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Study and application of joint assessment model for construction risk of Xiangxi Yangtze River Bridge

Author(s):


Medium: journal article
Language(s): English
Published in: Journal of Physics: Conference Series, , n. 1, v. 2680
Page(s): 012019
DOI: 10.1088/1742-6596/2680/1/012019
Abstract:

Closure of main arch rib is the key process of steel box girder arch bridge construction. The way to achieve quantitative and accurate prediction of closure error risk remains to be studied in depth. Taking the construction risk model of closure of main arch rib of Xiangxi Yangtze River Bridge as an example, firstly, the causal relationship between prior data and closure error of main arch rib is considered to construct Bayesian network topology; Further, the central limit theorem (CLT) is used to update the prior distribution of the parent nodes in the network using dynamic multi-source monitoring data; Then the finite element analysis and BP neural network are combined to reproduce the corresponding relationship between the arch rib closure error and the influencing factors. Finally, the probability distribution is determined by the Monte Carlo method, and the quantitative prediction of the joint construction risk error is realized. The analysis data shows that the risk probability of arch rib vertical error of Xiangxi Yangtze River Bridge is transmissible, and the risk level of arch rib closure error reaches Grade IV when it is 75∼125mm. Therefore, the prestress of anchor cable, tower buckle offset and local temperature should be closely monitored during construction. The calculated value of the joint prediction model of construction risk is consistent with the actual closure control error trend of the project, which can provide reference for the prediction and regulation of related bridge construction risks.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1088/1742-6596/2680/1/012019.
  • About this
    data sheet
  • Reference-ID
    10777554
  • Published on:
    12/05/2024
  • Last updated on:
    12/05/2024
 
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