Summary Wireless sensor technology‐based structural health monitoring (SHM) has been widely investigated recently. This paper proposes a fast‐moving wireless sensing technique for the SHM of bridges along a highway or in a city in which the wireless sensor nodes are installed on the bridges to automatically acquire data, and a fast‐moving vehicle with an onboard wireless base station periodically collects the data without interrupting traffic. For the fast‐moving wireless sensing technique, the reliable wireless data transmission between the sensor nodes and the fast‐moving base station is one of the key issues. In fast‐moving states, the data packet loss rates during wireless data transmission between the moving base station and the sensor nodes will increase remarkably. In this paper, the data packets loss in the fast‐moving states is first investigated through a series of experiments. To solve the data packets loss problem, the compressive sensing (CS)‐based lost data recovery approach is proposed. A field test on a cable‐stayed bridge is performed to further illustrate the data packet loss in the fast‐moving wireless sensing technique and the ability of the CS‐based approach for lost data recovery. The experimental and field test results indicate that the Doppler effect is the main reason causing data packet loss for the fast‐moving wireless sensing technique, and the feasibility and efficiency of the CS‐based lost data recovery approach are validated Copyright © 2014 John Wiley & Sons, Ltd.

Summary Structural health monitoring (SHM) of bridges has gained rapid development in the past few years. This paper describes application of SHM on long‐span bridges in China, with the aim to illustrate its practical value. A short review of its development and practice is firstly introduced. Three case studies are subsequently presented on utilization of SHM data in engineering practice. In the first case study, a ship collision incident is analyzed using SHM data. An alarm is sent and confirmed when the collision occurred, and mode parameters are identified with GPS measurements to evaluate the bridge condition. In the second case study, damage of expansion joints in a suspension bridge is assessed with girder end displacement measurements. Malfunction of viscous damper is found to correlate with cumulative displacement. The results show that cumulative displacement can be used for condition assessment of expansion joints. In the third case study, the performance of tuned mass dampers is evaluated with wind and vibration measurements before and after tuned mass damper installation. Through explanation of these case studies, the paper illustrates how to distill useful insights from SHM data, which could be instructive for further research in this field.

Lossy transmission is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without re-transmitting the data; the promise of this approach is to reduce communication and thus power savings. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. Specifically, this research targets to provide accurate compensation for stationary and compressible acceleration signals obtained from Structural Health Monitoring (SHM) systems with data loss ratio below 20%. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

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A partial differential equation-constrained optimization approach is presented for reconstructing mechanical properties (e.g., elastic moduli). The proposed method is based on the minimization of an error in constitutive equations functional augmented with a least squares data misfit term referred to as MECE for "modified error in constitutive equations." The main theme of this paper is to demonstrate several key strengths of the proposed method on experimental data. In addition, some illustrative examples are provided where the proposed method is compared with a common shear wave elastography (SWE) approach. To this end, both synthetic data, generated with transient finite element simulations, as well as ultrasonically tracked displacement data from an acoustic radiation force (ARF) experiment are used in a standard elasticity phantom. The results indicate that the MECE approach can produce accurate shear modulus reconstructions with significantly less bias than SWE.