Prediction of buckling damage of steel equal angle structural members using hybrid machine learning techniques.

Nang Xuan Ho, Tien-Thinh Le, The-Hung Dinh, Van-Hai Nguyen

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Precipitation modeling in Northeastern Bangladesh-India transboundary flood regions using bi-metaheuristic-optimized NMF-neural network.Scientific reports · 2025
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5 · Who and what money

Authors and funding

4 authors.

Nang Xuan HoFaculty of Vehicle and Energy Engineering, Phenikaa University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam.

Tien-Thinh LeFaculty of Mechanical Engineering and Mechatronics, Phenikaa University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam. thinh.letien@phenikaa-uni.edu.vn.

The-Hung DinhFaculty of Mechanical Engineering and Mechatronics, Phenikaa University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam.

Van-Hai NguyenFaculty of Mechanical Engineering and Mechatronics, Phenikaa University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam.

Funding

Phenikaa University PU2022-1-A-01

6 · The paper itself

Abstract

This article deals with prediction of buckling damage of steel equal angle structural members using a surrogate model combining machine learning and metaheuristic optimization technique. In particular, a hybrid Artificial Intelligence (AI)-based model involving Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) was developed and calibrated for the problem at hand. For this purpose, a database concerning compression tests of steel equal angle structural members was constructed from available resources with geometry variables such as length, width, thickness, mechanical properties of materials such as yield strength and initial imperfections (i.e. residual peak stress and initial geometric imperfections) and critical buckling load of columns. The hybrid PSOANN model was adopted because its prediction capability is higher than the traditional technique - i.e. scaled conjugate gradient (SCG). Indeed, ANN trained by PSO delivered better performance in terms of RMSE, MAE, ErrorStD, R

Indexed as

Artificial neural networkBuckling damageParticle swarm optimization.Steel equal angleSurrogate model

Identifiers

PMID39922853

DOI10.1038/s41598-025-87869-w

PMCPMC11807133

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.