Patch loading resistance prediction of plate girders with multiple longitudinal stiffeners using machine learning

Graciano, Carlos and Kurtoglu, Ahmet Emin and Kövesdi, Balázs Géza and Casanova, Euro (2023) Patch loading resistance prediction of plate girders with multiple longitudinal stiffeners using machine learning. STEEL AND COMPOSITE STRUCTURES, 49 (4). pp. 419-430. ISSN 1229-9367

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Official URL: https://doi.org/10.12989/scs.2023.49.4.419

Abstract

This paper is aimed at investigating the effect of multiple longitudinal stiffeners on the patch loading resistance of slender steel plate girders. Firstly, a numerical study is conducted through geometrically and materially nonlinear analysis with imperfections included (GMNIA), the model is validated with experimental results taken from the literature. The structural responses of girders with multiple longitudinal stiffeners are compared to the one of girders with a single longitudinal stiffener. Thereafter, a patch loading resistance model is developed through machine learning (ML) using symbolic regression (SR). An extensive numerical dataset covering a wide range of bridge girder geometries is employed to fit the resistance model using SR. Finally, the performance of the SR prediction model is evaluated by comparison of the resistances predicted using available formulae from the literature. Copyright © 2023 Techno-Press, Ltd.

Item Type:	Article
Additional Information:	Export Date: 19 January 2024 Correspondence Address: Graciano, C.; Departamento de Ingeniería Civil, Sede Medellín, A.A. 75267, Colombia; email: cagracianog@unal.edu.co
Uncontrolled Keywords:	RESISTANCE; RESISTANCE; Artificial intelligence; machine learning; machine learning; Nonlinear analysis; Machine-learning; Beams and girders; Plates (structural components); loading; Symbolic regression; Symbolic regression; Patch loading; Steel plate girders; Plate girder; Resistance models; Longitudinal stiffener; Longitudinal stiffener; multiple stiffeners; Loading resistance; Multiple stiffener; Patch loadings;
Subjects:	T Technology / alkalmazott, műszaki tudományok > TA Engineering (General). Civil engineering (General) / általános mérnöki tudományok T Technology / alkalmazott, műszaki tudományok > TD Environmental technology. Sanitary engineering / környezetvédelem, hulladékkezelés, egészségügyi mérnöki technika (ivóvízellátási és szennyvízkezelési technika) T Technology / alkalmazott, műszaki tudományok > TN Mining engineering. Metallurgy / bányászat, kohászat, fémipar
SWORD Depositor:	MTMT SWORD
Depositing User:	MTMT SWORD
Date Deposited:	03 Oct 2024 11:21
Last Modified:	03 Oct 2024 11:21
URI:	https://real.mtak.hu/id/eprint/206791

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