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SUMMARYWhen subjected to long-period ground motions, high-rise buildings' upper floors undergo large responses. Furniture and nonstructural components are susceptible to significant damage in such events. This paper proposes a full-scale substructure shaking table test to reproduce large floor responses of high-rise buildings. The response at the top floor of a virtual 30-story building model subjected to a synthesized long-period ground motion is taken as a target wave for reproduction. Since a shaking table has difficulties in directly reproducing such large responses due to various capacity limitations, a rubber-and-mass system is proposed to amplify the table motion. To achieve an accurate reproduction of the floor responses, a control algorithm called the open-loop inverse dynamics compensation via simulation (IDCS) algorithm is used to generate a special input wave for the shaking table. To implement the IDCS algorithm, the model matching method and the H ∞ method are adopted to construct the controller. A numerical example is presented to illustrate the open-loop IDCS algorithm and compare the performance of different methods of controller design. A series of full-scale substructure shaking table tests are conducted in E-Defense to verify the effectiveness of the proposed method and examine the seismic behavior of furniture. The test results demonstrate that the rubber-and-mass system is capable of amplifying the table motion by a factor of about 3.5 for the maximum velocity and displacement, and the substructure shaking table test can reproduce the large floor responses for a few minutes.

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The detection of visible damage (i.e., cracking, concrete spalling and crushing, reinforcement exposure, buckling and fracture) plays a key role in postearthquake safety assessment of reinforced concrete (RC) building structures. In this study, a novel approach based on computer-vision techniques was developed for pixel-level multicategory detection of visible seismic damage of RC components. A semantic segmentation database was constructed from test photos of RC structural components. Series of datasets were generated from the constructed database by applying image transformations and data-balancing techniques at the sample and pixel levels. A deep convolutional network architecture was designed for pixel-level detection of visible damage. Two sets of parameters were optimized separately, one to detect cracks and the other to detect all other types of damage. A postprocessing technique for crack detection was developed to refine crack boundaries, and thus improve the accuracy of crack characterization. Finally, the proposed vision-based approach was applied to test photos of a beam-to-wall joint specimen. The results demonstrate the accuracy of the vision-based approach to detect damage, and its high potential to estimate seismic damage states of RC components.