On account of the long time span of a project, successful data management plays a significant role in the operation and maintenance (O&M) performance of buildings. However, relatively scarce research has been conducted on systematic O&M data management processes or methodologies. Meanwhile, as a fundamental approach to O&M data management, considerable attention has been given to O&M data patterns and trends based on locational spaces.
Building information modeling (BIM) can provide an effective data management platform for every stage of building construction, including the O&M phase. One of the greatest advantages of BIM is that the data can be stored in the locational space or object. However, this advantage has not received sufficient attention in the O&M data management field.
The main objective of this study is to develop an O&M data management system that employs locational-space-based data analysis using Building Information Modeling (BIM) software (Revit). To achieve this objective, several detailed steps are presented. Firstly, this study confirms the importance of locational-space-based data analysis through a preliminary investigation on current O&M data management practices. Secondly, the limitations of the current O&M data management are disclosed by means of expert interviews and their solution is derived, that is, locational-space-based data analysis. Moreover, a novel O&M data structure and Unified Modeling Language (UML) diagram for development of the system to enable locational-space-based data analysis are designed. Consequently, the prototype of the system is programmed with the C# programming language. Finally, the system is implemented with hypothetical scenarios to demonstrate the improvements of decision making on O&M execution plan (OMEP), and it is validated through in-depth interviews with FM experts.
As a pioneering work, this study contributes to a growing area of research by providing an optimal data management methodology for the O&M phase and by developing a practical system to effectively analyze the O&M data based on locational spaces. From the perspective of industry, the results of this research—a locational-analysis-based O&M data management system (LAOMS)—facilitate accurate and effective O&M data analysis and thereby help decision-making in optimal O&M practices for commercial office buildings. Furthermore, the proposed system is expected to contribute to FM performance evaluation by providing an industrial standard database to address O&M problems.