Earthquake input and state estimation for buildings using absolute floor accelerations

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.

Automated summary

This study proposes a hybrid strategy to estimate earthquake input, states, and acceleration response at unmeasured floors using limited absolute floor acceleration measurements. Compared to an online model-based method, the proposed strategy demonstrates robustness and accuracy in challenging conditions.