Artificial Intelligence (AI) is revolutionizing Construction Management (CM) through automation, predictive analytics, and real-time decision-making throughout the project lifecycle.This study aims to provide a comprehensive and structured synthesis of AI models and their applications in CM. This paper presents a systematic review of 191 peer-reviewed articles published between 2020 and 2025, aiming to integrate the current state of AI implementation in CM, focusing on AI methods and models and their applications in CM. Compared to previous reviews that take these factors individually or focus narrowly on specific techniques, this study offers a comprehensive taxonomy that systematically maps AI techniques against CM functions and integration platforms. The results reveal that AI applications are primarily concentrated in risk and safety management, decision support, and monitoring and control, while domains such as legal analytics, robotics, and cybersecurity remain underexplored. Furthermore, Computer Vision (CV) and Deep Learning (DL) dominate tasks such as safety monitoring and defect detection, whereas Machine Learning (ML) and optimization algorithms are widely applied in cost estimation and scheduling. It also addresses developments rarely covered in construction research, including Generative AI (Gen-AI), Explainable AI (XAI), and transformer models, presenting a strategic framework for the widespread adoption of AI in the construction environment. This study contributes a structured taxonomy that systematically links AI models with CM functions and enabling technologies, providing a comprehensive synthesis of emerging trends and research gaps.

The Architecture, Engineering, and Construction (AEC) sector is increasingly adopting Artificial Intelligence (AI) technologies to enhance efficiency, decision-making, and operational performance across infrastructure and construction workflows. The objective of this study is to examine the challenges of implementing AI in civil and construction engineering, as associated with governance, ethics, and human interaction. The methodology integrates a structured conceptual framework developed through literature synthesis and exploratory survey analysis to assess the technical, governance, ethical, and sociotechnical implications of AI deployment in civil and construction engineering. The analysis focuses on key AI applications, including infrastructure inspection, bridge crack detection, construction safety monitoring, and predictive planning for maintenance and scheduling, while addressing critical dimensions such as explainability, liability, bias, ethical governance, and human factors. The results indicate that although AI-driven models can improve productivity and decision support, challenges related to transparency, accountability, data privacy, and overreliance on automated systems remain prevalent. The study concludes that the successful deployment of AI in civil and construction engineering requires rigorous validation practices, explainable and trustworthy system design, and sustained human-in-the-loop oversight to ensure responsible, reliable, and scalable integration into real-world engineering applications.