Standard terms as analytical variables for collective data sharing in construction management

Abstract

In order to facilitate data analysis techniques for solving construction management (CM) problems, especially in a collaborative manner, it is essential to define the data schemas of various construction information as the common variables. However, one of the critical barriers is that there is no standard set of variables addressed for collaborative research and practice. Therefore, defining standard variable terms is essential to share, utilize, and analyze construction data systematically and efficiently. In this context, the purpose of this paper is to propose a structured set of standard CM variable terms to enable the global collective analyses of CM data, not only for human managers but also for automated machine inferences. To address this issue, firstly, bibliographic data was extracted from Scopus, focusing on CM research, and the VOSviewer was used to analyze the bibliographic information. Proposed standard variable terms were then organized into a hierarchical structure with two levels, including eight variables in the first level and fifty-seven variables in the second level. Additionally, examples of utilizing standard variable terms were presented to the traditional analytical techniques, ontology, and artificial intelligence (AI) techniques to illustrate the viability of the proposed variable terms in CM. The proposed standard variable terms can be used as the basis for further detailed taxonomy toward collective advanced analytics implementation in CM. This approach will significantly enhance automated knowledge sharing between different applications and machines, resulting in global collective intelligence among unspecified CM professionals.