Estimation of Polypropylene Concentration of Modified Bitumen Images by Using k-NN and SVM Classifiers

Tapkin S., ŞENGÖZ B., ŞENGÜL G., TOPAL A., ÖZÇELİK E.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING, cilt.29, sa.5, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 5
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1061/(asce)cp.1943-5487.0000353
  • Dergi Adı: JOURNAL OF COMPUTING IN CIVIL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Polypropylene fibers, Optical microscopy, Morphology, Exhaustive search method, K-nearest neighbor, Multiclass support vector machine, Concentration estimation, STYRENE-BUTADIENE-STYRENE, DENSE BITUMINOUS MIXTURES, PREDICTION, BEHAVIOR, FIBERS
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

The goal of this study is to design an expert system that automatically classifies the microscopic images of polypropylene fiber (PPF) modified bitumen including seven different contents of fibers. Optical microscopy was used to capture the images from thin films of polypropylene fiber modified bitumen samples at a magnification scale of 100 x. A total of 313 images were pre-processed, and features were extracted and selected by the exhaustive search method. The k-nearest neighbor (k-NN) and multiclass support vector machine (SVM) classifiers were applied to quantify the representation capacity. The k-NN and multiclass SVM classifiers reached an accuracy rate of 87% and 86%, respectively. The results suggest that the proposed expert system can successfully estimate the concentration of PPF in bitumen images with good generalization characteristics. (C) 2014 American Society of Civil Engineers.