Abstract
This chapter attempts to cover the range from rigorous probabilistic collapse prediction of short period low-ductility buildings to simplified assessment of the collapse potential. The rigorous collapse predictions are based on modeling the deteriorating properties of structural elements with low ductility and predicting the collapse capacity for a given structural configuration and ground motion through incremental dynamic analysis. Consideration is given to the variation in collapse capacity due to randomness of the ground motion. A parameter study is summarized in which the effects of strength and deformation capacity on the collapse potential of frames with infill walls is evaluated. It is found that shear strength has the largest effect on the collapse capacity, and that deformation capacity and residual strength are of less importance.
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Acknowledgments
The development of the collapse prediction methodology for deteriorating systems was supported by the Pacific Earthquake Engineering Research (PEER) Center and the NEESR research project CMS–0421551, both sponsored by the US National Science Foundation, and was carried out at Stanford University’s John A. Blume Earthquake Engineering Center as part of a comprehensive effort to develop basic concepts for PBEE and supporting data on seismic demands and capacities Additional support was provided by the CUREE-Kajima Phase VI research program. The support of the sponsors is gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Krawinkler, H., Lignos, D.G. (2009). How to Predict the Probability of Collapse of Non-Ductile Building Structures. In: Ilki, A., Karadogan, F., Pala, S., Yuksel, E. (eds) Seismic Risk Assessment and Retrofitting. Geotechnical, Geological and Earthquake Engineering, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2681-1_17
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DOI: https://doi.org/10.1007/978-90-481-2681-1_17
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