Field application of smart SHM using field programmable gate array technology to monitor an RC bridge in New Mexico

Abstract

In this paper, an innovative field application of a structural health monitoring (SHM) system using field programmable gate array (FPGA) technology and wireless communication is presented. The new SHM system was installed to monitor a reinforced concrete (RC) bridge on Interstate 40 (I-40) in Tucumcari, New Mexico. This newly installed system allows continuous remote monitoring of this bridge using solar power. Details of the SHM component design and installation are discussed. The integration of FPGA and solar power technologies make it possible to remotely monitor infrastructure with limited access to power. Furthermore, the use of FPGA technology enables smart monitoring where data communication takes place on-need (when damage warning signs are met) and on-demand for periodic monitoring of the bridge. Such a system enables a significant cut in communication cost and power demands which are two challenges during SHM operation. Finally, a three-dimensional finite element (FE) model of the bridge was developed and calibrated using a static loading field test. This model is then used for simulating damage occurrence on the bridge. Using the proposed automation process for SHM will reduce human intervention significantly and can save millions of dollars currently spent on prescheduled inspection of critical infrastructure worldwide.

Wireless sensor network for structural health monitoring: A contemporary review of technologies, challenges, and future direction

“…182 Likewise, it is important to compare the efficiency of a research proposed approach in determining optimal sensor location with other sensor placement algorithms while using large numbers of sensors on the structure. 183 Moreover, balancing the requirements of civil engineering and considerations of network design is another challenge in sensor placement in SHM using WSNs. The importance of the effect of sensor placement on other parameters, such as wireless network design parameters, should not be underestimated.…”

Spiking Neural Network-based Structural Health Monitoring Hardware System

“…To enjoy high performance parallel computing with customized hardware implementation on programmable architecture has urged researcher to explore FPGA based SHM systems to boost data processing, feature extraction and classification for SHM systems. Number of feature extraction techniques including Fast Fourier Transform (FFT) [26], Hilbert-Huang Transform (HHT) [27], Bayesian statistics [28], non-linear time series analysis [29] and Discrete Wavelet Transform (DWT) to record acoustic emission are implemented on FPGA to analyze and classify sensory/imagery signals data to aid SHM systems. These classifiers require mapping of complex mathematical (differential) equations on FPGA thus incur hardware area and power.…”

“…The hardware SNN provides parallel processing thus enables multiple hardware architectures to process sensory data concurrently. Furthermore, these devices operate with low-power DC requirements and during power failure in disastrous situation they can be backup with battery pack or solar power to continuously work onsite [26]. Therefore, a single FPGA installed on critical infrastructure will be able to monitor realtime structural health.…”

“…Furthermore, the SNN-based classifier for SHM system requires least hardware resources as compared to existing state-of-the-art DWT-based classifier and HT-based classifier for SHM systems. The FPGA based models are power efficient and able to analyse structural health in extreme disastrous conditions without compromising health monitoring performance [26]. Future work includes deployment of FPGA based SNN on critical structures including bridges, hospital, school and community hall buildings to monitor damage performance on day-to-day load as well as under disaster condition.…”

Strain measurement based on cooperative operation with different smartphones

“…The precise measurement of strain plays important role in evaluating the mechanical properties, crack initiation, and residual stress of various structures in multiple engineering fields (Amezquita‐Sanchez et al., 2018; Jousset et al., 2018; Schenato et al., 2017; Tondini et al., 2015; Q. Zhang & Zhang, 2021; C. C. Zhang et al., 2016). To measure such parameters, three main types of mature strain measurement methods have been developed, including electrical strain gauges (Azarbayejani et al., 2011; Reis et al., 2018), fiber optic sensors (Bao et al., 2017; Ge et al., 2014; Glišić & Simon, 2000), and vibrating wire strain gauges (Lee et al., 2010, 2013). However, these traditional methods require expensive dedicated data acquisition equipment, which greatly hinders their widespread usage for important infrastructures.…”