Optimizing the treatment of recycled aggregate (>4 mm), artificial intelligence and analytical approaches

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Dilbas H.

MATERIALS, vol.16, no.8, pp.1-14, 2023 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 16 Issue: 8
  • Publication Date: 2023
  • Doi Number: 10.3390/ma16082994
  • Journal Name: MATERIALS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.1-14
  • Van Yüzüncü Yıl University Affiliated: Yes


Attached, old mortar removal methods are evolving to improve recycled aggregate qual-
ity. Despite the improved quality of recycled aggregate, treatment of recycled aggregate at the re-
quired level cannot be obtained and predicted well. In the present study, an analytical approach
was developed and proposed to use the Ball Mill Method smartly. As a result, more interesting
and unique results were found. One of the interesting results was the abrasion coefficient which
was composed according to experimental test results; and the Abrasion Coefficient enables quick
decision-making to get the best results for recycled aggregate before the Ball mill method applica-
tion on recycled aggregate. The proposed approach provided an adjustment in water absorption
of recycled aggregate, and the required reduction level in water absorption of recycled aggregate
was easily achieved by accurately composing Ball Mill Method combinations (drum rotation-steel
ball). In addition, artificial neural network models were built for the Ball Mill Method The artificial
neural network input parameters were Ball Mill Method drum rotations, steel ball numbers and/or
Abrasion Coefficient, and the output parameter was the water absorption of recycled aggregate.
Training and testing processes were conducted using the Ball Mill Method results, and the results
were compared with test data. Eventually, the developed approach gave the Ball Mill Method
more ability and more effectiveness. Also, the predicted results of the proposed Abrasion Coeffi-
cient were found close to the experimental and literature data. Besides, an artificial neural network
was found to be a useful tool for the prediction of water absorption of processed recycled aggre-