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Autoregressive statistical pattern recognition algorithms for damage detection in civil structures
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DOI：10.1016/j.ymssp.2012.02.014.png)
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Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Structural health monitoring
Damage detection
Statistical pattern recognition
Time series analysis
Autoregressive modeling
Monte Carlo method
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IF:
8.9
论文数:
1.3W
被引数:
6.6W
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The use of vibration data for damage detection in bridges: A comparison of system identification and pattern recognition approaches使用振动数据进行桥梁损伤检测: 系统识别和模式识别方法的比较
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