Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations
Ultra-high-performance concrete (UHPC) is a sustainable construction material; it can be applied as a substitute for cement concrete. Artificial intelligence methods have been used to evaluate concrete composites to reduce time and money in the construction industries. So, this study applied machine...
| Główni autorzy: | , |
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| Format: | Artykuł |
| Język: | English |
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Elsevier
2024-07-01
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| Seria: | Case Studies in Construction Materials |
| Hasła przedmiotowe: | |
| Dostęp online: | http://www.sciencedirect.com/science/article/pii/S221450952300904X |
| _version_ | 1827216908348293120 |
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| author | Pobithra Das Abul Kashem |
| author_facet | Pobithra Das Abul Kashem |
| author_sort | Pobithra Das |
| collection | DOAJ |
| description | Ultra-high-performance concrete (UHPC) is a sustainable construction material; it can be applied as a substitute for cement concrete. Artificial intelligence methods have been used to evaluate concrete composites to reduce time and money in the construction industries. So, this study applied machine learning (ML) and hybrid ML approaches to predict the compressive and flexural strength of UHPC. A dataset of 626 compressive strength and 317 flexural strength data points was collected from the published research articles, where fourteen important variables were selected as input parameters for the analysis of hybrid ML and ML algorithms. This research used XGBoost, LightGBM, and hybrid XGBoost- LightGBM algorithms to predict UHPC materials. Grid search (GS) techniques were used to adjust model hyper-parameters in search of improved high accuracy and efficiency. ML and hybrid ML models were train, and the test stage utilized statistical assessments such as coefficient of determination (R-square), root mean square error (RMSE), mean absolute error (MAE), and coefficient of efficiency (CE). The results presented hybrid ML algorithm was superior to the XGBoost and LightGBM algorithms in terms of R-square and RMSE values for both compressive and flexural strength prediction. A hybrid ML model and two ML models showed CS considerable R-square values above 0.94 at the testing stages and just over 0.97 at the training phase. Hybrid ML model performance accuracy for CS prediction R-square value found that almost 0.996 for training and 0.963 for testing phases. At the same time, the FS prediction result showed that the R-square value of the Hybrid ML model and two traditional ML models were found at almost 0.95 for the training phase and around 0.81 for the testing phase. But among them, the hybrid XGB-LGB model prediction performance was high accuracy and lowest error for CS and FS of UHPC trained and its hyperparameters optimized. Additionally, the SHAP investigation reveals the impact and relationship of the input variables with the output variables. SHAP analysis outcome reveals that curing age and steel fiber content input parameter had the highest positive impact on compressive strength and flexural strength of UHPC. |
| first_indexed | 2024-03-09T02:57:37Z |
| format | Article |
| id | doaj.art-ff2e17b0edb64e96a5374933c8670a6d |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2025-03-21T15:10:35Z |
| publishDate | 2024-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-ff2e17b0edb64e96a5374933c8670a6d2024-06-20T06:49:18ZengElsevierCase Studies in Construction Materials2214-50952024-07-0120e02723Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanationsPobithra Das0Abul Kashem1Corresponding author.; Department of Civil Engineering, Leading University, Sylhet 3112, BangladeshDepartment of Civil Engineering, Leading University, Sylhet 3112, BangladeshUltra-high-performance concrete (UHPC) is a sustainable construction material; it can be applied as a substitute for cement concrete. Artificial intelligence methods have been used to evaluate concrete composites to reduce time and money in the construction industries. So, this study applied machine learning (ML) and hybrid ML approaches to predict the compressive and flexural strength of UHPC. A dataset of 626 compressive strength and 317 flexural strength data points was collected from the published research articles, where fourteen important variables were selected as input parameters for the analysis of hybrid ML and ML algorithms. This research used XGBoost, LightGBM, and hybrid XGBoost- LightGBM algorithms to predict UHPC materials. Grid search (GS) techniques were used to adjust model hyper-parameters in search of improved high accuracy and efficiency. ML and hybrid ML models were train, and the test stage utilized statistical assessments such as coefficient of determination (R-square), root mean square error (RMSE), mean absolute error (MAE), and coefficient of efficiency (CE). The results presented hybrid ML algorithm was superior to the XGBoost and LightGBM algorithms in terms of R-square and RMSE values for both compressive and flexural strength prediction. A hybrid ML model and two ML models showed CS considerable R-square values above 0.94 at the testing stages and just over 0.97 at the training phase. Hybrid ML model performance accuracy for CS prediction R-square value found that almost 0.996 for training and 0.963 for testing phases. At the same time, the FS prediction result showed that the R-square value of the Hybrid ML model and two traditional ML models were found at almost 0.95 for the training phase and around 0.81 for the testing phase. But among them, the hybrid XGB-LGB model prediction performance was high accuracy and lowest error for CS and FS of UHPC trained and its hyperparameters optimized. Additionally, the SHAP investigation reveals the impact and relationship of the input variables with the output variables. SHAP analysis outcome reveals that curing age and steel fiber content input parameter had the highest positive impact on compressive strength and flexural strength of UHPC.http://www.sciencedirect.com/science/article/pii/S221450952300904XUltra-high-performance concreteMachine learningHybrid machine learningCompressive strengthFlexural strengthAnd SHapley Additive exPlanations |
| spellingShingle | Pobithra Das Abul Kashem Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations Case Studies in Construction Materials Ultra-high-performance concrete Machine learning Hybrid machine learning Compressive strength Flexural strength And SHapley Additive exPlanations |
| title | Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations |
| title_full | Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations |
| title_fullStr | Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations |
| title_full_unstemmed | Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations |
| title_short | Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations |
| title_sort | hybrid machine learning approach to prediction of the compressive and flexural strengths of uhpc and parametric analysis with shapley additive explanations |
| topic | Ultra-high-performance concrete Machine learning Hybrid machine learning Compressive strength Flexural strength And SHapley Additive exPlanations |
| url | http://www.sciencedirect.com/science/article/pii/S221450952300904X |
| work_keys_str_mv | AT pobithradas hybridmachinelearningapproachtopredictionofthecompressiveandflexuralstrengthsofuhpcandparametricanalysiswithshapleyadditiveexplanations AT abulkashem hybridmachinelearningapproachtopredictionofthecompressiveandflexuralstrengthsofuhpcandparametricanalysiswithshapleyadditiveexplanations |