WIT Press

Sustainable Evaluation Of Urban Buildings For Safety In Environment Management: The Case Of The Taiwan 921 Earthquake


Free (open access)

Paper DOI






Page Range

537 - 546




1,291 kb


C.-H. Tu & K.-W. Tsou


After the 921 earthquake in Taiwan, people have a great fear of earthquakes. In terms of disaster characteristics in earthquakes, the casualties were caused by breakages and the collapse of the buildings. The ability of the building to resist earthquakes becomes a fundamental requirement for earthquake prevention. The factors that influence the ability of a building to resist earthquakes can be categorized into two: the characters of natural environment and the characters of the building itself. The Binary Regression Method was used to construct a forecast model for the hazardousness of the building in an earthquake disaster, and to differentiate the relationship between damage to the building and the characters of the building by the coefficients in the model. In this study, which is in accordance with building damage records of the Gi-Gi earthquake, nine factors, namely the distance between the building and a river, the thickness of the soil, the structure of the building, the number of floors a building has, the age of the building, the use of the building, the plane configuration of the building, any skylights and the situation of any overprint on the roof, are determined in the logistic regression model, and the relationship between these factors and the grades of building damage are also analyzed by the model. In the aspect of model application, this study chooses parts of the areas in the east of Tainan City, which provide similar environmental conditions, calculates the danger probability and transforms these into the building prediction results in safety, danger and collapse. The prediction results are proved credible and valid through examination. Keywords: the building hazard in earthquake disasters, the logistic regression model.


the building hazard in earthquake disasters, the logistic regression model.