Risk assessment and early warning system design of curtain wall glass self-explosion in 350km/h high-speed railway station building based on machine learning

The traditional risk assessment method relies on experience and expert judgment, and lacks scientificity and accuracy. This study uses the powerful data processing and pattern recognition capabilities of machine learning to build a scientific and effective risk assessment model to achieve an accurate assessment of the risk of curtain wall glass self-explosion, and designs a sensitive and accurate early warning mechanism to reduce the losses and risks caused by curtain wall glass self-explosion. Support Vector Machine (SVM) is selected as the basic model, considering its advantages in dealing with small samples, nonlinear and high-dimensional pattern recognition problems. By collecting data such as historical cases, materials and environmental parameters, the characteristics related to self-explosion risk are screened out, and the SVM model is trained to realize the risk prediction of new data. The early warning system deploys sensor networks for real-time monitoring, establishes a big data platform to store and preprocess data, uses SVM model to analyze data to evaluate risks, and sets early warning thresholds according to the evaluation results to send out early warning information in time. Experimental analysis shows that the system can effectively assess the risk of curtain wall glass self-explosion, and trigger early warning when it exceeds the preset threshold, showing high accuracy and reliability. The system runs stably, and the links such as data processing, risk assessment and early warning release all operate normally, ensuring high reliability. In addition, the threshold dynamic adjustment mechanism of the system is automatically optimized according to the actual situation, which further improves the adaptability and flexibility of the system. This study provides a new solution for the risk assessment and early warning of curtain wall glass self-explosion, which is helpful to improve building safety and reduce related losses.