Retrospective BIM performance analysis based on construction big data

Purpose – The literature suggests employing big data and Building Information Modeling (BIM) to examine building projects from several perspectives. Nevertheless, the literature is deficient in thorough BIM performance evaluation methods grounded in big construction project data. This paper presents an evaluation framework outlining the data input requirements and necessary data to conduct research leveraging big data for the analysis of BIM performance. Design/methodology/approach – Data parameters and performance metrics included in the evaluation framework are derived from a synthesis of literature review, data overview and interviews. The construction data was analyzed using PowerBI after undergoing a quality control process. Analysis results were verified through interviews with the main contractor. The project data served to assess the evaluation framework. Findings – The evaluation framework has ten data parameters, and six performance metrics categorized into three main categories. The findings indicate that the evaluation framework can be utilized to comment on BIM performance in a project, with a level of accuracy. Results indicated that ensuring the quality of tracked project data is crucial for obtaining reliable analysis results. Determining performance metrics and data parameters prior to data recording processes can help simplify the analysis process and ensure accurate analysis results. Originality/value – The proposed framework offers a comprehensive performance evaluation methodology that leverages the innovative application of unique and challenging to acquire big data, allowing practitioners to assess BIM performance in relation to project time, cost and scope. Identified data parameters and novel performance metrics may provide the foundation of a guideline for construction project data logging to facilitate accurate BIM performance monitoring.