## 4 Conclusions  
In this study, machine learning (ML) methods were used to establish predictive models for evaluating the performance of foam concrete. By analyzing prediction accuracy and selecting the most suitable models for different indicators, the predictive precision of both mechanical strength and thermal conductivity was significantly improved. The major findings are concluded as follows:  
1. The Pearson correlation method was used to analyze the relationships between key parameters and performance indicators of foamed concrete. The key parameters include cement type, cement content, density, and water-to-cement ratio. By applying this method, the correlations between material properties and mechanical or thermal performance are identified and examined. These parameters were selected as optimal inputs due to their significant influence on foam concrete's mechanical and thermal properties. 2. The NNR model exhibited superior predictive performance for the mechanical properties of foam concrete. For compressive strength, it implemented a high coefficient of determination (R\({}^{2}\) = 0.9869), with an MAE of 1.6123 MPa and MAPE of 12.69%. In  
Figure 16: Feature importance scores. (**a**) Compressive strength; (**b**) flexural strength; (**c**) thermal conductivity coefficient. predicting flexural strength, the model also performed well, reaching an R\({}^{2}\) value of 0.9319, with corresponding MAE and MAPE values of 0.37 MPa and 18.83%, respectively. For thermal conductivity, the SVM model outperformed the other algorithms, attaining an R\({}^{2}\) of 0.9327, with a low MAE of 0.0228 W/m\(\cdot\)K and a MAPE of 22.11%. 3. Significant differences were identified in the predictive accuracy across different performance metrics. Compressive strength predictions were the most accurate, followed by flexural strength and thermal conductivity, the latter being the most challenging. The SVR model exhibited consistently stable performance across datasets, especially for predicting both compressive strength and thermal conductivity. Based on SVR, two high-performance predictive equations were selected for reliable evaluation of foam concrete properties. 4. DTR and NNR demonstrated the highest robustness, with an average \(\Delta\)R\({}^{2}\) = 0.102 and \(\Delta\)MAPE \(<\) 8.5%, significantly outperforming linear regression. Random Forest showed the strongest overfitting tendency (\(\Delta\)R\({}^{2}\) = 0.0319), while KNNR had the most stable generalization capability (\(\Delta\)R\({}^{2}\) = 0.1784). 5. K-fold cross-validation was employed to assess the performance of all eight models. Results confirmed that SVR outperformed other models in foam concrete property prediction. Based on error analysis, a hierarchical model selection strategy was proposed: NNR is recommended for mechanical properties, SVR for thermal performance, and DTR as a general-purpose model. The model selection framework provides reliable technical support for engineering applications of foam concrete, improving both accuracy and efficiency in prediction. Conceptualization, L.L. and A.A.; methodology, L.L., A.A., W.C. and Z.L. ; software, W.S. ; validation, W.S. and L.Y.G.-Z. ; formal analysis, W.S. and L.Y.G.-Z. ; investigation, W.S. ; resources, Z.L. ; data curation, W.S. ; writing--original draft, L.L. and W.S. ; writing--review and editing, A.A., W.C., Z.L. and L.Y.G.-Z. ; visualization, Z.L. ; supervision, Z.L. ; project administration, Z.L. All authors have read and agreed to the published version of the manuscript. No external funding was received for this study. Not applicable. The data presented in this study are available on request from the corresponding author (W.S.) subject to confidentiality agreements with industry partners. The authors gratefully acknowledge the invaluable contributions of our graduate researchers [PERSON], [PERSON], [PERSON], and [PERSON] in data validation. Their meticulous work ethic and technical expertise significantly enhanced this study's reliability.