Construction Safety Warning Of Segmental Precast Cantilever Continuous Girder Bridge

With the continuous enhancement of China’s economic strength,bridge construction has also been a good opportunity for development,and has made continuous breakthroughs in scale and technology.At the same time,bridge construction accidents also follow.Especially for the segmental precast cantilever assembly,which is a relatively new construction technology,the construction personnel lack sufficient construction experience,and there are great risks in the construction process.The resulting economic losses and social impact seriously hinder the realization of the safety goal of segmental precast cantilever assembly continuous beam bridge construction.But if we take reasonable and feasible measures to control the risk before the risk worsens,we can effectively reduce the possibility of such bridge construction accidents.This process is also the process of risk early warning management.However,the relevant theoretical and technical research on the construction safety early warning management of segmental precast cantilever continuous beam bridge in China is still relatively weak,and the traditional bridge construction early warning methods have not met the requirements of safety management in the era of big data.Therefore,through the comprehensive and in-depth research on the construction safety early warning method of segmental precast cantilever continuous beam bridge,this paper establishes the safety early warning management system from five systems,including early warning monitoring,analysis,alarm,decision-making and feedback,and selects the appropriate mathematical model as the key technology of safety early warning,so as to form a complete set of construction safety early warning system of segmental precast cantilever continuous beam bridge Full warning management theory.Based on the characteristics of the research object,based on the particularity of construction technology and construction risk of segmental precast cantilever continuous beam bridge,this paper makes a comprehensive analysis and Research on the risk sources involved in the construction process of this kind of bridge,and preliminarily obtains 32 risk factors,and verifies these 32 risk factors from two aspects of reliability analysis and validity test through questionnaire The risk factors have enough representativeness for the construction risk of this kind of bridge,so the construction safety early warning index system of segmental precast cantilever continuous beam bridge is established,and the safety early warning level is divided.Secondly,considering the attribute differences of early warning indicators,the audit inspection method and the technical detection method are used to obtain the monitoring data of qualitative indicators and quantitative indicators respectively,so as to realize the targeted monitoring and processing of early warning data.This paper takes the requirement of "information security management" in the era of big data as the research goal,and uses the early warning evaluation model as the evaluation method of early warning information in the early warning system.Combined with the questionnaire survey information,the weight of early warning index is determined by principal component analysis,and the Mass function values of qualitative index and quantitative index are obtained by using fuzzy comment set and matter-element extension theory respectively.The construction safety early warning model based on evidence theory model is further established,and the construction safety of segmental precast cantilever continuous beam bridge is realized through the evidence combination of Mass function Evaluation of the whole early warning level.Finally,combined with the West Fourth Ring Bridge Construction Project in Zhengzhou,the reliability of the early warning evaluation model and the early warning management method is verified,which provides a theoretical basis for the information early warning management of such bridge construction safety in the era of big data.