Damage detection on a full-scale highway sign structure with a distributed wireless sensor network

Abstract

Wireless sensor networks (WSNs) have emerged as a novel solution to many of the challenges of structural health monitoring (SHM) in civil engineering structures. While research projects using WSNs are ongoing worldwide, implementations of WSNs on full-scale structures are limited. In this study, a WSN is deployed on a full-scale 17.3m-long, 11-bay highway sign support structure to investigate the ability to use vibration response data to detect damage induced in the structure. A multi-level damage detection strategy is employed for this structure: the Angle-between-String-and-Horizon (ASH) flexibility-based algorithm as the Level I and the Axial Strain (AS) flexibility-based algorithm as the Level II. For the proposed multi-level damage detection strategy, a coarse resolution Level I damage detection will be conducted first to detect the damaged region(s). Subsequently, a fine resolution Level II damage detection will be conducted in the damaged region(s) to locate the damaged element(s). Several damage cases are created on the full-scale highway sign support structure to validate the multi-level detection strategy. The multi-level damage detection strategy is shown to be successful in detecting damage in the structure in these cases.Keywords: multi-level damage detection; full scale structure; wireless sensor network Recently a number of efforts have been conducted using WSNs for SHM in civil engineering community. Researchers at Clarkson University have developed a wireless monitoring system for bridge condition assessment. In the hybrid wireless system, accelerometers are used to obtain dynamic characteristic of the structure, and strain sensors are employed for assessing load rating and static analysis. The performances of the system have been tested on a single-span bridge with 20 wireless sensor nodes (Gangone et al. 2009). Researchers at the University of Illinois at Urbana-Champaign have deployed a large scale WSN for SHM in the new Jindo Bridge in South Korea. This implementation is the first dense deployment of WSNs on a cable-stayed bridge in the world and the largest (in the sense of the number of wireless sensors deployed) of its kind for civil infrastructure to date (Spencer 2009). Researchers at the University of Michigan have validated three output-only modal identification methods (peak-picking, random decrement, and frequency domain decomposition) using a distributed WSN. Experimental validation has also been performed on the balcony of a historic theater in metropolitan Detroit ). Researchers at Lehigh University have proposed an iterative modal identification algorithm for SHM with WSNs. Onboard computational power of wireless nodes is utilized to reduce communication burden and energy consumption for modal identification tasks (Dorvash et al. 2013). Researchers at Georgia Institute of Technology have implemented a wireless mobile sensor network for system identification on a space-frame bridge. Four mobile wireless nodes navigate autonomously to different sections of the bridge for measuring ...

Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges

“…Indeed, the health state of the bridge can be monitored by simply defying a fault index, which, for example, can be based on the variation of the displacements (or natural frequencies, acceleration, etc.) of the bridge and monitoring its variations over time, as shown by Sun et al 126 and Zhan et al 127 Fan et al 30 also present a comparative study between two fault detection methods based on failure index definition and three response-based methods. Finally, the health condition of the bridge can be also assessed by monitoring the evolution of the bridge modal parameters.…”

“…Sun et al 126 presented a multi-level fault detection strategy using natural frequencies and mode shapes of the structure, which have been obtained by manipulating the bridge acceleration data, retrieved by wireless sensors network (WSN). Particularly, the acceleration data of the bridge are used as the input to a fast Fourier transform, and therefore, the natural frequencies are obtained by looking for the peak of the amplitude spectral density, whereas the mode shapes are provided by the singular value decomposition of the cross-spectral density matrix.…”

Traffic Safety Evaluation for Railway Bridges Using Expanded Multisensor Data Fusion

“…Besides the multimetric sensing, Imote2 provides highly accurate post time synchronization with error less than 50 μs (Nagayama and Spencer, ; Li et al., ) and requires low‐power operation that consumes 6.9 mWh for sensing (Park et al., ). As such, the Imote2 smart sensor platform has been considered to be reliable and versatile, being widely applied to infrastructure monitoring research (Jang et al., ; Rice et al., ; Cho et al., ; Sim et al., ; Ho et al., ; Dorvash et al., ; Cho and Spencer, ; Sun et al., ; Sim and Spencer, ).…”

Probabilistic Assessment of High-Throughput Wireless Sensor Networks

“…Due to such features, numerous researchers have deployed short-term and long-term monitoring systems on structures using commercially available and academic prototype sensors. Summaries and examples of WSS development and full-scale monitoring can be found in [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Despite these successful deployments, full-scale implementations are still limited.…”