Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach
Basalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/16/13/4578 |
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| author | Celal Cakiroglu Yaren Aydın Gebrail Bekdaş Zong Woo Geem |
| author_facet | Celal Cakiroglu Yaren Aydın Gebrail Bekdaş Zong Woo Geem |
| author_sort | Celal Cakiroglu |
| collection | DOAJ |
| description | Basalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength. |
| first_indexed | 2024-03-11T01:35:53Z |
| format | Article |
| id | doaj.art-1d7084aeac184b48bd895c94427376d8 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-11T01:35:53Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-1d7084aeac184b48bd895c94427376d82023-11-18T16:56:46ZengMDPI AGMaterials1996-19442023-06-011613457810.3390/ma16134578Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP ApproachCelal Cakiroglu0Yaren Aydın1Gebrail Bekdaş2Zong Woo Geem3Department of Civil Engineering, Turkish-German University, 34820 Istanbul, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpasa, 34320 Istanbul, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpasa, 34320 Istanbul, TurkeyCollege of IT Convergence, Gachon University, Seongnam 13120, Republic of KoreaBasalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength.https://www.mdpi.com/1996-1944/16/13/4578FRPconcretesplitting tensile strengthmachine learningXGBoostSHAP |
| spellingShingle | Celal Cakiroglu Yaren Aydın Gebrail Bekdaş Zong Woo Geem Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach Materials FRP concrete splitting tensile strength machine learning XGBoost SHAP |
| title | Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach |
| title_full | Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach |
| title_fullStr | Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach |
| title_full_unstemmed | Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach |
| title_short | Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach |
| title_sort | interpretable predictive modelling of basalt fiber reinforced concrete splitting tensile strength using ensemble machine learning methods and shap approach |
| topic | FRP concrete splitting tensile strength machine learning XGBoost SHAP |
| url | https://www.mdpi.com/1996-1944/16/13/4578 |
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