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Research On The Influencing Factors And Diffusion Of BIM Technology

Posted on:2021-03-26Degree:MasterType:Thesis
Country:ChinaCandidate:C YinFull Text:PDF
GTID:2492306473458344Subject:Management Science and Engineering
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According to the survey,the total output value of China’s construction industry in 2018 was 235085 billion yuan,accounting for 26.1% of the total GDP in 2018 of 90.303 billion yuan.The number of employed people is 55.633 million,accounting for 7.17% of the total employed people in the society.From the above data,we can see that the construction industry is an important pillar industry of our national economy,and it occupies an important position in the development process of the national economy.With the development of the concept of Industry 4.0,global technology development has entered the information age,and Building Information Models(BIM for short)have emerged as the times require.BIM technology has the advantages of full-process intelligent control and full-process collaborative work.China’s construction industry is a traditional industry.Compared with the manufacturing industry,it has problems such as low technical level,labor-intensive,serious environmental pollution,low construction efficiency,and fragmented industrial chains.The introduction of BIM technology can solve the above problems.However,the slow spread of BIM technology in China has hindered the universal application of BIM technology.If no measures are taken,it will affect the development of the construction industry and even the development of the entire national economy.Therefore,it is significant to study the influencing factors and diffusion process of BIM diffusion.When studying the influencing factors of BIM technology diffusion,this paper first collects the influencing factors of BIM technology diffusion through literature research method,then collects data through questionnaire survey,and finally uses principal component analysis and multiple regressions to determine the main factors affecting BIM technology diffusion.Research shows that the main factors affecting the diffusion of BIM technology are:the superiority of BIM technology,government management measures and BIM maturity,the internal environment of enterprises,and the cost of using BIM technology.Among them,the superiority of BIM technology has the greatest impact.This article next studies BIM technology diffusion.By comparing various diffusion models,the Bass model is selected,and the BIM technology diffusion process is studied through the basic Bass model.In the parameter estimation,the maximum market potential value is 107453,and then the analogy method is used to estimate the innovation coefficient and imitation coefficient.When using the analogy method,the innovation coefficients and imitation coefficients of 27 innovative technologies are analyzed.First,the 27 innovative technologies are classified into 6 categories by cluster analysis,and similar technologies are identified(including the innovative technologies in the CAD technology category as the BIM similar technology),and then use the square Euclidean distance to determine the weight.Finally,the innovation coefficient and imitation coefficient of the similar technology are weighted average.The obtained innovation coefficient is p = 0.0020 and the imitation coefficient is q = 0.3126.The imitation coefficient is much larger than the innovation coefficient,which indicates that BIM technology is greatly affected by internal influences when it diffuses.Bringing the three parameters into the Bass model,it was obtained that at the t~* = 16.06 year,the diffusion rate of BIM technology was the fastest,and the cumulative number of enterprises adopted by that time was 53,383.In this paper,the basic Bass model is further improved.The imitation coefficient is regarded as an exponential function of time,and the time parameter a is introduced.The research shows that the diffusion speed of BIM technology is affected by the function of the imitation coefficient.
Keywords/Search Tags:BIM, Bass model, Multiple linear regression, Model to improve
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