Asphalt concrete stability estimation from non-destructive test methods with artificial neural networks

TERZİ S., Karasahin M., Gokova S., Tahta M., Morova N., UZUN İ.

NEURAL COMPUTING & APPLICATIONS, cilt.23, ss.989-997, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s00521-012-1023-1
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.989-997
  • İstanbul Üniversitesi Adresli: Evet

Özet

The core drilling method has often been used to determine the current status of asphalt concretes. However, this method is destructive so causes damage to the asphalt concretes. In addition, this method causes localized points of weakness in the asphalt concretes and is time consuming. In recent years, non-destructive testing methods have been used for pavement thickness estimation, determination of elasticity modulus, and density and moisture measurements. In this study, the above-mentioned non-destructive and destructive tests with data obtained by applying the Marshall stability to the same asphalt concretes were estimated using the artificial neural networks approach.

The core drilling method has often been used to determine the current status of asphalt concretes. However, this method is destructive so causes damage to the asphalt concretes. In addition, this method causes localized points of weakness in the asphalt concretes and is time consuming. In recent years, non-destructive testing methods have been used for pavement thickness estimation, determination of elasticity modulus, and density and moisture measurements. In this study, the above-mentioned non-destructive and destructive tests with data obtained by applying the Marshall stability to the same asphalt concretes were estimated using the artificial neural networks approach.