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.

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Nonlinear simulations for structures under disasters have been widely focused on in recent years. However, precise modeling for the nonlinear behavior of reinforced concrete (RC) shear walls, which are the major lateral-force-resistant structural member in high-rise buildings, still has not been successfully solved. In this paper, based on the principles of composite material mechanics, a multi-layer shell element model is proposed to simulate the coupled in-plane/out-plane bending and the coupled in-plane bending-shear nonlinear behaviors of RC shear wall. The multi-layer shell element is made up of many layers with different thickness. And different material models (concrete or rebar) are assigned to various layers so that the structural performance of the shear wall can be directly connected with the material constitutive law. And besides the traditional elasto-plastic-fracture constitutive model for concrete, which is efficient but does not give satisfying performance for concrete under complicated stress condition, a novel concrete constitutive model, referred as microplane model, which is originally proposed by Bazant et al., is developed to provide a better simulation for concrete in shear wall under complicated stress conditions and stress histories. Three walls under static push-over load and cyclic load were analyzed with the proposed shear wall model for demonstration. The simulation results show that the multi-layer shell elements can correctly simulate the coupled in-plane/out-plane bending failure for tall walls and the coupled in-plane bending-shear failure for short walls. And with microplane concrete constitutive law, the cycle behavior and the damage accumulation of shear wall can be precisely modeled, which is very important for the performance-based design of structures under disaster loads.Keywords: shear wall, nonlinear analysis, microplane, finite element, multi-layer shell element INTRODUCTION:Nonlinear simulations for structures under disasters have been widely focused on in recent years. However, precise modeling for the nonlinear behavior of reinforced concrete (RC) shear walls, which are the major lateral-force-resistant structural member in high-rise buildings, still has not been successfully solved. As the cross section of the shear wall member is much bigger than that of the beam and column member, its deformation behavior under the lateral load is more complicated and the research has focused on the nonlinear analysis model for shear wall at home and abroad until now. In this paper, based on the principles of composite material mechanics, a multi-layer shell element model is proposed to simulate the coupled in-plane/out-plane bending or the coupled in-plane bending-shear nonlinear behaviors of RC shear wall. At the element level, the model uses the shell element that is made up of multiple layers with different thickness and different material models (concrete or rebar) are assigned to various layers. Since the model relates the nonlinear behaviors of the shear wall elemen...

The evaluation of mechanical property degradation (i.e., stiffness and strength degradation) for seismically damaged reinforced concrete (RC) components is a critical step in the post-earthquake assessment of the residual seismic capacity of buildings. In this study, a novel approach based on deep learning (DL) was proposed to evaluate the stiffness and strength degradation of RC columns according to visible seismic damage. A database was constructed by linking the test photos of RC column specimens with the loading points on the hysteretic curves, from which the stiffness and strength reduction factors (𝜆 K and 𝜆 Q , respectively) were analyzed. Two novel convolutional network (CNN) modules were designed to enable feature extraction and integration of seismic damage with a reduced number of parameters, and multitask learning was introduced to enable adaptive feature fusion for stiffness and strength degradation individually.A deep convolutional network (DCNN) was therefore proposed to model the correlation between visible seismic damage and mechanical property degradation of flexural-dominated RC columns, which can integrate visual characteristics and spatial topologies of visible damage to estimate 𝜆 K and 𝜆 Q . The application to two test specimens validated the preferable accuracy and robustness of the proposed DL-based approach, and demonstrated its high potential for use in post-earthquake performance assessment of buildings.

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