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<p>A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. By revealing the intrinsic drivers of strength, ductility, and corrosion resistance, and introducing a utility function to balance performance and cost, the newly designed steel achieved a UTS of 837 MPa, 20% elongation, and a corrosion rate of 0.54 g/(m<sup>2</sup>·h) after 576 hours in a simulated marine environment, demonstrating excellent overall properties.</p>

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Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月6日

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  • Bingxiao Shi,
  • Xin Guo,
  • Luntao Wang,
  • Wenbo Huang,
  • Hong Luo,
  • Lizhi Qin,
  • Guowei Yang,
  • Xuequn Cheng,
  • Xiaogang Li

摘要

A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. By revealing the intrinsic drivers of strength, ductility, and corrosion resistance, and introducing a utility function to balance performance and cost, the newly designed steel achieved a UTS of 837 MPa, 20% elongation, and a corrosion rate of 0.54 g/(m2·h) after 576 hours in a simulated marine environment, demonstrating excellent overall properties.