Loading...

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

Jahanshahi, M ; Sharif University of Technology

712 Viewed
  1. Type of Document: Article
  2. DOI: 10.1007/s00521-016-2228-5
  3. Publisher: Springer-Verlag London Ltd
  4. Abstract:
  5. 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
  6. Keywords:
  7. Artificial neural networks ; Collapse load factor ; Collapse mechanism ; Genetic algorithms ; Heuristic methods ; Ant colony optimization ; Complex networks ; Factor analysis ; Heuristic algorithms ; Neural networks ; Ant colony systems ; Collapse loads ; Effective parameters ; Kinematical method ; Plastic analysis ; Plastic behavior ; Plastic limit analysis ; Algorithms
  8. Source: Neural Computing and Applications ; 2016 , Pages 1-19 ; 09410643 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s00521-016-2228-5