Detection of Asphalt Pavement Segregation Using Machine Learning Linear and Quadratic Discriminant Analyses

Austin Arunika, Jaka Fajar Fatriansyah, Venia Andira Ramadheena

Research output: Contribution to journalArticlepeer-review

Abstract

Segregation (hot-mix asphalt segregation) is one of the main problems affecting asphalt pavement performance. The early detection is important, but the tests are quite expensive and time-consuming. The visual examination is the cheapest method but too varied in judgement and can rise further problems. In this experiment, we developed machine learning linear and quadratic discriminant analyses to detect/classify segregated and non-segregated pavement asphalt. Six variables were employed: SD only, IR only, MAD only, IR-mean, MAD-mean, IR-mean, MAD-SDmean and IR-SD-mean. The results showed that the complexities of information affect machine learning performance. IR-SD-mean and MAD-SD-mean parameters gave best accuracy performance for training data at 99.2% (LDA)/98.5% (QDA) and testing data at 98.33% (LDA)/95% (QDA) respectively. In general, QDA gave more accuracy performance in comparison to LDA although our data dimension is small.

Original languageEnglish
Pages (from-to)213-218
Number of pages6
JournalEvergreen
Volume9
Issue number1
DOIs
Publication statusPublished - 2022

Keywords

  • Asphalt pavement segregation
  • Linear discriminant analysis
  • Machine Learning
  • quadratic discriminant analyses
  • segregation detection

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