Volume 12, Issue 3 (4-2022)
IJOCE 2022, 12(3): 435-455 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Moghbeli A, Hosseinpour M, Sharifi Y. DEVELOPMENT OF NEURAL NETWORK MODELS TO ESTIMATE LATERAL-DISTORTIONAL BUCKLING RESISTANCE OF CELLULAR STEEL BEAMS. IJOCE 2022; 12 (3) :435-455
URL: http://ijoce.iust.ac.ir/article-1-526-en.html
URL: http://ijoce.iust.ac.ir/article-1-526-en.html
Abstract: (8373 Views)
The lateral-torsional buckling (LTB) strength of cellular steel girders that were subjected to web distortion was rarely examined. Since no formulation has been presented for predicting the capacity of such beams, in the current paper an extensive numerical investigation containing 660 specimens was modeled using finite element analysis (FEA) to consider the ultimate lateral-distortional buckling (LDB) strength of such members. Then, a reliable algorithm based on the artificial neural networks (ANNs) was developed and the most accurate model was chosen to derive an efficient formula to evaluate the LDB capacity of steel cellular beams. The input and target data required in the ANN models were provided using the ANN analyzes. An attempt was made to include the proposed formula in all the variables affecting the LDB of cellular steel beams. In the next step, the validity of the proposed formula was proved by several statistical criteria, and also the most influential input variable was discussed. eventually, a comparison study was executed between the results provided by the ANN-based equation and the AS4100, EC3, and AISC codes. It was revealed that the presented equation is accurate enough and can be used by practical engineers.
Type of Study: Research |
Subject:
Applications
Received: 2022/05/21 | Accepted: 2022/04/21 | Published: 2022/04/21
Received: 2022/05/21 | Accepted: 2022/04/21 | Published: 2022/04/21
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
