyces cerevisiae yeast (LSC, n = 14) from −21 to 56 days relative to calving. Dry matter intake, milk yield and composition, and rumination time were monitored daily. Blood samples were collected at −21, −7, 3, 14, 28, 42, and 56 days relative to calving to evaluate the metabolic profile. Fecal samples were collected at 56 days relative to calving to measure volatile fatty acids and feed digestibility. No differences between groups were observed in dry matter intake. Compared with CTR, rumination time was lower in LSC in after calving. Although there were no differences in milk components between groups, LSC had greater milk yield in the last three weeks of the study than CTR. No differences were observed in inflammatory markers or other plasma metabolites, except for β-hydroxybutyrate, which was higher in LSC, and reactive oxygen metabolites (ROMs), which were lower in LSC. Overall, these outcomes suggest that live yeast supplementation had some positive effects on milk yield and oxidative status. [ABSTRACT FROM AUTHOR]

Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure's health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge's health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others' N − 1 measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness. [ABSTRACT FROM AUTHOR]

The present study deals with the structural safety evaluation of a 50-year-old river bridge, called Baghetto Bridge, located in north Italy on the Adda River. Generally speaking, hydraulic processes are the main cause of bridge failure. Scour and hydrodynamic loads have been largely studied by the hydraulic engineering community; however, in practice, integration with structural analysis is often missing. The aim of this research is to provide a multidisciplinary procedure based on hydraulic and dynamic investigations devoted to the structural verification and monitoring of river bridges with traditional mechanical bearings. The deck–river interaction is addressed, studying the influence of debris accumulation on the bridge and performing structural verification of the bearing supports. The actions exerted on the bridge deck by the river current were estimated following the recommendations of the Italian code and making some further assumptions. In addition, dynamic investigations and FE modelling were performed. The results show (1) a relatively fast procedure that can be applied by practitioners to perform structural verification of river bridges with traditional mechanical bearings, and (2) an investigation method to evaluate temperature–frequency correlation as a reference for future inspections. [ABSTRACT FROM AUTHOR]