## 1 Introduction  
In recent years, the scale of highway construction in China has expanded rapidly. With the continuous increase in traffic volume and load ratings, various forms of pavement distress have emerged. To enhance the performance and durability of pavement structures, an increasing number of new materials and technologies have been adopted in highway construction. Among these, the incorporation of steel fibers into cement concrete or asphalt concrete has proven effective in improving pavement performance. Extensive engineering practice has demonstrated that adding steel fibers to cement concrete pavements can reduce slab thickness, minimize or eliminate the need for longitudinal joints, and reduce transverse contraction joints, while also offering excellent abrasion resistance and freeze-thawresistance. Furthermore, steel fibers enhance the crack resistance and high-temperature rutting resistance of asphalt concrete, providing significant reinforcement and toughening benefits to pavement structures. Due to their high tensile strength, good toughness, and strong compatibility with concrete, steel fibers have become a widely demanded reinforcing material in practical engineering applications. Research has shown [1] that adding only 1-2% of steel fibers can significantly enhance the tensile strength, deformation property, energy dissipation capacity, and ductility of concrete. As a result, steel fiber reinforced concrete (SFRC) has been widely applied in various engineering fields, including pavement structures, bridge components, airport runways, tunnels, and hydraulic structures. Typical engineering applications include SFRC overlays, layered SFRC pavements, and SFRC-reinforced bridge piers and abutments, as well as shotcrete SFRC for stabilizing road slopes and tunnel linings. The compressive strength of SFRC is a critical parameter for structure design and quality testing, which is generally obtained by testing standard cubic or cylindrical specimens, which consumes a large amount of raw materials and is very time-consuming to produce and maintain [2]. Moreover, in practical engineering applications, the results are often influenced by a variety of factors such as construction techniques and environmental conditions. Some researchers [3] have proposed some empirical formulas on an experimental basis with the help of linear or nonlinear statistical regression methods. Traditional empirical formulas are mostly based on linear or polynomial regression models, which typically involve a limited number of variables and oversimplify the underlying physical mechanisms. They often neglect the combined influence of multiple factors--such as fiber characteristics and material properties--on the compressive strength of steel fiber reinforced concrete, and are incapable of quantifying the complex interactions among these variables. Moreover, the development of such formulas usually depends on a limited set of experimental data. Although this often yields concise expressions that are convenient for practical application, the limited data scope significantly restricts their generalization capability. However, due to the complex composition of concrete materials and the interdependence among various influencing factors, the behavior of SFRC exhibits strong nonlinearity. At present, there is still a lack of systematic and accurate theoretical or experimental models that can comprehensively and reliably capture the relationships among the various factors affecting its compressive strength. In recent years, with the emergence and development of artificial intelligence, machine learning (ML) provides new ideas and approaches for the rational prediction of SFRC [4]. ML is able to learn and mine potential laws directly from large amounts of data, and even approximate any continuous function [5] that connects input and output space with arbitrary accuracy. The current application of ML methods in civil engineering is also rapidly expanding. [PERSON] et al.