A theoretical holistic decision-making framework supporting collaborative design based on common data analysis (CDA) method

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.