# Correlation of Pavement Distress and Roughness Measurement
## 2 Literature Review
### The Visual Inspection and Roughness Relationship  
Research by [PERSON] was performed to find out the relationship between IRI and specific types of distress. Raveling, rutting, and cracking were among the distresses that occurred. Three relationships linking IRI and traveling, rutting, and cracking have been observed. According to the findings of this investigation, raveling and cracking are related to IRI and it might be categorized as \"ride quality distress.\" Furthermore, IRI was shown to have no statistically significant correlation with rutting [3]. [PERSON] and his team built an artificial neural network (ANNs) and regression models to predict IRI as a function of pavement distresses. The LTPP database was used to create these models. The models that were constructed included prediction of IRI as an age function, IRI initial, alligator fatigue cracking (all severities), transverse cracking (all severities), and rut depth standard deviation (RUTSD). The correlation coefficient (R2) of the regression analysis was 0.57, but the correlation coefficient (R2) of the ANNs model was 0.75 [4; 5]. In the same way [6], examined data from California's locations in the Bay area of San Francisco city to see if there was a correlation between IRI and pavement distress. To do this, the researchers set out with the objective of developing a surface roughness model that could be used to predict car operating expenses on highways in the Bay region of San Francisco city. In this study, 39 measurements were taken at 15 m intervals over a 152.4 m test section, the researchers developed a link between IRI and pavement distress. The outcome was the development of a linear link between IRI and a composite Pavement Condition Index (PCI. It was computed by taking into account the type and level of distress observed for each segment of the roadway. The PCI scale ranges from zero to one hundred, with one hundred representing ideal road conditions. The model obtained an adjusted R2 of 0.52, In other words, the aggregate pavement quality index could predict slightly more than half of the variation in IRI. In the United States, researchers investigated the association between pavement surface roughness and various types of distresses on the pavement [7]. The research included 462 pavement portions from 37 projects throughout Michigan. The sections of road pavement were studied to explore the connection between pavement surface roughness and distress. Assumption number one in this research was that an excess roughness results in increased dynamic loads on axles, which can then result in a tangible acceleration of pavement degradation. If this relationship is established, then it will be possible to plan preventive maintenance (PM) action to smooth the pavement surface. The transportation department in the State of Connecticut utilized data in order to determine a relationship between the pavement distresses and IRI. Rutting and total quantity of cracking were the only distress types used. They called the associations they discovered \"relatively weak.\" R2 values for various lengths of pavement sections were 0.177 for 10 m, 0.242 for 30 m, and 0.299 for 90 m. They also found that utilizing neural networks did not enhance the findings, thus, they concluded that the IRI could not be utilized to forecast the general condition of pavements. Their study concentrated on pavement segments less than 90 m in length, and a simple summation was used to aggregate the value of cracks [8]. Other researchers have discovered that when the IRI is compared to other performance measures, the R2 value is greater. For example [9], investigated the correlation between IRI and cracks, depth of rutting, potholes, patchwork, and raveling, and discovered that a R2 value of 0.77 was the best they could come up with. They checked asphalt roads in India for guidance. Another study [10] investigated the relationship between the IRI and the PSI in Dubai by using a nonlinear regression model.