While displacement is valuable information for the structural behavior, measuring displacements from large civil structures is often challenging and costly. To overcome difficulties found in direct measurements such as using linear variable differential transformer and LASER-based methods, indirect displacement estimation approaches are alternatively developed. Such indirect approaches in general rely on acceleration or strain that is relatively cost effective and convenient to measure. However, these measurements have own characteristics that limit wider application of the indirect estimation. For example, as the double integration of acceleration results in the low-frequency drift in the estimated displacement, high-pass filters are often used to suppress the drift, assuming displacements are close to a zero mean process; strain is difficult to use for high-frequency modes. These types of limitations can be resolved by the fusion of different types of measurements. This study develops an indirect displacement estimation method based on the multimetric data (i.e., acceleration and strain) that can estimate nonzero mean, dynamic displacements. The proposed approach is numerically validated, showing better estimation than the single measurement-based methods. Furthermore, the performance of the proposed approach is verified using dynamic response data measured from the Sorok Bridge, a cable-stayed bridge in Korea.

Structural health monitoring (SHM) is playing an increasingly important role in ensuring the safety of structures. A shift of SHM research away from traditional wired methods toward the use of wireless smart sensors (WSS) has been motivated by the attractive features of wireless smart sensor networks (WSSN). The progress achieved in Micro Electro-Mechanical System (MEMS) technologies and wireless data transmission, has extended the effectiveness and range of applicability of WSSNs. One of the most common sensors employed in SHM strategies is the accelerometer; however, most accelerometers in WSS nodes have inadequate resolution for measurement of the typical accelerations found in many SHM applications. In this study, a high-resolution and low-noise tri-axial digital MEMS accelerometer is incorporated in a next-generation WSS platform, the Xnode. In addition to meeting the acceleration sensing demands of large-scale civil infrastructure applications, this new WSS node provides powerful hardware and a robust software framework to enable edge computing that can deliver actionable information. Hardware and software integration challenges are presented, and the associate resolutions are discussed. The performance of the wireless accelerometer is demonstrated experimentally through comparison with high-sensitivity wired accelerometers. This new high-sensitivity wireless accelerometer will extend the use of WSSN to a broader class of SHM applications.

Displacement measurements are useful information for various engineering applications such as structural health monitoring (SHM), earthquake engineering and system identification. Most existing displacement measurement methods are costly, labor-intensive, and have difficulties particularly when applying to full-scale civil structures because the methods require stationary reference points. Indirect estimation methods converting acceleration to displacement can be a good alternative as acceleration transducers are generally cost-effective, easy to install, and have low noise. However, the application of acceleration-based methods to full-scale civil structures such as long span bridges is challenging due to the need to install cables to connect the sensors to a base station. This article proposes a low-cost wireless displacement measurement system using acceleration. Developed with smart sensors that are low-cost, wireless, and capable of on-board computation, the wireless displacement measurement system has significant potential to impact many applications that need displacement information at multiple locations of a structure. The system implements an FIR-filter type displacement estimation algorithm that can remove low frequency drifts typically caused by numerical integration of discrete acceleration signals. To verify the accuracy and feasibility of the proposed system, laboratory tests are carried out using a shaking table and on a three storey shear building model, experimentally confirming the effectiveness of the proposed system.