Applications of Artificial Intelligence for Enhancing Quality Control in the Construction Sector: A Comprehensive Analysis

Abstract

Quality control (QC) in the construction sector is essential for ensuring structural integrity, safety, and compliance with regulatory standards, yet traditional methods are often labor-intensive, subjective, and error-prone. Artificial Intelligence (AI) offers transformative potential to enhance QC processes through automation, precision, and data-driven decision-making. This paper investigates AI applications, including machine learning, computer vision, robotics, and predictive analytics, in improving QC across construction phases, from material inspection to structural monitoring. Through a systematic literature review, global case studies, and experimental data, the study evaluates AI’s efficacy in detecting defects, optimizing inspections, and reducing costs. Findings indicate that AI achieves 90–98% accuracy in defect detection, reduces inspection times by 30–50%, and lowers QC costs by 15–25%. Challenges such as data scarcity, integration complexity, and regulatory barriers are addressed, with recommendations for future research to advance AI adoption. This paper underscores AI’s role in revolutionizing construction QC, promoting safety, efficiency, and sustainability.