Artificial intelligence for quality control in construction: a systematic review

Purpose This systematic literature review aims to explore how artificial intelligence (AI) contributes to improving quality control in the construction industry. The review investigates the types of AI technologies applied, evaluates their effectiveness and identifies challenges and gaps in implementation. Design/methodology/approach Following PRISMA guidelines, a total of 248 peer-reviewed studies published between 2017 and 2024 were initially retrieved from major academic databases, including Scopus, Web of Science, IEEE Xplore, SpringerLink and Google Scholar. After removing duplicates and applying strict inclusion and exclusion criteria, 16 high-quality studies were selected for final analysis. Thematic coding and qualitative synthesis were employed to extract key insights. Findings The review indicates that AI technologies such as machine learning, computer vision and predictive analytics significantly enhance quality control processes in construction. Despite these benefits, challenges such as limited data transparency, integration difficulties and workforce resistance hinder widespread adoption. Originality/value This review provides a roadmap for the strategic deployment of AI in construction quality control. It emphasizes the importance of standardized datasets, interoperable systems, inclusive training programs and ethical governance to support successful AI integration. The results offer valuable guidance for researchers, practitioners and policymakers seeking to advance smart, efficient and resilient construction practices.