Bio: Li-Hong Sheng is an academic researcher from California Department of Transportation. The author has contributed to research in topics: Wireless sensor network & Structural health monitoring. The author has an hindex of 1, co-authored 1 publications receiving 30 citations.
TL;DR: The development of a large-scale wireless structural monitoring system for long-span bridges is reported; the system is entirely wireless which renders it low-cost and easy to install.
Abstract: A dense network of sensors installed in a bridge can continuously generate response data from which the health and condition of the bridge can be analyzed. This approach to structural health monitoring the efforts associated with periodic bridge inspections and can provide timely insight to regions of the bridge suspected of degradation or damage. Nevertheless, the deployment of fine sensor grids on large-scale structures is not feasible using wired monitoring systems because of the rapidly increasing installation labor and costs required. Moreover, the enormous size of raw sensor data, if not translated into meaningful forms of information, can paralyze the bridge manager's decision making. This paper reports the development of a large-scale wireless structural monitoring system for long-span bridges; the system is entirely wireless which renders it low-cost and easy to install. Unlike central tethered data acquisition systems where data processing occurs in the central server, the distributed network of wireless sensors supports data processing. In-network data processing reduces raw data streams into actionable information of immediate value to the bridge manager. The proposed wireless monitoring system has been deployed on the New Carquinez Suspension Bridge in California. Current efforts on the bridge site include: 1) long-term assessment of a dense wireless sensor network; 2) implementation of a sustainable power management solution using solar power; 3) performance evaluation of an internet-enabled cyber-environment; 4) system identification of the bridge; and 5) the development of data mining tools. A hierarchical cyber-environment supports peer-to-peer communication between wireless sensors deployed on the bridge and allows for the connection between sensors and remote database systems via the internet. At the remote server, model calibration and damage detection analyses that employ a reduced-order finite element bridge model are implemented.
TL;DR: The state of the art in WSNs-based bridge health monitoring systems is reviewed including wireless sensor, network topology, data processing technology, power management, and time synchronization.
Abstract: Structural health monitoring (SHM) systems have shown great potential to sense the responses of a bridge system, diagnose the current structural conditions, predict the expected future performance, provide information for maintenance, and validate design hypotheses. Wireless sensor networks (WSNs) that have the benefits of reducing implementation costs of SHM systems as well as improving data processing efficiency become an attractive alternative to traditional tethered sensor systems. This paper introduces recent technology developments in the field of bridge health monitoring using WSNs. As a special application of WSNs, the requirements and characteristics of WSNs when used for bridge health monitoring are firstly briefly discussed. Then, the state of the art in WSNs-based bridge health monitoring systems is reviewed including wireless sensor, network topology, data processing technology, power management, and time synchronization. Following that, the performance validations and applications of WSNs in bridge health monitoring through scale models and field deployment are presented. Finally, some existing problems and promising research efforts for promoting applications of WSNs technology in bridge health monitoring throughout the world are explored.
TL;DR: In this paper, the authors investigated the potential for using dense arrays of relatively low-precision GPS sensors to achieve high precision displacement estimates and showed that dynamic response resolution as low as 20-30 cm can be achieved and that the resolution improves with the number of sensors used.
Abstract: SUMMARY Many of the available SHM approaches neither readily support displacement monitoring nor work in concert with one another to take advantage of displacement-based SHM for various long-period structures. Although survey-quality GPS technology offers the possibility of measuring such displacements with sub-centimeter precision, the associated cost is too high to allow for routine deployment. Low-cost GPS chips commonly found in mobile phones and automobile navigation equipment are attractive in terms of size, cost, and power consumption; however, the displacement accuracy of these GPS chips is on the order of several meters, which is insufficient for SHM applications. Inspired by sensory information processing strategies of weakly electric fish, this paper investigates the potential for using dense arrays of relatively low-precision GPS sensors to achieve high-precision displacement estimates. Results show that dynamic response resolution as low as 20–30 cm can be achieved and that the resolution improves with the number of sensors used. Copyright © 2012 John Wiley & Sons, Ltd.
TL;DR: In this paper, a new high-sensitivity accelerometer board (SHM-H) for the Imote2 wireless smart sensor (WSS) platform is presented for structural health monitoring.
Abstract: State-of-the-art smart sensor technology enables deployment of dense arrays of sensors, which is critical for structural health monitoring (SHM) of complicated and large-scale civil structures. Despite recent successful implementation of various wireless smart sensor networks (WSSNs) for full-scale SHM, the low-cost micro-electro-mechanical systems (MEMS) sensors commonly used in smart sensors cannot readily measure low-level ambient vibrations because of their relatively low resolution. Combined use of conventional wired high- sensitivity sensors with low-cost wireless smart sensors has been shown to provide improved spectral estimates of response that can lead to improved experimental modal analysis. However, such a heterogeneous network of wired and wireless sensors requires central collection of an enormous amount of raw data and off-network processing to achieveglobal time synchronization; consequently, many of the advantages of WSSNs for SHM are lost. In this paper, the development of a new high-sensitivity accelerometer board (SHM-H) for the Imote2 wireless smart sensor (WSS) platform is presented. The use of a small number of these high-sensitivity WSSs, composed of the SHM-H and Imote2, as reference sensors in the Natural Excitation Technique—based decentralized WSSN strategy is explored and is shown to provide a cost- effective means of improving modal feature extraction in the decentralized WSSN for SHM. DOI: 10.1061/(ASCE)EM.1943-7889 .0000352. © 2012 American Society of Civil Engineers. CE Database subject headings: Structural health monitoring; Probe instruments; Identification; Stochastic models. Author keywords: Structural health monitoring; Wireless smart sensor network; High-sensitivity sensor; System identification; Decentralized sensor network.
TL;DR: Literature related to both ITS-informed SHM and SHM-informed ITS is reviewed and potential challenges and future research directions associated with ITS-SHM integration are discussed.
Abstract: Recent catastrophic bridge failures clearly indicate the urgent need for improving interval-based bridge inspection procedures that are qualitative and subjective in nature. Structural Health Monitoring (SHM) can mitigate the deficiencies of interval-based inspection techniques and provide real-time diagnostic information regarding the bridge structural health. SHM is not flawless however; the variability in the vehicle characteristics and traffic operational conditions makes it prone to false diagnosis. Recent advancements in the integration of SHM with intelligent transportation systems (ITS) demonstrate the successful use of ITS devices (e.g., traffic cameras, traffic detectors) in the analysis of bridge responses to multimodal traffic with varying loads or during the critical events that cause excess vibration beyond the normal limit. In an ITS-informed SHM system, the ITS device collected data can be integrated with SHM to increase the reliability and accuracy of the SHM system. This integration would reduce the possibility of false diagnosis of damages detected by the SHM system (e.g., vibrations caused by heavy vehicles on a bridge could be read by a SHM sensor as a structural health problem of the bridge), which would eventually decrease the bridge maintenance costs. Similarly, in SHM-informed ITS system, SHM sensors can provide data on bridge health condition for ITS applications, where ITS uses this bridge health condition information for real-time traffic management. In this paper, literature related to both ITS-informed SHM and SHM-informed ITS is reviewed. Based on the literature review, potential challenges and future research directions associated with ITS-SHM integration are also discussed.
TL;DR: In this paper, the deployment problem for finding node locations to reliably diagnose the health of a structure while consuming minimum energy during data collection is considered, and a simple shear structure is considered and modal analysis is performed.
Abstract: SUMMARY Structural health monitoring using wireless sensor networks has drawn considerable attention in recent years. The ease of deployment of tiny wireless devices that are coupled with sensors and actuators enhances the data collection process and makes prognostic and preventive maintenance of an infrastructure much easier. In this paper, the deployment problem is considered for finding node locations to reliably diagnose the health of a structure while consuming minimum energy during data collection. A simple shear structure is considered and modal analysis is performed. The example verifies the expectation that placing nodes further apart from each other reduces the mode shape errors but increases the energy consumption during data collection. A min–max, energy-balanced routing tree and an optimal grid separation formulation are proposed that minimize the energy consumption as well as provide fine grain measurements. Copyright © 2012 John Wiley & Sons, Ltd.