Artificial intelligence techniques applied to civil engineering problems
Journal: Journal of Building Technology DOI: 10.32629/jbt.v8i1.5208
Abstract
Artificial Intelligence (AI) is a branch of computer sciences that studies the creation and design of machines capable of solve problems by itself, basing its behavior in the human brain. The methods for modeling and optimizing complex structure systems require huge amounts of computer resources; and artificial-intelligence-based solutions can provide valuable alternatives for solve problems efficiently. This article provides an overview of different techniques of AI, like expert systems, artificial neural networks, fuzzy systems and genetic algorithms; used to solve Civil Engineering problems.
Keywords
artificial intelligence; neural networks; construction; reinforced concrete; beams
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[3] Bianchini, A., & Bandini, P. (2010). Prediction of Pavement Performance through Neuro‐Fuzzy Reasoning. Computer‐Aided Civil and Infrastructure Engineering , 25(1), 39-54.
[4] Charniak, E. & McDermott, D. (1985). Introduction to Artificial Intelligence. Massachusetts: AddisonWesley.
[5] Cheng, M. Y., Tsai, H. C. & Sudjono, E. (2010). Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry. Expert Systems with Applications, 37(6), 4224-4231.
[6] Demir, F. (2008). Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Construction and Building Materials, 22(7), 1428-1435.
[7] Fernández, D. F. (2007). Desarrollo de una nueva herramienta basada en redes neuronales para el diseño de protecciones ligeras cerámica-metal frente a impacto de alta velocidad. (Tesis inédita de doctorado). Universidad Carlos III de Madrid, Leganés, España.
[8] Gestal, M., Cancela, A., Andrade, J. M. & Gómez-Carracedo, M. P. (2006). Hybrid System with Artificial Neural
Networks and Evolutionary Computation in Civil Engineering. En J. Rabuñal & J. Dorado (Ed.), Artificial Neural
Networks in real-life applications (pp. 141-164). Coruña: Idea Group Publishing.
[9] González, L. O., Guerrero, A., Delvasto, S. & Will, A. (2012). Red Neuronal Artificial para estimar la resistencia a compresión, en concretos fibro-reforzados con polipropileno. Ventana Informática, 1(26), 11-28.
[10] Kewalramani, M. A., & Gupta, R. (2006). Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Automation in Construction, 15(3), 374-379.
[11] Klir, G. & Yuan, B. (1995). Fuzzy sets and fuzzy logic. New Jersey: Prentice Hall.
[12] Kumar, S., & Barai, S. V. (2010). Neural networks modeling of shear strength of SFRC corbels without stirrups. Applied Soft Computing, 10(1), 135-148.
[13] Kurkova, V. (1992). Kolmogorov theorem and multilayer neural networks. Neural Networks, 5(3), 1-5. Li, H. & Love, P. E. (1998). Site-level facilities layout using genetic algorithms. Journal of Computing in Civil Engineering, 12(4), 227-231.
[14] Luger, G. F. & Stubblefield W. A. (1998). Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Massachusetts: Addison-Wesley.
[15] Mansour, M. Y., Dicleli, M., Lee, J. Y., & Zhang, J. (2004). Predicting the shear strength of reinforced concrete beams using artificial neural networks. Engineering Structures, 26(6), 781-799.
[16] Montaño, J. J. (2002). Redes neuronales artificiales aplicadas al análisis de datos. (Tesis inédita de doctorado). Universitat de les Illes Balears, Palma de Mallorca, España.
[17] Mukherjee, A., Deshpande, J. M., & Anmala, J. (1996). Prediction of buckling load of columns using artificial neural networks. Journal of Structural Engineering, 122(11), 1385-1387.
[18] Rabuñal J. R. & Puertas, J. (2006). Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering. En J. Rabuñal & J. Dorado (Ed.), Artificial Neural Networks in real-life applications (pp. 166-187). Coruña: Idea Group Publishing.
[19] Rabuñal, J. R., Puertas, J., Suarez, J., & Rivero, D. (2007). Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrological processes, 21(4), 476-485.
[20] Russell, S. J. & Norvig, P. (1995). Artificial intelligence: a modern approach. New Jersey: Prentice Hall.
[21] Salas, R. (2004). Redes Neuronales Artificiales, (Inf. Téc. Nº 1). Valparaíso: Universidad de Valparaíso, Departamento de Computación.
[22] Sanad, A. & Saka, M. P. (2001). Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks. Journal of Structural Engineering, 127(7), 818-828.
[23] Senouci, A. & Al-Derham, H. R. (2008). Genetic algorithm-based multi-objective model for scheduling of linear construction projects. Advances in Engineering Software, 39(12), 1023-1028.
[24] Sobhani, J., & Ramezanianpour, A. A. (2011). Service life of the reinforced concrete bridge deck in corrosive environments: A soft computing system. Applied Soft Computing, 11(4), 3333-3346.
[25] Srivastav, A.K.L. (2012). Applications of artificial intelligence in structural engineering. The Experiment, 3(3), 199-202.
[26] Tang, C. W., Lin, Y., & Kuo, S. F. (2007). Investigation on correlation between pulse velocity and compressive strength of concrete using ANNs. Comput. Concr, 4(6), 437-456.
[27] Yang, K. H., Ashour, A. F., & Song, J. K. (2007). Shear capacity of reinforced concrete beams using neural network. Int. J. Concrete Struct. Mater, 1(1), 63-73.
[28] Zarandi, M. F., Türksen, I. B., Sobhani, J., & Ramezanianpour, A. A. (2008). Fuzzy polynomial neural networks
for approximation of the compressive strength of concrete. Applied Soft Computing, 8(1), 488-498.
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