Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete
In the present study, several hybrids and coupled forms of machine learning algorithms were developed to provide accurate elastic modulus of recycled aggregate concrete’s (ERAC) estimation, called multilayer perceptron neural networks (MLPNN). For this gain, a comprehensive dataset was collected fro...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-02-01
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| Series: | Journal of Asian Architecture and Building Engineering |
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| Online Access: | http://dx.doi.org/10.1080/13467581.2025.2458809 |
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| author | Mingke Zheng Jinzhao Yin Lei Zhang Lihua Wu Hao Liu |
| author_facet | Mingke Zheng Jinzhao Yin Lei Zhang Lihua Wu Hao Liu |
| author_sort | Mingke Zheng |
| collection | DOAJ |
| description | In the present study, several hybrids and coupled forms of machine learning algorithms were developed to provide accurate elastic modulus of recycled aggregate concrete’s (ERAC) estimation, called multilayer perceptron neural networks (MLPNN). For this gain, a comprehensive dataset was collected from literature containing 400 rows of samples. Networks with different hidden layer numbers were used to develop MLPNN (from one layer to three layers). The accuracy of MLPNN deeply related to neuron numbers of each hidden layer, wherein the present paper, three optimization algorithms; the arithmetic optimization algorithm (AOA), equilibrium optimizer (EO), and flow direction algorithm (FDA); are merged with MLPNN to obtain the ideal value of neuron numbers. Considering all models, all developed models have the acceptable ability for predicting the ERAC by the coefficient of determination of at least 0.9306 for the learning stage and 0.9411 for the examining stage. All in all, the MLPNN model with two numbers of hidden layers with a structure of 17-14-1 optimized with AOA can be proposed as the most appropriate model. The created MLPNN models may optimize material selection and quality control in building processes, reducing dependence on natural aggregates. |
| format | Article |
| id | doaj-art-5a49ada29bfc439c8d1c40de7906bb0d |
| institution | OA Journals |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-5a49ada29bfc439c8d1c40de7906bb0d2025-08-20T02:04:07ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-02-010012010.1080/13467581.2025.24588092458809Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concreteMingke Zheng0Jinzhao Yin1Lei Zhang2Lihua Wu3Hao Liu4Zhejiang Tongji Vocational College of Science and TechnologyZhejiang Tongji Vocational College of Science and TechnologyZhejiang Tongji Vocational College of Science and TechnologyZhejiang Tongji Vocational College of Science and TechnologyZhejiang Tongji Vocational College of Science and TechnologyIn the present study, several hybrids and coupled forms of machine learning algorithms were developed to provide accurate elastic modulus of recycled aggregate concrete’s (ERAC) estimation, called multilayer perceptron neural networks (MLPNN). For this gain, a comprehensive dataset was collected from literature containing 400 rows of samples. Networks with different hidden layer numbers were used to develop MLPNN (from one layer to three layers). The accuracy of MLPNN deeply related to neuron numbers of each hidden layer, wherein the present paper, three optimization algorithms; the arithmetic optimization algorithm (AOA), equilibrium optimizer (EO), and flow direction algorithm (FDA); are merged with MLPNN to obtain the ideal value of neuron numbers. Considering all models, all developed models have the acceptable ability for predicting the ERAC by the coefficient of determination of at least 0.9306 for the learning stage and 0.9411 for the examining stage. All in all, the MLPNN model with two numbers of hidden layers with a structure of 17-14-1 optimized with AOA can be proposed as the most appropriate model. The created MLPNN models may optimize material selection and quality control in building processes, reducing dependence on natural aggregates.http://dx.doi.org/10.1080/13467581.2025.2458809modulus of elasticity predictionrecycled aggregate concretemulti-layer neural networksmetaheuristic optimization |
| spellingShingle | Mingke Zheng Jinzhao Yin Lei Zhang Lihua Wu Hao Liu Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete Journal of Asian Architecture and Building Engineering modulus of elasticity prediction recycled aggregate concrete multi-layer neural networks metaheuristic optimization |
| title | Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete |
| title_full | Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete |
| title_fullStr | Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete |
| title_full_unstemmed | Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete |
| title_short | Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete |
| title_sort | hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete |
| topic | modulus of elasticity prediction recycled aggregate concrete multi-layer neural networks metaheuristic optimization |
| url | http://dx.doi.org/10.1080/13467581.2025.2458809 |
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