Next Article in Journal
Unsteady Stagnation-Point Flow and Heat Transfer Over a Permeable Exponential Stretching/Shrinking Sheet in Nanofluid with Slip Velocity Effect: A Stability Analysis
Next Article in Special Issue
Effect of Different Types of Recycled Concrete Aggregates on Equivalent Concrete Strength and Drying Shrinkage Properties
Previous Article in Journal
High-Speed Welding of Stainless Steel with Additional Compensatory Gas Jet Blow Molten Pool
Previous Article in Special Issue
Investigation of the Use of Recycled Concrete Aggregates Originating from a Single Ready-Mix Concrete Plant
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 8
Issue 11
10.3390/app8112171
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete

by
Zhenhua Duan
1,2,
Shaodan Hou
1,
Chi-Sun Poon
2,*,
Jianzhuang Xiao
1 and
Yun Liu
1,*
1
Department of Structural Engineering, Tongji University, Shanghai 200092, China
2
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2018, 8(11), 2171; https://doi.org/10.3390/app8112171
Submission received: 29 September 2018 / Revised: 22 October 2018 / Accepted: 1 November 2018 / Published: 6 November 2018
(This article belongs to the Special Issue New Trends in Recycled Aggregate Concrete)
Browse Figures
Versions Notes

Abstract

It has been proved that artificial neural networks (ANN) can be used to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates from different sources. This paper is a further study of the use of ANN to analyze the significance of each aggregate characteristic and determine the best combinations of factors that would affect the compressive strength and elastic modulus of RAC. The experiments were carried out with 46 mixes with several types of recycled aggregates. The experimental results were used to build ANN models for compressive strength and elastic modulus, respectively. Different combinations of factors were selected as input variables until the minimum error was reached. The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption and impurity content can reduce the prediction error of ANN to 5.43%. Moreover, for elastic modulus, water absorption and specific gravity are the most influential, and the network error with a combination of mixes, curing age, specific gravity and water absorption is only 3.89%.
Keywords: recycled aggregate; recycled aggregate concrete; artificial neural networks; aggregate characteristic; input variable recycled aggregate; recycled aggregate concrete; artificial neural networks; aggregate characteristic; input variable

Share and Cite

MDPI and ACS Style

Duan, Z.; Hou, S.; Poon, C.-S.; Xiao, J.; Liu, Y. Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete. Appl. Sci. 2018, 8, 2171. https://doi.org/10.3390/app8112171

AMA Style

Duan Z, Hou S, Poon C-S, Xiao J, Liu Y. Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete. Applied Sciences. 2018; 8(11):2171. https://doi.org/10.3390/app8112171

Chicago/Turabian Style

Duan, Zhenhua, Shaodan Hou, Chi-Sun Poon, Jianzhuang Xiao, and Yun Liu. 2018. "Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete" Applied Sciences 8, no. 11: 2171. https://doi.org/10.3390/app8112171

APA Style

Duan, Z., Hou, S., Poon, C.-S., Xiao, J., & Liu, Y. (2018). Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete. Applied Sciences, 8(11), 2171. https://doi.org/10.3390/app8112171

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop