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Experimental investigation and comparative machine learning prediction of the compressive strength of recycled aggregate concrete incorporated with fly ash, GGBS, and metakaolin

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Abstract

Recycled aggregates (RA) can provide a sustainable solution for replacing natural aggregates (NA) in the concrete mix. However, the stakeholders and inspection professionals lack confidence in predicting their compressive strength (CS) due to limited databases. Most of them solely focus on the concrete mix with natural aggregates only. Even though numerous researchers have proposed alternative mix designs for recycled aggregate concrete (RAC), utilizing RA is still not practicable. One of them is the lack of a simple and effective compressive strength prediction that uses RAC. This study focuses on the application of six different machine learning (ML) techniques: XG Boost, K-nearest neighbors (KNN), artificial neural network (ANN), support vector machine (SVM), linear regression, decision tree (DT), and random forest (RF), for predicting the CS of concrete mixed with RA. The input variables are weights of coarse RA, Portland cement, fly ash, ground granulated blast furnace slag, and metakaolin. The database is prepared by experimental testing of concrete cube specimens for 188 mixes in the concrete technology laboratory of IIT Bhubaneswar. For most of the mixes, coarse RA was the only coarse aggregate to get the compressive strength. It includes variations in water/binder from 0.25 to 0.75. It was observed that the addition of flyash, GGBS, and MK significantly impacted the CS at a later age. The ML model indicates that an accuracy of 0.95 was achieved on the current test database for predicting CS. Among all the machine-learning algorithms, XG Boost can be used for forecasting compressive strength since it provides excellent accuracy with minimal computation. This research can be used as a data-driven novel solution for developing concrete mixes to achieve a specified CS. However, this work employs only experimental data as a machine learning input, which can be improved further by including databases from the literature.

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Data availability

The experimental data used for the machine learning models are available in the mendeley database. (https://data.mendeley.com/datasets/5wkxzmzwnz/1). The python code for replication of results is available from the author upon reasonable request.

Abbreviations

ANN:

Artificial neural networks

CDW:

Construction and demolition waste

GGBS:

Ground granulated blast furnace slag

MK:

Metakaolin

NA:

Natural aggregate

RA:

Recycled aggregate

RAC:

Recycled aggregate concrete

KNN:

K Nearest neighbors

TCM:

Total cementitious material

CFA:

Coal flyash

DT:

Decision tree

ML:

Machine learning

NCA:

Natural coarse aggregate

OPC:

Ordinary Portland cement

RCA:

Recycled coarse aggregate

SVM:

Support vector machine

RF:

Random forest

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Acknowledgements

The authors acknowledge the support provided by IL&FS Environmental Infrastructure and Services Ltd. Plant (New Delhi), India, for providing treated RAs for smooth completion of the research project. The authors gratefully acknowledge the Ministry of Education, New Delhi, India, for providing financial help in the form of a fellowship to the first author. Also, the authors are grateful to the Indian Institute of Technology Bhubaneswar for providing research facilities.

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  1. School of Infrastructure, Indian Institute of Technology Bhubaneswar, Argul, Odisha, 752050, India

    Uma Shankar Biswal, Mayank Mishra, Manav Kumar Singh & Dinakar Pasla

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Correspondence to Mayank Mishra.

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Biswal, U.S., Mishra, M., Singh, M.K. et al. Experimental investigation and comparative machine learning prediction of the compressive strength of recycled aggregate concrete incorporated with fly ash, GGBS, and metakaolin. Innov. Infrastruct. Solut. 7, 242 (2022). https://doi.org/10.1007/s41062-022-00844-6

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