Purpose: This study addresses the current knowledge gap in planning and executing building permit-related inspections, which are essential for ensuring safety, legality and compliance with building regulations. The complexity of these inspections arises from their multidisciplinary nature and the variability of inspection processes across different jurisdictions.Design/methodology/approach: The research employs a systematic approach that combines desk research, literature review and expert interviews conducted across different countries. This mixed-methods approach enables the development of a conceptual framework that organizes the inspection processes into clear categories of responsibilities, processes and characteristics.Findings: The study identifies and formalizes key elements of building permit-related inspections, including diverse national or jurisdictional contexts, processes, inspector management and inspection categorization, before synthesizing them into a comprehensive conceptual framework. This framework illustrates the complex relationships between the different aspects of inspections, providing a structured method to effectively manage these complexities.Originality/value: This research contributes to the fields of construction management and building permitting by bridging theoretical knowledge and practical applications in the realm of construction inspections. By synthesizing dispersed knowledge into one coherent framework, the study establishes a theoretical foundation that enhances the efficiency and transparency of building inspections globally. The research also provides significant insights that will aid in the digital transformation of the building inspection processes that are crucial for compliance and quality assurance in the construction industry.

Image-based techniques have become integral to the construction sector, aiding in project planning, progress monitoring, quality control, and documentation. In this paper, we address two key challenges that limit our ability to fully exploit the potential of images. The first is the “semantic gap” between low-level image features and high-level semantic descriptions. The second is the lack of principled integration between images and other digital systems used in construction, such as construction schedules and building information modeling (BIM). These challenges make it difficult to effectively incorporate images into digital twins of construction (DTC), a critical concept that addresses the construction industry’s need for more efficient project management and decision-making. To address these challenges, we first propose an ontology-based construction image interpretation (CII) framework to formalize the interpretation and integration workflow. Then, the DiCon-SII ontology is developed to provide a formalized vocabulary for visual construction contents and features. DiCon-SII also acts as a bridge between images and other digital systems to help construct an image-involved DTC. To evaluate the practical application of DiCon-SII and CII in supporting construction management tasks and as a precursor to DTC, we conducted a case study involving drywall installation. Via this case study, we demonstrate how the proposed methods can be used to infer the operational stage of a construction process, estimate labor productivity, and retrieve specific images based on user queries.

Recently there has been a strong interest in using semantic technologies to improve information management in the construction domain. Ontologies provide a formalized domain knowledge representation that provides a structured information model to facilitate information management issues such as formalization and integration of construction workflow information and data and enables further applications such as information retrieval and reasoning. SPARQL Protocol And RDF Query Language (SPARQL) queries are the main approaches to conduct the information retrieval from the Resource Description Framework (RDF) format data. However, there is a barrier for end users to develop the SPARQL queries, as it requires proficient skills to code them. This challenge hinders the practical application of ontology-based approaches on construction sites. As a generative language model, ChatGPT has already illustrated its capability to process and generate human-like text, including the capability to generate the SPARQL for domain-specific tasks. However, there are no specific tests evaluating and assessing the SPARQL-generating capability of ChatGPT within the construction domain. Therefore, this paper focuses on exploring the usage of ChatGPT with a case of importing the Digital Construction Ontologies (DiCon) and generating SPARQL queries for specific construction workflow information retrieval. We evaluate the generated queries with metrics including syntactical correctness, plausible query structure, and coverage of correct answers