Multi-component deconvolution interferometry for data-driven prediction of seismic structural response

Abstract Prediction of structural response is necessary for evaluating condition and quantifying vulnerability of structural systems exposed to seismic loads. Traditional modeling techniques for infrastructure systems such as finite elements are typically limited by inherent modeling assumptions as well as the prohibitive computational effort required for analysis. This necessitates the development of surrogate models that serve as a basis for predicting structural response. Deconvolution interferometry is a viable data-driven approach for such a task that uses single component sensor data to generate a set of impulse response functions for a structure of interest that constitutes the required surrogate model of the structure. The resulting surrogate model aids in both dynamic characterization as well as for accurate response prediction. However, it is limited to cases where motions in various degrees of freedom of a structure can be decoupled. This decoupling requires dense sensor deployment as well as prior knowledge about the structure’s geometry. To overcome this limitation, in this paper we propose a multi-component deconvolution seismic interferometry approach to develop a surrogate model for response prediction for cases with sparse sensor deployment and limited information about the structure of interest. The resulting model incorporates various sources of uncertainties namely measurement noise, effects of variations of temperature and humidity, and human activity induced vibrations by predicting a probabilistic structural response. We demonstrate the efficacy of the proposed algorithm by applying it to field monitoring data collected from structures with sparse sensor deployment in the Groningen region of the Netherlands for a period of approximately 10 months on average.