The crack-damage resistance of early-age concrete is affected by multiple factors such as hydration, self-drying, temperature and humidity diffusion, and material properties, which are difficult to be accurately evaluated by traditional theories and numerical models. This paper proposed an adaptive physics-informed back propagation neural network (BPINN) to accurately evaluate the damage of early-age concrete under multi-physics field coupling. The temperature and humidity diffusion and shrinkage models are used as physics loss functions to guide the model in learning the physics laws. Furthermore, time-dependent factor weights are constructed for both the physics and boundary equations to enhance the model's ability to learn the spatiotemporal feature distribution of the sampling points. BPINN effectively simulates the influence of concrete strength grade and boundary conditions on temperature and humidity diffusion, with the average error less than 5 %. The LOSS differences of traditional physics informed neural network (PINN) and BPINN in time step, activation function, hidden layer and neuron number are quantified. Compared with the traditional PINN, the LOSS of BPINN is reduced by 62.4 %. On this basis, the predictive performance of BPINN and four types of data-driven models is compared to verify the influence of physics constraint, as BPINN has the smallest statistical loss and data discreteness. The model proposed in this paper enhances the learning ability of spatial-temporal features by balancing the weight between boundary and physics equations, providing new insights for the thermo-hygro-mechanical coupling field in early-age concrete.
Wang et al. (Sun,) studied this question.