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On the efficiency of artificial neural networks for plastic analysis of planar frames in comparison with genetic algorithms and ant colony systems

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Abstract

The investigation of plastic behavior and determining the collapse load factors are the important ingredients of every kinematical method that is employed for plastic analysis and design of frames. The determination of collapse load factors depends on many effective parameters such as the length of bays, height of stories, types of loads and plastic moments of individual members. As the number of bays and stories increases, the parameters that have to be considered make the analysis a complex and tedious task. In such a situation, the role of algorithms that can help to compute an approximate collapse load factor in a reasonable time span becomes more and more crucial. Due to their interesting properties, heuristic algorithms are good candidates for this purpose. They have found many applications in computing the collapse load factors of low-rise frames. In this work, artificial neural networks, genetic algorithms and ant colony systems are used to obtain the collapse load factors of two-dimensional frames. The latter two algorithms have already been employed in the analysis of frames, and hence, they provide a good basis for comparing the results of a newly developed algorithm. The structure of genetic algorithm, in the form presented here, is the same as previous works; however, some minor amendments have been applied to ant colony systems. The performance of each algorithm is studied through numerical examples. The focus is mainly on the behavior of artificial neural networks in the determination of collapse load factors of two-dimensional frames compared with other two algorithms. The investigation of results shows that a careful selection of the structure of artificial neural networks can lead to an efficient algorithm that predicts the load factors with higher accuracy. The structure should be selected with the aim to reduce the error of the network for a given frame. Such an algorithm is especially useful in designing and analyzing frames whose geometry is known a priori.

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References

  1. Baker J, Horn MR, Heyman J (1956) The steel skeleton plastic behavior and design. Cambridge University Press, Cambridge

    Google Scholar 

  2. Neal BG, Symonds PS (1952) The rapid calculation of plastic collapse load for a framed structure. In: Proceedings of the institution of civil engineers, London, pp 58–71

  3. Neal BG, Symonds PS (1952) The calculation of plastic loads for plane frames. In: International association for bridge and structural engineering, fourth congress, Cambridge and London

  4. Neal BG, Symonds PS (1951) The calculations of collapse loads for framed structures. J Inst Civ Eng 35:21–40

    Article  Google Scholar 

  5. Charnes A, Greenberg HJ (1959) Plastic collapse and linear programming. In: Summer meeting of the American mathematical society

  6. Heyman J (1960) On the minimum weight design of a simple portal frame. Int J Mech Sci 1:121–134

    Article  Google Scholar 

  7. Horne MR (1953) Determination of the shape of fixed ended beams for maximum economy according to the plastic theory. In: International association of bridge and structural engineering, fourth congress

  8. Baker J, Heyman J (1969) Plastic design of frames, fundamentals, vol 1. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  9. Jennings A (1983) Adapting the simplex method to plastic design. In: Proceedings of instability and plastic collapse of steel structures, pp 164–173

  10. Watwood VB (1979) Mechanism generation for limit analysis of frames. J Struct Div ASCE 109:1–15

    Google Scholar 

  11. Gorman MR (1981) Automated generation for limit analysis of frames. In: Proceedings of ASCE ST7, pp 1350–1354

  12. Thierauf G (1987) A method for optimal limit design of structures with alternative loads. Comput Meth Appl Mech Eng 16:134–149

    Google Scholar 

  13. Kaveh A (2006) Optimal structural analysis. Wiley, Chichester

    Book  MATH  Google Scholar 

  14. Kaveh A, Khanlari K (2004) Collapse load factor of planar frames using a modified genetic algorithm. Commun Numer Methods Eng 20:911–925

    Article  MATH  Google Scholar 

  15. Munro J (1977) Optimal plastic design of frames. In: Proceedings of the NATO advanced study in engineering plasticity by mathematical programming, pp 136–171

  16. Livesley RK (1977) Linear programming in structural analysis and design. In: Gallagher RH et al (eds) Proceedings of optimum structural design. Wiley, New York

    Google Scholar 

  17. Best MJ (1977) Engineering plasticity by mathematical programming. In: Proceedings of the NATO advanced study in engineering plasticity by mathematical programming, pp 517–522

  18. Maier G, Pastor J, Ponter ARS, Weichert D (2003) Direct methods in limit and shakedown analysis. In: de Borst R, Mang HA (eds) Numerical and computational methods; In: Milne I, Ritchie RO, Karihaloo B (eds) Comprehensive structural integrity, Elsevier-Pergamon, Amsterdam

  19. Mahini MR, Moharrami H, Cocchetti G (2013) A dissipated energy maximization approach to elastic-perfectly plastic analysis of planar frames. Arch Mech 65(3):171–194

    MathSciNet  MATH  Google Scholar 

  20. Kaveh A, Jahanshahi M (2008) Plastic limit analysis of frames using ant colony systems. Comput Struct 86:1152–1163

    Article  Google Scholar 

  21. Jahanshahi M, Pouraghajan M, Pouraghajan M (2013) Enhanced ACS algorithms for plastic analysis of planar frames. Comput Methods Civ Eng 4:65–82

    Google Scholar 

  22. Cao M, Qiao P (2008) Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems. Neural Comput Appl 17:509–519

    Article  Google Scholar 

  23. Cao M, Qiao P, Ren Q (2009) Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures. Neural Comput Appl 18:821–832

    Article  Google Scholar 

  24. Aydin K, Kisi O (2012) Damage detection in Timoshenko beam structures by multilayer perceptron and radial basis function networks. Neural Comput Appl 24:583–597

    Article  Google Scholar 

  25. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, USA

    MATH  Google Scholar 

  26. Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, USA

    Google Scholar 

  27. Kaveh A, Jahanshahi M (2004) Plastic analysis of planar frames using kinematic method and genetic algorithm. Asian J Civ Eng 5:145–160

    Google Scholar 

  28. Kaveh A, Jahanshahi M, Khanzadi M (2008) Plastic analysis of frames using genetic and ant colony algorithms. Asian J Civ Eng 9:229–249

    Google Scholar 

  29. Kohama Y, Takada T, Kozawa N, Miyamura A (1997) Collapse analysis of rigid frame by genetic algorithm. In: Proceedings of the computer aided optimum design of structures, pp 193–202

  30. Hofmeyer H, Davila Delgado JM (2015) Co-evolutionary and genetic algorithm based building spatial and structural design. AI EDAM 29:351–370

    Google Scholar 

  31. Hofmeyer H, Davila Delgado JM (2013) Automated design studies: topology versus one-step evolutionary structural optimisation. Adv Eng Inform 27(4):427–443

    Article  Google Scholar 

  32. Rafiq MY (2000) A design support tool for optimum building concept generation using a structured genetic algorithm. Int J Comput Integr Des Constr 2(2):92–102

    MathSciNet  Google Scholar 

  33. Turrin M, Von Buelow P, Stouffs R (2011) Design explorations of performance driven geometry in architectural design using parametric modelling and genetic algorithms. Adv Eng Inform 25(4):656–675

    Article  Google Scholar 

  34. Aminian P, Javid MR, Asghari A, Gandomi AH, Arab Esmaeili M (2011) A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method. Neural Comput Appl 20:1321–1332

    Article  Google Scholar 

  35. Aminian P, Niroomand H, Gandomi AH, Alavi AH, Arab Esmaeili M (2013) New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach. Neural Comput Appl 23:119–131

    Article  Google Scholar 

  36. Kaveh A, Bakhshpoori M, Kalateh-Ahani M (2013) Optimum plastic analysis of frames using ant colony system and charged system search algorithms. Sci Iran Trans A 20:414–421

    Google Scholar 

  37. Kaveh A, Jahanshahi M (2010) An ACS algorithm for the formation of subminimal-suboptimal cycle bases. In: Proceedings of the fourth international conference on structural engineering, mechanics and computations, Cape Town, South Africa

  38. Chen Y, Feng J, Wu Y (2012) Prestress stability of pin-jointed assemblies using ant colony systems. Mech Res Commun 41:30–36

    Article  Google Scholar 

  39. Chen Y, Feng J, Wu Y (2012) Novel form-finding of tensegrity structures using ant colony systems. J Mech Robot T ASME 4:031001

    Article  Google Scholar 

  40. Forcael E, González V, Orozco F, Vargas S, Pantoja A, Moscoso P (2014) Ant colony optimization model for tsunamis evacuation routes. Comput Aided Civ Inf 29:723–737

    Article  Google Scholar 

  41. Talatahari S, Kheirollahi M, Farahmandpour C, Gandomi AH (2013) A multi-stage particle swarm for optimum design of truss structures. Neural Comput Appl 23:1297–1309

    Article  Google Scholar 

  42. Pellegrino S, Calladine CR (1991) Structural computation of an assembly of rigid links, frictionless joints, and elastic springs. J Appl Mech ASME 58:749–753

    Article  Google Scholar 

  43. Deeks AJ (1996) Automatic computation of plastic collapse loads for frames. Comput Struct 60:91–102

    Article  MATH  Google Scholar 

  44. Chen S-C, Lin S-W, Tseng T-Y, Lin H-C (2006) Optimization of back-propagation network using simulated annealing approach. In: IEEE international conference of systems, man and cybernetics, pp 2819–2824

  45. Karlik B, Aydin S, Pakdemirli M (1998) Vibrations of beam-mass systems using artificial neural networks. Comput Struct 69:339–347

    Article  MATH  Google Scholar 

  46. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, New Jersey

    MATH  Google Scholar 

  47. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  48. de Lima LRO, da S Vellasco PCG, de Andrade SAL, da Silva JGS, Vellasco MMBR (2005) Neural networks assessment of beam-to-column joints. J Braz Soc Mech Sci Eng 27:314–324

    Article  Google Scholar 

  49. Niyati M, Moghadam AME (2009) Estimation of products final price using bayesian analysis generalized poisson model and artificial neural networks. J Ind Eng 2:55–60

    Google Scholar 

  50. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperative agents. IEEE Trans Syst Man Cybern Part B 26:1–13

    Article  Google Scholar 

  51. Dorigo M, Stutzle T (2005) Ant colony optimization. Prentice Hall, New York

    MATH  Google Scholar 

  52. Kaveh A (2004) Structural mechanics: graph and matrix methods. Research Studies Press, London

    MATH  Google Scholar 

  53. Kaveh A, Jahanshahi M (2006) Plastic design of frames using heuristic algorithms. In: Proceedings of the eight international conference on computational structures technology, Las Palmas, Spain, pp 239–240

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Authors and Affiliations

  1. Department of Civil Engineering, Sharif University of Technology, International Campus, P.O. Box 76417-76655, Kish Island, Iran

    M. Jahanshahi

  2. Department of Mechanical Engineering, Sharif University of Technology, International Campus, P.O. Box 76417-76655, Kish Island, Iran

    E. Maleki

  3. Department of Material Science and Engineering, Ege University, Izmir, Turkey

    A. Ghiami

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  1. M. Jahanshahi
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  2. E. Maleki
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  3. A. Ghiami
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Correspondence to M. Jahanshahi.

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Jahanshahi, M., Maleki, E. & Ghiami, A. On the efficiency of artificial neural networks for plastic analysis of planar frames in comparison with genetic algorithms and ant colony systems. Neural Comput & Applic 28, 3209–3227 (2017). https://doi.org/10.1007/s00521-016-2228-5

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  • DOI: https://doi.org/10.1007/s00521-016-2228-5

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