4.6 Article

Construction Risk Assessment of Deep Foundation Pit Projects Based on the Projection Pursuit Method and Improved Set Pair Analysis

Journal

APPLIED SCIENCES-BASEL
Volume 12, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/app12041922

Keywords

deep foundation pit project; construction risk; risk assessment; WBS-RBS; projection pursuit; set pair analysis

Funding

  1. Safety Risk Early Warning System and Monitoring of the Wuhan Rail [Line11 (20181h0281)]

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This research proposes a novel construction risk assessment method for deep foundation pit projects. By establishing an evaluation index system and improving the calculation method, it effectively deals with the complex risks of deep foundation pit construction and has been validated in a real case study.
Accurately evaluating the construction risk of deep foundation pit projects is crucial to formulate science-based risk response measures. Here, we propose a novel construction risk assessment method for deep foundation pit projects. A construction risk evaluation index system based on a work breakdown structure-risk breakdown structure matrix was established to deal with the complex risks of deep foundation pit construction. The projection pursuit method optimized by particle swarm optimization was used to extract the structural features from the evaluation data to obtain objective index weights. The calculation method of the five-element connection number in the set pair analysis was improved to evaluate the static construction risk. The partial derivatives of the five-element connection number were utilized to assess the dynamic construction risk. The Qi 'an Fu deep foundation pit project in China was selected as a case study. The results show that the construction risk was acceptable and decreased during the construction period, which was consistent with actual conditions, demonstrating the effectiveness of this novel method. The proposed model showed better performance than classical methods (analytic hierarchy process, entropy weight method, classical set pair analysis, fuzzy comprehensive evaluation, gray clustering method, backpropagation neural network, and support vector machine).

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