Quasi-Self-Powered Piezo-Floating-Gate Sensing Technology for Continuous Monitoring of Large-Scale Bridges

Developing a practical framework for long-term structural health monitoring (SHM) of large structures, such as a suspension bridge, poses several major challenges. The next generation of bridge SHM technology needs to continuously monitor conditions and issue early warnings prior to costly repair or catastrophic failures. Additionally, the technology has to interpret effects of rare, high-impact events like earthquakes or hurricanes. The development of this technology has become an even higher priority due to the fact that many of the world's bridges are reaching the end of their designed service lives. Current battery-powered wireless SHM methods use periodic sampling with relatively long sleep-cycles to increase a sensor's operational life. However, long sleep-cycles make the technology vulnerable to missing or misinterpreting the effect of a rare event. To address these practical issues, we present a novel quasi-self-powered sensing solution for long-term and cost-effective monitoring of large-scale bridges. The approach we propose combines our previously reported and validated self-powered Piezo-Floating-Gate (PFG) sensor in conjunction with an ultra-low-power, long-range wireless interface. The physics behind the PFG's operation enable it to continuously capture and store local, cumulative information regarding dynamic loading conditions of the bridge in non-volatile memory. Using extensive numerical and laboratory studies, we demonstrate the capabilities of the PFG sensor for predicting structural conditions. We then present a system level design that adapts PFG sensing for SHM in bridges. A challenging aspect of SHM in large-scale bridges is the need for long-range wireless interrogation, as many portions of the structure are not easily accessible for continual inspection and portions of the bridge cannot be frequently taken out-of-service. We show that by combining self-powered PFG sensors with a small battery and optimized long-range active wireless interface, we can realize a quasi-self-powered system that easily achieves a continuous operating lifespan in excess of 20 years. The efficiency and feasibility of the proposed method is verified in a case study of the Mackinac Bridge in Michigan, the longest suspension bridge across anchorages in the Western Hemisphere. Associated data from the deployment are discussed, in addition to limitations, challenges, and additional considerations for widespread field deployment of the proposed SHM framework

A Novel Data Reduction Approach for Structural Health Monitoring Systems

The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems