Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers
Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers
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使用冲击回波数据、概率和朴素贝叶斯分类器进行桥梁检查和缺陷识别
DOI：
10.3390/infrastructures6090132
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发表时间：
2021
期刊：
影响因子：
2.6
通讯作者：
S. Dorafshan
中科院分区：
文献类型：
--
作者：
Faezeh Jafari;S. Dorafshan
通过在频域分析IE信号来确定最大频率，对IE数据进行了解释。然而，目前的峰值频率方法可能不准确。本研究的目的是通过统计分析和朴素贝叶斯分类器，引入IE信号中可用于有效分类和解释桥面评估的特征。该数据集包含从先进传感技术FAST NDE实验室（FHWA）创建的8个平板收集的IE数据。使用一组时域统计特征、归一化峰值和预处理信号长度对IE数据进行统计分类。然后，采用朴素贝叶斯分类器识别缺陷区域。最后，将统计分类结果与频率法进行比较。结果表明，从缺陷区域采集的IE信号中，分别有19%和21%的信号具有多峰特征。然而，从音响中收集到的85%的IE信号只有一个峰值。使用概率分类器来寻找频率法和统计分析结果之间的关系。结果表明，10%的IE信号可用于估计声组的厚度。
Interpretation of IE data have been carried out by analyzing IE signals in frequency domain to determine the maximum frequency. However, the current peak frequency method can be inaccurate. The purpose of this research is to introduce features in IE signals that can be used for effective classification and interpretation for bridge deck evaluation through statistical analysis and Naive Bayes classifiers. The dataset contained IE data collected from eight slabs created at Advanced Sensing Technology FAST NDE laboratory (FHWA). A set of statistical features in time domain, normalized peak values, and length of preprocessed signals were used to classify the IE data, statistically. Then, Naive Bayes classifiers was employed to recognize defect area. Finally, the result of statistical classification was compared with frequency approach. The result shows that 19 and 21% of the IE signals collected from the defect area have multiple peaks, respectively. However, 85% of the IE signals collected from the sound set had only one peak. A probability classifier was used to find the relationship between the result of the frequency method and statistical analysis. The result shows that 10% of the IE signals were usable for estimating the thickness in the sound group.