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Using artificial neural networks to assess the applicability of recycled aggregate classification by different specifications

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Abstract

Due to their high variability, the use of recycled coarse aggregates (RAs) in new structural concrete is not so common. While there are guidelines published in various countries providing advice on the use of RAs, the classification and requirements differ significantly. This study presents a novel method of using artificial neural networks (ANN) to evaluate the feasible use of RAs, classified by several national standards/specifications, to fully or partially substitute natural coarse aggregates in concrete with different strength grades. The evaluation is conducted through the comparison of the predicted compressive strength and elastic modulus of natural aggregate concrete (NAC) with those of RAC by using self-developed ANN models, ANN16-f c and ANN16-E c, respectively. The predictions suggest that, most of the RAs classified by different specifications are of sufficient quality to be used in low-grade concrete (C30), but good quality control of RA is still necessary to produce RAC with equivalent compressive strength to that of NAC. As RAs investigated in this study are only considered in their minimum quality (critical) conditions, it is reasonable to be optimistic about the use of RAs in concrete production with a higher replacement level, especially for structural uses.

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Abbreviations

C&D:

Construction and demolition

RA:

Recycled coarse aggregate

RAC:

Recycled aggregate concrete

NA:

Natural coarse aggregate

NAC:

Natural aggregate concrete

OD:

Oven-dry

OC:

Old concrete

m :

Old masonry

δ :

Impurities including materials other than NA, old concrete and old masonry

RCA:

Mainly derived from old concrete rubble

RMA:

Mainly derived from old masonry rubble

MRA:

A mixture of RCA and RMA

ANN:

Artificial neural networks

C :

Content of ordinary Portland cement

W/C :

Water to cement ratio

A/C :

Total aggregate to cement ratio

S p :

Fine aggregate percentage

r :

The mass substitution rate of NA by RA

D CA :

The maximum particle size of the coarse aggregates

SGSSD :

Saturated surface-dry density of the coarse aggregates

W a :

Water absorption of the coarse aggregates

T NA :

Type of natural coarse aggregate

T RA :

Type of recycled coarse aggregate

k :

The moisture state of the coarse aggregates

C s :

Specimen size

G c :

Coefficient of cement depends on the strength grade

S c :

Coefficient of cement according to the rate of hydration

f c :

Compressive strength

E c :

Elastic modulus

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Funding

This study was funded by The Hong Kong Polytechnic University (Project of Strategic Importance).

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Zhenhua Duan & Chi Sun Poon

  2. Department of Building Engineering, Tongji University, Shanghai, People’s Republic of China

    Zhenhua Duan & Jianzhuang Xiao

Authors
  1. Zhenhua Duan
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  2. Chi Sun Poon
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  3. Jianzhuang Xiao
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Correspondence to Chi Sun Poon.

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Duan, Z., Poon, C.S. & Xiao, J. Using artificial neural networks to assess the applicability of recycled aggregate classification by different specifications. Mater Struct 50, 107 (2017). https://doi.org/10.1617/s11527-016-0972-8

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