Conditional Diffusion-Based inversion of operational parameters for shield attitude adjustment

Abstract

To mitigate catastrophic safety risks caused by shield attitude deviation in complex soft-soil tunneling, an intelligent operational parameter inversion framework termed physics-aware conditional diffusion for tunneling (PACD-T) is developed. Departing from conventional deterministic forward-mapping models, this study transforms the ill-posed inversion problem into a conditional probabilistic generation task via the reverse learning of a Markovian forward noising process. High-resolution field data from a municipal railway project in Shanghai are comprehensively processed to drive the model. Architecturally, a dual-path conditional module extracts heterogeneous contextual features, which are then dynamically re-injected layer-by-layer into a Transformer-based denoising network using Adaptive Layer Normalization (AdaLN). Crucially, a two-layer physical constraint combining a training-phase flexible barrier loss with an inference-phase hard projection enforces reflecting boundary conditions to guarantee engineering-compliant parameter generation. Experimental results demonstrate that PACD-T achieves superior inversion precision with R 2 exceeding 92.45% and a 0% boundary violation rate. By regulating sampling stochasticity, the framework successfully delivers both reliable primary execution schemes and diversified multi-strategy recommendations for real-world tunnel engineering.