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.