基于深度学习的长跨桥梁腐蚀检查与BIM集成
Kotaro Hattori1, Keiichi Oki1, Aya Sugita1
1IHI Infrastructure Systems Co., Ltd, 3 Ohama Nishimachi, Sakai, Osaka, 590-0977, Japan.
Heliyon
|August 22, 2024
概括
本研究介绍了一种自动化的桥梁检查方法,使用图像诊断技术来测量桥梁组件的腐蚀. 将这些数据集成到建筑信息建模 (BIM) 中可以提高基础设施维护的效率和准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构健康监测 结构健康监测
- 计算机视觉 计算机视觉
背景情况:
- 日本老化的基础设施需要高效的维护和运营,以确保社会安全.
- 目前的桥梁检查方法是劳动密集型的,容易出现错误,缺乏标准化的记录.
- 桥梁检查中的低效率阻碍了有效管理,特别是在长桥上.
研究的目的:
- 开发一种自动化方法来测量桥梁结构上腐蚀的部件的位置和面积.
- 提高桥梁检查和维护过程的效率和准确性.
- 为了更好的管理,将检查数据集成到3D建筑信息建模 (BIM) 模型中.
主要方法:
- 使用图像诊断技术分析桥梁检查车辆捕获的图像.
- 专注于捕捉硬化梁的下面表面的图像.
- 将自动测量结果集成到3D建筑信息建模 (BIM) 环境中.
主要成果:
- 成功开发了一种用于自动测量腐蚀部分位置和面积的方法.
- 在实际的长桥上证明了开发方法的有效性.
- 通过集成的3D BIM数据,实现了桥梁状况的高效管理.
结论:
- 开发的自动图像诊断方法显著提高了桥梁检查效率.
- 与BIM集成为基础设施管理提供了更有效和标准化的方法.
- 这项技术解决了维护老化的桥梁基础设施的关键挑战.
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