Research On Risk Analysis Of Hospital Construction Projects Based On PSO-BP Neural Network

The swift development of the social economy and the gradual enhancement of living standards have led to a greater emphasis on both physical and mental well-being.As a consequence,there is a growing demand for advanced medical systems,including medical facilities,services,and technologies.As relevant medical policies are introduced domestically,the development and construction of hospitals have also gradually risen.Hospitals across the globe are undergoing renovations and expansions in order to keep up with the growing demand for medical care and to be prepared for various challenges.The hospital construction projects are more complex than the general construction projects.Hospital construction projects are more complex than general construction projects,with features such as comprehensive multisubjects,long construction cycles,large investment funds,large construction scale,many participating units,complex construction techniques and difficult project management.As a result,various risks are bound to occur at different stages of a hospital construction project.To ensure project safety,it is imperative to analyze and study the project risks promptly.Taking hospital construction projects as the background,combined with a large amount of domestic and foreign related literature for statistical and comparative analysis,to determine the feasibility of the optimization algorithm combined with BP neural network for risk analysis of hospital construction projects and to lay the foundation of the research.Based on theories related to risk management and the content of hospital building design,identify project risk factors,combine multiple methods to understand risk factors from multiple perspectives,divide risks into 6 primary indicators and 30 secondary indicators,and establish a risk evaluation index system.Based on the questionnaire survey to score the impact of each risk indicator,so as to quantify each risk indicator in the system and obtain risk data;combined with particle swarm algorithm and BP neural network to establish PSO-BP neural network model,to train and test the risk data,can more accurately assess the impact of each risk indicator,and provide effective data support for risk management.Using the actual project Qujiang New Area Hospital construction project as an example,risk prediction was carried out to determine the risk level of the project as "medium risk(level III)";the hierarchical analysis method was used to calculate the weight of the risk factors in the secondary indexes of the project,and finally the level of each risk factor was derived.The study of hospital construction project risks based on BP neural network optimized by particle swarm algorithm,establishing risk evaluation index system,developing risk list,training and testing PSO-BP risk evaluation model,taking Qujiang New Area Hospital project as an example,analysing its risk level in the construction process as well as identifying important risk factors and putting forward risk prevention and control suggestions to achieve the purpose of reducing the impact of risks on hospital construction projects The project will also be tested to verify the reliability of the PSO-BP neural network model and the feasibility of using the optimization algorithm.