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.

Excessive materials are being manufactured, and along with it are the waste products that are being produced due to the rapid growth of industries. In the Philippines, wastes such as fly ash and damaged ceramics are being considered as a construction material since there are recent researches that proved their properties are comparable to cement and aggregates. In this study, compressive strength tests (ASTM C 39) were conducted to obtain the compressive strength of the concrete mixed with varying amounts fly ash and waste ceramics. Moreover, specimens were also subjected to varying days of curing to assess their strength development. Due to the availability of a wide range of data, machine learning model, such as the k-nearest neighbor, were also considered; it can predict an unknown target parameter without consuming tremendous time and resources. Thus, this study aimed to provide a k-nearest neighbor model that will serve as a reference 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 knearest neighbor model used was validated to ensure the predictions are acceptable.