High speed railway environment safety evaluation based on measurement attribute recognition model.

Area of Science:

• Engineering
• Environmental Science
• Transportation Safety

Background:

• High-speed rail operation safety is critical.
• Severe weather poses significant risks to railway infrastructure and operations.
• Existing safety evaluation methods may not fully capture environmental impacts.

Purpose of the Study:

• To establish a comprehensive environmental safety evaluation index system for high-speed railways.
• To analyze the impact mechanisms of severe weather events on high-speed rail operations.
• To develop a robust method for assessing the environmental safety of high-speed railways.

Main Methods:

• Analysis of impact mechanisms of severe weather (raining, thundering, lightning, earthquake, winding, snowing).
• Development of an attribute recognition method using Mahalanobis distance.
• Application of the Mahalanobis distance measurement function for non-correlation and dimensionless influence.
• Evaluation of China's high-speed railway environmental safety situation.

Main Results:

• The established index system effectively analyzes severe weather impacts.
• The Mahalanobis distance method accurately identifies sample similarities in multidimensional space.
• The evaluation of China's high-speed railway environmental safety aligns with actual conditions.

Conclusions:

• The proposed environmental safety evaluation system provides a scientific foundation for high-speed railway operation safety.
• The methodology is effective in assessing risks posed by severe weather.
• This research contributes to enhancing the reliability and safety of high-speed rail networks.