Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars
Abstract This study investigated the predictability of the compressive strength (CS) of geopolymeric mortars based on blast furnace slag (BFS) and steel mill slag (SMS). For this purpose, the study consists of two parts. In the first part of the study, BFS and SMS, two different types of slag were u...
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Nature Portfolio
2025-04-01
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| author | Atchadeou Yranawa Katatchambo Şinasi Bingöl |
| author_facet | Atchadeou Yranawa Katatchambo Şinasi Bingöl |
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| description | Abstract This study investigated the predictability of the compressive strength (CS) of geopolymeric mortars based on blast furnace slag (BFS) and steel mill slag (SMS). For this purpose, the study consists of two parts. In the first part of the study, BFS and SMS, two different types of slag were used as binders in 11 different proportions. At the end of the curing period, the weight, ultrasonic pulse velocity (UPV) and compressive strength of the mortars were determined. In the second part of the study, the compressive strength was predicted using regression analysis (CRA), multivariate adaptive regression spline (MARS), random forest (RF), multiple additive regression trees (TreeNet) and artificial neural networks (ANN). The model performance of the methods was compared using root mean square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE) performance statistics. When comparing the performance of the developed prediction models, the power function method was found to produce the best predictions among the regression-based methods. For the MARS, TreeNet and RF models, the TreeNet model produced the best prediction, while for the ANN_5 and ANN_10 models, the ANN_5 model produced the best prediction. In general, it can be concluded that the models developed with ANN can predict the compressive strength of mortars with a very high accuracy. Significant economic and time savings can be achieved with the developed models. In addition, the CS values of geopolymeric mortars prepared with different proportions of slag types and activator can be predicted without waiting for 7–28 days of curing. |
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| spelling | doaj-art-db9607f8e1894ccf8a218ea2e81187812025-08-20T02:08:08ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-96772-3Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortarsAtchadeou Yranawa Katatchambo0Şinasi Bingöl1Department of Civil Engineering, Tokat Gaziosmanpaşa UniversityDepartment of Civil Engineering, Tokat Gaziosmanpaşa UniversityAbstract This study investigated the predictability of the compressive strength (CS) of geopolymeric mortars based on blast furnace slag (BFS) and steel mill slag (SMS). For this purpose, the study consists of two parts. In the first part of the study, BFS and SMS, two different types of slag were used as binders in 11 different proportions. At the end of the curing period, the weight, ultrasonic pulse velocity (UPV) and compressive strength of the mortars were determined. In the second part of the study, the compressive strength was predicted using regression analysis (CRA), multivariate adaptive regression spline (MARS), random forest (RF), multiple additive regression trees (TreeNet) and artificial neural networks (ANN). The model performance of the methods was compared using root mean square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE) performance statistics. When comparing the performance of the developed prediction models, the power function method was found to produce the best predictions among the regression-based methods. For the MARS, TreeNet and RF models, the TreeNet model produced the best prediction, while for the ANN_5 and ANN_10 models, the ANN_5 model produced the best prediction. In general, it can be concluded that the models developed with ANN can predict the compressive strength of mortars with a very high accuracy. Significant economic and time savings can be achieved with the developed models. In addition, the CS values of geopolymeric mortars prepared with different proportions of slag types and activator can be predicted without waiting for 7–28 days of curing.https://doi.org/10.1038/s41598-025-96772-3Geopolmer mortarCompressive strengthANN modelingRegression analysis |
| spellingShingle | Atchadeou Yranawa Katatchambo Şinasi Bingöl Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars Scientific Reports Geopolmer mortar Compressive strength ANN modeling Regression analysis |
| title | Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars |
| title_full | Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars |
| title_fullStr | Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars |
| title_full_unstemmed | Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars |
| title_short | Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars |
| title_sort | comparison of regression based functions and ann models for predicting the compressive strength of geopolymer mortars |
| topic | Geopolmer mortar Compressive strength ANN modeling Regression analysis |
| url | https://doi.org/10.1038/s41598-025-96772-3 |
| work_keys_str_mv | AT atchadeouyranawakatatchambo comparisonofregressionbasedfunctionsandannmodelsforpredictingthecompressivestrengthofgeopolymermortars AT sinasibingol comparisonofregressionbasedfunctionsandannmodelsforpredictingthecompressivestrengthofgeopolymermortars |