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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Detalles Bibliográficos
Autores principales: Chen, Yuan, Wu, Jiaye, Zhang, Yingqian, Fu, Lei, Luo, Yunrong, Liu, Yong, Li, Lindan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267923/
https://www.ncbi.nlm.nih.gov/pubmed/35806843
http://dx.doi.org/10.3390/ma15134721

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267923/
https://www.ncbi.nlm.nih.gov/pubmed/35806843
http://dx.doi.org/10.3390/ma15134721

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