Optimizing the Treatment of Recycled Aggregate (>4 mm), Artificial Intelligence and Analytical Approaches

• Published in: Materials Vol. 16; no. 8; p. 2994
• Main Author: Dilbas, Hasan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.04.2023; MDPI
• Subjects: Abrasion; abrasion coefficient; Analysis; Artificial intelligence; Artificial neural networks; ball mill method; Coefficients; Concrete; Decision making; Energy consumption; Methods; Mortars (material); optimization; Parameters; recycled aggregate; Water absorption; Water reuse; Los Angeles California; United States > US; recycled aggregate; artificial neural networks; abrasion coefficient; ball mill method; optimization
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma16082994

Attached, old mortar removal methods are evolving to improve recycled aggregate quality. Despite the improved quality of recycled aggregate, treatment of recycled aggregate at the required 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 application 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 Coefficient 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 aggregate.