Journal of Northeastern University(Natural Science) ›› 2024, Vol. 45 ›› Issue (10): 1504-1512.DOI: 10.12068/j.issn.1005-3026.2024.10.017

• Resources & Civil Engineering • Previous Articles    

Risk Assessment Method of Deep Foundation Pit Construction Based on Two-Dimensional Cloud Model

Zhen HUANG1,2, Chen CAO1, Wei ZHANG1(), Shao-kun MA1,2   

  1. 1.School of Civil Engineering and Architecture,Guangxi University,Nanning 530004,China
    2.Key Laboratory of Disaster Prevention and Structural Safety,Guangxi University,Nanning 530004,China.
  • Received:2023-05-22 Online:2024-10-31 Published:2024-12-31
  • Contact: Wei ZHANG
  • About author:ZHANG Wei,E-mail:zw971126la_lune@163.com

Abstract:

In order to take into account the fuzzy and random uncertainty involved in risk assessment of deep foundation pit construction, a risk assessment method for deep foundation pit construction based on two?dimensional cloud model and 3En rule is proposed. Firstly, the risk sources of deep foundation pit construction are identified, and the construction risk assessment index system composed of risk factors is established. Secondly, based on the engineering situation, the Delphi method is used to preliminary determine. Furthermore, the subjective and objective weights of the assessment indexes are determined by the analytic hierarchy process and entropy weight method, and the comprehensive weights are obtained. Then, the two?dimensional standard cloud and synthesized cloud are generated according to the risk assessment criteria and the forward cloud generator. Finally, the 3En rule of the cloud model and the distribution probability of synthesized cloud droplets are used to judge the risk level membership. The proposed risk assessment method is applied to predict the construction risk level of a deep foundation pit project in Nanning city. Compared with the traditional fuzzy comprehensive risk assessment method, the reliability of the assessment method is verified.

Key words: foundation pit engineering, risk assessment, cloud model, uncertainty analysis, index system

CLC Number: