Compressive strength modelling of concrete mixed with fly ash and waste ceramics using K-nearest neighbor algorithm

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 p...

Full description

Saved in:
Bibliographic Details
Main Authors: Elevado, Kenneth Jae T., Galupino, Joenel G., Gallardo, Ronaldo S.
Format: text
Published: Animo Repository 2018
Subjects:
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2571
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
Description
Summary: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 k-nearest neighbor model used was validated to ensure the predictions are acceptable. © Int. J. of GEOMATE.