Investigation of the Relationships and Effects of Urban Transformation Parameters for Risky Structures: A Rapid Assessment Model

Most of the works on the literature on urban transformation focus on the outcomes of transformation in legal, psychosocial, socioeconomic, and geographical aspects; and employ rapid screening models to assess the areas and multiple structures subject to urban transformation. Aiming the contribute to the literature, this study investigates the causal relationships between the parameters used in risk assessment of the individual masonry structures undergoing transformation. The causal relationship, which expresses the cause-effect relationship between two variables, shows that the independent variable has a direct or indirect effect on the dependent variable. The results of statistical analysis and theory should be considered concurrently in building a causal relationship model. The structural assessment reports for risky structures of 183 individual masonry buildings were examined and the relationships between the dependent and independent variables were assessed using path analysis. In order to establish a rapid assessment technique for risk assessment, the variables were chosen by binary logistic regression analysis due to the discrete nature of the dependent variable, and the final model was built accordingly. According to the model analysed by binary logistic regression, direct and indirect effects between the variables were determined using path analysis. While path analysis is applied to continuous data and evaluates linear regression results, an evaluation was performed based on logistic regression with discrete data results in this study. According to the path model analysis, the city where the building was located had the largest direct effect (path coefficient). It was concluded that the model, built with 6 effective variables selected among 25 independent variables generating the risk result, was acceptable in terms of engineering, and the proposed rapid assessment model could be used for risk assessment because of its high correct classification rate.