Bridge infrastructures in Europe are facing ageing, progressive damaging processes, change of traffic loads as well as climate change effects; as such, a sound diagnostics process based on the analysis of accurate information acquired from monitoring systems is a key enabler to support the application of preventive maintenance plans and to guide efficient decisions on repairs or strengthening. The fast-paced development of cheaper but reliable devices has allowed to collect a huge amount of data to deepen the knowledge of the structural behavior over time of existing structures under service conditions. This paper shows the use of MEMS sensors, both clinometers and accelerometers, for continuous structural health monitoring on concrete bridges. A dense sensing monitoring approach is applied, and data are analyzed and compared in near-real time with a threshold set based on an updated reference FE model of the bridges. A case study is presented, where anomaly detection algorithms based on key performance indicator evolution in time have efficiently identified and localized damages triggering repeated proactive maintenance interventions. Attention is given to the seasonal influence on both the static and dynamic response of the bridges, and on the misleading effects on the damage detection and diagnostics processes. This approach is part of a wider framework aimed at an industrial application of SHM, in which the specific aspects covered in this paper have been identified and analyzed on multiple similar concrete bridges under continuous monitoring, of which evidence is provided. 

Concrete Bridges Continuous SHM Using MEMS Sensors: Anomaly Detection for Preventive Maintenance

Metrological Evaluation of New Industrial SHM Systems Based on MEMS and Microcontrollers

Real-time long-term monitoring can be an essential tool for the continuous assessment of existing and new structures. Numerical modelling of the real behavior of structures requires deep information that can be obtained with a pervasive monitoring accompanied by both field and laboratory testing. Real-time data allows to update the remaining service life estimation, supporting proactive maintenance strategies. Furthermore, the evaluation of the actual bearing capacity and the comparison between resistance and actions within the domain of relevant safety acceptance criteria can be performed with an increased knowledge of the structural performance. This paper presents an experimental study designed to investigate the combined effect of corrosion and sustained loads on a prestressed concrete element. The beam, subjected to a four-point bending test, has a 200 × 300 mm cross section, a total length of 3700 mm and a clear span of 2700 mm. The element was exposed to accelerated corrosion through electrolytic cells for up to 70 days. A sustained load equal to 150 kN, corresponding to approximately 62% of the ultimate load of an identical undamaged reference beam, was simultaneously applied. Aiming to evaluate the structural behavior according to the progress of the damage level, a MEMS technology real-time monitoring system has been set to collect data and analyze the beam response in continuous. A FE model was developed to optimize the monitoring system setup, to predict the behavior and to compare experimental results with numerical simulations. 

Laboratory Testing and SHM of a Prestressed Concrete Beam Under Accelerated Artificial Corrosion and Sustained Loads

Asset managers, owners and the engineering companies are facing the increasing need to accurately estimate the actual structural condition of existing infrastructures as well as assess their residual life with respect to predetermined reliability levels. In this context, existing concrete bridges require special attention due to the growing progressive ageing, deterioration mechanisms as well as the increased traffic loads, coupled with the reach of the nominal reference life. To this extent, a full-scale experimental testing campaign has been carried out on a reinforced concrete simply supported beam built around 55 years ago and with a span equal to 25.90 m. The reinforced concrete (RC) beam was removed during the demolition operations of the Mollere viaduct, along the A6 highway, in Italy. The testing campaign consisted of a load-controlled test where the beam, supported on isolated foundations, has been directly loaded with a system of cast iron masses calibrated to reach the near-collapse state at the end of the test. The beam has been instrumented with continuous monitoring systems to effectively observe its structural behaviour throughout the experimental test. This paper summarizes the load test setting and the main results in terms of both the static behaviour, showing the efficiency of the testing campaign as well the accuracy of the monitoring systems applied. 

Full-Scale Experimental Testing on a Reinforced Bridge RC Beam: Experimental and SHM Results

Bridge infrastructures in Europe are facing growing campaigns of maintenance plans, repairs and strengthening interventions to face progressive ageing, deterioration of materials, higher traffic loads with respect to the design assumptions, additional threats and hazards induced by the climate change as well as the results of inadequate periodic quality control. In this context, Structural Health Monitoring (SHM) systems are efficient tools to collect accurate data to support the application of preventive maintenance strategies as well as to support the informed management of transport infrastructures. Continuous SHM systems can provide a detailed characterization of the static and dynamic behavior of the bridge under service conditions, as well as being employed throughout the maintenance phase to deepen the knowledge of the structural response during the strengthening works. The data collection coupled with an updated numerical FE modelling of the bridge has the aim to confirm (or disconfirm) the assumptions made during the interventions design phase and to prove the actual efficiency of the repairs through an ongoing diagnosis process. This paper shows the use of Micro Electro Mechanical System (MEMS) sensors, both clinometers and accelerometers, for continuous SHM on a concrete bridge subject to service condition followed by strengthening works. A dense sensing monitoring approach is applied, where data are collected, analysed and compared with an updated digital twin finite element (FE) model of the bridge used to simulate the structural strengthening works as a reference. Attention is given to the bridge dynamic continuous response during the site activities, evaluated through Operational Modal Analysis (OMA) techniques, employed to control the expected effect of the structural interventions. This applied development in the domain of SHM shows the advantages of an industrial application of dense sensing techniques, to support both the service life as well as the maintenance phase of concrete bridges, to move towards a smarter and more efficient asset management process. 

Continuous MEMS SHM Systems to Support RC Bridges Retrofitting Interventions Through an Ongoing Diagnosis

The progress in the world of new sensors is running fast, offering good performances, and reliable solutions with initial costs orders of magnitude lower than those faced only a few years ago. The spread of new electronic devices, like microcontrollers, the increasing power of the networks for data transmission and management and in the end the availability of the new data-driven approaches have created a revolution in SHM approaches, not yet fully mastered. The way to design a SHM system is going to be deeply revised in an industrial perspective, within a complex framework in which everything has to be planned into details since the beginning, including the development of a metrological culture, the personnel education, the need of spare parts, re-calibration, …. This also means a revolution in data management: huge data flows not only create hardware problems related to their transfer; the software too requires a great deal of effort to compress data, also due to the actual cost of cloud resources. All these facts, accounting for the real metrological performances of the best MEMS sensors available at present, also require simplified data analyses, as software complexity is now mainly transferred to the network management. A trade-off must be looked for between the big redundancy offered by the actual networks and the need of a simple and prompt information, granting the structure safety: That is why as the data rates increase, the algorithms to be adopted must be simple, reliable, eventually adapted to edge computing at the sensor level, where hardware power is now present though at a reduced scale. The chapter shows such an approach in a real case from the system design, its birth, and its proper use for damage detection, up to the detection of a structural failure.

Isabella Alovisi

Papers

New Sensor Nodes, Cloud, and Data Analytics: Case Studies on Large Scale SHM Systems