Optimization Strategy of Tunnel Lining Structural Analysis Using Parametric Design-Driven MINN Surrogate Model

Traditional methods for the structural analysis of tunnel linings are often hampered by complexity and significant time consumption, creating the need for a more efficient analysis workflow. To address this issue, this study develops an application oriented surrogate modeling framework centered on parametric design-driven MINN. The framework of PD-driven MINN leverages parametric design to systematically generate a comprehensive dataset of 2000 parameter groups from FEA, providing a robust foundation for model training. Meanwhile, the MINN architecture is tailored to process these diverse inputs, effectively capturing complex parameter interactions while balancing computational speed and modeling accuracy. To validate the proposed strategy, the model’s predictions were rigorously compared against real-world engineering data from pre-buried sensor measurements in an actual transportation tunnel project. The results indicate the reliability of a parametric design-driven MINN surrogate model. A comparative analysis demonstrates its superior fitting performance and convergence over traditional artificial neural networks. This study demonstrates the practical value of adapting existing neural network techniques and integrating them with parametric design to support more efficient tunnel lining structural analysis.