Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks
The investigations on the performance of concrete incorporating waste materials hold promise in achieving sustainable construction. Although various studies have addressed the mechanical behaviour of concrete containing waste tyre rubber (WTR) and clay brick powder (CBP), an advanced understanding o...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Waste Management Bulletin |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949750725000082 |
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| author | David Sinkhonde Destine Mashava Tajebe Bezabih Derrick Mirindi |
| author_facet | David Sinkhonde Destine Mashava Tajebe Bezabih Derrick Mirindi |
| author_sort | David Sinkhonde |
| collection | DOAJ |
| description | The investigations on the performance of concrete incorporating waste materials hold promise in achieving sustainable construction. Although various studies have addressed the mechanical behaviour of concrete containing waste tyre rubber (WTR) and clay brick powder (CBP), an advanced understanding of predicting the compressive strength of such concrete remains underdeveloped. In this study, predicting the compressive strength of rubberized concrete incorporating CBP using the artificial neural network (ANN)-based models is proposed for the first time. The prediction is based on multilayer perceptron (MLP) and radial basis function (RBF) neural networks. It is shown that MLP is superior in predicting the compressive strength of rubberized concrete incorporating CBP compared with RBF. However, regardless of the algorithm used, the R2 and adjusted R2 values are higher than 0.75. Results on Pearson’s r values greater than 0.85 illustrate higher predictive abilities of the neural networks. Moreover, the study demonstrates that it is not possible to obtain significant relationships between the individual independent variables of rubberized concrete incorporating CBP and concrete compressive strength. The ANN-based models in this research contribute towards an understanding of predicting the compressive strength of rubberized concrete incorporating CBP, which can inspire further modeling studies involving such materials. |
| format | Article |
| id | doaj-art-e534b12ea769463eaa8d26503e1da614 |
| institution | DOAJ |
| issn | 2949-7507 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Waste Management Bulletin |
| spelling | doaj-art-e534b12ea769463eaa8d26503e1da6142025-08-20T02:59:49ZengElsevierWaste Management Bulletin2949-75072025-04-013121923310.1016/j.wmb.2025.01.008Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networksDavid Sinkhonde0Destine Mashava1Tajebe Bezabih2Derrick Mirindi3Department of Civil and Construction Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya; Corresponding author.Department of Mechanical Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, KenyaDepartment of Civil and Construction Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, KenyaSchool of Architecture and Planning, Morgan State University, USAThe investigations on the performance of concrete incorporating waste materials hold promise in achieving sustainable construction. Although various studies have addressed the mechanical behaviour of concrete containing waste tyre rubber (WTR) and clay brick powder (CBP), an advanced understanding of predicting the compressive strength of such concrete remains underdeveloped. In this study, predicting the compressive strength of rubberized concrete incorporating CBP using the artificial neural network (ANN)-based models is proposed for the first time. The prediction is based on multilayer perceptron (MLP) and radial basis function (RBF) neural networks. It is shown that MLP is superior in predicting the compressive strength of rubberized concrete incorporating CBP compared with RBF. However, regardless of the algorithm used, the R2 and adjusted R2 values are higher than 0.75. Results on Pearson’s r values greater than 0.85 illustrate higher predictive abilities of the neural networks. Moreover, the study demonstrates that it is not possible to obtain significant relationships between the individual independent variables of rubberized concrete incorporating CBP and concrete compressive strength. The ANN-based models in this research contribute towards an understanding of predicting the compressive strength of rubberized concrete incorporating CBP, which can inspire further modeling studies involving such materials.http://www.sciencedirect.com/science/article/pii/S2949750725000082Artificial neural networkClay brick powderConcreteMultilayer perceptronRadial basis functionWaste tyre rubber |
| spellingShingle | David Sinkhonde Destine Mashava Tajebe Bezabih Derrick Mirindi Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks Waste Management Bulletin Artificial neural network Clay brick powder Concrete Multilayer perceptron Radial basis function Waste tyre rubber |
| title | Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks |
| title_full | Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks |
| title_fullStr | Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks |
| title_full_unstemmed | Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks |
| title_short | Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks |
| title_sort | predicting the compressive strength of rubberized concrete incorporating brick powder based on mlp and rbf neural networks |
| topic | Artificial neural network Clay brick powder Concrete Multilayer perceptron Radial basis function Waste tyre rubber |
| url | http://www.sciencedirect.com/science/article/pii/S2949750725000082 |
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