BIM-based knowledge management for the use of construction and facilities management knowledge in construction projects

  • Hao Wang

Student thesis: Doctoral ThesisDoctor of Philosophy


Knowledge is one of the most important resources in any organizations. Construction is a knowledge-intensive industry, in which organizations are known for the delivery of products and services, relying on various knowledge. In construction projects, different disciplinary teams have knowledge learned and accumulated from their experience of construction practice or education. However, due to the fragmented nature of construction projects, it is difficult for the knowledge of each discipline to be integrated for solving problems in a specific project phase. For example, in the design stage, it is difficult to apply the knowledge of the contractor and the facility management (FM) team to solve potential problems in construction and FM proactively. Compared to generic information technology (IT) tools, building information modelling (BIM) provides an integrated platform that allows multiple disciplinary teams to manage project-related data collaboratively. In addition, BIM can be seen as a process throughout the project life cycle, in which the project lifecycle data are accumulated and recorded. Therefore, the study attempts to explore whether the knowledge related to construction and FM can be more easily applied to the design stage of the project with the support of BIM.

In order to understand how BIM can improve KM in construction projects, it is necessary to understand the status of KM in current construction projects, including the difficulties of KM and the strategies being used. Also, the process of involvement of contractors and FM teams in the design stage should be explored. As BIM has distinguishing features compared to generic IT, it is also necessary to explore how some of these features can help the proactive application of construction and FM-related knowledge to the design stage of the projects. In addition, the expectation of BIM-supported KM for early application of construction and FM-related knowledge in design stage should be identified. Based on the understanding of the above research questions, a KM system integrated to BIM is developed.

This research used mixed methods to achieve the research objectives mentioned above. In order to collect in-depth information from the construction industry, semi-structured interview as a part of the mixed methods was adopted to obtain construction practitioners’ views on the current status of KM in construction projects, as well as the strategies for early involvement of contractors and FM teams and the aspects that need to be improved. The practitioners’ expectations of BIM-supported KM were also identified through semi-structured interviews. Based on the results of the semi-structured interviews, in order to apply construction and FM-related knowledge in the design stage, it is needed to enable the knowledge generated during construction and FM phases to be captured. Also, the captured knowledge should be easily retrieved in the design stage. Accordingly, a KM system is developed to achieve this purpose. The waterfall method is used as a software development method to develop the KM system. The parametric modelling of BIM is used for improving knowledge capture in the developed system. The parameters used to capture knowledge cases are pre-defined in the BIM model and are compatible with the CBR and NLP modules. Moreover, since BIM is a process throughout the project life cycle, the knowledge generated during the construction and FM stages can be captured by BIM. The captured knowledge will be stored in a centralised knowledge base for the future use. On the other hand, this system combines case-based reasoning (CBR) with deep neural network-based natural language processing (NLP) as an innovative retrieval method. CBR presents the knowledge context through attributes, while NLP is used to evaluate the similarity of detailed text description between knowledge cases. The weights of CBR attributes affects the performance of CBR. Statistical technique on the large amounts of data collected is required for weight determination. For this reason, this research adopts a questionnaire survey as a quantitative method to determine the weight of each attribute in CBR to improve its accuracy. By using the developed system, the captured knowledge can be proactively discovered and applied in the design stage of future projects through the retrieval tool supported by the CBR and NLP. This developed system is validated through a case study.

This research combines conventional KM tools, such as CBR and NLP, with BIM to facilitate the proactive application of construction and FM related knowledge in the design stage of projects. The parametric modelling of BIM is applied in this research for knowledge capture. Besides, this research identified distinctive features of BIM that can be used to improve KM. These distinctive features provide BIM-supported KM with some advantages over generic IT-based KM and point out the research roadmap for future research on BIM-supported KM. It is hoped that this study becomes one of the frontier research attempts that initiate an idea of the transformation from BIM into building knowledge modelling (BKM).
Date of AwardJul 2021
Original languageEnglish
Awarding Institution
  • Queen's University Belfast
SponsorsQueen's University & China Scholarship Council
SupervisorXianhai Meng (Supervisor) & Patrick McGetrick (Supervisor)


  • building information modelling
  • knowledge management
  • case-based reasoning
  • natural language processing
  • deep learning

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