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Although existing monitoring systems partially mitigate various challenges in the Architecture, Engineering, and Construction (AEC) industry, accidents continue to pose a significant threat, resulting in substantial human casualties and economic losses. Based on these challenges, earlier research has shown the capability of Cyber-Physical Systems (CPS) and Digital Twins (DT) to enhance monitoring systems by enabling real-time data collection, improving predictive accuracy, and strengthening anomaly detection. However, a systematic understanding of how these technologies can be effectively combined to optimize monitoring in the AEC industry remains limited. To bridge this gap, this study conducts a comprehensive review of CPS-DT integration for monitoring in the AEC sector, exploring current research trends, key applications, and existing challenges. The findings reveal that current CPS and DT applications for monitoring primarily focus on structural health monitoring, safety assessments, and real-time performance evaluation. However, challenges such as network reliability constraints and energy efficiency limitations significantly hinder their full-scale deployment in dynamic construction environments. Based on these insights, this study identified key future research directions, including the development of efficient predictive models to improve risk assessment, the optimization of real-time data processing architectures to enhance responsiveness, and the improvement of wireless communication infrastructures for seamless data transmission. Addressing these challenges will facilitate the development of intelligent, scalable, and cost-effective monitoring systems, ultimately enhancing safety, efficiency, and sustainability in the AEC industry.
Details
Construction engineering;
Data processing;
Performance evaluation;
Wireless communications;
Computer architecture;
Cyber-physical systems;
Prediction models;
Digital twins;
Network reliability;
Optimization;
Data transmission;
Anomalies;
Real time;
Monitoring systems;
Economic impact;
Data collection