A machine learning method for predicting the chloride migration coefficient of concrete

Taffese, Woubishet Zewdu; Espinosa-Leal, Leonardo

doi:10.1016/j.conbuildmat.2022.128566

Cited by 46 publications

(12 citation statements)

References 48 publications

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“…Machine learning algorithms have solved several civil engineering problems [84] such as, Geotechnical engineering [20,21], pavement structures [22], structural engineering [23][24], composite structural elements [25], material science [26][27][28][29][30], tra c engineering [31][32][33][34], etc.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Regressors for Half Cell Potential Prediction

Murthy,

Pandey,

Gandhi

2024

Preprint

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This study addresses the critical issue of steel corrosion in concrete structures, a major concern in the construction industry. By integrating advanced machine learning techniques, particularly ensemble methods, the research aims to enhance the accuracy and reliability of corrosion prediction models for reinforced concrete structures. Through experimentation and meticulous data collection, key input parameters such as distances from the anode, relative humidity, temperature, and concrete age were identified. Various ensemble learning methods including Boosted Trees, Bagged Trees, and Optimizable Ensembles were employed and evaluated using performance metrics such as RMSE, R-squared, MSE, MAE, prediction speed, and training time. LSBoost with Bayesian optimization emerged as the top-performing method, achieving the lowest RMSE of 0.018097, highest R-squared of 0.97, lowest MSE of 0.00032752, and smallest MAE of 0.013769. Despite its longer training time, LSBoost with Bayesian optimization offers superior predictive accuracy compared to other methods, warranting consideration for applications where accuracy is paramount. Bagged Trees and Boosted Trees also demonstrated good performance, balancing prediction speed and accuracy, making them suitable for time-sensitive applications. This research provides valuable insights for developing cost-effective maintenance and rehabilitation strategies, ultimately improving the durability and strength of concrete structures.

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Section: Introductionmentioning

confidence: 99%

Ensemble Regressors for Half Cell Potential Prediction

Murthy,

Pandey,

Gandhi

2024

Preprint

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“…Many civil engineering problems have been solved using ML algorithms. These include geotechnical engineering [ 17 , 18 ], pavement structures [ 19 ], structural engineering [ 20 , 21 , 22 , 23 ], composite structural elements [ 24 ], material science [ 25 , 26 , 27 , 28 , 29 ], and traffic engineering [ 30 , 31 , 32 , 33 ]. Moreover, previous research has been conducted to predict the Dcl in concrete using an AI-based approach [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Fuhaid

Alanazi

2023

Materials

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The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C3A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R2 values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design.

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“…The final strength of concrete depends heavily on the mix design. An optimized mix design model requires numerous laboratory experiments for optimal concrete compressive strength, which can be time-consuming, error-prone, and expensive [ 10 ]. Since then, researchers have presented empirical regression approaches to predict concrete compressive strength.…”

Section: Introductionmentioning

confidence: 99%

Estimation of concrete materials uniaxial compressive strength using soft computing techniques

Raju,

Rahman,

Hasan

et al. 2023

Heliyon

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A machine learning method for predicting the chloride migration coefficient of concrete

Cited by 46 publications

References 48 publications

Ensemble Regressors for Half Cell Potential Prediction

Ensemble Regressors for Half Cell Potential Prediction

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Estimation of concrete materials uniaxial compressive strength using soft computing techniques

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