Han Wei

There are different degrees of cracks in prestressed concrete (PC) beams, especially the transverse cracks in the bottom plate and the vertical cracks in the web of prestressed concrete box girders, which will reduce the bearing capacity of the structure. In order to clarify the evolution law of cracks in prestressed concrete box girders after cracking, a 25 m prestressed concrete box girder was poured and a full-scale model test was carried out. At the 1/4 span position of the test beam, the test beam was cracked in 19 steps to 1900 kN. Then at the 3/4 span position, the test beam was cracked in 18 steps to 1800 kN, and after that the bending load test was carried out in 17 steps to analyze the influence of existing cracks on the bending cracks of the test beam. Combined with fractal theory, the evolution law of cracks, the development law of fractal dimensions, and their relationship under different loading conditions were analyzed. The results showed that the crack distribution of the test beam satisfies self-similarity and had fractal characteristics in a statistical sense, which could reflect the process of crack occurrence, propagation and damage, and could quantify the crack shape. The fractal dimension of cracks gradually increased with the increase in concentrated load on the test beam, and the fractal dimension of cracks had a linear relationship with the concentrated load. The crack propagation process of the concrete beam reflected the fractal dimension increasing process; that is, the crack damage evolution process of test beam was a dimension increasing process, and the fractal dimension of crack distribution increased linearly with the applied load. The functional relationship between prestressed concrete crack width and fractal dimension was constructed, which provided a new path for bridge safety evaluation. The research results provide engineering reference for the cause analysis and maintenance treatment of similar bridge cracks.

Precast multi-girder bridges are widely utilized in highway infrastructure but are susceptible to transverse connection deterioration, which can lead to single-girder load-bearing failures. Existing structural health monitoring methods based on the correlation of total dynamic strain responses often fail to identify early-stage damage due to the static masking effect, where dominant, in-phase quasi-static components overshadow subtle, damage-sensitive dynamic features. To overcome this limitation, this paper proposes a novel condition indicator based on the correlation of high-frequency dynamic strain increments. An online streaming processing pipeline is developed, incorporating automated single-vehicle crossing event extraction, frequency-targeted signal decoupling, and indicator smoothing. Theoretical derivations on a dual-beam model demonstrate that the proposed indicator is a structural-intrinsic metric, exhibiting high sensitivity to joint stiffness while remaining robust against variations in vehicle weight and speed. Numerical simulations on an 8-slab finite element bridge model under stochastic traffic flow further verify the effectiveness of the framework. Results indicate that the proposed indicator can localize both progressive degradation and sudden brittle failures. Additionally, the method maintains reliability down to a signal-to-noise ratio of 30dB and robustness to hyper-parameter selection. While the current framework is established based purely on numerical validation and has not yet been tested using real bridge strain data, it shows numerical feasibility and provides a solid theoretical and algorithmic foundation for the automated condition evaluation of precast multi-girder bridges, supporting future field validation for both long-term maintenance and emergency response.

A massive number of early-constructed small-to-medium-span bridges are collectively entering an “aging” phase in China. Meanwhile, vast amounts of unstructured bottom-level inspection texts remain underutilized. To address them, this paper proposes a data governance method. Large Language Models were leveraged to process unstructured defect data from 18,238 real-world bridges nationwide. The data were structurally cleaned and mapped into discrete features, revealing multidimensional vulnerabilities. On this basis, the Stable Contrastive Pattern Risk (SCPR) intelligent decision-making model was developed. The results demonstrate that, following robust filtration, 6 nationwide common risk rules were extracted from 2064 initial candidate combinations. These rules converge into three core risk patterns: the heavy-duty aging pattern, the substructure-dominated pattern, and the over-water small-span low-seismic-design pattern. Guided by these robust rules and specific damage enrichment characteristics, risk stratification and differentiated management strategies were further formulated for Class III bridges. This research facilitates a paradigm shift in bridge maintenance. It moves from reactive, post-event symptom characterization toward data-driven, proactive early warnings. This shift provides a substantive scientific foundation for optimizing resource allocation and enabling precise investment decisions at the road network level.