耦合稀疏编码在桁架桥梁损伤智能检测中的应用

M. Fallahian, E. Ahmadi, Saeid Talaei, F. Khoshnoudian, M. Kashani
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引用次数: 0

摘要

桥梁结构的损伤检测对桥梁结构的及时维护起到至关重要的作用,从而防止损伤的进一步扩大,防止结构的倒塌。目前,机器学习算法在结构智能损伤检测中的应用越来越多。这项工作的重点是应用一种新的机器学习算法来识别桁架桥梁的损伤位置和严重程度。使用频率响应函数(frf)作为损伤特征,并使用主成分分析(PCA)进行压缩。采用耦合稀疏编码(CSC)作为分类方法,学习桥梁损伤特征与损伤状态之间的关系。以两座桁架桥梁为例,验证了该方法在桁架桥梁损伤检测中的准确性。结果表明,该方法能够可靠地检测桁架桥梁的损伤位置和严重程度。
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Application of Couple Sparse Coding in Smart Damage Detection of Truss Bridges
Damage detection of bridge structures plays a crucial role in in-time maintenance of such structures, which subsequently prevents further propagation of the damage, and likely collapse of the structure. Currently, the application of machine learning algorithms are growing in smart damage detection of structures. This work focuses on application of a new machine learning algorithm to identify the location and severity of damage in truss bridges. Frequency Response Functions (FRFs) are used as damage features, and are compressed using Principal Component Analysis (PCA). Couple Sparse Coding (CSC) is adopted as a classification method to learn the relationship between the bridge damage features and its damage states. Two truss bridges are used to test the proposed method and determine its accuracy in damage detection of truss bridges. It is found that the proposed method provides a reliable detection of damage location and severity in truss bridges.
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来源期刊
CiteScore
3.00
自引率
10.00%
发文量
48
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