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首页> 外文期刊>Iranian Journal of Science and Technology, Transactions of Civil Engineering >Condition Assessment of Civil Structures for Structural Health Monitoring Using Supervised Learning Classification Methods
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Condition Assessment of Civil Structures for Structural Health Monitoring Using Supervised Learning Classification Methods

机译：采用监督学习分类方法对结构健康监测的条件评估

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摘要

Structural health monitoring is an essential process for ensuring the safety and serviceability of civil structures. When a structure suffers from damage, it is necessary to implement maintenance programs for returning the structural performance and integrity to its initial normal condition. An important challenge is that the structure of interest may be damaged even after a sophisticated maintenance program. This conveys the great necessity of performing the second level of structural condition assessment and damage detection of maintained structures. To achieve this aim, this article proposes a novel methodology using the concept of supervised learning. The main objective of the proposed methodology is to train various supervised learning classifiers using a training dataset that consists of features regarding both the undamaged and damaged states of the structure before the maintenance program in the first level. Once the classifiers have been trained, one attempts to predict the class labels of test samples associated with the current state of the structure after the maintenance program during the second level. According to the predefined class labels of the training and test samples in the first stage, it is feasible to recognize the current state of the maintained structure in the second level and detect potential damage. The major contribution of this article is to introduce the concept of supervised learning for damage detection in an innovative manner. A numerical concrete beam and an experimental laboratory frame are used to demonstrate the effectiveness and applicability of the proposed methodology. Results show that this methodology is a practical and reliable tool for structural condition assessment and damage detection of maintained structures.
机译：结构健康监测是确保民用结构安全和可维护性的重要过程。当结构遭受损坏时，有必要实施用于将结构性能和完整性返回其初始正常情况的维护程序。一个重要的挑战是，即使在复杂的维护计划之后，感兴趣的结构可能会损坏。这传送了执行第二级结构条件评估和维持结构的损伤检测的巨大必要性。为实现这一目标，本文提出了一种利用监督学习概念的新型方法。该方法的主要目的是使用培训数据集培训各种监督学习分类器，该数据集包括关于在第一级维护程序之前的结构的未损坏和损坏状态的特征。一旦分类器被训练，一次尝试预测在第二级维护程序之后在维护程序之后预测与当前结构的当前状态相关的测试样本的类标签。根据第一阶段的训练和测试样本的预定义类标签，可行的是在第二级中识别维持的结构的当前状态并检测潜在的损坏。本文的主要贡献是以创新的方式介绍监督学习的概念。使用数值混凝土梁和实验实验室框架用于证明所提出的方法的有效性和适用性。结果表明，该方法是一种实用且可靠的工具，用于结构条件评估和维持结构的损伤检测。

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