School of Civil Engineering, Changsha University of Science & Technology, Changsha
National Program on Key Basic Research Project of China
In order to solve the problem of distortion of thermal parameters of mass concrete in bridge engineering, an inverse analysis method of thermal parameters of mass concrete based on uniform design theory and BP neural network was proposed. Firstly, BP neural network model is established based on Matlab, use a tan-sigmod transfer function. Then the thermal parameter samples are designed by uniform design method. The finite element software is used to analyze the temperature samples of concrete feature points and train the complex nonlinear relationship between them. Finally, the measured temperature values are input into the trained neural network, and the inversion values of the thermal parameters are obtained. The thermal conductivity, reaction rate and adiabatic temperature rise were inverted during the mass concrete construction of the scaffold saddle support pier of the Taihong Yangtze River Bridge. The calculated temperature based on the inversion value is in good agreement with the actual measured value, which guides the temperature control construction and ensures project safety. Engineering practice shows that the inverse analysis method combining uniform design and BP neural network reduces the sample data. In the training phase of BP neural network, the additional momentum method is used to significantly shorten the network training time and improve the efficiency.