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A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete. | LitMetric

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete.

Materials (Basel)

Department of Mining Technology, Topography and Structures, Campus de Vegazana s/n, University of León, 24071 León, Spain.

Published: June 2022

AI Article Synopsis

  • This study compared four machine learning methods (XG Boost, GB, CB, and ETR) to predict the splitting tensile strength of 28-day-old self-compacting concrete made from recycled aggregates.
  • A dataset of 381 samples was used, split into training (70%), validation (15%), and test (15%) sets, and various metrics were employed to evaluate the models' performance.
  • XG Boost outperformed the other models with the highest coefficient of determination (R = 0.8423) and lowest error metrics, confirming it as the best predictor for the tensile strength, with cement identified as the most influential variable.

Article Abstract

Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R value of R = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229901PMC
http://dx.doi.org/10.3390/ma15124164DOI Listing

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