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Assessment of present pavement condition using machine learning techniques

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dc.contributor.author Sharma, M
dc.contributor.author Kumar, P
dc.contributor.editor Pasindu, HR
dc.contributor.editor Bandara, S
dc.contributor.editor Mampearachchi, WK
dc.contributor.editor Fwa, TF
dc.date.accessioned 2023-01-24T04:29:06Z
dc.date.available 2023-01-24T04:29:06Z
dc.date.issued 2021
dc.identifier.citation ***** en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/20250
dc.description.abstract Quantification of present pavement condition in terms of an index term i.e., Pavement Condition Index (PCI) is one of the most important and primary steps while taking decision related to Maintenance and Rehabilitation of Pavements. PCI as proposed by ASTM D6433 rates pavement in seven conditions viz. Good, Satisfactory, Fair, Poor, Very Poor, Serious and Failed. Determination of rating condition of pavement using distress severity and extent turns out to be tedious process. Hence, present study investigates application machine learning techniques for assessment of present pavement condition. Three different algorithms i.e., Logistic Regression, Naïve Bayes and K-Nearest Neighbor have been tested in the present study using Long Term Pavement Performance database consisting of over 10,000 datapoints. The dataset was divided into 7:3 ratio for training and testing phase. Employed algorithms were tested based on accuracy, precision, recall and f-measure. Logistic Regression Classifier was found to have highest accuracy of 0.92 among three classifiers used in the study. en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.subject Pavement condition index en_US
dc.subject Naïve Bayes en_US
dc.subject Logistic regression en_US
dc.subject K-nearest neighbor en_US
dc.subject Pavement condition assessment en_US
dc.title Assessment of present pavement condition using machine learning techniques en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.year 2021 en_US
dc.identifier.conference Road and Airfield Pavement Technology en_US
dc.identifier.pgnos pp. 71-82 en_US
dc.identifier.proceeding Proceedings of 12th International Conference on Road and Airfield Pavement Technology, 2021 en_US
dc.identifier.doi https://doi.org/10.1007/978-3-030-87379-0_5 en_US


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