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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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使用机器学习与多点移动波分解方法的堆损坏检测.

Juntao Wu¹, M Hesham El Naggar², Kuihua Wang¹

¹College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.

Sensors (Basel, Switzerland)

|October 14, 2023

概括

此摘要是机器生成的。

洞内多点移动波分解 (MPTWD) 方法有效检测钢筋混凝土堆损坏. 使用统计和信号处理技术的机器学习框架准确量化损坏,增强结构完整性的评估.

关键词:

损害的特征损害的特征基于数据的建模.机器学习是机器学习.堆完整性测试试验堆完整性测试试验旅行的波浪分解分解.

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科学领域:

结构工程结构工程
地质技术工程地质技术工程
材料科学材料科学材料科学

背景情况:

现场造钢筋混凝土 (RC) 是关键基础设施组件.
评估RC的完整性和损坏对于结构安全至关重要.
目前用于检测损坏的方法在描述下部完整性方面存在局限性.

研究的目的:

开发和验证一个洞内多点移动波分解 (MPTWD) 方法用于RC堆损坏评估.
建立一个数据驱动的机器学习框架,用于检测和量化堆损坏.
优化机器学习框架,使用分析解决方案和多种特征提取技术.

主要方法:

在洞内使用MPTWD重建技术来评估的完整性.
使用分析解决方案生成合成数据来增强有限的现场样本.
对LR,XGBoost和MLP分类器应用了两个特征提取方法 (分布式采样,统计/信号处理).

主要成果:

洞内MPTWD方法在评估下部堆完整性方面表现出有效性.
机器学习分类器在使用统计和信号处理功能时表现得更好.
总共有24个提取的特征被发现足以准确检测和量化损伤.

结论:

开发的洞内MPTWD方法与机器学习框架相结合,为RC堆损坏评估提供了强大的方法.
统计和信号处理特征提取显著提高了机器学习模型在这个应用程序的性能.
该研究验证了关键基础设施数据驱动损害检测框架的可行性并优化了其性能.