This paper presents the health monitoring of an experimental bridge model using Hilbert-Huang transform of transient vibration data. The Hilbert-Huang transform involves decomposition of vibration data into 'intrinsic mode functions' through the process of empirical mode decomposition. The Hilbert transform of intrinsic mode functions yields magnitude and frequency of oscillations as a function of time, which is called the Hilbert spectrum. Marginal Hilbert spectrum is obtained by integration of the Hilbert spectrum over time. A 4 × 1 m single span-bridge instrumented with 10 wireless sensor nodes is used for the study. Three levels of damage are introduced by removing bolts connecting the midspan floor beam with one of the girders. Bridge vertical accelerations are measured as a wheel and axle is gently rolled across its length (called one 'run') to simulate passing vehicles. The Hilbert spectrum for multiple runs, joint time-frequency analysis for individual runs, marginal Hilbert spectrum, and instantaneous phase were examined for the baseline (healthy) bridge and the three damage cases. The results demonstrate that the method can detect and locate damage under transient vibration loads. Figure 7. Magnitude versus frequency for sensor 10 (17 runs for each case): (a) baseline, (b) damage level 1, (c) damage level 2, and (d) damage level 3. 8 A. KUNWAR ET AL.

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Over recent years, there has been much interest in the use of low-cost wireless transceivers for communication of sensor data to alleviate the expense of widely distributed cable-based sensors in structural monitoring systems. However, while the number of unique wireless sensor platforms has continued to expand rapidly, the lack of success in replicating the number of deployed sensors and sampling rates utilized in previous cable-based systems has led to disillusionment over their use for this application. This article presents a wireless sensing system designed for concurrent measurement of both static and dynamic structural response through strain transducers, accelerometers, and temperature sensors. The network protocol developed supports real-time, high-rate data acquisition from large wireless sensor arrays with essentially no data loss. The current network software enables high-rate acquisition of up to 40 channels across 20 wireless units on a single peer-to-peer network with system expansion enabled through additional networks operating simultaneously on adjacent communication channels. Elements of the system design have been specifically tailored towards addressing condition assessment of highway bridges through strain-based load ratings as well as vibration-based dynamic analysis. However, the flexible system architecture enables the system to serve essentially as an off-the-shelf solution for a wide array of wireless sensing tasks. The wireless sensing units and network performance have been validated through laboratory tests as well as dense large-scale field deployments on an in-service highway bridge.

This article focuses on the deployment of a wireless sensor system (WSS) developed at Clarkson University for structural monitoring purposes. The WSS is designed specifically for diagnostic bridge monitoring, providing independent conditioning for accelerometers, strain transducers, and temperature sensors in addition to high-rate wireless data transmission and is capable of supporting large-scale sensor arrays. A threespan simply supported structure was subjected to diagnostic load testing as well as ambient vibration monitoring. A total of 90 wireless and several wired sensors, including accelerometers and strain transducers were used in the deployment. Strain measurements provided capacity and demand characteristics of the structure in the form of neutral axis locations, load distributions, and dynamic allowances which ultimately produced an inventory and operating load rating for the structure. Additionally, modal characteristics of the structure, including natural frequencies and mode shapes, were derived from measured accelerations and discussed briefly.

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