@article{Apichat Janpila_Piyawat Foytong_Supakorn Tirapat_Nuttawut Thanasisathit_Anat Ruangrassamee_2020, title={THE OPTIMAL METHOD FOR BUILDING DAMAGE FRAGILITY CURVE DEVELOPMENT}, volume={18}, url={https://geomatejournal.com/geomate/article/view/1488}, abstractNote={<p>A fragility curve is a primary component in the risk assessment, which is useful for evacuation<br>planning, estimation of potential losses, and estimation of the damage to residential buildings caused by<br>natural hazards. In general, a fragility curve represents the relationship between the probability of exceeding<br>a specific damage state of a structure and natural hazard intensity. For determining such a curve, two<br>parameters: the median and standard deviation are estimated. A fragility curve can be constructed using<br>empirical data and analytical data. Numerical fitting data is used to develop the fragility curve. Various<br>methods have been proposed using numerical fitting data to approximate the fragility curves. However, the<br>most widely used methods for developing fragility curves are the least-squares method and the maximum<br>likelihood method. In this present study, these two different numerical fitting data methods for fragility curve<br>development are analyzed and compared. Basic assumptions and limitations of each method are also<br>discussed. The building damage data used in all methods to derive the fragility curve is generated from<br>hypothetical damage data assuming a lognormal distribution. Finally, the maximum likelihood method is<br>proven to be optimal for developing fragility curve based on structural damage data.<br><br></p>}, number={69}, journal={GEOMATE Journal}, author={Apichat Janpila and Piyawat Foytong and Supakorn Tirapat and Nuttawut Thanasisathit and Anat Ruangrassamee}, year={2020}, month={May}, pages={74–80} }