文章基本信息

标题：Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection
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作者：Azadeh Noori Hoshyar ; Sergey Kharkovsky ; Bijan Samali 等
期刊名称：Journal of Signal and Information Processing
印刷版ISSN：2159-4465
电子版ISSN：2159-4481
出版年度：2018
卷号：09
期号：02
页码：111-121
DOI：10.4236/jsip.2018.92007
语种：English
出版社：Scientific Research Publishing
摘要：In recent years, the interest in damage identification of structural components through innovative techniques has grown significantly. Damage identification has always been a crucial concern in quality assessment and load capacity rating of infrastructure. In this regard, researchers focus on proposing efficient tools to identify the damages in early stages to prevent the sudden failure in structural components, ensuring the public safety and reducing the asset management costs. The sensing technologies along with the data analysis through various techniques and machine learning approaches have been the area of interest for these innovative techniques. The purpose of this research is to develop a robust method for automatic condition assessment of real-life concrete structures for the detection of relatively small cracks at early stages. A damage identification algorithm is proposed using the hybrid approaches to analyze the sensors data. The data obtained from transducers mounted on concrete beams under static loading in laboratory. These data are used as the input parameters. The method relies only on the measured time responses. After filtering and normalization of the data, the damage sensitive statistical features are extracted from the signals and used as the inputs of Self-Advising Support Vector Machine (SA-SVM) for the classification purpose in civil Engineering area. Finally, the results are compared with traditional methods to investigate the feasibility of the hybrid proposed algorithm. It is demonstrated that the presented method can reliably detect the crack in the structure and thereby enable the real-time infrastructure health monitoring.
关键词：Signal Processing;Machine Learning;Statistical Features;Feature Extraction;Classification;Crack Detection