Application of machine learning technique for predicting and evaluating chloride ingress in concrete

doi:10.1007/s11709-022-0830-4

Front. Struct. Civ. Eng.

2022, Vol. 16

Issue (9) : 1153-1169 https://doi.org/10.1007/s11709-022-0830-4

RESEARCH ARTICLE

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Van Quan TRAN¹(

), Van Loi GIAP¹, Dinh Phien VU¹, Riya Catherine GEORGE², Lanh Si HO^1,²(

)

¹. Department of Civil Engineering, 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

The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel. Therefore, the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel. This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and random forest (RF) in combination with particle swarm optimization (PSO). The input variables for modeling include exposure condition, water/binder ratio (W/B), cement content, silica fume, time exposure, and depth of measurement. The results indicate that three models performed well with high accuracy of prediction (R²≥ 0.90). Among three hybrid models, the model using GB_PSO achieved the highest prediction accuracy (R² = 0.9551, RMSE = 0.0327, and MAE = 0.0181). Based on the results of sensitivity analysis using SHapley Additive exPlanation (SHAP) and partial dependence plots 1D (PDP-1D), it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content. When the number of different exposure conditions is larger than two, the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes. This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.

Keywords gradient boosting random forest chloride content concrete sensitivity analysis.

Corresponding Author(s): Van Quan TRAN,Lanh Si HO

Just Accepted Date: 09 September 2022 Online First Date: 15 November 2022 Issue Date: 22 December 2022

Cite this article:

Van Quan TRAN,Van Loi GIAP,Dinh Phien VU, et al. Application of machine learning technique for predicting and evaluating chloride ingress in concrete[J]. Front. Struct. Civ. Eng., 2022, 16(9): 1153-1169.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0830-4
https://academic.hep.com.cn/fsce/EN/Y2022/V16/I9/1153

Fig.1 Histogram and sample distribution of input variables in the database: (a) exposure condition; (b) W/B; (c) cement content; (d) silica fume content; (e) time exposure; (f) depth of measurement; (g) chloride content.

Tab.1 Input and output variable description of the present study

Fig.2 Correlation between input and output variables.

Fig.3 DC between input and output variables.

Fig.4 Illustration of ANN algorithm.

Fig.5 RF flow chart.

Fig.6 Particle movement in PSO algorithm.

Fig.7 Methodology flowchart.

Tab.2 Hyper-parameters space of ML models

Fig.8 Influence of Po on convergence R² score.

Tab.3 Optimum hyper-parameters of ML models

Fig.9 R² score of hybrid models versus the number of iterations using 500 Monte Carlo simulations for different models.

Fig.10 Performance and StD value of three MF models: (a) RMSE value; (b) MAE value; (c) R² value for training and testing part.

Tab.4 Summary of R² value in 500 simulations for three ML models

Tab.5 Summary of RMSE value (× 10^?3) in 500 Monte Carlo simulations for three ML models

Tab.6 Summary of MAE value (× 10^?3) in 500 simulations for three ML models

Fig.11 Experimental and predicted chloride content results as a function of the sample index for the training and testing datasets.

Fig.12 Regression graphs for the case of the best parameters of GB_PSO model: (a) training part; (b) testing part; (c) all data.

Tab.7 Comparison between the results in this study and previous literature

Fig.13 SHAP value for feature importance analysis.

Fig.14 Partial Dependence Plots (PDP) of the input variables on predicted chloride content. (a) Depth of measurement; (b) exposure condition; (c) W/B; (d) cement content; (e) time exposure; (f) silica fume content.

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