This study presents the application of Genetic Algorithms (GAs) for the optimum cost design of reinforced concrete beams and columns based on the standard specifications of the American Concrete Institute (ACI 318-11). The produced optimization procedure satisfies the strength, serviceability, ductility, durability, and other constraints related to good design and detailing practice. While most of the approaches reported in this field have considered steel reinforcement only or cross-sectional dimensions of the members as design variables and for the flexural aspect in general, the dimensions and reinforcing steel in this study were introduced as design variables, considering the axial, flexural, shear, and torsion effects on the members. The aim of this study is to find the effect of material’s price on the optimum cost of beams and columns according to the local market using the GAs, by limiting the design procedure with many constraints that control the optimum design variables to a certain limits. It was found that the Genetic Algorithms is a sufficient method for finding the optimum solution smoothly and flawless with many complicated constraints. Also, increasing the applied torsion on a beam section with a constant cost ratio r will increase the optimum cost by about 3.8%.
Published in | American Journal of Civil Engineering (Volume 5, Issue 6) |
DOI | 10.11648/j.ajce.20170506.13 |
Page(s) | 331-338 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Optimum Design, Genetic Algorithms, Material Price, Concrete Design, Optimum Cost
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[4] | Building Code Requirements for Structural Concrete (ACI 318 M–11), Reported by ACI Committee 318. |
[5] | Adeli H. and Sarma K. C., 2006, “Cost Optimization of Structures”, John Wiley & Sons Ltd. |
[6] | Wight J. K. and MacGregor J. G., 2009, “Reinforced Concrete – Mechanics And Design”, Fifth Edition, Pearson Education Inc. |
[7] | Najem, Rabi’ M. and Yousif, Salim T., 2015, “Optimum Structural Cost: A Genetic Algorithms Approach”, Scholar’s Press, Deutschland, Germany. |
[8] | Global Optimization Tool Box 3, 2010, “User’s Guide”, The Math Works Inc. |
[9] | Wight, James K. and MacGregor, James G, 2009, “Reinforced Concrete: Mechanics And Design”, Fifth Edition, Pearson Education Hall. |
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APA Style
Salim Tayeb Yousif, Rabi Muyad Najem. (2017). Material Price Enfluence on the Optimum Design of Different Structural Members. American Journal of Civil Engineering, 5(6), 331-338. https://doi.org/10.11648/j.ajce.20170506.13
ACS Style
Salim Tayeb Yousif; Rabi Muyad Najem. Material Price Enfluence on the Optimum Design of Different Structural Members. Am. J. Civ. Eng. 2017, 5(6), 331-338. doi: 10.11648/j.ajce.20170506.13
AMA Style
Salim Tayeb Yousif, Rabi Muyad Najem. Material Price Enfluence on the Optimum Design of Different Structural Members. Am J Civ Eng. 2017;5(6):331-338. doi: 10.11648/j.ajce.20170506.13
@article{10.11648/j.ajce.20170506.13, author = {Salim Tayeb Yousif and Rabi Muyad Najem}, title = {Material Price Enfluence on the Optimum Design of Different Structural Members}, journal = {American Journal of Civil Engineering}, volume = {5}, number = {6}, pages = {331-338}, doi = {10.11648/j.ajce.20170506.13}, url = {https://doi.org/10.11648/j.ajce.20170506.13}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20170506.13}, abstract = {This study presents the application of Genetic Algorithms (GAs) for the optimum cost design of reinforced concrete beams and columns based on the standard specifications of the American Concrete Institute (ACI 318-11). The produced optimization procedure satisfies the strength, serviceability, ductility, durability, and other constraints related to good design and detailing practice. While most of the approaches reported in this field have considered steel reinforcement only or cross-sectional dimensions of the members as design variables and for the flexural aspect in general, the dimensions and reinforcing steel in this study were introduced as design variables, considering the axial, flexural, shear, and torsion effects on the members. The aim of this study is to find the effect of material’s price on the optimum cost of beams and columns according to the local market using the GAs, by limiting the design procedure with many constraints that control the optimum design variables to a certain limits. It was found that the Genetic Algorithms is a sufficient method for finding the optimum solution smoothly and flawless with many complicated constraints. Also, increasing the applied torsion on a beam section with a constant cost ratio r will increase the optimum cost by about 3.8%.}, year = {2017} }
TY - JOUR T1 - Material Price Enfluence on the Optimum Design of Different Structural Members AU - Salim Tayeb Yousif AU - Rabi Muyad Najem Y1 - 2017/10/13 PY - 2017 N1 - https://doi.org/10.11648/j.ajce.20170506.13 DO - 10.11648/j.ajce.20170506.13 T2 - American Journal of Civil Engineering JF - American Journal of Civil Engineering JO - American Journal of Civil Engineering SP - 331 EP - 338 PB - Science Publishing Group SN - 2330-8737 UR - https://doi.org/10.11648/j.ajce.20170506.13 AB - This study presents the application of Genetic Algorithms (GAs) for the optimum cost design of reinforced concrete beams and columns based on the standard specifications of the American Concrete Institute (ACI 318-11). The produced optimization procedure satisfies the strength, serviceability, ductility, durability, and other constraints related to good design and detailing practice. While most of the approaches reported in this field have considered steel reinforcement only or cross-sectional dimensions of the members as design variables and for the flexural aspect in general, the dimensions and reinforcing steel in this study were introduced as design variables, considering the axial, flexural, shear, and torsion effects on the members. The aim of this study is to find the effect of material’s price on the optimum cost of beams and columns according to the local market using the GAs, by limiting the design procedure with many constraints that control the optimum design variables to a certain limits. It was found that the Genetic Algorithms is a sufficient method for finding the optimum solution smoothly and flawless with many complicated constraints. Also, increasing the applied torsion on a beam section with a constant cost ratio r will increase the optimum cost by about 3.8%. VL - 5 IS - 6 ER -