###### Abstract  
This study investigates the quantitative relationship between material composition and the performance of foam concrete based on 170 validated experimental datasets extracted from the existing literature. The statistical approach combined with machine learning modeling was employed to systematically analyze and predict key performance indicators. Pearson correlation analysis was used to identify the parameters affecting mechanical and thermal properties. The analysis revealed that the water-to-cement ratio (W/C) and cement content were the most influential factors for mechanical properties, while density and the coarse-to-fine aggregate ratio (Cag/Fag) had the greatest impact on thermal conductivity. To overcome the limitations of traditional empirical models in capturing complex non-linear relationships, a predictive framework with eight machine learning algorithms was established. Among these, Neural Network Regression exhibited the highest accuracy for mechanical property prediction, with a coefficient of determination of R\({}^{2}\) = 0.987 for compressive strength and R\({}^{2}\) = 0.932 for flexural strength. For thermal conductivity, support vector regression achieved the best predictive performance with R\({}^{2}\) = 0.933. Error analysis demonstrated significant differences in prediction accuracy across performance indicators: compressive strength was the easiest to predict, followed by flexural strength, while thermal conductivity was the most challenging. Based on practical engineering requirements, a hierarchical model selection strategy was proposed. Specifically, Neural Network Regression is prioritized for mechanical properties, and support vector regression is prioritized for thermal properties. Decision Tree Regression is recommended as a general-purpose model. The predictive model used in this study provides reliable technical support for the optimization and engineering application of foam concrete, enhancing both prediction accuracy and practical efficiency. keywords: foam concrete; sustainability; data analysis; mechanical strength; thermal conductivity; predictive model +
Footnote †: journal: Applied sciences  
resource depletion and environmental impacts [1; 2; 3]. In particular, the large-scale extraction of natural aggregates for concrete production imposes considerable ecological stress. To address sustainable challenges in modern construction engineering, the use of alternative materials has gained increasing attention. Foam concrete has been recognized as a promising solution due to its lightweight, thermal insulation capability, and environmental friendliness [4; 5]. The performance of foam concrete is primarily evaluated through its mechanical properties and thermal conductivity. They are critical for their application in structural and non-structural components, such as walls, floors, and roofs. Foam concrete demonstrated promising compressive strength and effective thermal insulation in several studies. However, its performance is strongly influenced by factors such as mix proportions, manufacturing processes, and environmental conditions. Accurate prediction and systematic optimization are essential to guarantee reliable practical implementation. In recent years, foam concrete has garnered increasing attention in both academic and industrial domains due to its multifunctional advantages, particularly in lightweight structural applications and sustainable construction. Numerous studies have highlighted its exceptional thermal insulation, lightweight nature, and adaptability to complex geometries, which makes it an attractive solution for modern building challenges. [PERSON] et al. [6] provided a comprehensive review emphasizing that ultra-light foamed concrete (ULFC) with optimized pore structures can achieve thermal conductivity values as low as 0.1 W/m\(\cdot\)K while maintaining adequate mechanical properties for non-structural applications. Similarly, [PERSON] et al.