通过3D激光扫描和卷积神经网络的老化钢桥的结构完整性
Georgios Tzortzinis1,2, Angelos Filippatos3, Jan Wittig4
1Institute of Lightweight Engineering and Polymer Technology, Technische Universität Dresden, Dresden, Germany. georgios.tzortzinis@tu-dresden.de.
Communications engineering
|August 1, 2024
概括
这项研究引入了检查钢桥的新框架,使用3D激光扫描和人工智能更准确,更有效地检测腐蚀. 这种先进的方法旨在通过防止由结构退化引起的桥梁故障来提高公共安全.
科学领域:
- 结构工程 结构工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 钢桥腐蚀是结构性故障的主要原因,导致安全风险和死亡事故.
- 目前的检查方法是劳动密集型的,导致交通中断,并且对结构能力的预测准确性有限.
- 需要先进,高效和准确的方法来在现场评估腐蚀的桥梁成员.
研究的目的:
- 开发使用先进技术的钢铁桥梁的持续检查和评估框架.
- 提高对腐蚀的评估和预测桥梁结构剩余容量的准确性和效率.
- 通过减轻与腐蚀的桥梁基础设施相关的风险来提高公共安全.
主要方法:
- 用3D激光扫描对已退役的钢梁进行全面实验测试.
- 开发了一个基于点云数据的1,421个腐蚀场景的计算数据库.
- 在腐蚀场景数据库上训练有素的分类和回归卷积神经网络 (CNN).
主要成果:
- 在腐蚀评估中,预测误差很低,分类误差高达2.0%和回归误差高达3.3%.
- 在八个现实世界的腐蚀桥梁成员端上验证了方法.
- 成功实施了评估使用中的桥梁的框架,证明了其实际适用性.
结论:
- 拟议的框架在评估老化的桥梁基础设施方面取得了重大进展.
- 与传统方法相比,3D激光扫描和CNN的整合提供了更高的准确性和效率.
- 这种创新方法有望通过持续的监测和评估来提高钢桥的安全性和寿命.
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