Optimization of beam and column sections for compliance drift of reinforced concrete buildings using Artificial Neural Networks
DOI:
https://doi.org/10.17533/udea.redin.16382Keywords:
artificial neural networks (ANN), drift, seismic design, framed structures, optimizationAbstract
This article presents the application of Artificial Neural Networks (ANN) to estimate optimal sections of beams and reinforced concrete columns for symmetric framed buildings with 1-6 floors taking into consideration the minimum requirements of the NSR-10 related with drift and seismic design. It is also studied the sensitivity of drift to the values of dimensions of beamsand columns providing a better understanding of this relationship in order to obtain optimal designs more quickly, easily and reliably as compared to current used procedures.
Downloads
References
N. Lagaros, M. Papadrakakis, G. Kokossalakis. “Structural optimization using evolutionary algorithms”. Computers and Structures. Vol. 80. 2002. pp. 571-589. DOI: https://doi.org/10.1016/S0045-7949(02)00027-5
C. Camp S. Pezeshk, H. Hansson. “Flexural design of reinforced concrete frames using a genetic algorithm”. Journal of Structural Engineering. Vol. 129. 2003. pp. 105-115. DOI: https://doi.org/10.1061/(ASCE)0733-9445(2003)129:1(105)
N. Ahmadi, R. Moghadas, A. Lavaei. “Dynamic analysis of structures using neural networks”. American Journal of Applied Sciences. Vol. 5. 2008. pp. 1251-1256. DOI: https://doi.org/10.3844/ajassp.2008.1251.1256
M. Sonmez. “Discrete optimum design of truss structures using artificial bee colony algorithm”. Structural and Multidisciplinary Optimization. Vol. 43. 2011. pp. 85-97. DOI: https://doi.org/10.1007/s00158-010-0551-5
D. Suji, S. Natesan, R. Murugesan, R. Sanjai. “Optimal design of fibrous concrete beams through simulated annealing”, Asian Journal of Civil Engineering– Building and Housing. Vol. 9. 2008. pp. 193-213
C. Ferreira, M. Barros, A. Barros. “Optimal design of reinforced concrete T-sections in bending”. Engineering Structures. Vol. 25. 2003. pp. 951-964. DOI: https://doi.org/10.1016/S0141-0296(03)00039-7
M. Lepš, M. Šejnoha. “New approach to optimization of reinforced concrete beams”. Computers and Structures. Vol. 81. 2003. pp. 1957-1966. DOI: https://doi.org/10.1016/S0045-7949(03)00215-3
C. Coello, A. Christiansen. “Multiobjective optimization of trusses using genetic algorithms”. Computers and Structures. Vol. 75. 2000. pp. 647-660. DOI: https://doi.org/10.1016/S0045-7949(99)00110-8
M. Ghozi, P. Aji, P. Suprobo. “Evolutionary Parallel SAP2000 for truss structure optimization”, International Journal Academic Research. Vol. 3. 2011. pp. 1140-1145.
C. Chan, M. Wong. “Evolutionary optimization of braced steel frameworks for tall buildings using a hybrid OC-GA method”. Advances in Engineering Structures, Mechanics and Construction–Solid Mechanics and Its Applications. Vol. 140. 2006. pp. 205-214. DOI: https://doi.org/10.1007/1-4020-4891-2_17
J. Lee. C. Yun. “Damage localization for bridges using probabilistic neural networks”. KSCE Journal of Civil Engineering. Vol. 11. 2007. pp. 111-120. DOI: https://doi.org/10.1007/BF02823854
N. Caglar, M. Elmasa, Z. Yamana, M. Saribiyikb. “Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings”. Construction and Building Materials. Vol. 22. 2008. pp. 788-800. DOI: https://doi.org/10.1016/j.conbuildmat.2007.01.029
G. Vanderplaats. “Structural optimization: where we’ve been and where we’re going”. Computer Aided Optimum Design of Structures V. Vol. 28. 1977. pp. 45-54.
L. Schmit, B. Farshi. “Some approximation concepts for structural synthesis”. Journal of American Institute of Aeronautics and Astronautics. Vol. 12. 1974. pp. 692-699. DOI: https://doi.org/10.2514/3.49321
H. Adeli. “Neural Networks in Civil Engineering: 1989-2000”. Computer-Aided Civil and Infrastructure Engineering. Vol. 16. 2001. pp. 126-142. DOI: https://doi.org/10.1111/0885-9507.00219
M. Hadi. “Neural networks applications in concrete structures”. Computers and Structures. Vol. 81. 2003. pp. 373-381. DOI: https://doi.org/10.1016/S0045-7949(02)00451-0
N. Caglar. “Neural network based approach for determining the shear strength of circular reinforced concrete columns”. Construction and Building Materials. Vol. 23. 2009. pp. 3225-3232. DOI: https://doi.org/10.1016/j.conbuildmat.2009.06.002
M. Hakan. “An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks”. Engineering Structures. Vol. 32. 2010. pp. 1888-1898. DOI: https://doi.org/10.1016/j.engstruct.2010.03.010
O. Möller, R. Foschi, L. Quiroz, M. Rubinstein. “Structural optimization for performance-based design in earthquake engineering: Applications of neural networks”. Structural Safety. Vol. 31. 2009. pp. 409- 499. DOI: https://doi.org/10.1016/j.strusafe.2009.06.007
N. Lagaros, M. Papadrakakis. “Neural network based prediction schemes of the non-linear seismic response of 3D buildings”. Advances in Engineering Software. Vol. 44. 2012. pp. 92-115. DOI: https://doi.org/10.1016/j.advengsoft.2011.05.033
N. Lagaros, M. Papadrakakis. “Reliability-based structural optimization using neural networks and Monte Carlo simulation”. Computer Methods in Applied Mechanics and Engineering. Vol. 191. 2002. pp. 3491-3507. DOI: https://doi.org/10.1016/S0045-7825(02)00287-6
L. Zhang, G. Subbarayan. “An evaluation of backpropagation neural networks for the optimal design of structural systems: Part I. Training procedures”. Computer Methods in Applied Mechanics and Engineering. Vol. 191. 2002. pp. 2873-2886. DOI: https://doi.org/10.1016/S0045-7825(01)00372-3
L. Zhang, G. Subbarayan. “An evaluation of backpropagation neural networks for the optimal design of structural systems: Part II. Numerical evaluation”. Computer Methods in Applied Mechanics and Engineering. Vol. 191. 2002. pp. 2887-2904. DOI: https://doi.org/10.1016/S0045-7825(02)00213-X
E. Caicedo, J. López. “Perceptrón Multicapa y Algoritmo Backpropagation”. Una aproximación práctica a las Redes Neuronales Artificiales. 1a ed. Ed. Universidad del Valle. Cali, Colombia. 2009. pp. 75- 136. DOI: https://doi.org/10.25100/peu.64
B. del Brío, A. Sanz. “Fundamentos de las Redes Neuronales Artificiales”, “Redes Neuronales Supervisadas”, “Aplicaciones de Redes Neuronales Artificiales”, Redes neuronales y sistemas difusos. 2a ed. Ed. Alfaomega. Ciudad de México, México. 2002. pp. 13-32, pp. 63-84, 209-240.
S. Haykin. “Introduction”, “Learning Processes”, “Single Layer Perceptrons”, “Multilayer Perceptrons”. Neural Networks. A comprehesive foundation. 2a ed. Ed. Pearson Prentice Hall. Singapur, Singapur. 1999. pp. 23-71, 72-124, 136-170, 178-266.
B. Gottfried, J. Weisman. “Introduction”, “Optimization fundamentals”. Introduction to optimization theory. 1st ed. Ed. Prentice Hall. New Jersey, USA. 1973. pp. 4-22, 26-57.
R. Haftka, Z. Gürdal. “Classical tools in Structural Optimization”. Elements of Structural Optimization. 3rd ed. Ed. Kluwer Academic Publishers. Amsterdam, The Netherlands. 1992. pp. 23-61. DOI: https://doi.org/10.1007/978-94-011-2550-5_2
AIS. “Título A”, “Título C”. Normas Colombianas de Diseño y Construcción Sismo-Resistente. Bogotá, Colombia. 2010. pp. A15-A30, A76, C42, C69, C178.
M. Hudson, M. Hagan, H. Demuth. “Multilayer Networks and Backpropagation Training”. Neural Network Toolbox, User’s Guide. Ed. The Mathworks Inc. Natick, USA. 2011. pp. 3.2-3.28.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Revista Facultad de Ingeniería
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Revista Facultad de Ingeniería, Universidad de Antioquia is licensed under the Creative Commons Attribution BY-NC-SA 4.0 license. https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
The material published in the journal can be distributed, copied and exhibited by third parties if the respective credits are given to the journal. No commercial benefit can be obtained and derivative works must be under the same license terms as the original work.
Twitter