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. Copyright © 2011 John Wiley & Sons, Ltd.

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.

A real-time wireless sensor network platform capable of maintaining lossless data transmission over several minutes of continuous, high-rate sampling is presented in this paper. The platform was designed specifically to provide the capability to enable expeditious system identification, as well as load rating of highway bridges without compromising the typical data acquisition parameters employed in comparable cable-based tests. Consequently, the hardware signal conditioning interface permits data collection from a variety of sensors typical to structural health monitoring, including accelerometers, strain transducers, and temperature sensors. The embedded software features a proprietary network transmission protocol capable of lossless, real-time delivery of up to 40 measurement channels at an effective sampling rate of 128 samples per second per channel. Documented in this paper is a field study on an end-of-service highway bridge in which ambient vibration monitoring was performed using 60 accelerometers interfaced with 30 wireless sensor nodes operating within one of two simultaneously operating star topology networks. In addition, an experimental load rating of the entire structure was performed through large-scale strain measurement facilitated by the same wireless sensor network platform.

Development of diagnostic and prognostic routines for application to in-service measurements from highway bridges necessitates analysis of experimental measurements from in-service highway bridges under natural or prescribed induced damage. This is generally limited to the unique opportunity of investigating end-of-service life bridges prior to reconstruction and consequently only a limited library of such case studies exist. This paper documents a field test of an end-of-service bridge span with prescribed progressive damage to a bearing as well as several diaphragm connections. Thirty dual-axis accelerometers were distributed across the bridge span with data acquisition and transmission facilitated by a real-time lossless wireless sensor network. A highway department service truck applied traffic excitation to the structure through routine passes on a consistent lane of traffic. Output-only system identification was applied to the baseline time history response to develop a state-space model of the brid...