Search

• Curvature is identified from dynamic and quasi-static acceleration response. • Filtering and empirical mode decomposition provide curvature influence lines. • Sparse modal parameters and dense influence lines are fused using Kalman filter. • The system state represents dense and robust curvature estimate. • Monitoring the state evolution allows high-resolution damage localization. Curvature is one of the most popular damage-sensitive features in vibration-based structural health monitoring applications, typically calculated from identified modal features. While the relevant strategic or historical importance of bridges may justify dense sensor networks, a limited budget is generally assigned to monitor "minor" viaducts, thus involving inexpensive devices or extremely sparse sensing solutions. Modal parameters can only be obtained at instrumented locations. Thereby, damage assessment methods based on identified features typically have a low spatial resolution, especially when using low-cost monitoring setups with a modest number of sensing devices. This paper proposes an original identification method for the curvature of bridges based on sparse acceleration measurements that can be collected using standard accelerometers. The raw acceleration signal is processed through a particular filter bank that extracts dynamic and quasi-static signal components. The first components are employed to identify modal parameters, from which sparse yet robust estimates of the structural curvature are retrieved. On the other hand, the quasi-static acceleration generated by the structural deflection induced by traffic load is used to identify the curvature influence lines of the bridge, which are fused with modal estimates using a Kalman filter. The state variable of the analyzed system, representing a dense curvature profile of the structure subjected to concentrated loads, can be used as a damage-sensitive feature for high-resolution damage localization. The method is applied to a steel truss bridge subject to different damage configurations. [ABSTRACT FROM AUTHOR]

• Instantaneous identification of time-varying modal parameters is pursued. • A two-step output-only algorithm and a signal-adaptive variant are presented. • The algorithm is suitable for emerging decentralized systems based on smart sensors. • The algorithm is applied to a real bridge structure for damage identification. • The results demonstrated the effectiveness and usability of the proposed procedure. One of the latest trends in structural health monitoring involves the use of wireless decentralized sensing systems, developed to reduce costs and speed up the whole monitoring process. The main purpose of this paper is to present a novel decentralized procedure for the instantaneous modal identification of time-varying structures, also suitable in the presence of environmental variations and non-stationary ambient excitation. In particular, a modal assurance criterion (MAC)-based clustered filter bank (CFB) is obtained, capable of decomposing structural responses into modal components for the evaluation of time-varying natural frequencies and modal shapes through a nonlinear energy operator. The proposed algorithm is relatively simple and usable with low-cost smart sensing systems, as it requires low computational effort and works with few data at a time. To prove the effectiveness of the presented method, a simulated near-real-time modal identification procedure has been performed on a full-scale bridge under progressive damage scenarios. The estimated modal parameters have then been used for damage diagnosis. The results reveal a good correspondence between identified modal parameters and reference values, showing also promising outcomes for both damage detection and localization. [ABSTRACT FROM AUTHOR]