Application of Soft Computing Paradigm to Large Deformation Analysis of Cantilever Beam under Point Load. (29th November 2021)

• Record Type: Journal Article
• Authors: Cui, Yanmei; Hong, Yong; Khan, Naveed Ahmad; Sulaiman, Muhammad
• Other Names: Afan Haitham Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-11-29
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/2182693
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : In this paper, a mathematical model for large deformation of a cantilever beam subjected to tip-concentrated load is presented. The model is governed by nonlinear differential equations. Large deformation of a cantilever beam has number of applications is structural engineering. Since finding an exact solution to such nonlinear models is difficult task, this paper focuses on developing soft computing technique based on artificial neural networks (ANNs), generalized normal distribution optimization (GNDO) algorithm, and sequential quadratic programming (SQP). The strength of ANN modeling for governing the equation of cantilever beam is exploited by the global search ability of GNDO and further explored by the local search mechanism of SQP. Design scheme is evaluated for different cases depending on variations in dimensionless end-point load ρ . Furthermore, to validate the effectiveness and convergence of algorithm proposed technique, the results of the differential transformation method (DTM) and exact solutions are compared. The statistical analysis of performance indicators in terms of mean, median, and standard deviations further establishes the worth of ANN-GNDO-SQP algorithm.