Application analysis of funnel analysis model in key factor identification of construction projects

Abstract

This study constructs a funnel analysis model by collecting relevant data to achieve fault monitoring. BP neural networks are also used to identify structural damage in construction projects, and GA is used to optimize BP to improve issues such as slow convergence and long time consumption. The results indicate that the difference between third-order frequency and first-order curvature mode is the most suitable indicator for damage warning and identification. The difference in the first-order curvature mode of adjacent measurement points of the damaged component increases with the increase of the degree of damage. Compared with Genetic Algorithm-BP neural network(GA-BP) and BP neural network(BP), the former has a smaller error in identification and better performance. The maximum and minimum relative errors of GA-BP in identifying the damage degree of the structure are 8.06% and 1.61%, meeting the accuracy requirements of the project. The identification of key factors in construction projects based on the funnel analysis model is beneficial for identifying structural damage and ensuring the safety of engineering projects.