Using ANNs to predict the mechanical properties of recycled aggregate concrete prepared with old concrete with different strength grades

Zhen Hua Duan; Shi Cong Kou; Chi Sun Poon

Using ANNs to predict the mechanical properties of recycled aggregate concrete prepared with old concrete with different strength grades

Zhen Hua Duan, Shi Cong Kou, Chi Sun Poon

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

In this paper, five parent concretes with 28-day strengths of 30, 45, 60, 80 and 100 MPa were prepared and crushed to produce recycled aggregates (RA) in the sizes ranges of coarse aggregates (5-10 mm and 10-20 mm), which were then used to fully replace the natural aggregate (NA) in producing new concrete mixes. The physical and mechanical properties of RA were tested to study their influence on the mechanical properties of recycled aggregate concrete (RAC). The experimental results were then compared with those produced by Artificial Neural Networks (ANNs) models. The results indicated that ANNs has a fairly high accuracy on predicting the compressive strength and elastic modulus of RAC even when RA are derived from different old concrete with different strength grades.

Original language	English
Title of host publication	fib Symposium 2012
Subtitle of host publication	Concrete Structures for Sustainable Community - Proceedings
Pages	75-78
Number of pages	4
Publication status	Published - 1 Dec 2012
Event	fib Symposium 2012: Concrete Structures for Sustainable Community - Stockholm, Sweden Duration: 11 Jun 2012 → 14 Jun 2012

Conference

Conference	fib Symposium 2012: Concrete Structures for Sustainable Community
Country/Territory	Sweden
City	Stockholm
Period	11/06/12 → 14/06/12

Keywords

Artificial Neural Network
Parent concrete
Physical and mechanical properties
Recycled aggregate

ASJC Scopus subject areas

Civil and Structural Engineering
Building and Construction

Cite this

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title = "Using ANNs to predict the mechanical properties of recycled aggregate concrete prepared with old concrete with different strength grades",

abstract = "In this paper, five parent concretes with 28-day strengths of 30, 45, 60, 80 and 100 MPa were prepared and crushed to produce recycled aggregates (RA) in the sizes ranges of coarse aggregates (5-10 mm and 10-20 mm), which were then used to fully replace the natural aggregate (NA) in producing new concrete mixes. The physical and mechanical properties of RA were tested to study their influence on the mechanical properties of recycled aggregate concrete (RAC). The experimental results were then compared with those produced by Artificial Neural Networks (ANNs) models. The results indicated that ANNs has a fairly high accuracy on predicting the compressive strength and elastic modulus of RAC even when RA are derived from different old concrete with different strength grades.",

keywords = "Artificial Neural Network, Parent concrete, Physical and mechanical properties, Recycled aggregate",

author = "Duan, {Zhen Hua} and Kou, {Shi Cong} and Poon, {Chi Sun}",

year = "2012",

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day = "1",

language = "English",

isbn = "9789198009811",

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Using ANNs to predict the mechanical properties of recycled aggregate concrete prepared with old concrete with different strength grades. / Duan, Zhen Hua; Kou, Shi Cong; Poon, Chi Sun.
fib Symposium 2012: Concrete Structures for Sustainable Community - Proceedings. 2012. p. 75-78.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Using ANNs to predict the mechanical properties of recycled aggregate concrete prepared with old concrete with different strength grades

AU - Duan, Zhen Hua

AU - Kou, Shi Cong

AU - Poon, Chi Sun

PY - 2012/12/1

Y1 - 2012/12/1

N2 - In this paper, five parent concretes with 28-day strengths of 30, 45, 60, 80 and 100 MPa were prepared and crushed to produce recycled aggregates (RA) in the sizes ranges of coarse aggregates (5-10 mm and 10-20 mm), which were then used to fully replace the natural aggregate (NA) in producing new concrete mixes. The physical and mechanical properties of RA were tested to study their influence on the mechanical properties of recycled aggregate concrete (RAC). The experimental results were then compared with those produced by Artificial Neural Networks (ANNs) models. The results indicated that ANNs has a fairly high accuracy on predicting the compressive strength and elastic modulus of RAC even when RA are derived from different old concrete with different strength grades.

AB - In this paper, five parent concretes with 28-day strengths of 30, 45, 60, 80 and 100 MPa were prepared and crushed to produce recycled aggregates (RA) in the sizes ranges of coarse aggregates (5-10 mm and 10-20 mm), which were then used to fully replace the natural aggregate (NA) in producing new concrete mixes. The physical and mechanical properties of RA were tested to study their influence on the mechanical properties of recycled aggregate concrete (RAC). The experimental results were then compared with those produced by Artificial Neural Networks (ANNs) models. The results indicated that ANNs has a fairly high accuracy on predicting the compressive strength and elastic modulus of RAC even when RA are derived from different old concrete with different strength grades.

KW - Artificial Neural Network

KW - Parent concrete

KW - Physical and mechanical properties

KW - Recycled aggregate

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M3 - Conference article published in proceeding or book

SN - 9789198009811

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BT - fib Symposium 2012

T2 - fib Symposium 2012: Concrete Structures for Sustainable Community

Y2 - 11 June 2012 through 14 June 2012

ER -

Using ANNs to predict the mechanical properties of recycled aggregate concrete prepared with old concrete with different strength grades

Abstract

Conference

Keywords

ASJC Scopus subject areas

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