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Hybrid Artificial Neural Network for Fire Analysis of Steel Frames

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Book cover Adaptation and Hybridization in Computational Intelligence

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 18))

Abstract

Tuning parameters of artificial neural networks (ANN) is a very complex task that typically demands a lot of experimental work performed by developers. In order to avoid this hard work, the automatic tuning of these parameters is proposed. A real-coded genetic algorithm (GA) was developed for this purpose. This, so-called meta-GA, algorithm acts as a meta-heuristic that searches for the optimal values of ANN parameters using the genetic operators of crossover and mutation and evaluates quality of solutions, obtained after applying the ANN for fire analysis of steel frames. As matter of fact, steel exhibits very unusual wavy behavior which is a very difficult to model by a close form empirical models when heated to the temperatures between 250°C and 600°C. Therefore, the use of ANN was one of the possible solutions which proved to be very promising. However, the results of this ANN with manual parameter setting by an expert can significantly be improved when using the meta-GA for automatic searching the optimal parameter setting of the original ANN algorithm.

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References

  1. Kirby, B.R., Preston, R.R.: High temperature properties of hot-rolled, structural steels for use in fire engineering design studies. Fire Safety Journal 13, 27–37 (1988)

    Article  Google Scholar 

  2. Eurocode 3: Design of steel structures, Part 1.2: General rules-Structural fire design. European Committee for Standardisation, Brussels (2001)

    Google Scholar 

  3. British Standard Institute. BS5950-8:Structural Use of Steelwork in Building - Part 8: Code of Practice for Fire Resistance Design. British Standards Institution, London (2003)

    Google Scholar 

  4. Lippmann, R.P.: An Introduction to Computing with Neural Nets. IEEE Magazine on Acoustics, Signal and Speech Processing 4(2), 4–22 (1987)

    Google Scholar 

  5. Sarle, W.S.: Neural Network FAQ. Periodic posting to the Usnet newsgroup comp.ai.neural-nets (2002), ftp://ftp.sas.com/pub/neural/FAQ.html

  6. Huang, Z.F., Tan, K.H.: Effects of External Bending Moments and Heating Schemes on the Responses of Thermally-restrained Steel Columns. Engineering Structures 26(6), 769–780 (2004)

    Article  Google Scholar 

  7. Seed, G.M., Murphy, G.S.: The applicability of neural networks in modelling the growth of short fatigue cracks. Fatigue & Fracture of Engineering Materials & Structures 21, 183–190 (1998)

    Article  Google Scholar 

  8. Haque, M.E., Sudhakar, K.V.: ANN based prediction model for fatigue crack growth in DP steel. Fatigue & Fracture of Engineering Materials & Structures 23, 63–68 (2001)

    Article  Google Scholar 

  9. Sterjovski, Z., Nolan, D., Carpenter, K.R., Dunne, D.P., Norrish, J.: Artificial neural networks for modeling the mechanical properties of steels in various applications. Journal of Materials Processing Technology 170, 536–544 (2005)

    Article  Google Scholar 

  10. Sakla, S.S.S.: Neural network modeling of the load-carrying capacity of eccentrically-loaded single-angle struts. Journal of Constructional Steel Research 60, 965–987 (2004)

    Article  Google Scholar 

  11. Oreta, A., Kawashima, K.: Neural Network Modeling of Confined Compressive Strength and Strain of Circular Concrete Columns. Journal of Structural Engineering 129(4), 554–561 (2003)

    Article  Google Scholar 

  12. Tang, C.W., Chen, H.J., Yen, T.: Modeling Confinement Efficiency of Reinforced Concrete Columns with Rectilinear Transverse Steel Using Artificial Neural Networks. Journal of Structural Engineering 129(6), 775–783 (2003)

    Article  Google Scholar 

  13. Zhao, Z.: Steel columns under fire - a neural network based strength model. Advances in Engineering Software 37(2), 97–105 (2004)

    Article  Google Scholar 

  14. Mikami, I., Tanaka, S., Hiwatashi, T.: Neural Network System for Reasoning Residual Axial Forces of High-Strength Bolts in Steel Bridges. Computer-Aided Civil and Infrastructure Engineering 13, 237–246 (1998)

    Article  Google Scholar 

  15. Papadrakakis, M., Lagaros, N.D., Plevris, V.: Design optimization of steel structures considering uncertainties. Engineering Structures 27, 1408–1418 (2005)

    Article  Google Scholar 

  16. Hozjan, T., Turk, G., Srpčič, S.: Fire analysis of steel frames with the use of artificial neural networks. Journal of Constructional Steel Research 63, 1396–1403 (2007)

    Article  Google Scholar 

  17. Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer, Berlin (1992)

    Book  MATH  Google Scholar 

  18. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)

    MATH  Google Scholar 

  19. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc.,, New York (2001)

    MATH  Google Scholar 

  20. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of Genetic Algorithms 1 (FOGA-1), pp. 41–49 (1991)

    Google Scholar 

  21. Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms 2 (FOGA-2), pp. 187–202 (1993)

    Google Scholar 

  22. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9(2), 115–148 (1995)

    MATH  MathSciNet  Google Scholar 

  23. Deb, K., Kumar, A.: Real-coded genetic algorithms with simulated binary crossover: Studies on multi-modal and multi-objective problems. Complex Systems 9(6), 431–454 (1995)

    Google Scholar 

  24. Eiben, A., Smith, J.: Introduction to Evolutionary Computing. Springer, Berlin (2003)

    Book  MATH  Google Scholar 

  25. Fister, I., Mernik, M., Filipič, B.: Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm. Computational Optimization and Applications 54(3), 741–770 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  26. Fister, I., Mernik, M., Filipič, B.: A hybrid self-adaptive evolutionary algorithm for marker optimization in the clothing industry. Applied Soft Computing 10(2), 409–422 (2010)

    Article  Google Scholar 

  27. Fister Jr, I., Yang, X.-S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. Electrotechnical Review 80(3), 116–122 (2013)

    Google Scholar 

  28. Fister Jr, I., Suganthan, P.N., Strnad, D., Brest, J., Fister, I.: Artificial neural networks regression on ensemble strategies in differential evolution. In: 20th International Conference on Soft Computing, Mendel 2014, pp. 65–70. University of Technology, Faculty of Mechanical Engineering, Institute of Automation and Computer Science, Brno (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova cesta 2, p.p. 3422, 1000, Ljubljana, Slovenia

    Tomaž Hozjan & Goran Turk

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000, Maribor, Slovenia

    Iztok Fister

Authors
  1. Tomaž Hozjan
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  2. Goran Turk
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  3. Iztok Fister
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Corresponding author

Correspondence to Tomaž Hozjan .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister Jr.

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Hozjan, T., Turk, G., Fister, I. (2015). Hybrid Artificial Neural Network for Fire Analysis of Steel Frames. In: Fister, I., Fister Jr., I. (eds) Adaptation and Hybridization in Computational Intelligence. Adaptation, Learning, and Optimization, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-14400-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-14400-9_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14399-6

  • Online ISBN: 978-3-319-14400-9

  • eBook Packages: EngineeringEngineering (R0)

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