Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers
نویسندگان
چکیده
Interpretation of IE data have been carried out by analyzing signals in frequency domain to determine the maximum frequency. However, current peak method can be inaccurate. The purpose this research is introduce features that used for effective classification and interpretation bridge deck evaluation through statistical analysis Naive Bayes classifiers. dataset contained collected from eight slabs created at Advanced Sensing Technology FAST NDE laboratory (FHWA). A set time domain, normalized values, length preprocessed were classify data, statistically. Then, classifiers was employed recognize defect area. Finally, result compared with approach. shows 19 21% area multiple peaks, respectively. 85% sound had only one peak. probability classifier find relationship between analysis. 10% usable estimating thickness group.
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ژورنال
عنوان ژورنال: Infrastructures
سال: 2021
ISSN: ['2412-3811']
DOI: https://doi.org/10.3390/infrastructures6090132