评估使用光学和深度学习方法对混凝土预制起重机梁的裂纹发展
1Faculty of Civil Engineering, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland.
Materials (Basel, Switzerland)
|February 26, 2025
概括
这项研究引入了一种使用人工智能和数字图像相关性来评估老式混凝土起重机梁裂的新方法. 这种方法可以早期检测结构损坏,以便进行预测性维护.
科学领域:
- 结构工程 结构工程
- 人工智能在基础设施评估中的作用
- 非破坏性测试方法的非破坏性测试方法
背景情况:
- 起重机梁对于工业基础设施的寿命和安全至关重要.
- 传统的裂评估方法 (LVDT,张力计) 有其局限性.
- 老化的基础设施需要先进的结构健康监测技术.
研究的目的:
- 提出一种用于评估老化预制混凝土起重机梁结构健康性的新方法.
- 为了整合深度学习的裂细分与数字图像相关性应变分析.
- 为了使结构恶化的准确,非破坏性评估.
主要方法:
- 适应U-Net深层卷积神经网络用于裂检测和细分.
- 应用数字图像关联 (DIC) 技术来测量表面延伸和位移.
- 图像细分和DIC数据的综合分析,用于全面的结构健康评估.
主要成果:
- 综合方法提供了对具体结构元素的非破坏性和详细分析.
- 有助于早期检测起重机梁的损坏,提高安全评估.
- 现场测试结果验证了拟议的方法论对老化的基础设施的有效性.
结论:
- 这种新的方法在评估老化的预制起重机梁的结构完整性方面取得了重大进展.
- 这种集成的AI和DIC方法是工业环境中预测性维护的一个有希望的工具.
- 该研究强调了先进数字技术的潜力,以确保关键基础设施的安全性和寿命.
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