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Experimental testing is a primary approach to understanding the seismic performance of nonstructural elements, the damage to which may claim tremendous loss during earthquakes. This paper proposed a standard dynamic loading protocol for experiments on acceleration-sensitive nonstructural elements. It is described by closed-form equations and matches the floor response spectrum of a specific building. An empirical phase angle is also proposed for bidirectional horizontal loading. The results of a trial shake table test on suspended ceilings show that the proposed protocol provides a promising tool to gain insight into the time-varying nature of the dynamic properties of suspended ceilings. [ABSTRACT FROM AUTHOR]

In 2015, a full-scale ten-story reinforced concrete (RC) building structure was tested on the E-Defense shake table, the recorded test data from which provided a unique benchmark case to validate a state-of-the-art modeling approach. This paper presents the development and validation of a finite element model of the test building structure established on the OpenSees platform. In this model, RC beams and columns were simulated using the fiber-based beam-column element, and shear walls were modeled with the multi-layer shell element. The numerical model provided a reasonable estimate of the observed global responses of the test structure, including peak inter-story drifts and floor accelerations, for the wall direction. The multi-layer shell element effectively tracked the local strain, flexural and shear deformations of RC walls. Although the numerical model reasonably captured responses for the frame direction under base fixed JMA-Kobe 50% shaking, the simulation of RC frames was less accurate for base fixed JMA-Kobe 100% shaking when the test structure experienced significant damage at the maximum inter-story drift of 2.9%. Finally, a couple of important modeling issues for RC structure were discussed, including beam-column joint modeling and damping modeling. Use of the scissors model to represent the beam-column joints led to an improved estimation of the inter-story drifts of stories where the beam-to-column joints experienced severe damage. A transient Rayleigh damping model, in which a tangent stiffness matrix was used to formulate a system damping matrix, was recommended for structural nonlinear response history analysis. [ABSTRACT FROM AUTHOR]

In this paper, the existing postearthquake performance assessment framework for reinforced concrete (RC) building structures is improved by adding a new feature of the computer vision‐based damage detection. In this framework, visible seismic damage is classified and quantified from photographs of damaged RC components using the developed deep convolutional network (CNN) Damage‐Net of semantic segmentation, and then the mechanical property degradation factors of components determined from the detected damage states are used to update the numerical model. Pushover analysis of the updated model assesses the residual capacity of the damaged structure. Large‐scale shaking table tests of a three‐story RC building structure, which was heavily instrumented with sensors and recorded with a large volume of photographs, were used as a case study to demonstrate the improved postearthquake performance assessment framework. The vision‐based approach accurately detected multicategory seismic damage of the test structure and effectively estimated the residual crack widths and angles under various lighting, image acquisition, and surface conditions. The updated model, which incorporated the mechanical property degradation of the damaged components, provided accurate estimate on the fundamental vibrational frequencies of the damaged structure after various levels of seismic motion shaking, which matched well with the system identification results. Using the mechanical property reduction factor values recommended by FEMA 306 & Chiu et al., pushover analysis of the updated models provided residual capacity curves that reasonably captured the measured hysteretic responses of the structure. In addition, the damage states of components as estimated by the vision‐based methods were also compared with the measured plastic hinge rotation data. The successful implementation of the vision‐based assessment in this test case indicates its potential for application in the postearthquake evaluation of buildings. [ABSTRACT FROM AUTHOR]

A tuned viscous mass damper (TVMD) is a novel type of vibration absorber which exhibits outstanding vibration control performance. In this study, an experiment was conducted to investigate the robustness of a TVMD for the vibration control of a typical single degree of freedom (SDOF) structural system. To that end, a new eddy-current TVMD (EC-TVMD) was developed as a representative TVMD device. The TVMD-controlled SDOF system was investigated using a series of shaking table tests. The influence of variations in stiffness of the primary structure and TVMD damping (including damping amplitude and damping nonlinearity) was assessed. The variation in TVMD damping exerted a smaller influence on control performance than the variation in primary structure stiffness. The robustness of TVMD control was enhanced by increasing the inertance-to-mass ratio. Parametric analyses using a numerical model further confirmed the experimental observations, and indicated that a TVMD exhibits improved robustness compared with a conventional tuned mass damper (TMD) which is sensitive to the detuning effect. The vibration control mechanism and robustness characteristics of the TVMD were further revealed by a Kelvin-Voigt model. Finally, the influence of damping nonlinearity was verified by the nonlinear time history analysis of a finite element model of the test structure. The results indicate that damping nonlinearity has limited influence on the control of a TVMD with nonlinear damping as long as this TVMD has the same peak displacement amplification ratio as the optimal linear design. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced vision-based, post-earthquake performance assessment framework for RC building structures. This framework incorporates a damage inference method that can estimate the stiffness and strength reduction factors of components in stories without damage inspection based on the inter-story drift. The framework was validated by the application to the full-scale shaking table test of an E-Defense 2018 ten-story RC structure. The vision-based damage detection method effectively detected multicategory seismic damage on the component surfaces, and the damage states estimated by the vision-based method were consistent with the results estimated by the measured plastic hinge rotation. The proposed inter-story drift-based damage inference method reasonably estimated the reduction factors of components in the stories without damage photos. The numerical model updated by the reduction factors of damaged components provided accurate estimates of the fundamental vibrational frequencies after different levels of seismic shaking, and the drift-based inference method significantly improved the results compared with the trivial linear interpolation method. The residual capacity curves provided by pushover analysis of the updated model using the reduction factors as per FEMA 306 & Chiu et al. reasonably captured the hysteretic responses of the structure. ● An inter-story drift-based damage inference method for RC structures was developed. ● The method was integrated into a vision-based post-earthquake assessment framework. ● Formulas were proposed to link stiffness and strength reduction factors of RC components. ● The framework was validated by an E-Defense full-scale ten-story shaking table test. [ABSTRACT FROM AUTHOR]

• A novel structural displacement estimation approach integrating dense optical flow and template matching was proposed. • The proposed approach was evaluated in two large-scale shaking table tests. • The hybrid approach eliminated displacement drift caused by cumulative errors. • Stable and reliable estimation of structural displacement across various environments was achieved. Structural displacement is an important indicator of structural safety in structural health monitoring (SHM). As one of the vision-based methods, optical flow can provide displacement measurement of pixels in images. However, estimating structural displacement by predicting the optical flow between the current frame and the initial frame may be subject to limited accuracy due to environmental variations. Alternatively, calculating structural velocity by predicting the optical flow of adjacent frames and integrating to obtain displacement may introduce displacement drift attributable to cumulative errors. This study proposes a hybrid structural displacement estimation method to eliminate the effects of environmental variations and cumulative errors. In comparison to the existing methods, the novelty of the proposed method is to effectively integrate deep learning-based dense optical flow and correlation template matching (CM), for achieving both high accuracy and improved robustness. Deep learning-based dense optical flow was used for optical flow prediction between adjacent frames through correlation calculations and iterative updating to obtain the structural velocity. Pyramid-accelerated CM was employed to locate the regions of interest (ROI) of the structure in each frame, and the structural displacement was then estimated by counting temporal changes in these locations. By fusing estimated structural velocity and displacement using the Kalman filter, optimized structural displacement results were obtained, and temporal cumulative errors using dense optical flow could be eliminated. The proposed method was validated in an indoor shaking table test of a three-story reinforced concrete structure, and an outdoor shaking table test of a cold-formed steel wall system. The results indicated that the proposed method reduced the root mean square error of estimated displacement by over 89% compared with dense optical flow and by over 36% compared with CM. Furthermore, the proposed method was able to process 1080p high-definition images at a rate of 5.43 frames per second, indicating its high efficiency for applications. [ABSTRACT FROM AUTHOR]

• A novel arrangement of displacement meters was proposed with data correction method. • A deep learning-based super-resolution approach was developed for drift measurement. • The developed approaches were evaluated through large-scale shaking table tests. • The novel arrangement of displacement meters eliminated the slab rotation effect. • The super-resolution approach achieved a stable sub-pixel measurement of drifts. Accurate measurements of inter-story drift responses are critical in shaking table tests. This paper compared three commonly-used approaches of inter-story drift measurement, and developed the techniques for enhanced measurement. This study proposed a novel arrangement of displacement meters along with the associated data correction method. By setting the overhanging steel arms above and beneath a floor slab at the same position, the suggested approach could remove the influence of floor slab rotation and thus improve the accuracy of inter-story drift measurement. In addition, a novel computer vision-based target tracking approach based on a super-resolution (SR) image reconstruction technique was developed. This advanced deep learning-based SR method can transform blurry, low-resolution images into sharp, high-resolution ones for precise target tracking. The accuracy of these developed inter-story measurement approaches was evaluated through a case study of shaking table tests of a large-scale three-story reinforced concrete (RC) building structure. The results indicated that the novel arrangement of displacement meters and associated data correction method successfully eliminated the influence of floor slab rotation, which could result in an error of approximately 20% in the inter-story drift measurement if left uncorrected. The novel SR method overcame the limitation of video resolution and achieved a stable sub-pixel measurement result. In the case of seismic loading, the SR method improved the signal-to-noise ratio of the drift measurement by 68%, and reduced the root mean square error by 63%, compared with the conventional template matching technique. The modal parameters of the test structure were accurately identified from the small-magnitude displacement data of white noise vibration responses measured using the SR method. [ABSTRACT FROM AUTHOR]

SUMMARY A series of large-scale dynamic tests was conducted on a passively controlled five-story steel building on the E-Defense shaking table facility in Japan to accumulate knowledge of realistic seismic behavior of passively controlled structures. The specimen was tested by repeatedly inserting and replacing each of four damper types, that is, the buckling restrained braces, viscous dampers, oil dampers, and viscoelastic dampers. Finally, the bare steel moment frame was tested after removing all dampers. A variety of excitations was applied to the specimen, including white noise, various levels of seismic motion, and shaker excitation. System identification was implemented to extract dynamic properties of the specimen from the recorded floor acceleration data. Damping characteristics of the specimen were identified. In addition, simplified estimations of the supplemental damping ratios provided by added dampers were presented to provide insight into understanding the damping characteristics of the specimen. It is shown that damping ratios for the specimen equipped with velocity-dependent dampers decreased obviously with the increasing order of modes, exhibiting frequency dependency. Damping ratios for the specimen equipped with oil and viscoelastic dampers remained constant regardless of vibration amplitudes, whereas those for the specimen equipped with viscous dampers increased obviously with an increase in vibration amplitudes because of the viscosity nonlinearity of the dampers. In very small-amplitude vibrations, viscous and oil dampers provided much lower supplemental damping than the standard, whereas viscoelastic dampers could be very efficient. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The seismic performance of nonstructural elements is crucial for buildings to remain functional during and after earthquakes. Since the seismic damage to many nonstructural elements is sensitive to their acceleration and/or velocity responses, dynamic testing becomes inevitable in assessing their seismic performance. We propose a novel experimental method that enables the dynamic testing of various layouts of nonstructural elements through a passively controlled and adaptable testbed mounted on and driven by a shake table. The testbed is made of three consecutive floors of steel structure. The bottom two floors provide a space to accommodate a full-scale room. The third floor on top adds additional degrees of freedom for the ease of tuning the dynamic properties of the testbed. The input motion of the shake table is generated by an offline control algorithm to drive the testbed so that its bottom two floors trace the time histories of target floor motions of the building of interest. This paper introduces the key aspects of the new method, including the derivation of the desired structural properties of the testbed, and the control algorithm that generates the necessary input motion of the shake table. An example of simulating the motions of two consecutive floors in the middle of a 42-story tall building is adopted to assist illustrating and validating the method by numerical simulation and experimental demonstration. [ABSTRACT FROM AUTHOR]

Vision-based methods have shown great potential in vibration-based structural health monitoring (SHM). However, these methods are not standard practices yet, since their accuracy and robustness may be influenced by extreme environmental conditions. To this end, this paper proposed a method, named dynamic regularized total variation correlation filter (DTVCF). In DTVCF, an effective optimization problem, which contains the space and structural shape information, is defined for dynamically updating the regularization weight map. Then the regularization weight map is smoothed by a total variation (TV) optimization. These are to better track the dramatically changing or almost invisible structural shape, caused by extreme environmental conditions. Moreover, efficient subpixel image registration (ESR) is used in each tracked region of interest (ROI), over time, to achieve subpixel accuracy. The superiority of DTVCF was validated in extreme environmental conditions. DTVCF could achieve subpixel level structural displacement estimation with high accuracy. Furthermore, DTVCF could process approximately 9.00 frame/s, and 3.50 frame/s in two shaking table tests, indicating its high efficiency for SHM applications. [ABSTRACT FROM AUTHOR]