Compressive Strength Prediction and Mix Proportion Design of UHPC Based on GA-BP Neural Network

被引：0
作者：
Chen Q. [1 ,2 ]
Ma R. [1 ,2 ]
Jiang Z. [1 ,2 ]
Wang H. [1 ,2 ]
机构：
[1] Key Laboratory of Advanced Civil Engineering Materials of Ministry of Education, Tongji University, Shanghai
[2] School of Materials Science and Engineering, Tongji University, Shanghai
来源：
Jianzhu Cailiao Xuebao/Journal of Building Materials | 2020年 / 23卷 / 01期
关键词：
Compressive strength prediction; GA-BP neural network; Genetic algorithm; Mix proportion design; Ultra-high performance concrete(UHPC);
D O I：
10.3969/j.issn.1007-9629.2020.01.027
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Preparation and compressive strength test of UHPC with different mix proportions were carried out, and neural network training samples were formed by combining the existing experimental data. The topology structure of input layer(7 nodes), hidden layer(8 nodes) and output layer(1 node) of the neural network was designed according to the raw material composition and performance requirements of UHPC. The genetic algorithm(GA) was introduced to optimize the initial weight and threshold of the UHPC compressive strength prediction network. The GA-BP neural network for compressive strength prediction of UHPC with different mix proportions was simulated and trained with the experimental samples, and the mix design method based on different performance requirements was established. By comparing the experimental data with the results of traditional BP neural network method, it is confirmed that the proposed GA-BP neural network can better guide the compressive strength prediction and mix design of UHPC. © 2020, Editorial Department of Journal of Building Materials. All right reserved.
引用
收藏
页码：176 / 183and191
相关论文
共 23 条
  • [1] Cheng B., Wang J., Yang W., Et al., Experimental study on preparation of ultra-high strength concrete based on the theory of the tightest packing, Concrete, 1, pp. 101-105, (2014)
  • [2] Ghafari E., Costab H., Julioa E., RSM-based model to predict the performance of self-compacting UHPC reinforced with hybrid steel micro-fibers, Construction and Building Materials, 66, pp. 375-383, (2014)
  • [3] Chen Q., Zhu H.H., Ju J.W., Et al., A stochastic micromechanical model for fiber-reinforced concrete using maximum entropy principle, Acta Mechanica, 229, 7, pp. 2719-2735, (2018)
  • [4] Chen Q., Zhu H.H., Ju J.W., Et al., Stochastic micromechanical predictions for the effective properties of concrete considering the interfacial transition zone effects, International Journal of Damage Mechanics, 27, 8, pp. 1252-1271, (2018)
  • [5] Chen Q., Zhu H.H., Yan Z.G., Et al., A multiphase micromechanical model for hybrid fiber reinforced concrete considering the aggregate and ITZ effects, Construction and Building Materials, 114, pp. 839-850, (2016)
  • [6] Chen Q., Zhu H.H., Ju J.W., Et al., A stochastic micromechanical model for multiphase composite containing spherical inhomogeneities, Acta Mechanica, 226, 6, pp. 1861-1880, (2015)
  • [7] Duan Z.H., Kou S.C., Poon C.S., Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete, Construction and Building Materials, 44, pp. 524-532, (2013)
  • [8] Zhang J., Liu X., Zhu H., Et al., Prediction model of mechanical properties of UHPC based on neural network, Concrete, 3, pp. 18-22, (2013)
  • [9] Tan G., Ding H., Research on generalized neural network and its application in traffic flow prediction, Control and Decision-Making, 17, pp. 777-780, (2002)
  • [10] Liu J., Application of artificial intelligence neural network in renewable energy micro grid, China Informatization, 5, pp. 68-69, (2018)
123