Comparative Study of 24 Publications on the Applications of AI, Robots, and Automation Frameworks on Quality Management in Construction Sites
Description
The dataset presents a comprehensive review and analysis of the existing literature on the integration of advanced technologies in construction quality management. This dataset encompasses 24 peer-reviewed research papers, each scrutinizing the application of AI, robotics, and various automation frameworks within construction sites, with a focus on quality management processes. The data includes detailed information extracted from each paper, such as the title, authors, year of publication, country of origin, research methods used, technologies investigated, benefits, drawbacks, and the overall reception of the findings within the industry. The dataset also covers the application level of these technologies, the frameworks they relate to, key performance indicators (KPIs) and metrics developed, and their reported accuracy. This dataset is valuable for researchers and industry professionals who are interested in understanding the current state of technology integration in construction quality management. It highlights the advancements, challenges, and gaps in the application of AI, robotics, and automation within the construction sector, providing a critical resource for future studies and practical implementations. The data also reflects on the practical implications of these technologies, offering insights into their potential impact on the construction industry's quality management practices.
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The primary databases utilised were Web of Science and Emerald, selected for their coverage of high-quality academic literature. The search was conducted using carefully crafted Boolean phrases to ensure that all pertinent studies were captured. These phrases included combinations of keywords such as "AI," "artificial intelligence," "robots," "robotics," "automation," "construction quality management," and "construction defect detection." The search aimed to cover all relevant literature up to the time of the study. Inclusion and Exclusion Criteria Studies were included if they: Discussed the application of AI, robotics, or automation frameworks specifically within the context of construction quality management. Provided empirical data, detailed case studies, or in-depth discussions that were based on actual construction site scenarios. Were published in peer-reviewed journals or recognised conference proceedings to ensure academic credibility and methodological rigour. Studies were excluded if they: Lacked sufficient data or clear methodologies, making it difficult to assess their findings or replicate their results. Focused solely on theoretical concepts without practical application or empirical validation within construction sites. Were not directly related to the construction industry, such as those focused on manufacturing or unrelated industries. Phases of Data Collection and Analysis 1. Initial Screening: During this phase, the titles and abstracts of all identified studies were screened to exclude any articles that were clearly irrelevant to the research topic. This step ensured that only studies potentially relevant to the application of AI, robotics, and automation in construction quality management were selected for further review. 2. Full-Text Screening: In this phase, the full texts of the selected studies were reviewed in detail. The studies were assessed based on the inclusion and exclusion criteria, with a focus on the presence of empirical data and practical application. This phase was crucial in ensuring that only studies with a direct and relevant contribution to the topic were included in the final analysis. 3. Data Extraction: For each study that passed the full-text screening, key information was systematically extracted. This included details on the technologies investigated, the benefits and drawbacks reported, the level of application within the quality management framework, and the metrics or key performance indicators (KPIs) used. This structured data extraction allowed for a comprehensive comparison across studies, facilitating the identification of patterns, trends, and gaps within the literature.