Next Article in Journal
Estimation of the Longitudinal Elasticity Modulus of Braided Synthetic Fiber Rope Utilizing Classical Laminate Theory with the Unit N/tex
Previous Article in Journal
Modeling Properties with Artificial Neural Networks and Multilinear Least-Squares Regression: Advantages and Drawbacks of the Two Methods
Previous Article in Special Issue
Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(7), 1095; https://doi.org/10.3390/app8071095

Can We Truly Predict the Compressive Strength of Concrete without Knowing the Properties of Aggregates?

1
Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
2
Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emídio Navarro, 1950-062 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Received: 29 May 2018 / Revised: 13 June 2018 / Accepted: 29 June 2018 / Published: 5 July 2018
(This article belongs to the Special Issue New Trends in Recycled Aggregate Concrete)
View Full-Text   |   Download PDF [5723 KB, uploaded 5 July 2018]   |  

Abstract

This paper is focused on the influence of the geological nature and quality of the aggregates on the compressive strength of concrete and explains why it is important not to ignore the characteristics of aggregates in the estimation of the strength of concrete, even for virgin aggregates. For this purpose, three original (Abrams, American Concrete Institute Manual of concrete practice and Slater) and two modified (Bolomey and Feret) models were used to calculate the strength of concrete by considering results of various publications. The results show that the models do not properly predict the strength of concrete when the characteristics of aggregates are neglected. The scatter between the calculated and experimental compressive strength of concrete, even when made with natural aggregates (NAs) only, was significant. For the same mix composition (with similar cement paste quality), there was a significant difference between the results when NAs of various geological nature (e.g., limestone, basalt, granite, sandstone) were used in concrete. The same was true when different qualities (namely in terms of density, water absorption and Los Angles abrasion) of aggregates were used. The scatters significantly decreased when the mixes were classified based on the geological nature of the aggregates. The same occurred when the mixes were classified based on their quality. For both modified models, the calculated strength of mixes made with basalt was higher than that of the mixes containing other types of the aggregates, followed by mixes containing limestone, quartz and granite. In terms of the quality of the aggregates, the calculated strength of concrete increased (was overestimated) as the quality of the aggregates decreased. The influence of the aggregates on the compressive strength of concrete became much more discernible when recycled aggregates were used mainly due to their more heterogeneous characteristics. View Full-Text
Keywords: compressive strength; models; geological nature of aggregates; quality of aggregates; concrete; recycled aggregates compressive strength; models; geological nature of aggregates; quality of aggregates; concrete; recycled aggregates
Figures

Graphical abstract

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

Share & Cite This Article

MDPI and ACS Style

de Brito, J.; Kurda, R.; Raposeiro da Silva, P. Can We Truly Predict the Compressive Strength of Concrete without Knowing the Properties of Aggregates? Appl. Sci. 2018, 8, 1095.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top