Semantic enrichment of BIM models is a process designed to add meaningful semantics to the information represented in a building model. Although semantic enrichment provides a valuable opportunity for BIM technology to reach its full potential, it is considered an emergent field of research. As such, the body of knowledge on the subject is incomplete and lacks formal definition of the process, possible applications, contributions, and computational approaches. In this work, an extensive literature review is performed to begin forming the body of knowledge in this field. A bibliometric analysis of relevant publications is implemented to identify previously explored approaches and methods for enrichment. Papers describing previous work in the field demonstrate the application of semantic enrichment to building information stored in accordance to the Industry Foundation Classes (IFC) schema as well as based on a web ontology. A detailed content analysis illustrates the benefits of semantic enrichment for various tasks in the BIM domain, including improvement of data exchange routines, design analysis and processing data obtained by remote sensing techniques. A formal definition for "semantic enrichment of BIM" is suggested based on the common features identified during the literature review. This work discusses the significance of semantic enrichment to a BIM workflow, pinpoints its current research gaps and describes direction for future research.

The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of such models on classification tasks using geometric information. This research explores the hypothesis that incorporating contextual information into the ML models can improve classification accuracy. To test this, we created a graph data structure where each building element is represented as a node assigned with basic geometric information. The connections between the graph nodes (edges) represent the immediate neighbors of that node, capturing the contextual information expressed in the BIM model. We devised a process for extracting graphs from BIM files and used it to construct a graph dataset of over 42,000 building elements and used the data to train several types of ML models. We compared the classification results of models that rely only on geometry, to graph neural networks (GNNs) that leverage contextual information. This work demonstrates that graph-based models for building element classification generally outperform classic ML models. Furthermore, dividing the graphs that represent complete buildings into smaller subgraphs further improves classification accuracy. These results underscore the potential of leveraging contextual information via graphs for advancing ML capabilities in the BIM environment.

Building design must adhere to numerous codes, laws, and regulations. In practice, despite the available tools for Automated Code Checking (ACC) and the extensive research in the field, checking whether a given design complies with all relevant regulatory requirements remains a manual and time-consuming task. A comprehensive checking system that provides highly automated solutions for a wide range of regulations remains a distant goal. Recent studies have underscored the potential of engaging advanced technologies like Machine Learning (ML) and Natural Language Processing (NLP) to enhance ACC abilities. Hence, this work aims to review recent advancements in implementing ML technologies in code compliance checking, identify knowledge gaps, and suggest future research directions. By implementing a systematic literature review methodology, we identify three key research areas within the domain: processing regulatory text, processing design information, and an overall checking mechanism. Existing efforts explore each of these areas using a variety of ML algorithms to enhance their effectiveness. Despite the notable advancements, challenges persist due to the complexity of regulations, ambiguity of legal texts, and the scarcity of training data, all of which limit the scalability of the presented approaches. Additionally, while ML enhances flexibility by learning from data rather than relying on hardcoded rules, it introduces uncertainties in decision-making processes critical to building permitting. The review highlights the potential for hybrid approaches that combine the strengths of both rule-based systems and ML models to effectively address these challenges.

Purpose: Building permitting is mostly a manual, labor intensive and time-consuming process. Initiatives for streamlining the process are not always helpful since they often fail to address the core problems within the process. A framework for modeling the permitting process can be useful to identify bottlenecks, core challenges and best practices. Hence, the authors aim to demonstrate and validate a previously suggested workflow for permit process modeling using the permitting process in Israel as a test case.Design/methodology/approach: The authors implement qualitative expert interviews for data acquisition. The collected data are then processed for a qualitative data analysis. The results of the analysis are then validated using a focus group workshop in the field of building permits. In the test case the focus group consisted of Israeli experts.Findings: The authors present a detailed overview of the as-is building permit process in Israel and the existing challenges. Through this test case, the authors found that the framework is applicable in different countries and that it can provide valuable insights into the core problems within the process. In addition, application of the same framework in different countries can provide comparable results that would allow the authors to identify best practices.Originality/value: The major contribution of this work is the development and validation of a framework for building permitting process modeling which can be used to identify existing challenges and bottlenecks in the process. Implementing a structured and unified approach provides an opportunity to easily compare processes in different countries to identify best practices.