Abstract: This paper proposes a physics and information dual-driven intelligent diagnostic method for the longitudinal mechanical behavior of shield tunnels, aiming to address the current bottlenecks in its safety diagnosis. By embedding the physical equations that characterize the longitudinal mechanical behavior of the tunnel into physical neurons, and integrating the measured data as information neurons, a physics and information dual-driven neural network model called physics-informed neural networks (PINNs) is constructed. This model enables real-time updating and inversion of the structural parameters of shield tunnels, surrounding geological parameters, and load distribution patterns, thereby forward solving the longitudinal structural mechanics state of the tunnel. The inverted parameters obtained are further utilized to analyze the longitudinal mechanical behavior of other tunnel sections, so as to realize the diagnosis of the longitudinal long-distance shield tunnels. Case studies and engineering applications show that the proposed PINNs model can effectively solve the longitudinal structural problems in tunnels. Furthermore, compared to the traditional purely data-driven deep neural network (DNN) models, the PINNs model exhibits significant generalization capability and robustness, presenting promising prospects for engineering applications.

Key words: physics and information dual-driven, shield tunnel, longitudinal mechanical behavior, back analysis