Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

• Published in: Computers (Basel) Vol. 8; no. 1; p. 2
• Main Authors: Abambres, Miguel, Rajana, Komal, Tsavdaridis, Konstantinos, Ribeiro, Tiago
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 26-12-2018
• Subjects: Artificial neural networks; cellular steel beams; Computing time; Construction; Construction industry; Data points; Diameters; Economic impact; Elastic buckling; finite element analysis; Finite element method; Impact analysis; Independent variables; Load; Mathematical models; Neural networks; perforated beams; Personal computers; Steel beams; Steel construction; Stress concentration; Thickness; Vertical loads; Webs
• ISSN: 2073-431X; 2073-431X
• DOI: 10.3390/computers8010002

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localized and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.