This study develops a Bayesian parameter identification framework that uses static strain measurements to update pipeline structural models under complex boundary conditions. Because strain responses are directly linked to internal stress states and are much less sensitive to boundary condition uncertainty, the proposed approach retains high identification accuracy where conventional methods based on static displacements or modal data are difficult to apply. The method employs the Metropolis-Adjusted Langevin Algorithm (MALA), a gradient-based MCMC scheme with a Metropolis correction that ensures asymptotically exact sampling, to handle the high dimensional parameter space, and integrates a deep neural network (DNN) surrogate model to accelerate sampling. A numerical example demonstrates the efficiency of MALA in high dimensional settings by exploiting the gradient of the log posterior to guide proposals, successfully identifying the stiffness of 30 pipeline segments and showing that combining axial and hoop direction strain data yields more accurate estimates. An experimental case on a real pipeline corroborates the effectiveness of the approach, reducing the mean absolute error (MAE) of predicted strains from 27.3% to 4.2% after updating. Overall, by coupling MALA with a DNN surrogate, the study establishes a static-strain-based Bayesian inference framework for high dimensional parameter identification in pipelines with complex boundaries, providing a practical route for engineering applications and supporting reliable structural safety assessment.