Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN
This paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigat...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9952781 |
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| author | Krishna Prakash A Jane Helena H Paul Oluwaseun Awoyera |
| author_facet | Krishna Prakash A Jane Helena H Paul Oluwaseun Awoyera |
| author_sort | Krishna Prakash A |
| collection | DOAJ |
| description | This paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigation. The ANN model is based on the Levenberg–Marquardt principle which is executed using MATLAB. The inputs are given in the percentage ratio of cement: fly ash: crushed stone aggregate (FA): coarse aggregate (CA) for the process of learning, testing, and validation. The selected model is subjected to several trials in terms of mean square error, containing 4 input, 2 sets of 10 hidden layers, and one output components. In this study, a total of 2600 blocks of different mixes were tested as per IS 2185-1 (2005) to assess 3, 7, 14, 21, and 28 days’ strength. The experimental investigations were carried out in two phases. In the first phase, experimental investigations to identify the optimum mix proportions of cement, aggregate, fly ash, and ground granulated blast furnace slag to achieve desired compressive strength was carried out. In the second phase, the identified mix proportions were analysed using ANN to predict the compressive strength of interlocking blocks. The results indicate that the proposed ANN model developed to determine the mechanical strength and cost of interlocking blocks has excellent prediction ability. |
| format | Article |
| id | doaj-art-6d8abb53ddf04c1faa1f712c82f2cf59 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-6d8abb53ddf04c1faa1f712c82f2cf592025-02-03T06:10:45ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/99527819952781Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANNKrishna Prakash A0Jane Helena H1Paul Oluwaseun Awoyera2Department of Civil Engineering, Anna University, Chennai 600025, IndiaDepartment of Civil Engineering, Anna University, Chennai 600025, IndiaDepartment of Civil Engineering, Covenant University, Ota, NigeriaThis paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigation. The ANN model is based on the Levenberg–Marquardt principle which is executed using MATLAB. The inputs are given in the percentage ratio of cement: fly ash: crushed stone aggregate (FA): coarse aggregate (CA) for the process of learning, testing, and validation. The selected model is subjected to several trials in terms of mean square error, containing 4 input, 2 sets of 10 hidden layers, and one output components. In this study, a total of 2600 blocks of different mixes were tested as per IS 2185-1 (2005) to assess 3, 7, 14, 21, and 28 days’ strength. The experimental investigations were carried out in two phases. In the first phase, experimental investigations to identify the optimum mix proportions of cement, aggregate, fly ash, and ground granulated blast furnace slag to achieve desired compressive strength was carried out. In the second phase, the identified mix proportions were analysed using ANN to predict the compressive strength of interlocking blocks. The results indicate that the proposed ANN model developed to determine the mechanical strength and cost of interlocking blocks has excellent prediction ability.http://dx.doi.org/10.1155/2021/9952781 |
| spellingShingle | Krishna Prakash A Jane Helena H Paul Oluwaseun Awoyera Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN Advances in Civil Engineering |
| title | Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN |
| title_full | Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN |
| title_fullStr | Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN |
| title_full_unstemmed | Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN |
| title_short | Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN |
| title_sort | optimization of mix proportions for novel dry stack interlocking concrete blocks using ann |
| url | http://dx.doi.org/10.1155/2021/9952781 |
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