ABSTRACT
It is very important to locate the cause of disease in the management and maintenance of bridges. However, accurate judgment requires certain knowledge reserves and professional experience support. The knowledge graph and machine learning algorithm can be combined to realize the automation and intelligence of disease diagnosis. The present study aims at continuous beam bridges, uses the knowledge graph built on the basis of previous work to realize knowledge traction, and explores machine learning algorithms based on the management and maintenance data to achieve data-driven. The results show that the TF-IDF algorithm can provide effective decision support in bridge disease diagnosis for engineering personnel. The bridge disease cause diagnosis and evaluation model based on the knowledge graph and TF-IDF can effectively improve the efficiency and quality of bridge management, and improve the quality of management strategy formulation.
