Predicting compressive strength of concrete with fly ash, metakaolin and silica fume by using machine learning techniques

Majd, Ali Al-Saraireh

doi:10.1590/1679-78257022

Cited by 2 publications

(2 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, this analysis corroborates the previous observation that such additives affect compressive strength of concrete [14,21,47]. Moreover, previous studies involving compressive strength of concrete, machine learning as well as Fig.…”

Section: Influence Of Independent Variable Sensitivitysupporting

confidence: 91%

See 1 more Smart Citation

Comparing the performance of machine learning models for predicting the compressive strength of concrete

Loureiro,

Stefani

2024

Discov Civ Eng

View full text Add to dashboard Cite

This work aimed to investigate and compare the performance of different machine learning models in predicting the compressive strength of concrete using a data set of 1234 compressive strength values. The predictive variables were selected based on their relevance using the SelectKBest method, resulting in an analysis of eight and six predictive variables. The evaluation was conducted through linear correlation studies via simple linear regression and non-linear correlation studies using support vector regression (SVR), random forest (RF), gradient boosting (GB), and artificial neural networks (ANN). The results showed a coefficient of determination (R2) = 0.897 and a root mean square error (RMSE) = 6.535 MPa for SVR, R2 = 0.885 and RMSE = 5.437 MPa for GB, R2 = 0.868 and RMSE = 5.859 MPa for GB and R2 = 0.894 and RMSE = 5.192 MPa for ANN, all for test set and eight predictor variables. The comparison between the machine learning methods revealed significant differences. For instance, ANN stood out with a higher R2 value, demonstrating its remarkable ability to explain the variability in the data. ANN also showed the lowest RMSE value, indicating notable accuracy in the predictions. Although ANN has demonstrated higher performance, GB shows a closer performance, which no differences from a practical application. The choice between these approaches depends on considerations regarding the balance between explainability and accuracy. While GB provides a more in-depth understanding of the relationship between variables, ANN stands out for the accuracy of its predictions.

show abstract

Section: Influence Of Independent Variable Sensitivitysupporting

confidence: 91%

“…| https://doi.org/10.1007/s44290-024-00022-w Research experimental assays found the same influence of concentration and type of additives in compressive strength of concrete [16,21,23,47].…”

Section: Discussionmentioning

confidence: 91%

Comparing the performance of machine learning models for predicting the compressive strength of concrete

Loureiro,

Stefani

2024

Discov Civ Eng

View full text Add to dashboard Cite

show abstract

Advanced Ensemble Machine-Learning Models for Predicting Splitting Tensile Strength in Silica Fume-Modified Concrete

Al-Abdaly,

Seno,

Thwaini

et al. 2024

Buildings

View full text Add to dashboard Cite

The splitting tensile strength of concrete is crucial for structural integrity, as tensile stresses from load and environmental changes often lead to cracking. This study investigates the effectiveness of advanced ensemble machine-learning models, including LightGBM, GBRT, XGBoost, and AdaBoost, in accurately predicting the splitting tensile strength of silica fume-enhanced concrete. Using a robust database split into training (80%) and testing (20%) sets, we assessed model performance through R2, RMSE, and MAE metrics. Results demonstrate that GBRT and XGBoost achieved superior predictive accuracy, with R2 scores reaching 0.999 in training and high precision in testing (XGBoost: R2 = 0.965, RMSE = 0.337; GBRT: R2 = 0.955, RMSE = 0.381), surpassing both LightGBM and AdaBoost. This study highlights GBRT and XGBoost as reliable, efficient alternatives to traditional testing methods, offering substantial time and cost savings. Additionally, SHapley Additive exPlanations (SHAP) analysis was conducted to identify key input features and to elucidate their influence on splitting tensile strength, providing valuable insights into the predictive behavior of silica fume-enhanced concrete. The SHAP analysis reveals that the water-to-binder ratio and curing duration are the most critical factors influencing the splitting tensile strength of silica fume concrete.

show abstract

Predicting compressive strength of concrete with fly ash, metakaolin and silica fume by using machine learning techniques

Cited by 2 publications

References 52 publications

Comparing the performance of machine learning models for predicting the compressive strength of concrete

Comparing the performance of machine learning models for predicting the compressive strength of concrete

Advanced Ensemble Machine-Learning Models for Predicting Splitting Tensile Strength in Silica Fume-Modified Concrete

Contact Info

Product

Resources

About