**6. Conclusions**

The relationship between IRI and pavement distresses was investigated using statistical correlation analysis. The purpose of the research was to measure the level of distress related to riding quality. The relationship between distress density and IRI values was determined using a statistical correlation test. In the pavement portions, the model related IRI and distress types were calculated. The correlation between the value of IRI and distress types has been investigated by two important statistical tests:


The results of the study indicate that there is a significant correlation between IRI and cracking, patching, depression and raveling in both main and secondary street groups, with a 95% confidence level. It has also shown that potholes and rutting distress types did not have a significant relationship to IRI values in both the main and secondary streets. This has led to a conclusion that is based on the statistical investigation of the relationship between IRI and distress types: cracking, patching, depression, and raveling could possibly be characterized as ride-quality type distresses. While potholes and rutting distress, could probably be described as non-ride quality type distresses.

Roughness and the extent of correlation can be helpful in detecting the type of distress. This analysis concludes that IRI can either be used to evaluate pavement quality or to monitor pavement deterioration. Rapid measurements of IRI using the Automatic Road Analyzer (ARAN) can ease the process of traditional pavement visual inspection. Using sample visual inspection ratings from pavement distress images can also be used to determine an approximate IRI value without having a Road Analyzer and allow subsequent planning and evaluation to proceed. However, not all surface distresses can be detected by roughness, especially some types of distress that are of very low severity.

**Author Contributions:** Conceptualization; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; A.I.A.-M., Visualization, A.I.A.-M. and A.A.S.; Writing—original draft, A.I.A.-M.; Writing—review & editing, A.I.A.-M. and A.A.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors would like to acknowledge the support provided by Researchers Supporting Project Number (RSP2022R424), King Saud University, Riyadh, Saudi Arabia.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Available on request and with regulations.

**Acknowledgments:** Authors would like to thank Riyadh Municipality, General Administration for Operation and Maintenance for providing the data. The authors acknowledge King Saud University for providing the funding of this research paper.

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