Optimization of beam and column sections for compliance drift of reinforced concrete buildings using Artificial Neural Networks

Jorge  Arcila Zea; Carlos Alberto Riveros Jerez; Javier Enrique Rivero Jerez

doi:10.17533/udea.redin.16382

Autores/as

Jorge Arcila Zea Universidad EAFIT
Carlos Alberto Riveros Jerez Universidad de Antioquia https://orcid.org/0000-0002-7128-4165
Javier Enrique Rivero Jerez Universidad de Antioquia https://orcid.org/0000-0001-9524-374X

DOI:

https://doi.org/10.17533/udea.redin.16382

Palabras clave:

redes neuronales, edes neuronales artificiales (RNA), deriva, diseño sísmico, estructuras aporticadas, optimización

Resumen

Este artículo presenta los resultados del uso de Redes Neuronales Artificiales (RNA) para la estimación de secciones óptimas de vigas y columnas en concreto reforzado en edificaciones aporticadas simétricas de 1 a 6 pisos teniendo en cuenta los requisitos mínimos exigidos en la NSR-10 relacionados con la deriva y el diseño sísmico. Además se estudió la sensibilidad de la deriva respecto a los valores de dimensiones de vigas y de columnas, para que una vez se tenga una mejor comprensión de dicha relación, se puedan obtener diseños óptimos de manera más rápida, sencilla y confiable en comparación con los procedimientos utilizados actualmente.

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Biografía del autor/a

Jorge Arcila Zea, Universidad EAFIT

Ingeniero Civil-Universidad de Antioquia (2011). Actualmente, estudiante de Maestría en Ingeniería Sismo-Resistente.

Carlos Alberto Riveros Jerez, Universidad de Antioquia

Facultad de Ingeniería.

Javier Enrique Rivero Jerez, Universidad de Antioquia

Facultad de Ingeniería, Docente.

Citas

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.