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An integrated hardware and software system for a scalable wireless sensor network ͑WSN͒ is designed and developed for structural health monitoring. An accelerometer sensor node is designed, developed, and calibrated to meet the requirements for structural vibration monitoring and modal identification. The nodes have four channels of accelerometers in two directions and a microcontroller for processing and wireless communication in a multihop network. Software components have been implemented within the TinyOS operating system to provide a flexible software platform and scalable performance for structural health monitoring applications. These components include a protocol for reliable command dissemination through the network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. The prototype WSN was deployed on a long-span bridge with 64 nodes. The data acquired from the testbed were used to examine the scalability of the network and the data quality. Robust and scalable performance was demonstrated even with a large number of hops required for communication. The results showed that the WSN provides spatially dense and accurate ambient vibration data for identifying vibration modes of a bridge.

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A spatially dense wireless sensor network was designed, developed and installed on a long-span suspension bridge for a 3-month deployment to record ambient acceleration. A total 174 sets of data ͑1.3 GB͒ were collected from 64 sensor nodes on the main span and south tower of the Golden Gate Bridge. Analysis of the vibration data using power spectral densities and peak picking provide approximate estimates of vibration modes with minimal computation. For more detailed analysis of the data, autoregressive with moving average models ͑ARMA͒ give parametric estimates of vibration modes for frequencies up to 5 Hz. Statistical analysis of the multiple realizations give the distributions of the vibration frequencies, damping ratios, and mode shapes and 95% confidence intervals. The statistical results are compared with vibration properties using the peak picking method and previous studies of the bridge using measured data and a finite-element model. Analysis of the ambient vibration data and system identification results demonstrate that high spatial and temporal sensing using the wireless sensor network give a high resolution and confidence in the identified vibration modes. The estimation errors for the identified vibration properties are generally low, with frequencies being the most accurate and damping ratios the least accurate.