RT info:eu-repo/semantics/article
T1 A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete
A1 Martínez García, Rebeca
A1 Palencia Coto, María Covadonga
A1 Jagadesh, P
A1 De Prado Gil, Jesús
A2 Ingenieria Mecanica
K1 Ingeniería de minas
K1 Machine Learning
K1 Splitting Tensile Strength
K1 Self-compacting Concrete
K1 Recycled Aggregates
K1 Prediction
AB [EN] 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 (R2), 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 R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 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.
PB MDPI
LK https://hdl.handle.net/10612/20584
UL https://hdl.handle.net/10612/20584
NO de-Prado-Gil, J.; Palencia, C.; Jagadesh, P.; Martínez-García, R. A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete. Materials 2022, 15, 4164. https://doi.org/10.3390/ma15124164
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 30-jun-2024