Long-term health detection of railway-tunnel is the development direction and trend of future railway tunnel research. Based on the actual engineering of a railway tunnel, this study developed a safety evaluation model for railway tunnel structures using a fuzzy comprehensive evaluation method and examined a health state evaluation method suitable for most railway tunnel structures. The results showed that the evaluation method comprehensively reflected the impact of various factors, which had strong practicality. The evaluation results were clear, accurate, and consistent with engineering practice. When using the safety factor index to study the stress of a railway tunnel structure, Midas/civil analysis showed that different levels of the surrounding rock structural vault in railway tunnels were in a tensile, control-bearing capacity state. When calculating safety factors, the range of a 60° central angle of a railway tunnel vault was calculated according to the tensile control-bearing capacity. Theoretical formulas of the range of the center angle φ0 of the vault tension zone were derived and then verified by experiments and numerical analysis.
By integrating rough set theory and neural network theory, this study combined their advantages. Drawing on the existing theoretical results for bridge influencing factors, a method for numerical simulation and data fusion was used in the application of multifactor data fusion for cable-stayed bridge safety evaluation. Based on studying existing bridge safety evaluation methods, a neural network and rough set theory were combined to perform a safety evaluation of PC cable-stayed bridge cables, which provided a new means for bridge safety evaluation. First, a cable-stayed bridge in Shenyang was used as the engineering background, the safety level of its cables was divided into five levels, and a safety evaluation database was established, clustered by a Kohonen neural network. This provided specific evaluation indicators corresponding to the five safety levels. A rough neural network algorithm integrating the rough set and neural network was applied to data fusion of the database, with the attribute-reduction function of the rough set used to reduce the input dimension of the neural network. Conclusions. The neural network was then trained and the resulting trained network was applied to the safety evaluation of the cables of the cable-stayed bridge. Four specific attribute index values, corresponding to the bridge cables, were directly input to obtain the safety status of the bridge and provide corresponding management suggestions.
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