Active And Passive Design And Risk Prediction Model Under The Risk Of Infection In Hospital Waiting Space

In 2019 the COVID-19 pandemic swept the world.With the spread of the epidemic and repeated outbreaks,controlling airborne transmission of the virus has become a hot topic again.Hospital waiting room,as a highly crowded place,is the risk zone of infection outbreak.The survey found that there are many problems such as poor ventilation in the waiting space in China,which makes infection risk control in waiting space an urgent problem.This study aims to explore an effective design method to reduce the risk of infection in waiting Spaces through the combination of active and passive design.This study finally realized the early warning of infection risk in hospital waiting room under different personnel density,and provided a general risk control strategy to make the space infection risk lower than the warning value.Firstly,the main parameters affecting the risk of space infection were obtained through drawings and literature research.Such as space form,interior layout,waiting room area,length-width ratio of waiting room,tuyere layout,ventilation rate.Key parameters and related parameter thresholds were extracted to determine the ideal typical model,and the actual space with the highest matching degree with the ideal typical model was found from the actual hospital project as the typical model.Secondly,the standard simulation method is verified,and the key influence variables studied by the extended model are modified according to the pre-study.All the research conditions satisfying the spatial rationality were determined by using the exhaustive method.Then,Gambit modeling platform and Fluent17.0 simulation platform were used to restore indoor physical environment and simulate particle diffusion,and Anylogic was used to calculate the density distribution of waiting room staff as the basis for infection risk calculation.Finally,the particle distribution and human density distribution data are combined with the Wells-Riley classical infection equation.Build an extended model infection risk database with Matlab.In order to fully measure the control level of research variables on space infection risk,this study proposed new evaluation indexes: particle removal efficiency Pr,high-risk area ratio Hr and comprehensive evaluation index C of space infection risk.Firstly,descriptive analysis of the calculated results is carried out to determine the rough distribution of research data.Then,the difference analysis and factor weight analysis were used to explore whether there was a significant influence between the study variables and the risk of spatial infection.Finally,based on the comprehensive evaluation index C,5 optimal and 5 worst design schemes with better space infection risk control were summarized.At the same time,the influence of each parameter on the comprehensive evaluation index C is analyzed and discussed,which provides a basis for the selection of design strategy.At the application level,based on the expanded model infection risk database,the infection risk under different personnel densities was calculated.The influence relationship between independent variables and dependent variables was explored through machine learning,and the spatial infection risk prediction model under different personnel densities was established.The influence rule of variables and the application of prediction model were explained through a practical case study.The results can provide theoretical guidance for architects in the decision-making process of design strategy,and provide data and method support for the establishment of real-time space infection risk warning model..