**4. Conclusions**

The paper presents a framework to apply the multi-attribute utility theory (MAUT) for the categorization of the condition of railway embankments using multiple sources of data available to the infrastructure managers. The overall methodology proposes the use of five attributes. These include frequency of tamping activities, extent of external irregularities obtained from visual inspections, frequency of internal irregularities obtained from GPR lower frequency data, ballast quality (fouling), and ballast depth (pockets). Ballast quality and depth data were obtained from higher frequency GPR antennas. The selection of these attributes is made because of their relevance to the problem and that they are readily available. By weighting the importance of each of the attributes and by evaluating them on the investigated railway lines, the MAUT-based methodology provides calculation of the overall utility function values used to form a ranking list of the condition for the investigated railway embankments. To verify the usefulness of the methodology, the paper presents its application on 181 km of railway embankments in Croatia. The calculated overall utility function for each section provided the categorization of embankments in five categories, ranging from very poor to very good. The developed MAUT model provides a transparent and comprehensive procedure that can support decision-makers to plan maintenance works and further detailed investigation works and monitor programs and remediation measures. The framework is flexible and methodology could be expanded to consider additional attributes.

**Author Contributions:** Conceptualization, M.S.K. and M.B.; Data curation, M.S.K.; Formal analysis, M.S.K. and M.B.; Investigation, M.S.K. and M.B.; Methodology, M.S.K. and I.S.; Project administration, M.S.K.; Validation, M.B., I.S. and K.G.; Visualization, M.B. and K.G.; Writing—original draft, M.B. and I.S.; Writing—review & editing, M.S.K. and K.G.

**Funding:** The authors gratefully acknowledge the support from the H2020 Programme for DESTination RAIL project, funded under MG-2.1-2014 I2I Intelligent Infrastructure call, gran<sup>t</sup> agreemen<sup>t</sup> No 636285.

**Conflicts of Interest:** The authors declare no conflicts of interest.
