2026 Vol.9 Apr.N02

• Title: Safety evaluation and grading early warning method of bridge-tunnel connection section in mountainous area based on multi-source
• Name: Tao Peng 1,2 , Lijuan Zhang 1,2, Guotao Wang 1,2, Meng Wang 1,2,
• Company: 1.Guizhou Communications Polytechnic University,Guiyang 551400,China; （2.Guizhou Provincial Key Laboratory of Intelligent Construction andOperation & Maintenance for Bridge and Tunnel Engineering in Mountainous Areas, Guiyang 551400,China）
• Abstract:

  The early warning methods of bridge and tunnel connection in mountainous areas mostly rely on a single data source, and judge the safety status and give early warning according to the preset fixed threshold. Because the bridge-tunnel connection section in mountainous area is remote, the data of a single sensor is easy to be missing or abnormal, and the real safety state of the structure cannot be fully reflected by a single data, which leads to poor early warning accuracy. In view of this, a safety evaluation and grading early warning method of bridge-tunnel connection section in mountainous area based on multi-source data fusion is proposed. Collect multi-source heterogeneous monitoring data, covering three types of data: structural ontology, environmental incentive and traffic operation, calculate the weight of each index by entropy weight-analytic hierarchy process, and complete feature level fusion through weighted arithmetic average to get the comprehensive risk feature value. According to the safety score and risk probability, the four-level early warning grading standard is set, the dynamic threshold is used to replace the fixed threshold, and the real-time iterative correction threshold is combined with the change of extremely high risk posterior probability to realize real-time early warning. In the experiment, the early warning accuracy of the proposed method is tested. Through the test and comparison results, it is clear that when the proposed method is used for the safety early warning of the bridge-tunnel connection section in mountainous areas, the average coincidence degree of the safety status labels of the algorithm is as high as 88%, which has an ideal early warning effect.

• Keyword: multi-source data; Mountain area; Bridge-tunnel connection section; Safety evaluation; Graded early warning;
• DOI: 10.12250/jpciams2026090404
• Citation form: Tao Peng , Lijuan Zhang , Guotao Wang , Meng Wang , Lei Zhao , Zhanying Huang.Safety evaluation and grading early warning method of bridge-tunnel connection section in mountainous area based on multi-source data fusion[J]. Computer Informatization and Mec

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