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Home > Publications > International Concrete Abstracts Portal
The International Concrete Abstracts Portal is an ACI led collaboration with leading technical organizations from within the international concrete industry and offers the most comprehensive collection of published concrete abstracts.
Title: Towards an Accurate Determination of Collapse Vulnerable Reinforced Concrete Buildings
Author(s): Khalid M. Mosalam and Selim Gunay
Publication: Special Publication
Appears on pages(s): 1-14
Keywords: Collapse simulations; element removal; failure detection; gravity load failure; non-ductile reinforced concrete buildings; post-failure modeling; tornado diagram analysis.
Abstract:There are many vulnerable reinforced concrete (RC) buildings located in earthquake-prone areas around the world. These buildings are characterized by the lack of seismic details and corresponding non-ductile behavior and significant potential of partial and global collapse. One of the current challenges of the earthquake engineering profession and research communities is the identification of such buildings and determination of effective and economical retrofit methods for response enhancement. Identification of these buildings is not a trivial task due to the various sources of non-ductile behavior and the large number of involved sources of uncertainty. Furthermore, accurate determination of collapse-prone buildings is important from an economical perspective. Unfortunately, there are not enough economical resources to retrofit all the non-ductile buildings that have the symptoms for collapse potential. In order to use the available monetary resources in an effective manner, these buildings should be accurately and reliably ranked to identify those that are most vulnerable to collapse. This paper intends to provide a contribution to the accurate determination of the most collapse vulnerable non-ductile RC buildings by discussing the methods from existing literature and exploring the research needs related to (a) gravity load failure modeling and (b) consideration of sources of uncertainty in an efficient manner.
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