Artificial neural network (ANN) modelling of concrete mixed with waste ceramic tiles and fly ash

• Main Authors: Elevado, Kenneth Jae T., Galupino, Joenel G., Gallardo, Ronaldo S.
• Format: text
• Published: Animo Repository 2018
• Subjects: Waste products as building materials—Compression testing; Fly ash—Compression testing; Neural networks (Computer science); Civil Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1897
• Institution: De La Salle University

Waste generation has been the result of a growing demand in the construction industry. Thus, waste utilization has been one of the considerations in the construction industry towards sustainability. In the Philippines setting, many types of research were conducted to support the claim that wastes such as fly ash and waste ceramics have properties that are comparable to cement and aggregates. The American Concrete Institute standards were referred in the mix design of the specimens. This study incorporated the use of fly ash in the replacement of Type 1 Portland Cement and the substitution of waste ceramic tiles in replacing gravel as the coarse aggregates. Moreover, specimens were also subjected to varying days of curing to assess their strength development. Machine learning, namely Artificial Neural Network (ANN), was considered since there was an available wide range of data. This study aimed to provide an Artificial Neural Network (ANN) algorithm that will serve as a model to predict the compressive strength of concrete while incorporating waste ceramic tiles as a replacement to coarse aggregates while varying the amount of fly ash as a partial substitute to cement. The Artificial Neural Network (ANN) model used was validated to ensure the predictions are acceptable. © 2018, Int. J. of GEOMATE.