Acta Metrologica Sinica  2022, Vol. 43 Issue (11): 1464-1469    DOI: 10.3969/j.issn.1000-1158.2022.11.12
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Prediction of Concrete Mechanical Properties Based on Fusion RBF-PSO-AE Algorithm
HUANG Chen-liang1,GUO Li-qun2,Lü Yang-yang3,LIU Chang4
1. Zhumadian Keyuan Construction Engineering Quality Inspection Co., LTD, Zhumadian, Henan 463000, China
2. Huaqiao University, Quanzhou, Fujian 362021, China
3. Henan University of Technology, Zhengzhou, Henan 450001, China
4. Henan Province Georock Engineering Technology Co., LTD, Zhengzhou, Henan 450001,China
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Abstract  Aiming at the problem of accurate prediction of concrete material mechanical properties, a particle swarm optimization (PSO) optimization of radial basis function (RBF) and the autoencoder(AE) fusion predicting model (RBF-PSO-AE) is proposed to predict and analyz the fracture energy, instability toughness and crack initiation toughness of concrete. Firstly, RBF and AE are used to accelerate the convergence of data feature dimensionality reduction by using cross entropy loss function. Secondly, PSO is used to quickly optimize the network optimal weight of the model. Finally, the model is compared with a variety of single prediction models. The experimental results show that the prediction accuracy and generalization ability of the algorithm model are significantly improved, and the prediction accuracy is greater than 99.99%, with a root mean square error of 0.006%. It can effectively reduce the error of concrete mechanical property prediction, and has good robustness.
Key wordsmetrology      concrete materials      mechanical prediction model      radial basis function      particle swarm optimization      autoencoder     
Received: 01 March 2022      Published: 14 November 2022
PACS:  TB93  
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HUANG Chen-liang
GUO Li-qun
Lü Yang-yang
LIU Chang
Cite this article:   
HUANG Chen-liang,GUO Li-qun,Lü Yang-yang, et al. Prediction of Concrete Mechanical Properties Based on Fusion RBF-PSO-AE Algorithm[J]. Acta Metrologica Sinica, 2022, 43(11): 1464-1469.
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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.11.12     OR     http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I11/1464
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