Machine learning approaches for RCPT modeling of concrete

Chloride ion penetration can lead to the corrosion of embedded steel reinforcement in concrete and deteriorate its durability. One of the commonly used methods for determining the durability of concrete is the rapid chloride permeability test (RCPT). The accurate prediction of RCPT is essential in optimizing concrete mixture design. In this study, RCPT modeling was conducted using a dataset of 469 samples. The contributions of this study are to apply a large-scale dataset with essential variables, to identify the optimal variables to maximize the performance of RCPT prediction models, to determine the most accurate prediction method for RCPT prediction, and to introduce a novel algorithm to tune hyperparameters of machine learning methods. To this end, first, feature selection was implemented to select the optimal variables. Then, different machine learning methods were used to predict RCPT. A novel less-parameter algorithm (STML) was adjusted to tune hyperparameters considering multiple metrics, which was considerably more accurate than the conventional tuning method. The results suggested that eXtreme Gradient Boosting tuned by STML was the best-performing model, with an MAE of 38.893 coulombs. Subsequently, SHapley Additive exPlanation was synchronized with the best-performing model, and the results showed that the test temperature had the highest relative influence on RCPT, followed by fly ash to binder ratio, silica fume to cement ratio, and coarse aggregate content. Finally, the Partial Dependence Plots were applied to capture the influence direction of different variables on RCPT, allowing the identification of the optimal range of materials to minimize RCPT. • All the vital features to accurately predict RCPT are identified. • A novel less-parameter algorithm is developed to tune hyperparameters. • XGB tuned by STML is the best-performing model. • Test temperature has the highest relative influence on RCPT. • The optimal range of variables to minimize RCPT is determined.

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Publication Details

Journal
Construction and Building Materials
Published
2025-08-11
DOI
https://doi.org/10.1016/j.conbuildmat.2025.143126
Citations
12
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
11.44
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article

Machine learning approaches for RCPT modeling of concrete

Amir H. Gandomi, Hamed Naseri, Farzad Safi Jahanshahi
12 citations
Construction and Building Materials
Infrastructure Maintenance and Monitoring
11.44
article

Machine learning approaches for RCPT modeling of concrete

Amir H. Gandomi, Hamed Naseri, Farzad Safi Jahanshahi
article en
12 citations

Abstract

Chloride ion penetration can lead to the corrosion of embedded steel reinforcement in concrete and deteriorate its durability. One of the commonly used methods for determining the durability of concrete is the rapid chloride permeability test (RCPT). The accurate prediction of RCPT is essential in optimizing concrete mixture design. In this study, RCPT modeling was conducted using a dataset of 469 samples. The contributions of this study are to apply a large-scale dataset with essential variables, to identify the optimal variables to maximize the performance of RCPT prediction models, to determine the most accurate prediction method for RCPT prediction, and to introduce a novel algorithm to tune hyperparameters of machine learning methods. To this end, first, feature selection was implemented to select the optimal variables. Then, different machine learning methods were used to predict RCPT. A novel less-parameter algorithm (STML) was adjusted to tune hyperparameters considering multiple metrics, which was considerably more accurate than the conventional tuning method. The results suggested that eXtreme Gradient Boosting tuned by STML was the best-performing model, with an MAE of 38.893 coulombs. Subsequently, SHapley Additive exPlanation was synchronized with the best-performing model, and the results showed that the test temperature had the highest relative influence on RCPT, followed by fly ash to binder ratio, silica fume to cement ratio, and coarse aggregate content. Finally, the Partial Dependence Plots were applied to capture the influence direction of different variables on RCPT, allowing the identification of the optimal range of materials to minimize RCPT. • All the vital features to accurately predict RCPT are identified. • A novel less-parameter algorithm is developed to tune hyperparameters. • XGB tuned by STML is the best-performing model. • Test temperature has the highest relative influence on RCPT. • The optimal range of variables to minimize RCPT is determined.

Construction and Building MaterialsVol. 492
University of Technology Sydney (AU), Sirjan University of Technology (IR), Polytechnique Montréal (CA)
Climate action
Openalex Percentile: Top 1%
Infrastructure Maintenance and Monitoring
11.44
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