|Table of Contents|

Dynamic data analysis method of bridge damage based on CART algorithm(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年4期
Page:
50-59
Research Field:
桥梁与隧道工程
Publishing date:

Info

Title:
Dynamic data analysis method of bridge damage based on CART algorithm
Author(s):
XIANG Chang-sheng1; 2;  LIU Hai-long2;  ZHAO Chi1;  SU Tian-tao1
(1. School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu, China; 2. Western Center of Disaster Mitigation in Civil Engineering, Ministry of Education, Lanzhou University of Technology, Lanzhou 730050, Gansu, China)
Keywords:
bridge engineering;  damage recognition;  CART algorithm;  add-mass method;  modal strain energy
PACS:
U411
DOI:
10.19721/j.cnki.1671-8879.2023.04.006
Abstract:
Aimed at the defect that traditional damage detection methods were difficult to accurately identify the damage degree of bridge structure, a damage detection method was proposed, which can learn and classify the damage dynamic information of bridge by calculating Gini coefficient to sort data samples through selecting appropriate features, great advantages of CART decision tree algorithm were taken in data mining. Firstly, the add-mass method was used to construct structural dynamic response data set, and modal strain energy index ξ of add-mass was calculated to locate structural damage. Then, ξ was taken as the feature of decision tree and input into the CART algorithm for training, and then the damage degree was classified and identified, meanwhile, anti-noise of this method was verified. Finally, the simple supported beam and continuous beam were used for verification analysis. The results show that the damage identification index ξ based on added mass can accurately locate the damage, and the CART classifier can effectively identify the damage degree of the bridge structure. The damage classification accuracy of the two cases can reach 99%, 95% and 95%, 90% with 2% and 5% noise levels, respectively, which means this method achieves a high accuracy and strong robustness, and can provide a new reference for identifying damage degree of bridge structures.4 tabs, 19 figs, 23 refs.

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Last Update: 2023-08-20