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After earthquakes, structural response such as interstory drift is critical for accurate structural assessment for buildings. Typically, direct integration of absolute floor accelerations does not yield reliable floor displacements due to the long‐period drifts caused by noise, a widely acknowledged challenge. In this case, model‐based estimation strategies can be employed, which often require the ground input for better accuracy. However, in many cases the ground input may not be available for lack of instrumentation or even be unmeasurable due to soil‐structure interaction, hence needs to be estimated. Earthquake input estimation in this case is particularly challenging due to the lack of direct feedthrough term, leading to low observability of system input. As a result, input estimation is sensitive to modeling error, measurement noise, and incomplete measurements. To address this challenge, a hybrid strategy is proposed to estimate earthquake input, states, and acceleration response at unmeasured floors using limited absolute floor acceleration measurements. First, the earthquake input is estimated through a maximum a posteriori (MAP) estimation method, and then the estimated input is combined with Kalman filter to further estimate states and unmeasured responses. A comprehensive assessment was performed through a series of numerical and experimental tests including a comparative study with a popular online model‐based method. While the online method demonstrated certain sensitivity to modeling error and measurement noise due to weak observability, the proposed strategy showed robustness and accuracy under realistic and challenging conditions. Further verification is also performed using a real‐world building structure that experienced earthquake events.

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.

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