An integrated quality control and supervision system in the construction of high-rise buildings using BIM digital twins and unmanned monitoring to ensure compliance with safety sustainability and energy efficiency requirements

Abstract

The study aims to develop and empirically test a comprehensive quality control and supervision system in high-rise construction that integrates BIM modeling, digital twins, and drone-based monitoring to ensure compliance with safety, structural stability, and energy-efficiency requirements; the goal is to demonstrate that integrating these technologies enables a shift from reactive defect remediation to proactive prevention, thereby substantially reducing costs and schedules. Materials include six high-rise projects(35-62 storeys) in diverse climatic and seismic contexts(2019-2024), split into control and experimental groups; BIM LOD 400(Revit, Navisworks), a cloud-based digital twin with streaming data integration, a network of>2,500 IoT sensors(strain gauges, inclinometers, temperature/humidity sensors), as well as quadcopters with a 45 MP RGB camera, LiDAR, and a thermal imager were employed; photogrammetry and scanning were processed in Pix4Dmapper and Trimble RealWorks, and statistical analysis was based on correlation-regression models and hypothesis testing. The results show a 78,76% reduction in critical defects(14,88→3,16 per 10,000 m²), a 67.71% reduction in rectification time(56,45→18,23 person-hours), and a 92,41% reduction in budget overruns due to errors (11,73%→0,89%); drone monitoring increased façade survey productivity by 53.06×, improved survey control accuracy by 1,78×, and raised the completeness of surface defect detection to 96,15%. Energy performance converged toward design values: the deviation of actual annual heating consumption fell from+29,47% in the control to+4,68% under the integrated supervision regime, while the number of thermal bridges decreased from 12,4 to 1,9 per 1,000 m² of façade. The digital twin delivered an average synchronization latency of 215,8 ms, handled up to 3,150 parallel IoT streams, automatically generated 88,42% of as-built documentation, and auto-classified 95,77% of deviations; a strong relationship was established between the frequency of drone monitoring and defect rectification speed(r=0.87), and each additional percentage point of automated clash checks at the preconstruction stage reduced final cost by 0,18%. Conclusion: the synergy of BIM, digital twins, and UAVs radically improves quality and safety, reduces lifecycle costs, and should be standardized with further AI integration for predictive analytics.