Research on Hyperparameter Optimization of Concrete Slump Prediction Model Based on Response Surface Method

Research on Hyperparameter Optimization of Concrete Slump Prediction Model Based on Response Surface Method

In this paper, eight variables of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and flow are used as network input and slump is used as network output to construct a back-propagation (BP) neural network. On this basis, the learning rate, momentum fact...

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Bibliographic Details
Main Authors: Yuan Chen, Jiaye Wu, Yingqian Zhang, Lei Fu, Yunrong Luo, Yong Liu, Lindan Li
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Materials
Subjects:
BP neural network
response surface method
parameter optimization
slump
determination coefficient
root mean square error
Online Access:https://www.mdpi.com/1996-1944/15/13/4721

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https://www.mdpi.com/1996-1944/15/13/4721

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