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...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90923 |
| _version_ | 1848765461829779456 |
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| author | Sun, J. Wang, J. Zhu, Z. He, R. Peng, C. Zhang, C. Huang, J. Wang, Yufei Wang, Xiangyu |
| author_facet | Sun, J. Wang, J. Zhu, Z. He, R. Peng, C. Zhang, C. Huang, J. Wang, Yufei Wang, Xiangyu |
| author_sort | Sun, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| 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 | 2025-11-14T11:35:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-90923 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:37Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-909232023-05-09T08:00:49Z Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network Sun, J. Wang, J. Zhu, Z. He, R. Peng, C. Zhang, C. Huang, J. Wang, Yufei Wang, Xiangyu Science & Technology Technology Construction & Building Technology Engineering, Civil Engineering high-strength concrete unconfined compressive strength beetle antennae search backpropagation neural network sensitivity analysis ENGINEERED CEMENTITIOUS COMPOSITES ANGLE SHEAR CONNECTORS FUZZY INFERENCE SYSTEM COMPRESSIVE STRENGTH COLUMN CONNECTIONS FIREFLY ALGORITHM BEAMS OPTIMIZATION BEHAVIOR SEARCH 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/90923 10.3390/buildings12010065 English http://purl.org/au-research/grants/arc/LP180100222 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Technology Construction & Building Technology Engineering, Civil Engineering high-strength concrete unconfined compressive strength beetle antennae search backpropagation neural network sensitivity analysis ENGINEERED CEMENTITIOUS COMPOSITES ANGLE SHEAR CONNECTORS FUZZY INFERENCE SYSTEM COMPRESSIVE STRENGTH COLUMN CONNECTIONS FIREFLY ALGORITHM BEAMS OPTIMIZATION BEHAVIOR SEARCH Sun, J. Wang, J. Zhu, Z. He, R. Peng, C. Zhang, C. Huang, J. Wang, Yufei Wang, Xiangyu Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network |
| 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 | Science & Technology Technology Construction & Building Technology Engineering, Civil Engineering high-strength concrete unconfined compressive strength beetle antennae search backpropagation neural network sensitivity analysis ENGINEERED CEMENTITIOUS COMPOSITES ANGLE SHEAR CONNECTORS FUZZY INFERENCE SYSTEM COMPRESSIVE STRENGTH COLUMN CONNECTIONS FIREFLY ALGORITHM BEAMS OPTIMIZATION BEHAVIOR SEARCH |
| url | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90923 |