Developing artificial neural networks to estimate the fatigue strength of structural steel details using the new European database

被引:0
作者
Bartsch, Helen [1 ]
Voelkel, Justus [1 ]
Feldmann, Markus [1 ]
机构
[1] Rhein Westfal TH Aachen, Inst Steel Construct, Mies van der Rohe Str 1, D-52074 Aachen, Germany
来源
STEEL CONSTRUCTION-DESIGN AND RESEARCH | 2025年 / 18卷 / 01期
关键词
artificial intelligence; deep learning; artificial neural networks; fatigue; database; LOW-CYCLE FATIGUE; LIFE; INTELLIGENCE; PREDICTION; BEHAVIOR; JOINTS; STIFFENERS; MODEL;
D O I
10.1002/stco.202400029
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This article outlines a method for utilizing machine learning, particularly artificial neural networks, to estimate the fatigue strength of structural steel details. Data have been taken out of a structured database of fatigue tests, depicting the background of EN 1993-1-9. The artificial neural network has been trained and verified on the basis of experimental fatigue test results on the example of the transverse stiffener. Results show that the neural network is capable of predicting the fatigue strength of random transverse stiffener details. Comparisons have been made to a numerical approach applying the effective notch stress approach, showing also limits. This study helps paving the way for a thorough investigation into the complex relationship between different influencing factors and fatigue strength, highlighting the benefits and limitations of using machine learning tools.
引用
收藏
页码:1 / 13
页数:13
相关论文
共 101 条
  • [1] Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams
    Abambres, Miguel
    Rajana, Komal
    Tsavdaridis, Konstantinos Daniel
    Ribeiro, Tiago Pinto
    [J]. COMPUTERS, 2018, 8 (01)
  • [2] Artificial Neural Network Predictions of Fatigue Life of Steel Bars Based on Hysteretic Energy
    Abdalla, Jamal A.
    Hawileh, Rami A.
    [J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2013, 27 (05) : 489 - 496
  • [3] Neural network model for optimization of cold-formed steel beams
    Adeli, H
    Karim, A
    [J]. JOURNAL OF STRUCTURAL ENGINEERING-ASCE, 1997, 123 (11): : 1535 - 1543
  • [4] ALBRECHT P, 1979, J STRUCT DIV-ASCE, V105, P2657
  • [5] FATIGUE OF 8-YEAR WEATHERED A588-STEEL STIFFENERS IN SALT-WATER
    ALBRECHT, P
    SIDANI, M
    [J]. JOURNAL OF STRUCTURAL ENGINEERING, 1989, 115 (07) : 1756 - 1767
  • [6] [Anonymous], 2024, 1993192023 PREN
  • [7] [Anonymous], 2024, 19931142022 PREN
  • [8] Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network
    Barbosa, Joelton Fonseca
    Correia, Jose A. F. O.
    Freire Junior, R. C. S.
    De Jesus, Abilio M. P.
    [J]. INTERNATIONAL JOURNAL OF FATIGUE, 2020, 135
  • [9] BARGEL HJ, 1976, STAHL EISEN, V96, P1038
  • [10] Bartsch H., 2021, J CONSTR STEEL RES, V189