Abstract
Abstract This paper presents a comprehensive scientific analysis of research on automatic monitoring in construction projects. Through a methodical examination of 857 bibliographic records from three databases, we find important trends, novel themes, and key research areas in this field. Our findings reveal that machine learning (ML), building information modelling (BIM), and deep learning are the most the most popular techniques for automatic monitoring. Furthermore, we identify construction technologies and artificial intelligence (AI) as the primary research foci. So, the study uncovers collaboration patterns among researchers and institutions, highlighting key players and their contributions, identifies research gaps and challenges, such as the need for integrating AI, big data, and cloud computing into construction project monitoring, and proposes future research directions to address these challenges and enhance the effectiveness of automatic monitoring systems. By providing a systematic review and insightful analysis, this study contributes to the advancement of construction project monitoring. It offers valuable insights for researchers, practitioners, and policymakers to foster innovation, improve project performance, and ensure sustainable construction practices.
Published Version
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