###### Abstract
The mixture of an SMC normal-temperature-modified asphalt and styrene-butadiene styrene block copolymer (SBS)-modified asphalt (SMCSBS) composite modified asphalt mixture, compared with traditional hot mix asphalt mixtures, not only has the material composition structure, strength structure, and basic characteristics [13], but also has the advantages of an SMC normal temperature-modified asphalt with a low construction temperature, good road performance, good energy savings and an emission reduction effect [14]. [PERSON] [15] proposed to apply an SMC normal temperature-modified asphalt in a highway covering construction and elaborated the specific construction points, pointing out that an SMC normal temperature-modified asphalt has the advantages of energy savings and environmental protection, convenient construction, and significant economic benefits. [PERSON] [16] used SBS and SMC to prepare a compound-modified constant temperature warm-mix asphalt and applied it to an AC-13 (the mixture ratio of asphalt mixture is asphalt concrete (AC-13)) asphalt mixture. Through laboratory tests, the high-temperature performance, low-temperature performance, and moisture susceptibility of the mixture were evaluated, and a performance analysis was conducted for the road material. At present, the research on an SMC normal temperature asphalt modifier is still in the exploratory stage [17], and there are few studies on SMCSBS-modified asphalt mixtures. This paper tries to study the fatigue performance of an SMCSBS-modified asphalt mixture. At present, there are many kinds of fatigue life prediction models for asphalt mixtures. As early as the 1960s, [PERSON], [PERSON] et al. [18] established a classic fatigue life prediction model based on strain control. [PERSON], [PERSON] et al. [19] used double load envelopes and twin cohesive models to predict composite fatigue delamination. Artificial intelligence has a wide range of applications in various fields. Compared with classical statistical methods, artificial intelligence models have better a prediction and recognition ability. In order to save testing time, meet equipment requirements, and cost and complexity, various prediction models, such as Bayesian, artificial neural network (ANN) [20], and support vector machine regression (SVM), have been introduced. [PERSON] et al. proposed a Bayesian damage identification method based on the analysis probability model of scattering coefficient estimation and ultra-fast wave scattering simulation scheme [21]. [PERSON] et al. pointed out that the error between the prediction results of the low-temperature flexural strain of asphalt and the actual measurement results based on the BP (back propagation) neural network model of the MATLAB platform is within the accuracy range of the engineering requirements [22]. As a powerful new learning machine, the support vector machine (SVM), based on statistical learning theory, has attracted extensive attention from its initial application in pattern recognition, to its application in regression estimation [23]. For example, [PERSON] investigates classification based on support vector machines to predict the liquefaction potential from the actual standard penetration test and cone penetration test field data [24]. [PERSON], [PERSON] and [PERSON] investigated the application of SVM in the resilient modulus (M\({}_{\text{R}}\)) prediction of hot mix asphalt samples [25]. In a study by [PERSON] et al., an Support Vector Machine Firefly Algorithm (SVM-FFA) was used to predict the stiffness of polyethylene terephthalate (PET)-modified asphalt mixtures [26]. Compared with the artificial neural network, genetic algorithm, and the support vector machine, this method is effective in stiffness prediction. To sum up, in this paper, the fatigue performance of an asphalt mixture is studied from the following aspects: SMC content (SMC accounts for the mass percentage of SMCSBS composite modified asphalt)/%, asphalt stone ratio, stress ratio, and load frequency/Hz.