Machine learning-assisted fatigue performance optimization for cutout geometry of orthotropic steel bridge decks

Diaphragm cutout is a typical fatigue detail of orthotropic steel decks (OSDs). The cutout geometry could alter the structural responses and fatigue performance, significantly challenging the design process. This study proposes an efficient computational framework addressing fatigue performance optimization for the cutout geometry design. Sensitivity analysis is first carried out to identify the significant parameters, and the datasets are established according to the random sampling technique and the finite element (FE) model. Then, a comparison of prediction performance between the back-propagation neural networks (BPNNs) and radial basis function neural networks (RBFNNs) is employed to present the applicability of two types of artificial neural networks (ANNs) in the prediction of structural responses. Finally, the cutout geometry optimization is performed by integrating the prediction model and the multi-objective particle swarm optimization (MOPSO) algorithm. The validity and applicability of the framework are demonstrated with a real-world application. The optimization results show that the fatigue life is increased from 119.8 to 150.2 years for cutout detail 1 and from 37.4 to 70.2 years for cutout detail 2. The proposed framework can significantly reduce the computational burden and deliver an optimized scheme for the cutout design.

• Record URL:
  https://doi.org/10.1016/j.cscm.2023.e01962
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2214509523001419
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22145095
• Supplemental Notes:
  • © 2023 The Authors. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Xudong
  • 0000-0001-6018-1251
  • Miao, Changqing
  • Hao, DiDi
• Publication Date: 2023-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: e01962
• Serial:
  • Case Studies in Construction Materials
  • Volume: 18
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-5095
  • Serial URL: http://www.sciencedirect.com/science/journal/22145095

Subject/Index Terms

• TRT Terms: Bridge decks; Fatigue cracking; Machine learning; Neural networks; Orthotropic plates; Performance; Steel bridges
• Subject Areas: Bridges and other structures; Data and Information Technology; Design; Highways; Materials;

Filing Info

• Accession Number: 01877400
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 27 2023 3:10PM