This paper presents the numerical simulations of the reinforced concrete (RC) panels and the RC shear walls under monotonic and cyclic loading in order to validate the ability of the proposed model which is based on the Rigid-Body-Spring Model (RBSM) to predict the crack propagation behaviours. The authors have already developed constitutive models for the three-dimensional RBSM with random geometry in order to quantitatively evaluate the mechanical responses including softening and localization fractures, and have shown that the model can well simulate the cracking and failure behaviours of RC members. In this study, the constitutive models were extended to include cyclic effects and the model was validated through the simulations of the RC panel tests under cyclic loadings, which were reported in the literatures. Furthermore, the simulations of the RC shear wall tests, which were tested in the context of the international benchmark ConCrack (http://www. concrack.org/) were carried out, and the capability of the model to predict the detailed cracking information, such as crack width, spacing and direction of propagation is discussed.
Taking Shanghai Tianzifang as an example, this study attempted to utilize theories, such as the Space Syntax Theory, to build a multivariate model with street spatial characteristics as variables, and investigate the correlation between street spatial characteristics and pedestrian density in commercial blocks using multivariate regression analyses of the variables in this model. This study inspected two aspects of spatial characteristics of street space. First, in terms of commercial use characteristics, this study examined how pedestrian density is affected by three variables: store density, overflow ratio of store-front space. Density of building exits and entrances. Next, in terms of structural characteristics, this study probed the relationship between commercial pedestrian density and three other variables: street integration, height of buildings on both sides of the streets, and the stores' distance from block entrances. Last, a multivariate regression analysis was conducted on the research data of the six variables. The results validated that four of the variables are correlated with commercial pedestrian density, in the order of their degree of influence: street integration, store density, density of building exits and entrances, and height of buildings on both sides of the streets.
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