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Prediction of Carbonation Depth for Concrete Containing Mineral Admixtures Based on Machine Learning

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Abstract

This study developed a prediction model of the carbonation depth of concrete containing mineral admixtures based on an intelligent algorithm. A carbonation test database of mineral admixture concrete was established considering the influence of 17 parameters. The intelligent algorithm and three existing carbonation depth prediction models were analysed based on the database. The evaluation results indicated that the prediction accuracy of the back-propagation neural network is higher than that of the support vector machine, and the prediction accuracies of the two intelligent algorithms are higher than those of the existing numerical prediction models for carbonation depth. A variable importance analysis indicated that the content of fly ash in mineral admixture has a relatively large influence on the carbonation depth, and the carbonation time is the most critical factor affecting the carbonation depth of concrete containing mineral admixture.

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Abbreviations

W :

Water

W/C :

Water-binder ratio

\({K}_{{\mathrm{CO}}_{2}}\) :

Factor of CO2 concentration

C 0 :

CO2 concentration by volume, %

\({{\varvec{K}}}_{{\varvec{k}}1}\) :

Location factor

\({{\varvec{K}}}_{{\varvec{k}}{\varvec{t}}}\) :

Curing and casting factor

\({{\varvec{K}}}_{{\varvec{k}}{\varvec{s}}}\) :

Stress factor

K F :

Fly ash replacement factor

F :

Fly ash replacement ratio, %

\({{\varvec{f}}}_{\mathbf{c}\mathbf{u}.{\varvec{k}}}\) :

Standard value of cube compressive strength, MPa

w :

Water content, kg/m3

c :

Cement content, kg/m3

\({{\varvec{\gamma}}}_{\mathbf{c}}\) :

Coefficient for cement type

\({\gamma }_{\mathrm{HD}}\) :

Coefficient of the degree of hydration

\({n}_{0}\) :

CO2 concentration by volume, %

\({r}_{1}\) :

Influence coefficient of cement variety

\({r}_{2}\) :

Influence coefficient of fly ash

\({r}_{3}\) :

Influence coefficient of climatic conditions

\(d\left(t\right)\) :

Carbonation depth at service time t

t :

Carbonization time, d

\({K}_{\mathrm{c}}\) :

Cement variety

Ma:

Types of mineral admixtures

\({f}_{\mathrm{cu}}\) :

Cube compressive strength, MPa

Cco2 :

Carbon dioxide concentration

T :

Temperature

R 2 :

Coefficient of determination

R :

Correlation coefficient

BP:

Back-propagation

SVM:

Support vector machine

C:

Cement

FA:

Fly ash

GGBS:

Ground granulated blast furnace slag

SP:

Steel slag powder

SF:

Silica fume

MK:

Metakaolin

Fine:

Fine aggregate

Coarse:

Coarse aggregate

CR:

Cement replacement rate

CA:

Concrete admixture

MSE:

Mean square error

MAPE:

Mean absolute percentage error

IAE:

Integral absolute error

SD:

Standard deviation

RMSD:

Root mean square deviation

SRC:

Standardised regression coefficient

ET:

Exposure time

CD:

Carbonation depth

CT:

Concrete curing time

RH:

Relative humidity

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Acknowledgements

The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (Project No. 52108132), the Natural Science Foundation of Hebei Province (Project No. E2021202067), the Colleges and Universities in Hebei Province Science and Technology Research (Project No. QN2021037).

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Authors and Affiliations

  1. School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China

    Yu Wei, Pang Chen, Shaojun Cao, Hui Wang, Yinbo Liu, Zhengxuan Wang & Wenzhong Zhao

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  1. Yu Wei
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  2. Pang Chen
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  3. Shaojun Cao
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  4. Hui Wang
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  5. Yinbo Liu
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  7. Wenzhong Zhao
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Contributions

P.C. and S.C. designed the whole test scheme; Y.W., H.W., Y.L., Z.W. and W.Z. performed the experiments. P.C. made comments and amendments on the paper.

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Correspondence to Pang Chen.

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Wei, Y., Chen, P., Cao, S. et al. Prediction of Carbonation Depth for Concrete Containing Mineral Admixtures Based on Machine Learning. Arab J Sci Eng 48, 13211–13225 (2023). https://doi.org/10.1007/s13369-023-07645-8

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