Next Article in Journal
Correction: Electrochemical Investigation of the Corrosion of Different Microstructural Phases of X65 Pipeline Steel under Saturated Carbon Dioxide Conditions. Materials 2015, 8, 2635–2649
Next Article in Special Issue
Utilization of Palm Oil Clinker as Cement Replacement Material
Previous Article in Journal
Acid Denaturation Inducing Self-Assembly of Curcumin-Loaded Hemoglobin Nanoparticles
Previous Article in Special Issue
Effects of Medium Temperature and Industrial By-Products on the Key Hardened Properties of High Performance Concrete
Article Menu

Export Article

Open AccessArticle
Materials 2015, 8(12), 8714-8727; doi:10.3390/ma8125483

Prediction of the Chloride Resistance of Concrete Modified with High Calcium Fly Ash Using Machine Learning

1
Research and Academic Computer Network,Wawozowa 18, Warsaw 02-796, Poland
2
Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, Warsaw 02-106, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Prabir Sarker
Received: 9 October 2015 / Revised: 16 November 2015 / Accepted: 30 November 2015 / Published: 11 December 2015
(This article belongs to the Special Issue Utilisation of By-Product Materials in Concrete)
View Full-Text   |   Download PDF [519 KB, uploaded 11 December 2015]   |  

Abstract

The aim of the study was to generate rules for the prediction of the chloride resistance of concrete modified with high calcium fly ash using machine learning methods. The rapid chloride permeability test, according to the Nordtest Method Build 492, was used for determining the chloride ions’ penetration in concrete containing high calcium fly ash (HCFA) for partial replacement of Portland cement. The results of the performed tests were used as the training set to generate rules describing the relation between material composition and the chloride resistance. Multiple methods for rule generation were applied and compared. The rules generated by algorithm J48 from the Weka workbench provided the means for adequate classification of plain concretes and concretes modified with high calcium fly ash as materials of good, acceptable or unacceptable resistance to chloride penetration. View Full-Text
Keywords: chloride penetration; concrete; durability; high calcium fly ash; machine learning chloride penetration; concrete; durability; high calcium fly ash; machine learning
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Marks, M.; Glinicki, M.A.; Gibas, K. Prediction of the Chloride Resistance of Concrete Modified with High Calcium Fly Ash Using Machine Learning. Materials 2015, 8, 8714-8727.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Materials EISSN 1996-1944 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top