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Founded in 1904 and headquartered in Farmington Hills, Michigan, USA, the American Concrete Institute is a leading authority and resource worldwide for the development, dissemination, and adoption of its consensus-based standards, technical resources, educational programs, and proven expertise for individuals and organizations involved in concrete design, construction, and materials, who share a commitment to pursuing the best use of concrete.
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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: Uncertainties in Predicting Structural Disproportionate Collapse
Author(s): Shalva Marjanishvili and Serdar Astarlioglu
Publication: Special Publication
Appears on pages(s): 1-12
Keywords: robustness, uncertainty, reliability; disproportionate collapse; column loss; alternate path method, probabilistic modeling
Abstract:The possibility of a local structural failure causing global collapse of a structural system has fueled the continued development of improved computational methods to model building behavior, as well as "best practices" engineering standards. In spite of these efforts, recent events are bringing the issue of collapse prevention to the forefront and highlighting the shortcomings of existing design practices. The catastrophic nature of structural collapse dictates the need for more reliable methodologies to quantify the likelihood of structural failures, and strategies to minimize potential consequences. This paper presents the results of a stochastic nonlinear dynamic analysis study of a simple reinforced concrete structural model to predict catastrophic failure. The performed analysis indicates that, at the point of incipient failure, uncertainties caused by the variability of the design parameters become increasingly large. Consequently, it may not be possible to accurately predict when (and if) failure may occur. Recognizing the need to understand uncertainties associated with risk and probabilities of unlikely events (low probability and high consequence events), this paper sets the stage to better understand the limitations of current numerical analysis methods and discuss innovative alternatives.
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