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.

Taking the main beams of historical buildings as the engineering background, existing theoretical research results related to influencing structural factors were used along with numerical simulation and data fusion methods to examine their integrity. Thus, the application of multifactor data fusion in the safety assessment of the main beams of historical buildings was performed. On the basis of existing structural safety assessment methods, neural networks and rough set theory were combined and applied to the safety assessment of the main beams of historical buildings. The bearing capacity of the main beams was divided into five levels according to the degree to which they met current requirements. The safety assessment database established by a Kohonen neural network was clustered. Thus, the specific evaluation indices corresponding to the five types of safety levels were presented. The rough neural network algorithm, integrating the rough set and neural network, was applied for data fusion with this database. The attribute reduction function of the rough set was used to reduce the input dimension of the neural network, which was trained, underwent a learning process, and then used for predictions. The trained neural network was applied for the safety assessment of the main beams of historical buildings, and six specific attribute index values corresponding to the main beams were directly input to obtain the current safety statuses of the buildings. Corresponding management suggestions were also provided.