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Article

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

1
Institute for Smart City of Chongqing University, Chongqing University, Liyang 213300, China
2
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
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School of Architectural Engineering, Nanjing Institute of Technology, Nanjing 211167, China
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Hunan Provincial Key Laboratory of Geotechnical Engineering for Stability Control and Health Monitoring, Hunan University of Science and Technology, Xiangtan 411201, China
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College of Civil Engineering, Fuzhou University, 2 Xue Yuan Rd., University Town, Fuzhou 350116, China
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School of Design and Built Environment, Curtin University, Perth 6102, Australia
*
Authors to whom correspondence should be addressed.
Academic Editor: Elena Ferretti
Buildings 2022, 12(1), 65; https://doi.org/10.3390/buildings12010065
Received: 30 November 2021 / Revised: 26 December 2021 / Accepted: 5 January 2022 / Published: 10 January 2022
(This article belongs to the Special Issue The Impact of Building Materials on Construction Sustainability)
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. View Full-Text
Keywords: high-strength concrete; unconfined compressive strength; beetle antennae search; backpropagation neural network; sensitivity analysis high-strength concrete; unconfined compressive strength; beetle antennae search; backpropagation neural network; sensitivity analysis
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MDPI and ACS Style

Sun, J.; Wang, J.; Zhu, Z.; He, R.; Peng, C.; Zhang, C.; Huang, J.; Wang, Y.; Wang, X. Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network. Buildings 2022, 12, 65. https://doi.org/10.3390/buildings12010065

AMA Style

Sun J, Wang J, Zhu Z, He R, Peng C, Zhang C, Huang J, Wang Y, Wang X. Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network. Buildings. 2022; 12(1):65. https://doi.org/10.3390/buildings12010065

Chicago/Turabian Style

Sun, Junbo, Jiaqing Wang, Zhaoyue Zhu, Rui He, Cheng Peng, Chao Zhang, Jizhuo Huang, Yufei Wang, and Xiangyu Wang. 2022. "Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network" Buildings 12, no. 1: 65. https://doi.org/10.3390/buildings12010065

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