# Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
## 1 Introduction  
Truck haulage is a pivotal transportation method for both ore and waste materials in open-pit mining operations [1, 2, 3]. The cost of truck transportation accounts for 45% to 60% of the total production cost, with fuel consumption being a significant component [4, 5]. Despite the gradual implementation of electric-driven trucks in open-pit mines [6, 7, 8], many of these mines are expected to continue utilizing fuel-driven trucks for an extended period. Hence, the investigation of methods to decrease fuel consumption holds significant value in managing the operational expenses associated with open-pit mining. Road roughness is one of the most critical factors affecting the fuel consumption of trucks [9, 10], and the quality of the road not only increases the fuel consumption but also reduces the transport efficiency of trucks. The investigation of roughness encompasses numerous international standards [11, 12]. One prominent approach to quantifying road roughness is the International Roughness Index (IRI), a well-established indicator that has found extensive application, particularly in the field of highway engineering [13, 14, 15]. The application of the IRI may vary across different countries, depending on the specific contextual factors within each region [16; 17]. As a case in point, [PERSON], [PERSON] and [PERSON] [18] applied the IRI in Canada, identifying IRI thresholds for categorizing pavement conditions into good, fair, and poor based on public perception. [PERSON], [PERSON], [PERSON], and [PERSON] [19] used IRI to assess the pavement roughness along an extensive network of roads spanning 13,191.34 km in Serbia. Similarly, [PERSON], [PERSON], and [PERSON] [20] studied the effect of pavement roughness by IRI on the dynamic impact coefficient of bridges in Korea. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [21] detected the pavement roughness in China and recommended appropriate IRI thresholds for road rehabilitation. Furthermore, [PERSON], [PERSON], and [PERSON] [22] developed a model that predicts IRI deterioration and calibrates its parameters based on pavement structural factors. This model combined empirical-mechanistic and regressive empirical approaches and was validated against road sections within the Pavement Management System of Netivei Israel, representing various climate zones. Based on the aforementioned literature, the IRI has been widely applied in road quality evaluation, but its application in open-pit mines remains limited. To implement IRI in open-pit coal mines, it is first necessary to define its application objectives, followed by establishing IRI standards. However, in practice, variations in road materials and haulage vehicles across different open-pit mines make it challenging to develop a universal standard applicable to most cases [23; 24]. Instead, a more pressing need lies in establishing an IRI-based design methodology. Given that fuel consumption constitutes the primary cost factor in truck haulage operations in open-pit mines, and considering the evident positive correlation between road quality and fuel consumption, this study innovatively proposes a quantitative relationship between road quality and fuel consumption. This approach ultimately enables fuel-efficient road classification for open-pit mines. The fuel-efficient road classification method proposed in this study had the aim of reducing fuel consumption in open-pit mining operations. The approach involves establishing a Digital Elevation Model (DEM) and calculating road IRI using point cloud data obtained through UAV mapping. A linear relationship between IRI and fuel consumption is derived through linear regression. Subsequently, K-means clustering is employed to classify truck fuel consumption. The fuel efficiency classification is determined by applying node values regarding fuel consumption to the established linear relationship.