## 4 Discussion  
The results confirm that the proposed BeiDou/GNSS-based monitoring method significantly enhances the accuracy and reliability of climbing scaffold monitoring in super-high-rise buildings. Through experimental validation on a real-world construction site, the system demonstrated stable performance in tracking scaffold movements and effectively captured displacement changes under varying environmental conditions. Compared to traditional manual or single-sensor monitoring approaches, the BeiDou/GNSS-based system provides continuous, high-precision positioning, reducing potential human errors and improving the real-time safety assessment. In contrast to the approach reported in [10], which employed a 16-level sensor system solely for analyzing the vertical displacement of the main load-bearing layer and, thus, only provided point-to-point measurements without a comprehensive assessment of the scaffold's climbing posture, the present study offers a more holistic monitoring solution. Specifically, while [10] used fixed threshold values for the graded warning that directly triggered corrective actions when exceeded, our method leverages a CUSUM-based anomaly detection mechanism to precisely capture and accumulate even minor displacement variations. This allows for the more accurate identification and early warning of anomalies. Furthermore, by setting graded warning thresholds tailored to the monitoring precision in different directions, our approach is better aligned with practical requirements for scaffold safety management.  
While the proposed BeiDou/GNSS-based monitoring system demonstrates high accuracy in tracking climbing scaffold movements, several limitations remain. First, the validation was conducted on a single construction project, which may not fully capture the variations in structural designs, construction methods, and environmental conditions. This limited scope raises concerns about the system's generalizability across different projects. Second, relying solely on BeiDou/GNSS for displacement monitoring presents challenges in capturing short-term dynamic movements due to the inherent sensor limitations. Additionally, external factors such as signal obstructions and multipath effects may impact measurement accuracy, necessitating the integration of complementary monitoring approaches.  
To address these limitations, several improvements can be implemented. Expanding validation across multiple high-rise construction projects with varying structural designs and environmental conditions will enhance the system's generalizability and reliability. Additionally, integrating MEMS-based IMUs with GNSS data can mitigate short-term positioning errors and improve real-time displacement tracking. Furthermore, incorporating cloud computing and IoT technologies will enable real-time data processing, automated anomaly detection, and remote monitoring, enhancing overall safety and operational efficiency.  
By addressing these areas, future research can further improve the accuracy, reliability, and applicability of BeiDou/GNSS-based climbing scaffold monitoring systems, contributing to safer and smarter super-high-rise construction.