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Optimization and Artificial Neural Network Models for Reinforced Concrete Members

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Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

The optimization methods for structural engineering problem are effective, but the optimization process must be repeated for each case of design variables. For that reason, artificial intelligence methods can be used to develop prediction models for optimum design variables of engineering problem. For this purpose, sets of optimum results are needed to be used in machine learning training. Metaheuristic methods are employed in optimization, and an artificial neural network (ANNs) model can be constructed. In this chapter, a prediction model used for optimum reinforced concrete (RC) members is presented. After the literature survey of machine learning and artificial intelligence methods used in structural optimization are summarized, ANNs are briefly explained. As examples, different RC problems are optimized, and ANNs models are developed for these examples. The results show that the prediction models may be a great source in the decision of design engineers in practical application.

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References

  1. Ramezani M, Bathaei A, Ghorbani-Tanha AK (2018) Application of artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load. Earthq Eng Eng Vib 17(4):903–915

    Article  Google Scholar 

  2. Yucel M, Bekdaş G, Nigdeli SM, Sevgen S (2019) Estimation of optimum tuned mass damper parameters via machine learning. J Build Eng 26:100847

    Article  Google Scholar 

  3. Yang L, Qi C, Lin X, Li J, Dong X (2019) Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model. Eng Struct 189:309–318

    Article  Google Scholar 

  4. Khalilzade Vahidi E, Rahimi F (2016) Investigation of ultimate shear capacity of RC deep beams with opening using artificial neural networks. Adv Comput Sci Int J 5(4):57–65

    Google Scholar 

  5. Yucel M, Bekdas G, Nigdeli SM, Sevgen S (2018) Artificial neural network model for optimum design of tubular columns. Int J Theor Appl Mech 3:82–86

    Google Scholar 

  6. Chen XL, Fu JP, Yao JL, Gan JF (2018) Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model. Eng Comput 34(2):367–383

    Article  Google Scholar 

  7. Behnood A, Golafshani EM (2018) Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. J Clean Prod 202:54–64

    Article  Google Scholar 

  8. Yücel M, Nigdeli SM, Bekdaş G (2019) Estimation model for generation optimization of design variables for I-beam vertical deflection minimization. In: 4th Eurasian conference on civil and environmental engineering (ECOCEE), İstanbul, Turkey

    Google Scholar 

  9. Yucel M, Nigdeli SM, Bekdaş G (2019) Generation of an artificial neural network model for optimum design of I-beam with minimum vertical deflection. In: 12th HSTAM international congress on mechanics. Thessaloniki, Greece

    Google Scholar 

  10. Nguyen HQ, Ly HB, Tran VQ, Nguyen TA, Le TT, Pham BT (2020) Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression. Materials 13(5):1205

    Article  Google Scholar 

  11. Graupe, D (2013) Backpropagation. In: Principles of artificial neural networks, vol 7, 3rd ed. World Scientific, Singapore, p 59

    Google Scholar 

  12. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks No. IV, Perth Australia, November 27–December 1, pp 1942–1948

    Google Scholar 

  13. Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Heidelberg-Berlin, Springer, pp 240–249

    Google Scholar 

  14. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  15. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press

    Google Scholar 

  16. Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica D 22(1–3):187–204

    Article  MathSciNet  Google Scholar 

  17. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  18. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg

    Google Scholar 

  19. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  20. Birbil Şİ, Fang SC (2003) An electromagnetism-like mechanism for global optimization. J Global Optim 25(3):263–282

    Article  MathSciNet  Google Scholar 

  21. Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

  22. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  23. Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  24. EN (Veranst.): EN 1992-1-1 Eurocode 2 (2005) Design of concrete structures. CEN, Brussels

    Google Scholar 

  25. Fedghouche F, Tiliouine B (2012) Minimum cost design of reinforced concrete T-beams at ultimate loads using Eurocode2. Eng Struct 42:43–50

    Article  Google Scholar 

  26. Kayabekir AE, Bekdaş G, Nigdeli SM (2019) Optimum design of T-beams using Jaya algorithm. In: 3rd international conference on engineering technology and innovation (ICETI), Belgrad, Serbia

    Google Scholar 

  27. The MathWorks, Matlab R2018a (2018) Natick, MA

    Google Scholar 

  28. ACI-318 (2005) Building code requirements for structural concrete and commentary, metric version. American Concrete Institute

    Google Scholar 

  29. Yucel M, Kayabekir AE, Nigdeli SM, Bekdaş G (2020) Optimum design of carbon fiber-reinforced polymer (CFRP) beams for shear capacity via machine learning methods: optimum prediction methods on advance ensemble algorithms–bagging combinations. In: Artificial intelligence and machine learning applications in civil, mechanical, and industrial engineering. IGI Global, pp 84–102

    Google Scholar 

  30. Khalifa A, Nanni A (2000) Improving shear capacity of existing RC T-section beams using CFRP composites. Cem Concr Compos 22(3):165–174

    Article  Google Scholar 

  31. Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques, 4th edn. Morgan Kaufmann, Cambridge, Massachusetts, MA

    Google Scholar 

  32. Cichosz P (2015) Regression model evaluation. In: Data mining algorithms: explained using R. Wiley, Chichester, United Kingdom, pp 299–300

    Google Scholar 

  33. Witten IH, Frank E (2005) Credibility: evaluating what’ s been learned. Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco, California, pp 177–178

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320, Avcılar, Istanbul, Turkey

    Melda Yücel, Sinan Melih Nigdeli, Aylin Ece Kayabekir & Gebrail Bekdaş

Authors
  1. Melda Yücel
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  2. Sinan Melih Nigdeli
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  3. Aylin Ece Kayabekir
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  4. Gebrail Bekdaş
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Corresponding author

Correspondence to Melda Yücel .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Serdar Carbas

  2. Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Abdurrahim Toktas

  3. Department of Computer Engineering, Tarsus University, Tarsus, Turkey

    Deniz Ustun

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Yücel, M., Nigdeli, S.M., Kayabekir, A.E., Bekdaş, G. (2021). Optimization and Artificial Neural Network Models for Reinforced Concrete Members. In: Carbas, S., Toktas, A., Ustun, D. (eds) Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-33-6773-9_9

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