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Influencing Factors Of Organization Leadership In Complex Construction Project Based On Bayesian Belief Network

Posted on:2022-08-23Degree:MasterType:Thesis
Country:ChinaCandidate:Z H TuFull Text:PDF
GTID:2492306539981689Subject:Architecture and Civil Engineering
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With the steady growth of complex construction projects,the construction and management of engineering projects are also facing more severe challenges.In recent years,such as project schedule delay,cost over budget,engineering quality substandard and other phenomena are increasingly serious.However,it is because of the dynamic change of the complexity of complex construction projects that the project managers can not make effective measures in time,which brings serious challenges to the project management.Research shows that organizational leadership is the most important management ability of project managers in complex construction projects,which can make dynamic adjustments with the dynamic changes of external environment.Therefore,based on Bayesian network theory research to the influential factors of complex construction project organization leadership,can effectively reveal the influence of organizational leadership path and explore the sensitive factors influencing the organizational leadership and strategy for complex construction project management to carry on the preliminary inference analysis,so as to realize the management of a more sophisticated to deal with all kinds of complicated problems.In this paper,literature review method,content analysis method,Bayesian network analysis method and empirical research are used to study the above problems.The main contents of the study are as follows:(1)Based on literature review and content analysis,relevant influencing factors of organizational leadership of complex construction projects were extracted,and the influencing factors were divided into three levels: individual,team and organization.Through the form of questionnaire design,this paper conducts a questionnaire survey on the influential factors of organizational leadership in complex construction projects.Through statistical analysis,reliability and validity analysis and other mathematical methods,this paper analyzes the relevant statistical data of the influencing factors of organizational leadership of complex construction projects,and proves the credibility and effectiveness of the questionnaire.Finally,25 influencing factors of organizational leadership of complex construction projects are determined from the three levels of individual,team and organization.(2)Based on the identified influencing factors of organizational leadership,118 questionnaires on organizational leadership of complex construction projects were used and combined with the influencing factors of organizational leadership of complex construction projects.The BBN model of organizational leadership of complex construction projects was constructed by machine learning,and 20 sample data were used to verify the model.Based on the learning reasoning function of Bayesian network,the prediction analysis,diagnosis reasoning analysis,sensitivity analysis and influence chain analysis of the BBN model are carried out.The results show that the Project management decision-makers should focus on personal communication and information transfer ability,reasonable handling of conflicts,communication and coordination ability,and improve the team in communication and coordination and task summary feedback ability,so as to improve the level of industry quality of the team.(3)Case analysis.The BBN model is applied to the construction project of Wudongde Hydropower Station as a practical case,and the credibility of the BBN model and the validity of Bayesian network reasoning method are verified.The research results will make up for the gap in the field of leadership related research and improve the leadership ability and adaptability of project managers in complex construction projects.
Keywords/Search Tags:Complex Construction Projects, Organizational Leadership, Bayes Network, Sensitivity Analysis, Impact Chain Analysis
PDF Full Text Request
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