Assessment of the condition of railway substructure by developing performance indicators based on data from multiple...

Area of Science:

• Civil Engineering
• Railway Engineering
• Asset Management

Background:

• Traditional track geometry measurements identify defect locations and severity but not their root causes.
• Existing multi-domain diagnosis approaches require individualized analysis for effective, long-term maintenance planning.
• A need exists for enhanced diagnostic information to complement current railway infrastructure management practices.

Purpose of the Study:

• To develop novel performance indicators for railway granular components.
• To provide a more detailed understanding of defect origins within the railway substructure.
• To create a decision-support tool for individualized infrastructure management.

Main Methods:

• Functional and reliability methods were employed to assess granular component performance.
• Expert analysis and multi-criteria data analysis were integrated into the methodology.
• A 35 km single-track railway section was used for validation, comparing results with multi-domain diagnosis.

Main Results:

• The developed indicators demonstrated sensitivity and strong correlation with maintenance decision-making.
• Good performance indicators corresponded to no necessary interventions, while poor performance indicated intervention needs.
• The individualized approach successfully identified specific layers linked to defects, aiding in understanding degradation mechanisms.

Conclusions:

• The proposed performance indicators offer a valuable, individualized approach to railway substructure diagnosis.
• This methodology facilitates targeted corrective actions and resilient maintenance planning.
• The framework serves as a specialized decision-support module for infrastructure management, complementing existing techniques.