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.

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.

Process choice and dimensioning of wastewater treatment plants (WWTPs) is difficult while ensuring regulatory standards are met and cost-efficiency is maintained. This step only accounts for a small fraction of the upfront costs, but can lead to substantial savings. This paper illustrates the results of a systematic methodology to evaluate system upgrade options by means of dynamic modelling. In contrast to conventional practice, the presented approach allows the most appropriate trade-off between cost of measures and effluent quality to be chosen and the reliability of a process layout to be assessed by means of uncertainty analysis. In a hypothetical case study, thirteen WWTP upgrade options are compared in terms of their effluent quality and economic performance. A further comparison of two options with regard to the resulting receiving water quality reveals the paramount importance of this aspect, and highlights the inadequacy of evaluation frameworks limited to the performance relative to a sub-system (WWTP effluent) when a wider perspective (as induced by the EU Water Framework Directive) has to be adopted

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.

Vibration-based bridge modal identification is a crucial tool in monitoring and managing transportation infrastructure. Traditionally, this entails deploying a fixed array of sensors to measure bridge responses such as accelerations, determine dynamic characteristics, and subsequently infer bridge conditions that will facilitate prognosis and decision-making. However, such a paradigm is not scalable, possesses limited spatial resolution, and typically entails high effort and cost. Recently, mobile sensing-based paradigms have demonstrated promise in laboratory and field settings as an alternative. These methods can leverage big data from crowdsourcing vibration data acquired from smartphone devices belonging to pedestrians and passengers traveling over a bridge, constituting a significantly large data stream of indirectly sensed bridge response. Although the efficacy of such a paradigm has been demonstrated for a limited set of case studies, ubiquitous implementation requires analyzing the impact of vehicle dynamics and quantifying data sources that can be used for the purpose of bridge modal identification. This paper presents a road map for achieving this through dynamically diverse datastreams such as passenger cars, buses, bikes, and scooters. Existing datastreams point towards the implementation of crowdsourced mobile sensing paradigms in urban settings, which would facilitate effective decision-making for enhanced transportation infrastructure resilience.