Prediction Method for Reinforced Concrete Corrosion-induced Crack Based on Stacking Integrated Model Fusion

doi:10.11902/1005.4537.2024.017

Journal of Chinese Society for Corrosion and protection

2024, Vol. 44

Issue (6): 1601-1609 DOI: 10.11902/1005.4537.2024.017

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Prediction Method for Reinforced Concrete Corrosion-induced Crack Based on Stacking Integrated Model Fusion

LIANG Zihao¹^,²^,³, YING Zongquan¹^,²^,³, LIU Meimei¹^,²^,³(

), YANG Shuai¹^,²^,³

1. CCCC Fourth Harbor Engineering Institute Co., Ltd., Guangzhou 510230, China
2. Key Laboratory of Construction Materials, CCCC, Guangzhou 510230, China
3. Key Laboratory of Harbor & Marine Structure Durability Technology, Ministry of Transport, Guangzhou 510230, China

Cite this article:

LIANG Zihao, YING Zongquan, LIU Meimei, YANG Shuai. Prediction Method for Reinforced Concrete Corrosion-induced Crack Based on Stacking Integrated Model Fusion. Journal of Chinese Society for Corrosion and protection, 2024, 44(6): 1601-1609.

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Abstract

In predicting corrosion-induced cracking of reinforced concrete, traditional empirical formulas used are varied with limited precision of prediction. To address these limitations, this paper presents a method based on the stacking of models to predict the cracking of reinforced concrete due to corrosion induced expansion. Firstly, 223 sets of test data on the cracking of reinforced concrete due to corrosion induced expansion were collected from published articles and processed in advance. Next, Bayesian optimization of hyperparameters, model training, and evaluation were conducted separately based on Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) algorithms. Determination coefficient (R²), mean absolute error (MAE), and root mean square error (RMSE) were utilized for a comparative analysis of the prediction performances of three machine learning models. On this basis, a prediction model integrating multiple algorithms with the Stacking method was proposed. Finally, the generalization performances of the proposed prediction model and traditional empirical formula models were verified, and the XGBoost model was employed to analyze the interpretability of the proposed model. As revealed in the results, the proposed model has better prediction accuracy and generalization performance than other machine learning models. The interpretability analysis result demonstrates that the prediction of the proposed model logic matches the practical engineering experience. This finding is conducive to improve the prediction accuracy of thecorrosion-induced cracking of reinforced concrete, and can provide scientific theoretical guidance for decision-makers in practical engineering.

Key words: reinforced concrete corrosion-induced cracking machine learing stacking algorithm XGBoost algorithm interpretability analysis

Received: 12 January 2024 32134.14.1005.4537.2024.017

ZTFLH:

TU375

Fund: National Key R&D Program of China(2022YFB2603000)

Corresponding Authors: LIU Meimei, E-mail: lmeimei@cccc4.com

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https://www.jcscp.org/EN/10.11902/1005.4537.2024.017 OR https://www.jcscp.org/EN/Y2024/V44/I6/1601

Fig.1 Model input and output feature data statistics: (a) concrete strength, (b) protection thickness, (c) crack width, (d) reinforcement diameters, (e) corrosion rate

Fig.2 Heat map for correlation analysis

Fig.3 Stacking integrated modeling framework

Arithmetic	Hyperparameterization	Optimal parameter values	$R 2$	RMSE/%	MAE/%
Stacking	/	/	0.906	1.17	0.88
XGBoost	n_estimator	222	0.883	1.31	0.95
	max_depth	9
	gamma	3
	learning_rate	0.3
RF	max_depth	9	0.837	1.55	1.09
	min_samples_split	2
	min_sample_leaf	1
	n_estimators	125
SVR	C	32	0.773	1.83	1.46
	Gamma	1	0.773	1.83	1.46

Table 1 Comparison of prediction results of machine learning models

Fig.4 Standardized Taylor diagram

Traditional empirical formulas	Calculation formula
Model one^[5]	$β = 1 d 32.43 + 0.303 f c u + 0.65 c + 27.45 w$
Model two^[27]	$β = 94.82 × 1 . 018 c f c u 0.248 d - 1.588$
Model three^[38]	$w = 4.5045 δ + 0.04955, β = 1 - 1 - 2 δ d 2 × 100 %$

Table 2 Three traditional empirical formulas

Fig.5 Predicted values, experimental values and absolute errors obtained by machine learning and traditional empirical formula models: (a) Stacking, (b) XGBoost, (c) RF, (d) Model one^[5], (e) Model two^[27], (f) Model three^[38]

Table 3 Comparison of errors of machine learning and empirical formula models

Fig.6 Comparison of generalization performance validations of various models

Fig.7 Single-feature dependency graph

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