For design for safety (DFS), automated compliance checking methods have received extensive attention. Although many research efforts have indicated the potential of BIM and ontology for automated compliance checking, an efficient methodology is still required for the interoperability and semantic representation of data from different sources. Therefore, a natural language processing (NLP)-based semantic framework is proposed in this paper, which implements rules-based automated compliance checking for building information modeling (BIM) at the design stage. Semantic-rich information can be extracted from safety regulations by NLP methods, which were analyzed to generate conceptual classes and individuals of ontology and provide a corpus basis for rule classification. The data on BIM was extracted from Revit to a spreadsheet using the Dynamo tool and then mapped to the ontology using the Cellfie tool. The interoperability of different source data was well improved through the isomorphism of information in the framework of semantic integration, causing data processed by the semantic web rule language to be transformed from safety regulations to achieve the purpose that automated compliance checking is implemented in the design documents. The practicability and scientific feasibility of the proposed framework was verified through a 95.21% recall and a 90.63% precision in compliance checking of a case study in China. Compared with traditional compliance checking methods, the proposed framework had high efficiency, response speed, data interoperability, and interaction.

To combat climate change and meet energy conservation and emission reduction goals, the building sector must adopt low-carbon technologies and low-carbon management methods. To systematically explore existing research areas and track future research trends of carbon emission in the construction stage (CECS), this study conducts a bibliometric and content analysis of CECS studies. 563 relevant publications published between 2000 and 2022 are examined and analyzed using data from the Web of Science (WoS) core collection database. The findings reveal that studies of CECS have evolved through three stages: preliminary exploratory period, stable development period, and rapid development period. In addition, the literature co-citation network and content analysis classify the 13 found co-citation clusters into four knowledge domains: sources definition, data statistics, assessment methods, and carbon reduction strategies. Finally, a knowledge map of CECS studies is presented, outlining significant aspects of research, existing gaps in knowledge, and directions for future study. This work will make it easier for academics and professionals to pinpoint promising areas of study, fill in knowledge gaps, and broaden the scope of existing research on CECS.

The concept of building health has gained increasing attention due to its strong association with occupant well-being, safety, and the long-term performance of buildings. However, despite the existence of models that assess individual aspects of building conditions, standardized frameworks for comprehensively evaluating overall building health remain limited. A systematic assessment of building health can help identify potential risks and prevent unexpected consequences caused by system failures. This study provides a systematic review of existing approaches to building health assessment by synthesizing relevant indicators and evaluation methods reported in the literature. Key variables are standardized and organized into six major performance dimensions: safety performance, environmental quality, occupant-centred performance, system and infrastructure performance, energy performance, and economic performance. In addition, the review examines the roles of widely used assessment approaches, including Post-Occupancy Evaluation (POE), Internet of Things (IoT)- based monitoring, and machine learning techniques, in supporting building health evaluation. Finally, future directions are proposed to support the development of more comprehensive, data-driven building health assessment frameworks.