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The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

The purpose of this study is to reflect on and analyse the sensory experiences related to the transition to remote work during the COVID-19 pandemic. The research seeks to understand how these experiences have influenced the integration of work practices into home and family life and the subsequent adaptations and embodied learning that arise in response.

The authors' research approach incorporates autoethnographic methods to explore the sensory, affective and emotional experiences of transitioning to remote work. The authors draw on principles of embodied learning, as influenced by Gilles Deleuze, and utilise a range of ethnographic tools including note-taking, audio memos, photography, shared conversations and written reflections to gather their data.

The study illuminates the ways bodies learn to accommodate the new organisational contexts that arise when the spaces, affects and forces of home and work intersect. It demonstrates how the integration of work into the private domain resulted in new affective and material arrangements, involving novel sensory experiences and substantial embodied learning.

This study provides a distinct, sensory-oriented perspective on the challenges and transformations of remote work practices amid the pandemic. By focussing on the affective resonances and embodied learning that emerge in this context, it contributes to the emerging discourse around post-lockdown work practices and remote work in general.

The study adopts an intersectional approach to identify the key dimension(s) that reproduce inequalities in women's subsistence entrepreneurship within urban-poor settings in the global south.

The in-depth case study is based on 44 semi-structured interviews and four focus-group discussions with women entrepreneurs based within urban-poor dwellings in the twin cities of Islamabad and Rawalpindi in the Islamic Republic of Pakistan.

The authors contribute to the literature by identifying how intersecting socio-class and socioeconomic inequalities, and patriarchal norms of izzat (meaning: honour, respect) and purdah (or veil), perpetuate disadvantage for women entrepreneurs producing and/or selling business goods and services.

The findings challenge the view of entrepreneurship as a meritocratic and neutral activity for social emancipation. The authors argue that multiple social hierarchies and inequalities operate simultaneously, but how these are understood, exercised and reproduce disadvantage for women entrepreneurs, depends on their social class. The authors propose a triple bind of domestic, market and societal inequalities as a heuristic framework for understanding intersecting inequalities, patriarchy and subsistence entrepreneurship in Pakistan, specifically the global south.

This study aimed to address a gap in subcontractor management by focusing on previously unexplored complexities surrounding subcontractor management in developing countries. While past studies concentrated on selection and relationships, this study delved into how effective subcontractor management impacts project success.

This study used the Bayesian Network analysis approach, through a meticulously developed questionnaire survey refined through a piloting stage involving experienced industry professionals. The survey was ultimately distributed among participants based in Accra, Ghana, resulting in a response rate of approximately 63%.

The research identified diverse components contributing to subcontractor disruptions, highlighted the necessity of a clear regulatory framework, emphasized the impact of financial and leadership assessments on performance, and underscored the crucial role of main contractors in Integrated Project and Labour Cost Management with Subcontractor Oversight and Coordination.

Previous studies have not considered the challenges subcontractors face in projects. This investigation bridges this gap from multiple perspectives, using Bayesian network analysis to enhance subcontractor management, thereby contributing to the successful completion of construction projects.