Automated Design of Reinforced Concrete Deep Beams

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Copyright: Islam, S. M. Shahidul
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Abstract
Recent developments of the finite element method and computer-aided design have reduced the need for long and expensive tests in many areas of engineering; for instance, many numerical simulations of metal-forming processes have been conducted and used extensively for the analysis and design of industrial parts. This is also true for the automobile industry which often simulates crash tests even though it is possible to develop a product solely through prototyping. However, the potential of these techniques has not been fully realised by civil engineering structural designers as the nonlinear finite element is still used as a verification rather than design tool. The aim of this study was to develop an automated design method for optimising the reinforcements in reinforced concrete structures. Deep beam were chosen because their behaviour encompasses all the difficulties associated with modelling reinforced concrete structures under a state of generalised stress, and also for their ease of manufacture and testing. The rationale for the method was that the steel bars carrying the loads once the concrete is cracked should be strained as close as possible to the steel s yield strain. It was found that, in particular, as the amounts of extra steel needed to prevent yielding in the regions did not increase uniformly due to stress redistribution, the developed method could save a substantial amount of reinforcement. To validate the method, three beam specimens with the optimised reinforcement were cast and tested in the laboratory. It was found that the experimental results corroborated those predicted by the automated design method and the design resulted in more ductile behaviour. The measured steel strains were also found to be in the vicinity of the yield strains predicted by the method. Most importantly, it was experimentally proven that the method used the reinforcing steel more efficiently.
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Author(s)
Islam, S. M. Shahidul
Supervisor(s)
Khennane, Amar
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Publication Year
2012
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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