混凝土缺陷智能化快速检测与定量识别技术研究
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摘要
针对目前混凝土缺陷无损检测技术的不足及相关模型实验研究十分缺乏的现状,结合实际工程中混凝土结构常见的质量缺陷,制作了一系列含有不同类型、性质缺陷及无缺陷的混凝土模型试件,开展了基于先进的信号处理技术和人工智能技术的混凝土缺陷无损检测的模型实验研究。针对冲击回波测试信号非稳态的复杂特性,应用小波变换技术有效地提取了缺陷信号的特征值;并应用极限学习机(ELM)作为分类模型,由此在理论分析和模型试验的基础上,建立了基于小波分析和极限学习机的混凝土缺陷智能化快速检测与定量分类识别系统。结果表明:该系统具有较好的分类识别性能,初步实现了对混凝土缺陷类型、性质和范围的智能化快速定量识别与评价,极大地提高了混凝土缺陷检测与评估的速度及精度。
Aiming at the current situation of deficiency of nondestructive testing technology of concrete defects and extreme lack of related model test study at home and abroad,representative RC plate structure in the civil and hydraulic engineering as the research object was taken and maken a series of concrete model specimens.Thus experimental study on concrete defects nondestructive testing is performed based on advanced signal processing and artificial intelligence technologies.In view of the fact that impact echo testing signal has a complex non-stationary characteristic,wavelet transform was adopted based feature extraction for extracting a number of features out of impact echo waveforms and use extreme learning machine( ELM) as classification models.Thus concrete defect detection was proposed and intelligent quick identification model system is established on the basis of theoretical analysis and model test.The system,built on advanced signal processing and artificial intelligence technologies,can achieve better defect detection,defect diagnosis,defect sizing & location,and can perform both qualitative and quantitative identification and evaluation quickly.The verification results show that the system is more applicable,efficient and effective in nondestructive testing of concrete defects.
Aiming at the current situation of deficiency of nondestructive testing technology of concrete defects and extreme lack of related model test study at home and abroad,representative RC plate structure in the civil and hydraulic engineering as the research object was taken and maken a series of concrete model specimens.Thus experimental study on concrete defects nondestructive testing is performed based on advanced signal processing and artificial intelligence technologies.In view of the fact that impact echo testing signal has a complex non-stationary characteristic,wavelet transform was adopted based feature extraction for extracting a number of features out of impact echo waveforms and use extreme learning machine( ELM) as classification models.Thus concrete defect detection was proposed and intelligent quick identification model system is established on the basis of theoretical analysis and model test.The system,built on advanced signal processing and artificial intelligence technologies,can achieve better defect detection,defect diagnosis,defect sizing & location,and can perform both qualitative and quantitative identification and evaluation quickly.The verification results show that the system is more applicable,efficient and effective in nondestructive testing of concrete defects.
引文
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10 向阳,彭勇,史习智.基于小波变换的混凝土缺陷特征抽取研究.武汉理工大学学报(交通科学与工程版),2002;26(2):147—150Xiang Yang,Peng Yong,Shi Xizhi.Research on wave1et-based feature extraction for concrete flaw detection.Journal of Wuhan University of Technology,2002;26(2):147—150
2 Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:theory and applications.Neurocomputing,2006;70(12):489—501
3 Huang G B,Zhou H M,Ding X J,et al.Extreme learning machine for regression and multiclass classification.IEEE Transactions on Systems,Man,and Cybernetics-Part B:Cybernetics,2012;42(04):513—529
4 孙健,王成华,杜庆波.基于小波包能量谱和NPE的模拟电路故障诊断.仪器仪表学报,2013;34(9):2021—2027Sun Jian,Wang Chenghua Du Qingbo.Analog circuit fault diagnosis based on wavelet packet energy spectrum and NPE.Chinese Journal of Scientific Instrument,2013;34(09):2021—2027
5 李永龙,邵忍平,曹精明.基于小波包与支持向量机结合的齿轮故障分类研究.西北工业大学学报,2010;28(4):530—535Li Yonglong,Shao Renping,Cao Jingming.Anew and effective method of gear fault diagnosis using wavelet packet transform combined with SVM.Journal of Northwestern Polytechnical University,2010;28(4):530—535
6 Widodo A,Yang B S,Han T.Combination of independent component analysis and support vector machines for intelligent faults diagnosis induction motors.Expert Systems with Applications,2007;32:299—312
7 曹茂森.基于动力指纹小波分析的结构损伤特征提取与辨识基本问题研究.南京:河海大学,2005Cao Maosen.Study on fundamental issues in extraction and identification of structural damage feature based on wavelet analysis of dynamic fingerprint.Nanjing:Hohai University,2005
8 Hertlein B H.Stress wave testing of concrete:a 25-year review and a peels into the future.Construction and Building Materials,2013;38:7240—7245
9 薛刚,蔡美峰.小波分析在钢筋混凝土梁损伤识别中的应用.北京科技大学学报,2007;29(12):1191—1194Xue Gang,Cai Meifeng.Application of wavelet analysis to damage detection of reinforced concrete beams.Journal of University of Science and Technology Beijing,2007;29(12):1191—1194
10 向阳,彭勇,史习智.基于小波变换的混凝土缺陷特征抽取研究.武汉理工大学学报(交通科学与工程版),2002;26(2):147—150Xiang Yang,Peng Yong,Shi Xizhi.Research on wave1et-based feature extraction for concrete flaw detection.Journal of Wuhan University of Technology,2002;26(2):147—150