With the acceleration of urbanization, the importance of risk management in underground construction projects has become increasingly prominent. In the process of risk assessment for underground construction projects, the uncertainty of subjective factors from experts poses a significant challenge to the accuracy of assessment outcomes. This paper takes a section of the Nanchang Metro Line 2 as the research object, aiming to address the subjectivity issues in the risk assessment of underground construction projects and to enhance the scientific rigor and accuracy of the assessment. The study initially conducts a comprehensive identification and analysis of risk factors in underground engineering through a literature review and expert consultation method. Based on this, this paper introduces the theory of Pythagorean fuzzy sets to improve the Delphi method in order to reduce the impact of subjectivity in expert assessments. Furthermore, this paper constructs a Bayesian network model, incorporating risk factors into the network, and quantifies the construction risks through a probabilistic inference mechanism. The research findings indicate a total of 12 key risk factors that have been identified across four dimensions: geological and groundwater conditions, tunnel construction technical risks, construction management measures, and the surrounding environment. The Bayesian network assessment results indicate that the effectiveness of engineering quality management and the state of safety management at the construction site are the two most influential factors. Based on the assessment results, this paper further conducts a risk control analysis and proposes targeted risk management measures.

The accumulation of years imbues historical buildings within universities with a profound sense of heritage, evident not only in the temporal imprints within their internal spaces but also in the evolution of their external surroundings. This cultural legacy subtly enriches students’ spatial awareness of history and fosters a collective memory of campus context. Current scholarly inquiry into university historical buildings primarily revolves around comprehensive considerations encompassing the preservation of these edifices, the overarching planning of academic institutions, and the safeguarding of the distinctive features inherent to historical structures. However, the predominant focus lies on qualitative analyses, leaving a pressing need for quantitative assessments and the establishment of an evaluation framework to gauge the efficacy of historical building preservation in academia. Addressing this gap, this study employs the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) to formulate the University Historical Building Protection Evaluation Framework (UHBPEF). Drawing from the examination and categorization of the primary instructional edifices within the Yujiatou campus of Wuhan University of Technology through the Research Classification Method (RCM), along with the consolidation of insights from experts and academic stakeholders, this study underscores the imperative of enhancing the scientific precision and pertinence of the university’s strategy for preserving historical buildings. By employing both qualitative and quantitative methodologies, this study offers innovative insights into the challenges facing historical building preservation in university settings, therefore propounding effective preservation strategies and offering a roadmap for future endeavors in this domain.

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 paper presents a Bayesian-based finite element model updating method that integrates static displacement measurements and dynamic modal data to simultaneously identify structural mass and stiffness parameters. By leveraging Bayesian inference, a posterior probability density function (PDF) is constructed by integrating static displacement and modal parameters, thereby effectively decoupling the identification of structural mass and stiffness. The Delayed Rejection Adaptive Metropolis (DRAM) Markov Chain Monte Carlo (MCMC) sampling algorithm is utilized to derive the posterior distributions of the updated parameters. To mitigate the computational burden associated with repetitive finite element (FE) analyses during large-scale MCMC sampling, a Kriging surrogate model is employed to efficiently approximate the time-consuming FE simulations. Numerical examples involving a cantilever beam and an actual concrete three-span single-box girder bridge illustrate that the proposed method accurately identifies simultaneous variations in mass and stiffness at multiple structural locations, effectively addressing parameter coupling and misidentification issues encountered when using either static or dynamic data alone. Moreover, the Kriging surrogate significantly improves computational efficiency. Experimental validation on an aluminum alloy cantilever beam further corroborates the effectiveness and practical applicability of the proposed method.

Based on the up-floating incident of the basement in a high-rise residential building, the finite element (FE) model of the up-floating destruction region is established to investigate the damage mechanism. The stress states and the deformations of the basement structure are obtained under complex loads including water buoyancy forces, vehicle loads and construction loads. To assess the extent of damage, a novel damage indicator is defined based on two levels: the cracking bending moments and the yield bending moment. The first-level cracking bending moment, second-level cracking bending moment and the yield bending moment can be determined using the section stratification method. By comparing the maximum bending moment of the component with its corresponding cracking moment, one can determine whether the cracks have occurred and assess their severity. Meanwhile, the antifloating failure model is constructed to analyze the mechanism of the up-floating destruction. Finally, a detailed reinforcement treatment plan of ‘decompression first and then reinforcement’ is presented to reinforce and repair the damaged basement structure. The mechanism analysis of the up-floating destruction and the comprehensive reinforcement treatments ensure the simulation of the life cycle of emergence, development and treatment to ensure structural safety.

To investigate the influence of transverse confinement on the bond performance between glass-fiber reinforced polymer (GFRP) bars and concrete, an experimental study involving 28 beam-type bond specimens was designed and conducted. The effects of key parameters, including the spacing of transverse stirrup confinement (60, 80, and 120 mm), concrete strength (C30 and C50), concrete cover thickness (1.5d and 2.5d), surface characteristics of GFRP bars (ribbed and sand coated), bar diameter (16 and 20 mm), and bond length (5d and 10d), on the bond-slip behavior and failure modes were systematically examined. The results indicate that transverse stirrup confinement effectively restrains the development of splitting cracks, thereby significantly enhancing bond strength and improving the ductility of the bond interface. Both the bond strength and residual bond strength increase as stirrup spacing decreases. Under confined conditions, the bond strength of ribbed GFRP bars is 17.26% to 41.72% higher than that of sand-coated bars. Additionally, bond strength increases with higher concrete strength and greater concrete cover thickness but decreases with longer bond lengths. This study provides an experimental basis and theoretical reference for the design of GFRP-reinforced concrete structures that consider the transverse confinement effect.