# Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
## 3 Case Study
### IRI Calculation for Haul Roads  
The determination of the IRI necessitates a thorough assessment of a road section, as per Equation (2). This process entails the division of the entire road into smaller sections at regular intervals. The choice of appropriate intervals significantly impacts the accuracy of IRI calculations. To ensure that the selected road intervals are representative of the majority of transport roads within open-pit mines, as illustrated in Figure 10, this study examined two non-working roads, two mining face roads, and two dump roads from the pit. The  
Figure 8: Experimental road.  
Figure 9: Model of experimental road.  
objective was to analyze the distribution of IRIs across transport roads under different interval settings.  
Typically, employing smaller statistical intervals yields more precise feedback on road roughness, whereas larger intervals result in less precise feedback. Notably, there are no specific regulations on this matter. The choice of interval size is typically adapted to the specific context or requirements. For example, in China's current highway standards, a statistical interval of 100 m is commonly employed. In this study, the IRI values with different statistical intervals of 10 m, 20 m, 40 m, 50 m, 100 m, and 200 m were tested to determine the most appropriate statistical interval.  
The statistical process was based on the Chinese 3 DMine 2016 version software, a mining design software similar to Surpac developed by Beijing 3 DMine Software Ltd. [32]. Initially, the point cloud data provided by the mine was imported into the software, and a DEM was generated. Subsequently, the route within the selected road area was delineated at 1 m point intervals. In the final step, the elevation change curve of the road was obtained by mapping all the points along the line onto the DEM. The elevation of each point, representing the sampling point as described in Equation (2), was used for the calculation of the IRI. The statistical results for the six distinct road sections are visualized in Figure 11.  
In addition to reflecting the overall distribution of the data, the box plot can clearly display outliers within the dataset. As shown in Figure 11, a considerable number of outliers are observed when the interval ranges between 10 and 50 m, indicating that the classification method at this stage fails to adequately represent the variations in road quality. When the interval is set to 200 m, the range of variation exhibits a statistically distinct pattern compared to intervals of 100 and 150 m, as illustrated in Figure 11c,e,f, suggesting that intervals of 100 and 150 m are relatively reasonable. Comparing the 100 m and 150 m intervals, this study concludes that the larger interval is more practical for real-world applications. Therefore, a 150 m interval is ultimately selected as the statistical interval.  
Certainly, it is important to emphasize that in actual practice, the interval derived from the above data analysis should only be considered as a reference. We still need to adjust it based on real-world feedback. However, this method offers a reliable experimental range for application, accelerating the process of determining the optimal interval.  
Figure 10: Elevation of experimental road.