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Machine learning techniques for recycled aggregate concrete strength prediction and its characteristics between the hardened features of concrete

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Abstract

This study aims to arrive at models to correlate the mechanical properties of recycled aggregate concrete (RAC). An experiment was performed on the recycled coarse aggregate (RCA) ratio of 0%, 25%, 50%, and 100% with 400kg/m3 of cement content, varying water-to-cement ratio (w/c) 0.3, 0.4, 0.48, and super plasticizer (SP) dosage to produce 15 different mixes were investigated. Furthermore, the compressive strength (both cube fcu and cylinder fcyl), modulus of elasticity (Ec), split tensile strength (fct), and flexural strength (fcr) were tested and investigated. In view of the results, new models representing the impact of RCA were created. The results showed that in terms of replacement ratio, at 56d, the mix with 25% of RCA with water-to-cement ratio (w/c) 0.3 and super plasticizer (SP) 1.5%, recorded maximum strength of 59.86MPa, 4.81MPa, and 5.416MPa under cube compressive strength, split tensile, and flexural strength respectively. The proposed models can effectively predict the Ec, fct, fcr, fcyl, and fcu of RAC. Scanning electron microscope (SEM) was conducted to scrutinize the microstructure of selected mixes which shows comparatively low voids, micro-cracks, and pores. Also, machine learning techniques like multi-linear regression (MLR) and extreme gradient boosting (XGB) algorithms were utilized for the compressive strength prediction of concrete (CSC). Results indicated that XGB for cylinder compressive strength was found to be 2.7% greater than cube compressive strength and MLR for cylinder compressive strength was found to be 1.5% greater than cube compressive strength

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Data availability

Data for the experimental results are available with the authors and can be provided for review purposes.

Abbreviations

RAC:

Recycled aggregate concrete

RCA:

Recycled coarse aggregate

W/C:

Water-to-cement ratio

WC:

Water content

SP:

Super plasticizer

NAC:

Natural aggregate concrete

NCA:

Natural coarse aggregate

Fcu :

Cube compressive strength

Fcyl :

Cylinder compressive strength

Fct :

Split tensile strength

Fcr :

Flexural strength

MOE:

Modulus of elasticity

GA:

Genetic algorithm

ANFIS:

Adaptive neuro fuzzy interference system

MLR:

Multi-linear regression

XGB:

Extreme gradient boost

SEM:

Scanning electron microscope

CSC:

Compressive strength of concrete

FA:

Fine aggregate

CDW:

Construction and demolition waste

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, 620015, India

    Shamili Syed Rizvon & Karthikeyan Jayakumar

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  1. Shamili Syed Rizvon
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Both the authors contributed equally to this work.

Conceived the ideas and experimental ideas of the study.

Performed the experiments and data were collected.

Data analysis and interpretation were done.

The Paper has been written by the corresponding author.

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Correspondence to Shamili Syed Rizvon.

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Communicated by Amjad Kallel

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Rizvon, S.S., Jayakumar, K. Machine learning techniques for recycled aggregate concrete strength prediction and its characteristics between the hardened features of concrete. Arab J Geosci 14, 2390 (2021). https://doi.org/10.1007/s12517-021-08674-z

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