Investigation of waste ceramic tile additive in hot mix asphalt using fuzzy logic approach


Kara Ç., Karacasu M.

CONSTRUCTION AND BUILDING MATERIALS, cilt.141, ss.598-607, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 141
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1016/j.conbuildmat.2017.03.025
  • Dergi Adı: CONSTRUCTION AND BUILDING MATERIALS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.598-607
  • Anahtar Kelimeler: Waste, Asphalt, Fuzzy logic, Ceramic tile, Recycling, Pollution, ARTIFICIAL NEURAL-NETWORKS, COMPRESSIVE STRENGTH, SILICA FUME, RECYCLED AGGREGATE, PREDICTION, CONCRETE, CONSTRUCTION, INDUSTRY
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

The aim of this paper is to explore the effects of waste ceramic tile additives on performance properties of asphalt mixtures. Within this context varied rates of waste ceramic tile aggregates (WCA) were used as aggregate for preparation and experiments for hot mix asphalt (HMA) specimens by using Marshall Design Method. Static and Dynamic Creep Tests were applied to the new 30 specimens prepared according to optimum bitumen contents (OBC) and all experimental results were evaluated. Additionally, effects of two important variables (WCA ratio and bitumen ratio) on Marshall Stability (MS), on air void content (AVC) and on voids of filled with asphalt cement (VFA) were modelled. It was concluded that, since specimens with HMA have good enough mechanical conditions according to Turkish Highway Construction Specifications in HMA for wearing course, up to 30% of natural aggregates can be replaced by WCA. By using WCA, country resource will be used effectively within the context of sustainable environment. Besides experimental results were evaluated and predicted with high accuracy by using fuzzy logic (FL) approach. Thanks to the FL model, we could predict values which are wanted to have knowledge about materials characterization with less specimens and less tests without spending much time and workforce. (C) 2017 Elsevier Ltd. All rights reserved.