In recent decades, wood structures have gained significant attention for their ecological benefits and architectural versatility. The performance of wood, a popular construction material, often depends on the integrity of its connections. This study focuses on bolted glulam timber connections, which are strong but prone to cracks that pose structural health challenges. Traditional crack evaluation methods are manual, time-consuming, and error-prone. To address these issues, this research proposes a two-stage performance evaluation method. In the first stage, an innovative approach called ‘Enhanced Deep Line Segment Detection’ (Deep LSD), a non-supervised machine learning technique, is used for crack detection without relying on large, annotated datasets, thus enhancing efficiency and adaptability. In the second stage, cyclic loading assays simulate varying damage stages to collect data and establish a correlation between crack states and connection damage. The Park and Ang damage model is employed within this framework to assess the extent of damage. The efficacy of enhanced deep LSD is confirmed by comparing detected crack areas with ground truth measurements, yielding a high R-squared value of 0.98 and a minimal error margin of 1.41. Additionally, a damage index based on the Chinese standard (GB/T 24335-2009) is used to classify damage across different connection groups, ensuring robustness and alignment with established practices.

This paper presents an integrated computational framework for predicting temperature fields in glulam beam–column connections under fire conditions, combining finite element modeling, automated parametric analysis, and deep learning techniques. A high-fidelity heat transfer finite element model was developed, incorporating the anisotropic thermal properties of wood and temperature-dependent material behavior, validated against experimental data with strong agreement. To enable large-scale parametric studies, an automated Abaqus model modification and data processing system was implemented, improving computational efficiency through the batch processing of geometric and material parameters. The extracted temperature field data was used to train a DeepONet neural network, which achieved accurate temperature predictions (with a L2 relative error of 1.5689% and an R² score of 0.9991) while operating faster than conventional finite element analysis. This research establishes a complete workflow from fundamental heat transfer analysis to efficient data generation and machine learning prediction, providing structural engineers with practical tools for the performance-based fire safety design of timber connections. The framework’s computational efficiency enables comprehensive parametric studies and design optimizations that were previously impractical, offering significant advancements for structural fire engineering applications.

Increasing the span of large wooden structures raises the risk of brittle failure in the glulam beams’ tensile zone, while the compressive zone materials remain underutilized, resulting in suboptimal structural efficiency and material wastage when constructing large-span wooden roof systems solely with wooden components. The steel-wood composite beam-string structure is a suitable solution for such needs. However, there is still a gap in research regarding the overall mechanical performance of steel-wood composite beam-string structures on a larger scale, with limited attention given to studying their mechanical properties and force mechanisms comprehensively. This paper introduces a steel-wood composite beam-string structure based on a practical engineering project of the Capital Museum East Branch. It includes a detailed analysis of forces and structural calculations, along with static loading tests on a glulam curved beam roof system using beam-string structures. Structural analysis shows that, under frequently occurred earthquake, the maximum inter-story drift of the overall structure is 1/370, with the stress ratio of most components below 0.8. Experimental results indicate that when the load increases to twice the design load, there is a tendency for separation at the connection, but significant damage is not observed. The cross-section generally satisfies the assumption of a plane section. The pre-stressed strands set at the bottom of the wooden beams strengthen the tension zone of the beams, causing the neutral axis of the beams to generally shift towards the tension zone. The experimental findings will inform the museum project and provide useful design insights for similar steel-wood composite beam-string structures.