中国部分滨海地区（如粤港澳大湾区）常年遭受强台风侵袭，这对区域内公共建筑的安全构成严重威胁。位于台风高频登陆区的机场航站楼大跨度钢结构屋盖及大面积玻璃幕墙对风荷载极为敏感，极易引发局部围护结构失效甚至主体结构损伤，成为值得重点关注和保护的公共建筑之一，其在施工和运营期间产生的、以屋面抗风揭为重点的防风减灾安全问题，已成为工程中亟待解决的技术难题。当前，构建数智化安全监测系统是实现风灾事前预防的有效手段之一。该文以珠海金湾机场T2航站楼实际工程为背景，研发了适用于滨海航站楼的结构安全监测及预警系统。该系统通过14类共250余套传感器，协同监测结构表面风速、风向、风压、结构应力应变及结构动力响应等关键参数，结合安全评定软件及智能报警功能等，实现了对围护系统和主体结构的全要素动态监测。此外，创新性地将增强纤维光纤智能传感筋固定在连续焊缝屋面板上以提升抗风揭性能，并在同一监测点布置多类型传感器以实现多源异构数据融合。实证分析表明，在2025年台风“韦帕”过境期间，该系统成功采集到屋面台风的风速时程和风压峰值等数据，验证了结构的安全稳定性。同时通过多源异构数据融合的监测模式，精准地呈现了航站楼主体结构及围护结构的风致响应，实现了数智化安全监测及预警。该成果为滨海地区类似大跨空间钢结构的抗风设计及安全监测提供了参考。

Objective: The Guangdong–Hong Kong–Macao Greater Bay Area is geographically situated in a region frequently impacted by severe tropical cyclones, which pose persistent threats to infrastructure resilience and public safety. Among critical facilities, airport terminals—especially those located along coastal zones with high typhoon landfall probability—face significant challenges regarding structural integrity under extreme wind events. Wind uplift forces during typhoons can cause catastrophic damage to roofing systems, cladding elements, and even primary structural components if not adequately mitigated. Studies have highlighted the necessity of proactive monitoring mechanisms that capture real-time structural behavior under extreme wind loads. Nonetheless, integrated solutions tailored specifically for large-span airport terminals remain limited. This study addresses this gap by proposing a comprehensive structural safety monitoring and early warning system explicitly designed for coastal airport environments, with a focus on improving preparedness for wind disasters, enhancing structural performance, and minimizing operational downtime during extreme weather events. Methods: The study is grounded in the engineering context of Zhuhai Airport Terminal 2, a representative large-span spatial steel structure exposed to frequent typhoons. A multi-layered structural health monitoring framework was developed, comprising more than 250 sensor units across 14 distinct categories, strategically deployed to capture critical environmental and structural parameters. The sensing system includes three-dimensional anemometers for wind speed and direction measurements, differential pressure sensors for surface wind pressure, strain gauges and fiber-optic sensors for stress–strain monitoring, and accelerometers for dynamic response characterization. An advanced data acquisition and processing platform integrates safety assessment algorithms with intelligent alarm logic, enabling continuous evaluation of structural conditions. Notably, the system employs enhanced fiber-optic intelligent sensing reinforcements directly bonded to continuously welded roof panels, providing high-resolution strain measurements while improving resistance to wind uplift. Multiple sensor types are co-located at identical monitoring points to enable multi-source heterogeneous data fusion, thereby enhancing redundancy, accuracy, and robustness in data interpretation. Results: The proposed system was empirically validated during Typhoon Wipha in 2025, which made landfall near the Pearl River estuary with sustained winds exceeding design-level thresholds. The monitoring system successfully recorded detailed time histories of wind velocity, gust factors, and localized pressure peaks across different roof zones. The data indicate that maximum wind pressures occurred at roof corners and leading edges, consistent with aerodynamic theory, although their magnitudes slightly exceeded initial design assumptions. Strain measurements further show that the proposed fiber-optic reinforcement effectively reduced peak tensile stresses in roof panels, confirming its dual function in structural health monitoring and mechanical enhancement. Multi-source heterogeneous data fusion enabled precise correlation between wind field characteristics and structural dynamic responses, allowing the identification of potential overstress conditions before they reached critical levels. Within seconds of their detection, anomalous trends triggered intelligent alarms, demonstrating the system's responsiveness and reliability. Comparative analysis against conventional single-source monitoring approaches showed a marked improvement in detection accuracy and situational awareness. Conclusions: This study demonstrates that an integrated, sensor-rich monitoring and early warning system can significantly improve the resilience of coastal airport terminals to typhoon-induced wind hazards. By combining advanced sensing technologies, intelligent data fusion, and targeted structural reinforcement, the proposed framework not only delivers real-time safety assurance but also generates valuable datasets for design code and maintenance strategy refinement. The approach is readily adaptable to other large-span spatial structures in hurricane-prone regions, offering broad applicability in civil infrastructure protection. Future work may explore integration with predictive modeling tools, machine learning-based anomaly detection, and automated mitigation measures to further strengthen disaster preparedness.