Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (2): 572-580.doi: 10.13229/j.cnki.jdxbgxb20181017

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Bridge extreme stress prediction based on new data assimilation algorithm

Xue-ping FAN1,2(),Guang QU1,2,Yue-fei LIU1,2   

  1. 1.Key Laboratory of Mechanics on Disaster and Environment in Western China of the Ministry of Education, Lanzhou University, Lanzhou 730030, China
    2.School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730030, China
  • Received:2018-10-09 Online:2020-03-01 Published:2020-03-08

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Abstract:

To reasonably and dynamically predict the extreme stresses of service-bridge structure, the monitored extreme stress data is taken as a time series, a new data assimilation algorithm about bridge extreme stresses prediction is proposed. The dynamic nonlinear model is built with the monitored extreme stress data of bridges, and K-MEANS algorithm and Expectation Maximization (EM) algorithm are introduced and embedded in the Gaussian Mixed Particle Filter (GMPF), then the Improved Gaussian Mixed Particle Filter (IGMPF) prediction approach can be obtained. The structural stresses are dynamically predicted based on the monitored extreme stress data. The monitored stress data of an actual bridge is provided to illustrate the feasibility of the proposed method, which shows that the improved algorithm is more feasible and accurate than the other algorithms.

Key words: structural engineering, dynamic nonlinear model, Gaussian mixed particle filter, dynamic prediction of bridge extreme stresses

CLC Number: 

  • TU391

Fig.1

Layout of monitored section about Tianjin Fumin bridge"

Fig.2

Sensors layout about section D"

Fig.3

Initial data and monitored data"

Fig.4

Predicted results of IGMPF"

Fig.5

Predicted results"

Fig.6

Sensors about main cable"

Fig.7

Initial data and monitored data"

Fig.8

Predicted results of IMGPF"

Fig.9

Predicted results"

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