Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network
High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC perfor...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Online Access: | https://www.mdpi.com/2075-5309/12/1/65 |
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| author | Junbo Sun Jiaqing Wang Zhaoyue Zhu Rui He Cheng Peng Chao Zhang Jizhuo Huang Yufei Wang Xiangyu Wang |
| author_facet | Junbo Sun Jiaqing Wang Zhaoyue Zhu Rui He Cheng Peng Chao Zhang Jizhuo Huang Yufei Wang Xiangyu Wang |
| author_sort | Junbo Sun |
| collection | DOAJ |
| description | High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented. |
| first_indexed | 2024-03-10T01:48:35Z |
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| language | English |
| last_indexed | 2024-03-10T01:48:35Z |
| publishDate | 2022-01-01 |
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| series | Buildings |
| spelling | doaj.art-dbf29fc099674588905259594f041b772023-11-23T13:11:42ZengMDPI AGBuildings2075-53092022-01-011216510.3390/buildings12010065Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural NetworkJunbo Sun0Jiaqing Wang1Zhaoyue Zhu2Rui He3Cheng Peng4Chao Zhang5Jizhuo Huang6Yufei Wang7Xiangyu Wang8Institute for Smart City of Chongqing University, Chongqing University, Liyang 213300, ChinaCollege of Civil Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Architectural Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Architectural Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Architectural Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaHunan Provincial Key Laboratory of Geotechnical Engineering for Stability Control and Health Monitoring, Hunan University of Science and Technology, Xiangtan 411201, ChinaCollege of Civil Engineering, Fuzhou University, 2 Xue Yuan Rd., University Town, Fuzhou 350116, ChinaSchool of Design and Built Environment, Curtin University, Perth 6102, AustraliaSchool of Design and Built Environment, Curtin University, Perth 6102, AustraliaHigh-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.https://www.mdpi.com/2075-5309/12/1/65high-strength concreteunconfined compressive strengthbeetle antennae searchbackpropagation neural networksensitivity analysis |
| spellingShingle | Junbo Sun Jiaqing Wang Zhaoyue Zhu Rui He Cheng Peng Chao Zhang Jizhuo Huang Yufei Wang Xiangyu Wang Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network Buildings high-strength concrete unconfined compressive strength beetle antennae search backpropagation neural network sensitivity analysis |
| title | Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network |
| title_full | Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network |
| title_fullStr | Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network |
| title_full_unstemmed | Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network |
| title_short | Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network |
| title_sort | mechanical performance prediction for sustainable high strength concrete using bio inspired neural network |
| topic | high-strength concrete unconfined compressive strength beetle antennae search backpropagation neural network sensitivity analysis |
| url | https://www.mdpi.com/2075-5309/12/1/65 |
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