Date of Award
Master of Science
ABSTRACTA computational procedure is developed that automatically generates structural designs for reinforced concrete frames using Big Bang-Big Crunch ( BB-BC) optimization. The objective of the optimization is to minimize the total cost or the CO2 emissions associated with construction of reinforced concrete frames subjected to constraints based on the specifications and guidelines prescribed by the American Concrete Institute (ACI 318-08). BB-BC optimization is an iterative population-based heuristic search method that has a numerically simple algorithm with relatively few control parameters as compared to the other evolutionary methods.Designs for several reinforced concrete frames that minimize the cost and the CO2 emissions associated with construction are presented. In the first frame example, low-cost designs developed using BB-BC optimization are compared deisgns developed using genetic algorithms. In the second set of frame designs, both low-cost and low-CO2 emissions designs using BB-BC optimization are compared to designs developed using simulated annealing. In both cases, the BB-BC algorithm generated designs that reduced the cost and the CO2 emissions of construction for example frames.
Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Huq, Farah Ishtiwana, "Cost and CO2 Optimization of Reinforced Concrete Frames Using a Big Bang-Big Crunch Algorithm" (2011). Electronic Theses and Dissertations. 236.