Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

doi:10.1007/s11709-022-0901-6

Front. Struct. Civ. Eng.

2023, Vol. 17

Issue (2) : 284-305 https://doi.org/10.1007/s11709-022-0901-6

RESEARCH ARTICLE

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

Hai-Van Thi MAI¹, May Huu NGUYEN^1,², Son Hoang TRINH¹, Hai-Bang LY¹(

)

¹. Civil Engineering Department, University of Transport Technology, Hanoi 100000, Vietnam
². Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima 739-8527, Japan

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Abstract

Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS.

Keywords compressive strength self-compacting concrete artificial neural network decision tree CatBoost

Corresponding Author(s): Hai-Bang LY

Just Accepted Date: 07 December 2022 Online First Date: 20 February 2023 Issue Date: 03 April 2023

Cite this article:

Hai-Van Thi MAI,May Huu NGUYEN,Son Hoang TRINH, et al. Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete[J]. Front. Struct. Civ. Eng., 2023, 17(2): 284-305.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0901-6
https://academic.hep.com.cn/fsce/EN/Y2023/V17/I2/284

Tab.1 Summary of recent studies on predicting the CS of FRSCC in the literature

Tab.2 Collected dataset and corresponding references used to construct the FRSCC CS database.

Tab.3 Input and output parameters used in the development of ML models.

Fig.1 Histograms of the parameters used in the dataset. (a) Cement; (b) coarse aggregate; (c) fine aggregate; (d) water; (e) fly ash; (f) glass fiber; (g) PP fiber; (h) steel fiber; (i) limestone; (j) nano-silica; (k) nano-CuO; (l) metakaolin; (m) superplasticizer; (n) VMA; (o) testing age.

Fig.2 Correlation matrix using the Pearson correlation coefficient between input and output variables in the database.

Fig.3 Typical neural network architecture for the problem in this study.

Fig.4 Proposed workflow of this study.

Fig.5 Investigation of the results over 10-fold CV scores using different ANN structures on the training dataset: (a) number of neurons; (b) activation function; (c) solver for weight optimization; (d) number of epochs.

Fig.6 Investigation of the results of 10-fold CV using different RF parameters: (a) number of estimators; (b) maximum tree depth; (c) minimum sample split; (d) minimum sample leaf; (e) maximum leaf nodes.

Fig.7 Investigation of the results over 10-fold CV using different CatBoost growing policy: (a) number of iterations; (b) CatBoost’s trees’ depth; (c) CatBoost’s lossguide maximum leaves; (d) CatBoost’s grow policy.

Fig.8 Regression analysis for the prediction results of the optimized CatBoost algorithm: (a) training part; (b) testing part.

Fig.9 Relative errors between actual and CatBoost simulation results: (a) training part; (b) testing part.

Fig.10 Error histograms between predicted and experimental CS for the best CatBoost model: (a) training part; (b) testing part.

Fig.11 Comparison with other ML models using regression analysis: (a) XGB training; (b) XGB testing; (c) LGB training; (d) LGB testing; (e) DT training; (f) DT testing.

Fig.12 PDP analysis of 15 input variables considered in this study: (a) cement; (b) coarse aggregate; (c) fine aggregate; (d) water; (e) fly ash; (f) glass fiber; (g) PP fiber; (h) steel fiber; (i) limestone; (j) nano-silica; (k) nano-CuO; (l) metakaolin; (m) superplasticizer; (n) VMA; (o) testing age.

No.	inputs	input variation		PDP CS variation			effect	rank
No.	inputs	min	max	min	max	$\| Δ \|$	effect	rank
1	cement (kg/m³)	220	754	55.6	75.0	19.4	positive	1
2	coarse aggregate (kg/m³)	0	1311.9	59.8	68.8	9.00	negative	7
3	fine aggregate (kg/m³)	0.83	1220	61.3	67.5	6.2	negative	9
4	water (kg/m³)	137.2	239	56.5	71.5	15.0	negative	4
5	fly ash (kg/m³)	0	306	65.0	73.0	8.0	positive	8
6	glass fiber (kg/m³)	0	7.95	64.6	68.8	4.2	mix	11
7	PP fiber (kg/m³)	0	12	62.6	66.6	4.0	mix	12
8	steel fiber (kg/m³)	0	156	64.5	75.0	10.5	positive	5
9	limestone (kg/m³)	0	288.9	64.7	65.6	0.9	mix	14
10	nano-silica (kg/m³)	0	90	64.5	74.5	10.0	positive	6
11	nano-CuO (kg/m³)	0	13.8	63.5	66.0	2.5	mix	13
12	metakaolin (kg/m³)	0	90	65.5	71.0	5.5	positive	10
13	superplasticizer (kg/m³)	0	33	61.0	77.5	16.5	positive	3
14	VMA (L/m³)	0	0.9	65.0	65.8	0.8	mix	15
15	testing age (d)	1	90	56.3	75.0	18.7	positive	2

Tab.4 Summary of effect of input variables on the CS of FRSCC based on PDP analysis

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