A theoretical holistic decision-making framework supporting collaborative design based on common data analysis (CDA) method. (1st April 2022)

• Record Type: Journal Article
• Authors: Khudhair, Ali; Li, Haijiang; Bower, Thomas; Ren, Guoqian
• Is Part Of: Journal of building engineering. Volume 46(2022)
• Publication Date: 2022-04-01
• Subjects: Building information modelling (BIM) -- Industry foundation classes (IFC) -- Data exchange -- Semantic subset -- Semantic intersection -- Single truth of information -- Partial truth of information -- Model view definition (MVD); Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2021.103686
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: The enormous expansion of information, which is assembled from various design tools, has caused challenges in data exchange and compelled companies to find various solutions to improve collaboration. Data exchange within Building Information Modelling (BIM) context has been mainly focused on individual disciplines. Even though several attempts have been made to develop data exchange requirements for BIM models, there is still a lack of homogeneity since no method for classifying and sharing those requirements is clearly outlined. A clearly defined "single truth of information" is still not acknowledged yet. Software tools require unambiguous clarity of the semantics, which can help various stakeholders to proceed with their design tasks. However, there is still a lack of multi-dimensional knowledgebase for holistic decision-making within a BIM workflow. Therefore, this paper presents a common data analysis (CDA) referencing various concepts such as the standardised Information Delivery Manual (IDM) method, model view definition (MVD) and the concept of semantic intersection to conclude "single truth of information" and "partial truth of information" data sets that form the basis for a theoretical holistic decision-making framework to support collaborative design. The information defined in this research was validated based on existing resources and literature. Following the concluded data sets, a model can be transformed automatically at the minimum commonality level, creating a starting point for other professions. Following the analysis, a theoretical holistic decision-making framework was proposed. The innovation of the proposed framework lies in providing a holistic decision-making system that combines both data extraction using the concluded data sets and semantic web technology to eliminate inefficiencies in data sharing and improve the decision-making process in the early design stage by providing the stakeholders with rational solutions with less effort and time. This paper provides the essential requirement for a holistic decision framework from a data processing perspective. Highlights: Designing a CDA to understand for each profession what sort of data is required and what information needs to be exchanged in order to define a "single truth of information" and "partial truth of information" data sets. Identifying a "single truth of information" that can be shared throughout the lifecycle, which can help developers to use the same mapped data set to fit their specific use case, eliminating the time wasted on rebuilding this common information. Proposing a theoretical holistic decision-making framework using combined data extraction and a semantic web approach to eliminate inefficiencies in data sharing and improve the decision-making process in the early design stage.