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A platform for research: civil engineering, architecture and urbanism
Forecasting unconfined compressive strength of calcium sulfoaluminate cement mixtures using ensemble machine learning techniques integrated with shapely-additive explanations
https://orcid.org/0000-0002-6524-0529
https://orcid.org/0000-0003-1127-4755
Highlights 723 unique data points were gathered from the literature to compile the dataset. LASSO method consistently improved the performance of all machine learning models. LASSO-XGBR model showed the highest accuracy in predicting the UCS of CSA cement mixture. Feature importance ranking was established by the SHAP analysis. Relationships between input features and UCS were explained using SHAP plots.
Abstract Calcium sulfoaluminate (CSA) cement mixture design is challenging due to the influence of multiple features on its unconfined compressive strength (UCS). Consequently, the relationships between input features and the UCS exhibit non-linear behavior, making it difficult to understand using experimental methods alone. Therefore, for the first time, this study constructed non-linear ensemble machine learning (ML) models on a dataset compiled from experimental literature to accurately predict the UCS of CSA cement mixtures. After applying feature selection techniques, four different ensemble models were built on the modified datasets to predict the UCS. The extreme gradient boosting model built on the dataset modified by the least absolute shrinkage and selection operator method achieved the best prediction accuracy (coefficient of determination; R2 = 0.95) on testing data. Finally, the SHapely Additive exPlanations analysis could interpret the selected ML model both quantitatively and qualitatively, by explaining the independent relationships between each input feature and UCS.
Forecasting unconfined compressive strength of calcium sulfoaluminate cement mixtures using ensemble machine learning techniques integrated with shapely-additive explanations
https://orcid.org/0000-0002-6524-0529
https://orcid.org/0000-0003-1127-4755
Highlights 723 unique data points were gathered from the literature to compile the dataset. LASSO method consistently improved the performance of all machine learning models. LASSO-XGBR model showed the highest accuracy in predicting the UCS of CSA cement mixture. Feature importance ranking was established by the SHAP analysis. Relationships between input features and UCS were explained using SHAP plots.
Abstract Calcium sulfoaluminate (CSA) cement mixture design is challenging due to the influence of multiple features on its unconfined compressive strength (UCS). Consequently, the relationships between input features and the UCS exhibit non-linear behavior, making it difficult to understand using experimental methods alone. Therefore, for the first time, this study constructed non-linear ensemble machine learning (ML) models on a dataset compiled from experimental literature to accurately predict the UCS of CSA cement mixtures. After applying feature selection techniques, four different ensemble models were built on the modified datasets to predict the UCS. The extreme gradient boosting model built on the dataset modified by the least absolute shrinkage and selection operator method achieved the best prediction accuracy (coefficient of determination; R2 = 0.95) on testing data. Finally, the SHapely Additive exPlanations analysis could interpret the selected ML model both quantitatively and qualitatively, by explaining the independent relationships between each input feature and UCS.
Forecasting unconfined compressive strength of calcium sulfoaluminate cement mixtures using ensemble machine learning techniques integrated with shapely-additive explanations
https://orcid.org/0000-0002-6524-0529
https://orcid.org/0000-0003-1127-4755
Highlights 723 unique data points were gathered from the literature to compile the dataset. LASSO method consistently improved the performance of all machine learning models. LASSO-XGBR model showed the highest accuracy in predicting the UCS of CSA cement mixture. Feature importance ranking was established by the SHAP analysis. Relationships between input features and UCS were explained using SHAP plots.
Abstract Calcium sulfoaluminate (CSA) cement mixture design is challenging due to the influence of multiple features on its unconfined compressive strength (UCS). Consequently, the relationships between input features and the UCS exhibit non-linear behavior, making it difficult to understand using experimental methods alone. Therefore, for the first time, this study constructed non-linear ensemble machine learning (ML) models on a dataset compiled from experimental literature to accurately predict the UCS of CSA cement mixtures. After applying feature selection techniques, four different ensemble models were built on the modified datasets to predict the UCS. The extreme gradient boosting model built on the dataset modified by the least absolute shrinkage and selection operator method achieved the best prediction accuracy (coefficient of determination; R2 = 0.95) on testing data. Finally, the SHapely Additive exPlanations analysis could interpret the selected ML model both quantitatively and qualitatively, by explaining the independent relationships between each input feature and UCS.
Forecasting unconfined compressive strength of calcium sulfoaluminate cement mixtures using ensemble machine learning techniques integrated with shapely-additive explanations
Balasooriya Arachchilage, Chathuranga (author) / Huang, Guangping (author) / Fan, Chengkai (author) / Liu, Wei Victor (author)
2023-11-03
Article (Journal)
Electronic Resource
English