# Applications of Metaheuristic Optimization Algorithms in Civil EngineeringCost and CO2 Emission Optimization of Reinforced Concrete Frames Using Enhanced Colliding Bodies Optimization Algorithm

Applications of Metaheuristic Optimization Algorithms in Civil Engineering: Cost and CO2 Emission... [This chapter investigates discrete design optimization of reinforcement concrete frames using the recently developed metaheuristic called Enhanced Colliding Bodies Optimization (ECBO) and the Non-dominated Sorting Enhanced Colliding Bodies Optimization (NSECBO) algorithm. The objective function of algorithms consists of construction material costs of reinforced concrete structural elements and carbon dioxide (CO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $${\mathrm{CO}}_2$$ \end{document}) emissions through different phases of a building life cycle that meets the standards and requirements of the American Concrete Institute’s Building Code. The proposed method uses predetermined section database (DB) for design variables that are taken as the area of steel and the geometry of cross sections of beams and columns. A number of benchmark test problems are optimized to verify the good performance of this methodology. The use of ECBO algorithm for designing reinforced concrete frames indicates an improvement in the computational efficiency over the designs performed by Big Bang–Big Crunch (BB–BC) algorithm. The analysis also reveals that the two objective functions are quite relevant and designs focused on mitigating CO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $${\mathrm{CO}}_2$$ \end{document} emissions could be achieved at an acceptable cost increment in practice. Pareto results of the NSECBO algorithm indicate that both objective yield similar solutions [1].] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

# Applications of Metaheuristic Optimization Algorithms in Civil EngineeringCost and CO2 Emission Optimization of Reinforced Concrete Frames Using Enhanced Colliding Bodies Optimization Algorithm

31 pages

/lp/springer-journals/applications-of-metaheuristic-optimization-algorithms-in-civil-eI6U5bnS1W

# References (17)

Publisher
Springer International Publishing
© Springer International Publishing Switzerland 2017
ISBN
978-3-319-48011-4
Pages
319 –350
DOI
10.1007/978-3-319-48012-1_17
Publisher site
See Chapter on Publisher Site

### Abstract

[This chapter investigates discrete design optimization of reinforcement concrete frames using the recently developed metaheuristic called Enhanced Colliding Bodies Optimization (ECBO) and the Non-dominated Sorting Enhanced Colliding Bodies Optimization (NSECBO) algorithm. The objective function of algorithms consists of construction material costs of reinforced concrete structural elements and carbon dioxide (CO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $${\mathrm{CO}}_2$$ \end{document}) emissions through different phases of a building life cycle that meets the standards and requirements of the American Concrete Institute’s Building Code. The proposed method uses predetermined section database (DB) for design variables that are taken as the area of steel and the geometry of cross sections of beams and columns. A number of benchmark test problems are optimized to verify the good performance of this methodology. The use of ECBO algorithm for designing reinforced concrete frames indicates an improvement in the computational efficiency over the designs performed by Big Bang–Big Crunch (BB–BC) algorithm. The analysis also reveals that the two objective functions are quite relevant and designs focused on mitigating CO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $${\mathrm{CO}}_2$$ \end{document} emissions could be achieved at an acceptable cost increment in practice. Pareto results of the NSECBO algorithm indicate that both objective yield similar solutions [1].]

Published: Dec 2, 2016

Keywords: Design Variable; Wind Load; Moment Capacity; Concrete Frame; Reinforced Concrete Frame