"运输基础设施和材料故障分析中的人工智能"的介绍
Yue Hou1, Qiao Dong2, Dawei Wang3,4
1Department of Civil Engineering, Swansea University, Swansea SA1 8EN, UK.
概括
人工智能 (AI) 为分析运输基础设施故障提供了强大的工具. 人工智能增强了材料特征,性能评估和预测性维护,有望提高安全性和寿命.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 交通基础设施对社会至关重要,但故障可能会造成重大损害.
- 传统的分析方法包括机械,基于材料和统计的方法.
- 交通基础设施的工程故障对公共安全构成风险.
研究的目的:
- 探索人工智能 (AI) 在分析运输基础设施和材料工程故障中的应用.
- 突出AI在多尺度材料表征和故障信息提取方面的优势.
- 介绍人工智能的最新发展,用于在这一领域的故障分析.
主要方法:
- 利用人工智能进行基础设施材料的多尺度表征.
- 使用人工智能从图像和点云数据中提取故障信息.
- 利用人工智能进行传感器信号评估和大数据驱动的性能预测.
主要成果:
- 人工智能有助于在多个尺度上方便地表征基础设施材料.
- 人工智能有效地从视觉和空间数据中提取关键的故障信息.
- 人工智能能够使用传感器数据进行性能评估,并预测长期的基础设施行为.
结论:
- 人工智能技术在基础设施工程中的数据收集,传输,融合,采矿和分析方面越来越有效.
- 人工智能有望显著帮助工程师检测,分析和防止运输基础设施故障.
- 未来的人工智能应用将提高基础设施维护战略的优化,以提高安全性和耐用性.
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