## 1 Introduction  
In the increasingly specialized field of construction material science, ultra-high-performance concrete (UHPC) represents a significant advancement. Renowned for its exceptional strength and durability, UHPC offers substantial advantages over conventional concrete, making it a preferred choice for contemporary engineering projects, where performance and longevity are critical [1]. However, accurately predicting the mechanical properties of UHPC, particularly its compressive strength, remains a significant challenge. This challenge is crucial not only for ensuring the design and structural integrity of buildings and infrastructure but also for reducing costs and enhancing the sustainability of construction practices. Recent studies have explored the role of various additives in UHPC, such as silica fume, slag, and superplasticizers (SPs), which are traditionally employed to enhance the mechanical properties of conventional concrete. Silica fume, known for its filling and pozzolanic effects, and slag, recognized for its contribution to long-term strength development, both contribute to a denser microstructure in UHPC. However, due to the already highly optimized mix design of UHPC, the marginal gains in strength from these additives are relatively limited. Additionally, while SPs significantly improve workability by reducing water content, their direct impact on compressive strength is also minimal. This limited impact of silica fume, slag, and SPs on UHPC strength, as demonstrated in previous experimental results, underscores the need for advanced predictive models that can accurately capture these nuanced relationships. [PERSON] et al. [2] believed that the amount of fiber could improve its mechanical properties and reduce the autologous contraction of UHPC. The pursuit to develop accurate predictive models for the compressive strength of UHPC has driven the investigation of diverse computational approaches [3]. Among these, artificial neural networks (ANNs) have gained prominence for their capacity to mimic human learning mechanisms [4]. In material science, ANNs have proven highly effective in simulating complex behaviors, owing to their self-learning, self-organizing, and adaptive properties. These capabilities allow ANNs to capture complex patterns and model nonlinear relationships within large datasets, surpassing the capabilities of traditional statistical methods. Nonetheless, ANNs face challenges such as overfitting, opacity in their decision-making processes, and the requirement for extensive training data, which can compromise their stability and accuracy [5]. To address these limitations, increasing attention has been directed towards support vector regression (SVR). SVR utilizes the principles of machine learning rooted in statistical learning theory, aiming to minimize error, enhance predictive accuracy, and ensure model robustness, even when working with limited datasets [6]. By constructing a regression surface within a high-dimensional space, SVR effectively manages nonlinear relationships, making it particularly well-suited for forecasting complex properties such as the compressive strength of UHPC [7]. The incorporation of SVR into predictive modeling marks a substantial advancement in material science [8]. Its utilization extends beyond theoretical research, offering significant practical benefits for construction projects. Improved predictive models facilitate more effective planning and quality control, optimize material formulations, and ensure that structural components adhere to safety and performance standards without excessive material use. This study aims to investigate and validate the efficacy of an optimized SVR model, enhanced through advanced algorithmic refinements, to predict the compressive strength of UHPC with greater accuracy and dependability than existing models.