基于机器视觉和机器学习的结构损坏检测和安全评估方法
Shengmin Wang1, Moxiao Li2,3, Di Le4
1School of Management, Wuhan Textile University, Wuhan, China.
PloS one
|February 2, 2026
概括
本研究引入了一种新的基于视觉的框架,用于检测结构损坏. 它使用深度学习和机器学习准确评估基础设施安全,在损害分类和细分方面实现高性能.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 基础设施的安全性和耐用性至关重要.
- 精确的结构损坏检测对于维护至关重要.
- 现有的方法可能缺乏解释性或准确性.
研究的目的:
- 为结构安全评估开发一个新的基于多个尺度的视觉框架.
- 整合深度学习和机器学习,以准确检测和分类损害.
- 为自动化结构健康评估提供可解释和通用化的解决方案.
主要方法:
- 集成ResNet-50和SegFormer模型用于损坏分类和细分.
- 量化提取七个关键损伤参数 (例如,裂长度,裂面积).
- 开发一个随机森林 (RF) 模型,将视觉特征映射到安全级别.
主要成果:
- 基于射频的安全评估模型实现了87.0%的准确性,0.76的F1得分和0.83的AUC.
- 与传统的机器学习方法相比,表现出更高的性能.
- 强调了拟议框架的强大概括和分类能力.
结论:
- 开发的框架为自动化结构损坏检测提供了一个全面的解决方案.
- 该方法提供了准确和可解释的结构安全评估.
- 这项工作有助于推进自动化基础设施健康监测.
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