Comparative Study of 24 Publications on the Applications of AI, Robots, and Automation Frameworks on Quality Management in Construction Sites
Description
This dataset provides a structured review of 24 peer-reviewed studies published between 2006 and 2024 on the application of advanced technologies in construction quality management. It collates information on a wide range of technologies, including artificial intelligence (AI), machine learning, computer vision, robotics, Internet of Things (IoT), blockchain, and augmented/virtual reality (AR/VR), as they have been applied to quality-related tasks in construction. For each study, the dataset records bibliographic details (title, authors, year, and country of origin), methodological approach, the technology investigated, application level within quality management processes, and any frameworks referenced. In addition, it documents the benefits and drawbacks reported, the key performance indicators (KPIs) or metrics used, and where available, measures of accuracy or performance. The dataset also captures information about industry reception and collaboration, as well as institutional affiliations. By consolidating these diverse sources into a single structured resource, the dataset enables researchers and practitioners to trace how different technologies have been applied to quality management in construction, what indicators have been used to evaluate them, and where evidence has been concentrated. It serves as a reference point for future academic studies and industry initiatives concerned with digitalisation and quality in the built environment.
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Steps to reproduce
The dataset was compiled through a systematic review of peer-reviewed literature on advanced technologies in construction quality management. Searches were conducted in Scopus and Web of Science for the period 2006–2024, using combined terms for technologies (artificial intelligence, machine learning, computer vision, robotics, IoT, blockchain, augmented/virtual reality) and quality (construction AND quality management/control/assurance). Records were screened using predefined criteria: studies were included if they reported empirical applications of digital technologies in construction with a clear link to quality management; non-English publications, abstracts without full papers, and studies outside the built environment were excluded. Data from eligible studies were extracted into a structured template covering bibliographic information, research method, technology investigated, application level, frameworks referenced, reported benefits and drawbacks, KPIs, and accuracy metrics where available. Terminology was normalised by mapping application levels to inspection, control, assurance, and management, and grouping technologies into clusters (AI/ML, robotics, IoT, blockchain, AR/VR). The cleaned entries were consolidated into an Excel file, providing a reproducible dataset of published evidence on digital technology applications in construction quality management.