Assessment of Waterlogging Risk in the Deep Foundation Pit Projects Based on Projection Pursuit Model

As a result of global climate change and urbanization, waterlogging disasters have occurred frequently around the world, and deep foundation pit projects with lower terrain suffer even more. This study puts forward a method for the waterlogging risk assessment of deep foundation pit projects via the combination of a projection pursuit model, particle swarm optimization, and an interpolation algorithm. First, through a comprehensive analysis of the water circulation process in waterlogging and the characteristics of deep foundation pit projects, a risk index system with 11 indicators is identified and constructed. Then, a projection pursuit model optimized by particle swarm optimization is leveraged to determine the weights of the indicators and the best projection values of evaluation objects, and the mathematical function between the best projection values and the risk levels is constructed by an interpolation algorithm. Finally, three deep foundation pit projects of the Chengdu Metro Line 11 in China are selected as case studies. The results demonstrate that the frequency of storms, intensity of rainfall, preparation of emergency rescue plans, and proportion of older workers have the greatest impacts on waterlogging risk in deep foundation pits. The risk ranking of the case studies is found to be consistent with the actual situations, which proves the objectivity and effectiveness of the proposed method.

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.