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

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.

[1]  Jian Feng,et al.  Prestress stability of pin-jointed assemblies using ant colony systems , 2012 .

[2]  Francisco Orozco,et al.  Ant Colony Optimization Model for Tsunamis Evacuation Routes , 2014, Comput. Aided Civ. Infrastructure Eng..

[3]  Pizhong Qiao,et al.  Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems , 2008, Neural Computing and Applications.

[4]  V. B. Watwood,et al.  Mechanism Generation for Limit Analysis of Frames , 1979 .

[5]  Amir Hossein Gandomi,et al.  A multi-stage particle swarm for optimum design of truss structures , 2013, Neural Computing and Applications.

[6]  L. Piazza.  A dissipated energy maximization approach to elastic-perfectly plastic analysis of planar frames , 2013 .

[7]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Özgür Kisi,et al.  Damage detection in Timoshenko beam structures by multilayer perceptron and radial basis function networks , 2012, Neural Computing and Applications.

[10]  P S Symonds,et al.  THE CALCULATION OF COLLAPSE LOADS FOR FRAMED STRUCTURES. (INCLUDES APPENDIX). , 1950 .

[11]  P S Symonds,et al.  THE RAPID CALCULATION OF PLASTIC COLLAPSE LOADS FOR A FRAMED STRUCTURES , 1952 .

[12]  Jian Feng,et al.  Novel Form-Finding of Tensegrity Structures Using Ant Colony Systems , 2011 .

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  Sergio Pellegrino,et al.  Structural computation of an assembly of rigid links, frictionless joints and elastic springs , 1991 .

[15]  J. Baker,et al.  Plastic Design of Frames 1 Fundamentals by J. Baker , 1969 .

[16]  Niyati Marjan,et al.  Estimation of Products Final Price Using Bayesian Analysis Generalized Poisson Model and Artificial Neural Networks , 2009 .

[17]  Ali Kaveh,et al.  Structural Mechanics: Graph and Matrix Methods , 1995 .

[18]  Hèrm Hofmeyer,et al.  Coevolutionary and genetic algorithm based building spatial and structural design , 2015, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[19]  P S Symonds,et al.  THE CALCULATIONS OF COLLAPSE LOADS FOR FRAMED STRUCTURES , 1951 .

[20]  Amir Hossein Gandomi,et al.  A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method , 2011, Neural Computing and Applications.

[21]  G. Thierauf,et al.  A METHOD FOR OPTIMAL LIMIT DESIGN OF STRUCTURES WITH ALTERNATIVE LOADS , 1978 .

[22]  M. Jahanshahi,et al.  Enhanced ACS algorithms for plastic analysis of planar frames , 2013 .

[23]  Juan Manuel Davila Delgado,et al.  Automated design studies: Topology versus One-Step Evolutionary Structural Optimisation , 2013, Adv. Eng. Informatics.

[24]  A Kaveh,et al.  PLASTIC ANALYSIS OF FRAMES USING GENETIC AND ANT COLONY ALGORITHMS , 2008 .

[25]  Toyofumi Takada,et al.  10 Failure load analysis of rigid frame by genetic algorithm , 1997 .

[26]  Shih-Wei Lin,et al.  Optimization of Back-Propagation Network Using Simulated Annealing Approach , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[27]  Rudi Stouffs,et al.  Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms , 2011, Adv. Eng. Informatics.

[28]  Ali Kaveh,et al.  Optimal Structural Analysis , 1997 .

[29]  G. Maier,et al.  3.12 – Direct Methods of Limit and Shakedown Analysis , 2003 .

[30]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[31]  M. Jahanshahi,et al.  PLASTIC ANALYSIS OF PLANAR FRAMES USING KINEMATIC METHOD AND GENETIC ALGORITHM , 2004 .

[32]  Jacques Heyman,et al.  Plastic Design of Frames: Frontmatter , 1969 .

[33]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[34]  T. Bakhshpoori,et al.  Optimum plastic analysis of planar frames using ant colony system and charged system search algorithms , 2013 .

[35]  M R Gorman.  AUTOMATED GENERATION FOR LIMIT ANALYSIS OF FRAMES , 1981 .

[36]  P A Jennings.  ADAPTING THE SIMPLEX METHOD TO PLASTIC DESIGN , 1983 .

[37]  Andrew Deeks,et al.  Automatic computation of plastic collapse loads for frames , 1996 .

[38]  Marley M. B. R. Vellasco,et al.  Neural networks assessment of beam-to-column joints , 2005 .

[39]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[40]  Pizhong Qiao,et al.  Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures , 2009, Neural Computing and Applications.

[41]  Amir Hossein Gandomi,et al.  New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach , 2012, Neural Computing and Applications.

[42]  Ali Kaveh,et al.  COLLAPSE LOAD FACTOR OF PLANAR FRAMES USING A MODIFIED GENETIC ALGORITHM , 2004 .

[43]  M. Z. Cohn,et al.  Engineering Plasticity by Mathematical Programming , 1981 .

[44]  Ali Kaveh,et al.  Plastic limit analysis of frames using ant colony systems , 2008 .

[45]  Thomas Stützle,et al.  Ant Colony Optimization Theory , 2004 .

[46]  A. Kaveh.  Optimal Structural Analysis: Kaveh/Optimal Structural Analysis , 2006 .

[47]  Jacques Heyman.  On the minimum-weight design of a simple portal frame , 1960 .

[48]  Bekir Karlik,et al.  VIBRATIONS OF A BEAM-MASS SYSTEMS USING ARTIFICIAL NEURAL NETWORKS , 1998 .