Dergiler / Journal of Investigations on Engineering and Technology / 2018 / Cilt: 1 - Sayı: 2

Artificial Neural Network-Based New Methodology for Modeling of Asphalt Mixtures and Comparison with IKE Method

Sayfa
1–13
Yayın Tarihi
2018-12-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Thus, powerful, efficient and alternative approaches were presented to the literature for modeling the asphalt mixtures.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Artificial Neural Networks (ANNs) are the most adopted approach in modeling of engineering problems.
📊
Temel Bulgular & Veri:The legibility, comprehensibility, consistency, estimation performance, standard deviation etc. of the presented approach has been compared with the previous study.
💡
Sonuç & Katkı:Thus, powerful, efficient and alternative approaches were presented to the literature for modeling the asphalt mixtures.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Intuitive k-nearest neighbor estimator (IKE) · Artificial neural networks (ANN) · Asphalt mixtures

Abstract

Artificial Neural Networks (ANNs) are the most adopted approach in modeling of engineering problems. In this paper, we have developed ANN-based a novel modeling approach for asphalt mixtures. The Flow, Stability and MQ of the mixtures have been modeled and predicted by the introduced ANN-based approach. The legibility, comprehensibility, consistency, estimation performance, standard deviation etc. of the presented approach has been compared with the previous study. The experimental studies have shown that the proposed approach provides robustness, stability and a high accuracy ratio for estimation the Flow, Stability and MQ. While this paper has presented a novel approach to modeling the asphalt mixtures, it has also verified the results of literature. Thus, powerful, efficient and alternative approaches were presented to the literature for modeling the asphalt mixtures.

🔖

Bu Makaleye Atıf Yap

İSKENDER, E., AKSOY, A., ÖZŞAHİN, Ş., KAHRAMAN, H. T., KONAK, S. D. (2018). Artificial Neural Network-Based New Methodology for Modeling of Asphalt Mixtures and Comparison with IKE Method. Journal of Investigations on Engineering and Technology, Cilt: 1 - Sayı: 2, 1–13. https://ajindex.com/articles/777506

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (12)

  1. Ibrahim M. A. Performance evaluation of SUPERPAVE and Marshall asphalt mix designs to suite Jordan climatic and traffic conditions. Constr Build Mater 2007;21:1732–1740.
  2. Tapkın S, Cevik A, Usar U. Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks. Expert Syst Appl 2010;37(6):4660–4670. Ozgan E. Artificial neural network based modeling of the Marshall Stability of asphalt concrete. Expert Syst Appl 2011;38:6025–6030.
  3. Hınıslıoglu S, Agar E. Use of waste high density polyethylene as bitumen modifier in asphalt concrete mix. Mater Lett 2004;58:267–271. Kuloglu N. Effect of astragalus on characteristics of asphalt concrete. J Mater Civil Eng 1999;11(4):283-286.
  4. Robertus C, Mulder EA, Koole RC. SBS modified bitumen for heavy duty asphalt pavements. In: Second international conference on roads and airfield pavement technology, Singapore; September 1995.
  5. Zoorob SE, Suparma LB. Laboratory design and investigation of the properties of continuously graded asphaltic concrete containing recycled plastics aggregate replacement (plastiphalt). Cem Concr Compos 2000;22:233–242. Nijboer LW. Some considerations of the Marshall test method for investigating bituminous masses. Strasse Autobahn 1957:210–4.
  6. Haddadi S, Ghorbel E, Laradi N. Effects of the manufacturing process on the performances of the bituminous binders modified with EVA. Constr Build Mater 2008;22:1212–9.
  7. Mirzahosseini MR, Aghaeifar A, Alavi AH, Gandomi AH, Seyednour R. Permanent deformation analysis of asphalt mixtures using soft computing techniques. Expert Syst Appl 2011;38: 6081–6100.
  8. Ozsahin TS, Oruc S. Neural network model for resilient modulus of emulsified asphalt mixtures, Constr Build Mater 2008;22:1436–1445.
  9. Gopalakrishnan K, Manik A. Co-variance matrix adaptation evolution strategy for pavement backcalculation. Constr Build Mater 2010;24:2177–2187. Attoh-Okine NO. Grouping pavement condition variables for performance modeling using self-organizing maps. Comput-Aided Civ Inf 2001;16(2):112–125.
  10. Lee BJ, Lee HD. Position-invariant neural network for digital pavement crack analysis. Comput-Aided Civ Inf 2004; 19(2):105–118.
  11. Mei X, Gunaratne M, Lu JJ, Dietrich B. Neural network for rapid evaluation of shallow cracks in asphalt pavements. Comput-Aided Civ Inf 2004;19(3):223–230.
  12. Ceylan H, Guclu A, Tutumluer E, Thompson MR. Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stressdependent subgrade behavior. The Int