Dr. Hao Sun, Postdoctoral Associate in the Department of Civil and Environmental Engineering at MIT, presents "Building monitoring through seismic interferometry" at the MIT Earth Resources Laboratory on April 22, 2016.
"Continuous monitoring of building provides a crucial alternative to assess its health condition as well as evaluate its safety throughout the whole service life. To link the field measurements to the characteristics of a building, one option is to characterize and update a model so that it can best describe the behavior and performance of the structure. In this talk, I would present a novel computational strategy for Bayesian characterization of building models with response functions extracted from ambient noise measurements using seismic interferometry. The intrinsic building impulse response functions (IRFs) are first extracted from ambient noises by deconvolving the motion recorded at different floors with respect to the measured ambient ground motion. The IRF represents the representative building response (shear wave propagation) to an input delta function at the ground floor, which can be extracted based on deconvolution and temporal averaging. From the extracted IRFs, the building characteristics, such as the wave velocity, resonant frequencies, mode shapes and damping ratios, can be measured. A hierarchical Bayesian framework with Laplace priors is then proposed for updating a finite element model. A Markov chain Monte Carlo technique with adaptive random-walk steps is employed to sample the model parameters for uncertainty quantification. An illustrative example is studied to validate the effectiveness of the proposed algorithm for temporal monitoring and probabilistic characterization of buildings. The structure considered in this study is a 21-storey instrumented concrete building at the MIT campus. The methodology described here allows for continuous temporal health monitoring, robust model updating as well as post-earthquake damage detection of buildings."