A comparative study of web-post buckling resistance in circular and elliptical openings : using artificial neural networks for predictive modeling

การศึกษาเปรียบเทียบความต้านทานการโก่งตัวของเว็บโพสต์ในช่องเปิดรูปวงกลมและรูปวงรี : การใช้โครงข่ายประสาทเทียมในการพยากรณ์

Name: Guntorn Promsatit

keyword: Web-post buckling

; Elliptical web openings

; Finite element analysis

; Artificial neural network

; Data-driven structural design

Abstract: This research investigates the potential of elliptical web openings to enhance webpost buckling resistance in steel beams under concentrated loads at mid-span compared to circular openings of the same area. The study systematically varies key geometric parameters: the ratio of circular web opening diameter to beam height (d/h), the ratio of spacing to circular opening diameter (s/d), the aspect ratio of elliptical openings (m j/r), and the orientation angle (θ) of elliptical openings. Utilizing finite element analysis (FEA) with ANSYS software, the research conducts both buckling and post-buckling analyses using a geometric nonlinear approach with imperfections, based on S355 steel properties. The parametric study encompasses a comprehensive set of 4,455 numerical models. These models investigate the influence of the aforementioned geometric parameters on the buckling resistance of steel beams. The findings indicate that elliptical openings with optimized aspect ratios significantly improve buckling resistance compared to circular openings. Higher d/h ratios tend to decrease buckling resistance by reducing the height of the tee section, while increased s/d ratios improve shear capacity by more effectively distributing stresses. Tailored m j/r ratios for elliptical openings further enhance buckling resistance without altering the spacing between openings. Additionally, elliptical openings with counterclockwise orientation angles demonstrate better performance in mitigating stress concentrations and improving stability. An Artificial Neural Network (ANN) model, developed using MATLAB’s Neural Net Fitting Tool (nftool), enhances the predictive accuracy for web-post buckling loads. This model utilizes the extensive dataset generated from the parametric study to predict buckling loads with high precision. The results of this research provide valuable insights for optimizing the design of perforated steel beams. By highlighting the superior performance of elliptical openings with optimized geometric parameters, this study contributes to the development of more efficient and resilient structural engineering solutions. The enhanced understanding of web-post buckling phenomena and the introduction of a robust predictive tool underscore the significance of this research in advancing the field of structural engineering.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Thunyaseth Sethaput

Role: advisor

Created: 2025

Modified: 2025-11-26

Issued: 2025-11-26

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2024.576

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Engineering and Technology

Grantor: Thammasat University

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