Application of fund analysis model to identify key factors in construction projects

Abstract

This study constructs a funnel analysis model by collecting relevant data to achieve fault monitoring. Back-propagation (BP) neural networks are also used to identify structural damage in construction projects, and a genetic algorithm (GA) is used to optimise BP to improve issues such as slow convergence and long time consumption. The results indicate that the difference between the third-order frequency and the 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 in the degree of damage. Comparing the GA–BP neural network and BP neural network, 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%, respectively, meeting the accuracy requirements of the project. The identification of the key factors in construction projects based on the funnel analysis model is beneficial for identifying structural damage and ensuring the safety of engineering projects.