Requirements of Machine Learning and Semantic Enrichment for BIM-Based Automated Code Compliance Checking: A Focus Group Study

Abstract

Several regulations, standards, and requirements govern the lifecycle of the built environment. Such instances include legislations, government development control rules, environmental compliances, project and contractual requirements, and performance standards to be followed during construction. Compliance checking is a complex task that is often conducted manually, making it a resource-intensive, error-prone, and time-consuming affair. Past researchers have worked on methods and processes of automated compliance checking systems (ACCS); however, there has been a negligible adaptation in the industry. To this end, this study tries to recognize the reasons for the gap in the implementation of the pre-construction permit compliance systems in the Indian construction industry. The study understands the reasons from the end-user’s perspective through the means of a focus group study. Key findings indicate manual pre-processing of data is a significant hurdle in Building Information Models (BIM) for application in ACCS. ACCS applications developed are restricted in their area of usage due to the limitation in applicability beyond explicit building code clauses. Rule-based validation of regulation requires enriched structured data, which is generally absent from the models developed by architects in the design phase. The study indicates the necessity of automated data pre-processing step that includes intelligent model filling suggestions and semantic enrichment to increase the adoption of ACCS. This study points out that automatic semantic enrichment (SE) can be achieved by applying machine learning (ML). Areas of application of SE in ACCS are identified in the study, which can enhance the industry’s user experience and adaptation of ACCS.