Reliability based analysis is a well-established tool in probability-based structural design. The load equation from the ACI Code along with several others ACI design methods are analyzed in order to verify that the reliability index using limit state functions are consistent across both determinate and indeterminate beams. This research provides an in-depth analysis and comparison of reliability indices for types of structures. Literature review describes the different probabilistic models used in the analysis. Several different probabilistic models are used in order to determine the reliability index of determinate and indeterminate structures. Standard reliability methods, such as the first-order reliability method (FORM), modified Rackwitz-Fiessler method, and Monte Carlo Simulations, are used in order to evaluate the reliability index of determinate beams and indeterminate beams designed with moment redistribution.
The research also presents the methodology and results of the reliability index of structures using different analysis methods. Reliability analysis methods are used to verify the safety of structures under different loading conditions for both determinate and indeterminate structures. The key outcomes of the research outlined in this Master’s thesis are the following:
1) Reliability index a. Closed-form solution is independent of the structure type b. Remains consistent across range of live load to dead load ratios 2) Reliability index did not vary greatly, regardless of: a. Structure b. Loading type c. Moment redistribution 3) The closed-form solution is more conservative and results in a lower reliability index. The refined method using the ACI design results in a higher reliability index.
|Advisor:||Silva, Pedro F., Manzari, Majid T.|
|School:||The George Washington University|
|Department:||Civil and Environmental Engineering|
|School Location:||United States -- District of Columbia|
|Source:||MAI 55/06M(E), Masters Abstracts International|
|Keywords:||ACI design codes, LRFD, Load resistance factor design|
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