<p>Infrastructure management is one of the main problems faced by railway managers. Generally, decision making is based on track geometry measurements made with recording machines. This perspective is able to detect where the problems are and their magnitude, but does not provide information on the origin of the defects. To alleviate this diagnostic shortcoming, approaches such as multi-domain diagnosis, which takes into account multiple parameters, have been developed. However, the developers of this technique point out that it is necessary to perform an individualized analysis so that the programming of interventions is as effective as possible and lasts over time. It is on the basis of this problem that the need arises to create information that complements what already exists, which is why it has been decided to develop performance indicators that provide more information than a traditional parameter or indicator. In this research work, the functional and reliability method has been used to obtain the performance of granular components, together with expert analysis and multi-criteria data analysis. To assess the methodology, a 35 km long single-track study site was evaluated. This road has all the input parameters for the implementation of the developed methodology and, in addition, the multi-domain diagnostic analysis was taken into account as an operational reference classification. The results indicate that the indicators developed are sensitive and appropriately matched to decision making; that is, when an indicator is categorized as good performance, no intervention on the road is necessary, whereas, poor performance states are predominantly associated with intervention cases. Furthermore, this individualized approach supports the identification of specific layers likely linked to observed defects, facilitating a better interpretation of the underlying degradation mechanisms within the railway substructure. By supporting the identification of affected layers, the methodology informs targeted corrective actions and fosters more resilient maintenance planning. While its practical application relies on integrating complementary inspection data and multi-diagnostic techniques, the proposed framework is designed specifically as a specialized module for substructure diagnosis. Consequently, it should be viewed as a high-level decision-support tool for infrastructure management rather than a standalone system for the entire track assembly.</p>