Structural cracks significantly threaten the safety and longevity of historical buildings, which are essential parts of cultural heritage. Conventional inspection techniques, which depend heavily on manual visual evaluations, tend to be inefficient and subjective. This research introduces an automated framework for crack and damage detection using advanced YOLO (You Only Look Once) models, aiming to improve both the accuracy and efficiency of monitoring heritage structures. A dataset comprising 2500 high-resolution images was gathered from historical buildings and categorized into four levels of damage: no damage, minor, moderate, and severe. Following preprocessing and data augmentation, a total of 5000 labeled images were utilized to train and evaluate four YOLO variants: YOLOv5, YOLOv8, YOLOv10, and YOLOv11. The models’ performances were measured using metrics such as precision, recall, mAP@50, mAP@50–95, as well as losses related to bounding box regression, classification, and distribution. Experimental findings reveal that YOLOv10 surpasses other models in multi-target detection and identifying minor damage, achieving higher localization accuracy and faster inference speeds. YOLOv8 and YOLOv11 demonstrate consistent performance and strong adaptability, whereas YOLOv5 converges rapidly but shows weaker validation results. Further testing confirms YOLOv10’s effectiveness across different structural components, including walls, beams, and ceilings. This study highlights the practicality of deep learning-based crack detection methods for preserving building heritage. Future advancements could include combining semantic segmentation networks (e.g., U-Net) with attention mechanisms to further refine detection accuracy in complex scenarios.

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.