## 1 Introduction  
Pavement distress is an accumulated result of mechanistic responses in pavement structure subjected to dynamic loads of moving vehicles and climatic conditions. Pavement surface evenness deteriorates as pavement distress propagates. The variance of dynamic loads on rougher pavement surface condition leads to even faster pavement deterioration. It is vital to consider vehicle-pavement interaction to estimate pavement distress in pavement design. In the Mechanical-Empirical Pavement Design Guide (MEPDG), the principles of engineering mechanics are applied to calculate pavement response under traffic loading. The empirical transfer functions are then used to predict long-term pavement performance through local calibrations. The axle load spectra obtained from weigh in motion (WIM) is utilized as traffic inputs in MEPDG. The WIM system captures gross vehicle weights, individual axle loads, and axle spacing when trucks are passing over the station [1]. In general, raw data collected by the WIM system need to be calibrated by comparing field measurements of static scales to guarantee the required accuracy [2]. Therefore, WIM data indicate static axle load spectra, which may not reflect the variation of dynamic loads on real pavements due to surface roughness. Previous studies have evaluated the impact of dynamic loads on flexible pavement deterioration and service life. [PERSON] et al. found that the service life of asphalt pavement was shortened by 40% after taking dynamic loads into consideration [3]. [PERSON], [PERSON], and [PERSON] concluded that pavement life could be reduced by 29% due to fatigue cracking and by 20% due to rutting after considering dynamic loads [4]. [PERSON] et al. introduced the distribution of dynamic loads under the wheel with a mean value equal to static load and a standard deviation, which follows normal distribution [5]. [PERSON] et al. evaluated the pavement damage around manholes and suggested that the maximum of dynamic load was 1.29 times that of the static load, which increased the pavement damage coefficient up to 2.12 times that of the static load [6]. To investigate the impact of dynamic loads on pavement surface conditions, the dynamic loading coefficient (DLC) was defined as a ratio by dividing the standard deviation of dynamic loads by the mean value. Regression models were developed to correlate DLC with contributing factors such as vehicle speed and pavement roughness conditions [7; 8; 9]. [PERSON] et al. found that pavement life decreased from 1.31 million to 0.63 million passes for a terminal nut depth of 15 mm, while pavement life decreased from 1.07 million to 0.64 million passes for a terminal of 40% at a vehicle speed of 90 km/h [10]. However, limited studies have taken the influence of axle loads on the DLC into consideration. Limited studies have considered dynamic loads induced by vehicles on rough pavement in pavement design. International roughness index (IRI) is a standard scale for roughness according to the response of a generic motor vehicle subjected to road surface roughness and determined by the measurement of road profile, simulation of vehicle responses, and accumulation of suspension travel [11]. It is quantified by measuring vertical deviations over a road section by response-type road measuring systems such as road meters and profilometers [12]. [PERSON] considered dynamic loads in the determination of axle load spectra in the M-E pavement design by including the static axle load spectra and distribution of dynamic loads from specific axle load intervals. It was indicated that the cracked area and nut depth in medium pavement structure increased by 67% and 8% under dynamic loads when the IRI was 4 m/km [13]. The constant value of DLC was utilized in the transformation. However, axle loads were expected to have an influence on the variance of dynamic loads and DLC [14; 15].