A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Diagnosing construction performance by using causal models
To date, most of the research that has addressed construction performance diagnosis has focused on identifying important factors having impact on construction performance and on establishing related performance models. The majority of the models were developed from a predictive perspective, not an explanatory one. The general goal of this research is to develop a construction performance diagnostic approach capable of assisting in identifying likely actual causes along with supporting evidence, and capturing and modeling experience-based diagnostic knowledge for current and future project use. The diagnostic approach implemented is based on a holistic structured causal model based diagnostic process which is applicable to key project performance measures. The approach is comprised of three layers: 1. a performance measure layer to determine if there exists a performance deviation to explain; 2. a quantitative casual models layer that makes use of quantitative causal relationships to identify causal variable variances, and 3. a user-defined experience-based causal models layer that makes use of experience-based knowledge to help further explain reasons (causal factors) for the causal variable variances. The design of the diagnostic approach involves five connected components which include an integrated information platform that treats the heterogeneous data collected in support of different construction management functions, a component related to making use of quantitative causal models, two components related to an experience-based causal modeling approach that allows the flexible formulation, automatic selection and use of experience-based causal models to help further explain performance variances, and a component responsible for searching and reporting evidence with the guidance of the experience-based causal models. A realistically-sized building project was used to demonstrate the workability of the diagnostic approach for time performance as the representative measure studied in this thesis. The incremental value of the approach compared with current diagnostic practice was demonstrated through an experiment involving individuals with knowledge of construction. The approach was also assessed in terms of some tests formulated to assess the fit of a diagnostic approach with the construction industry context, which is important if the research findings are to have any impact on practice. ; Applied Science, Faculty of ; Civil Engineering, Department of ; Graduate
Diagnosing construction performance by using causal models
To date, most of the research that has addressed construction performance diagnosis has focused on identifying important factors having impact on construction performance and on establishing related performance models. The majority of the models were developed from a predictive perspective, not an explanatory one. The general goal of this research is to develop a construction performance diagnostic approach capable of assisting in identifying likely actual causes along with supporting evidence, and capturing and modeling experience-based diagnostic knowledge for current and future project use. The diagnostic approach implemented is based on a holistic structured causal model based diagnostic process which is applicable to key project performance measures. The approach is comprised of three layers: 1. a performance measure layer to determine if there exists a performance deviation to explain; 2. a quantitative casual models layer that makes use of quantitative causal relationships to identify causal variable variances, and 3. a user-defined experience-based causal models layer that makes use of experience-based knowledge to help further explain reasons (causal factors) for the causal variable variances. The design of the diagnostic approach involves five connected components which include an integrated information platform that treats the heterogeneous data collected in support of different construction management functions, a component related to making use of quantitative causal models, two components related to an experience-based causal modeling approach that allows the flexible formulation, automatic selection and use of experience-based causal models to help further explain performance variances, and a component responsible for searching and reporting evidence with the guidance of the experience-based causal models. A realistically-sized building project was used to demonstrate the workability of the diagnostic approach for time performance as the representative measure studied in this thesis. The incremental value of the approach compared with current diagnostic practice was demonstrated through an experiment involving individuals with knowledge of construction. The approach was also assessed in terms of some tests formulated to assess the fit of a diagnostic approach with the construction industry context, which is important if the research findings are to have any impact on practice. ; Applied Science, Faculty of ; Civil Engineering, Department of ; Graduate
Diagnosing construction performance by using causal models
Li, Mingen (author)
2009-01-01
Theses
Electronic Resource
English
DDC:
690
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