In view of the complexity of the concrete structure of the subway station and the particularity of the under-ground environment,the traditional indoor test method is difficult to accurately measure the concrete temperature pa-rameters in the actual project.In order to improve the accuracy of temperature field simulation of early age concrete,the accurate value of concrete thermal parameters is obtained by using BP neural network.Firstly,the selection range of thermal parameters (thermal conductivity,specific heat,heat release of cementitious material) of concrete is deter-mined.Secondly,25 groups of samples are generated by orthogonal design to simulate the temperature field,and the temperature change data under different working conditions are obtained.Thirdly,the BP neural network is trained by using the temperature data,and the nonlinear mapping relationship between the thermal parameters of concrete and the temperature change is established and the thermal parameters are inverted.Finally,the inversion parameters are veri-fied by a number of field measured temperatures.The results show that the error between the simulated value and the measured temperature is small.This method not only improves the accuracy of the simulation,but also is more eco-nomical and efficient than the traditional test.
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