An interpretable machine-learning framework for predicting the splitting strength of asphalt concrete and supporting data-driven mixture design and a GUI platform integrating prediction and SHAP-based explanation was developed to improve the accessibility and practical applicability of the proposed framework.
Abstract
This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was established, and 14 input variables related to asphalt properties, aggregate gradation, and fiber characteristics were selected for modeling. Six machine-learning models, namely TabPFN, ANN, SVR, RF, XGBoost, and LightGBM, were developed and compared. Hyperparameter optimization was performed for five models using NSGA-II, while TabPFN was directly applied with its default configuration. The results show that all six models achieved satisfactory predictive capability, whereas TabPFN delivered the best overall performance on the testing set, with the lowest RMSE of 0.28, MAE of 0.21, MAPE of 18.01%, MAD of 0.14, the highest R^2 of 0.88, and the highest composite score of 0.91. SHAP analysis further revealed that nine dominant variables accounted for 92.0% of the total average contribution, among which Ag9.5, FT, Ag4.75, AC, and Du were the most influential. In addition, favorable parameter ranges for improving ST were quantified, such as Ag9.5<66.8%, Ag4.75<45.0%, AC<5.4 wt.%, AV<3.6%, and Du>134.7 cm. Finally, a GUI platform integrating prediction and SHAP-based explanation was developed to improve the accessibility and practical applicability of the proposed framework.
A Streamlit-based graphical user interface was developed to enable real-time prediction and MS–ITS trade-off visualization, providing a reference for preliminary mix design of asphalt concrete, and results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS.
J. Xing, Xiao Tan, Mu Guo et al.· Materials· 0 citations
This study applies several machine learning models to predict the Marshall stability of Stone Mastic Asphalt mixtures incorporating steel slag as a partial replacement for conventional coarse aggregates, with CatBoost providing the best performance.
T. Nguyen, Hoang-Long Nguyen, N. Trần et al.· Journal of Science and Trans...· 0 citations
An interpretable machine learning framework integrating Extreme Gradient Boosting with Shapley Additive Explanations to predict the 28-day compressive strength of fly ash-based geopolymer concrete (FA-GPC) is developed and experimentally validates.
X. Shi, Haoxiang Hu, Zhenhua Duan et al.· 0 citations
Six machine learning algorithms were employed to construct artificial intelligence models for the precise prediction of self-compacting concrete (SCC) flow properties, and the extreme gradient boosting (XGB) model was identified as exhibiting superior predictive accuracy and generalization performance.
Jinlei Mu, Xin Fang, Ke-qiang Cao et al.· Materials Research Express· 0 citations
This study develops an interpretable machine learning framework to predict the compressive strength of concrete incorporating blast-furnace slag (BFS). To address the critical issue of data leakage prevalent in conventional random splitting, a rigorous grouped validation strategy, specifically the GroupKFold algorithm, was implemented based on unique mixture proportions. Seven machine learning algorithms, including linear baselines and tree-based ensembles, were comprehensively evaluated. Results indicate that the XGBoost model achieved the highest predictive accuracy and stability, yielding a mean Root Mean Squared Error (RMSE) of 5.39 MPa and a minimal standard deviation of 0.54 MPa. A multi-criteria decision-making (MCDM) approach mathematically confirmed XGBoost as the optimal model. Furthermore, SHapley Additive exPlanations (SHAP) combined with data-density rug plots were utilized to uncover the non-linear interactions between BFS and other components. Rather than asserting direct causality, the SHAP analysis provides robust model-based associations that align with macroscopic physical expectations while strictly preventing over-interpretation in sparse data regions. Finally, a conceptual graphical user interface (GUI) is proposed to bridge the gap between theoretical models and future batch-plant deployment. This research balances rigorous high-precision prediction with transparent interpretability for BFS concrete design.
Qiyin Yuan, Jiannan Yin, Peng Gao et al.· Buildings· 0 citations
This study develops an integrated machine learning-experimental framework to predict the compressive strength (CS) of concrete incorporating ternary industrial wastes glass powder, marble powder, and iron ore slag. For this purpose, a dataset comprising 366 mix ratios and corresponding CS values was compiled from various sources for analysis. Advanced machine learning (ML) algorithms, including extreme gradient boosting (XGB), gradient boosting, and random forest (RF), were employed alongside hybrid techniques such as XGB-GBR and XGB-RF to evaluate the influence of these materials on strength. Based on the outcomes of the analysis, the hybrid XGB-GBR model demonstrates the highest balanced performance for both training (R2 = 0.911) and testing (R2 = 0.869) data sets. For validating the ML modeling and developing an interactive graphical user interface (GUI), experimental evaluation of CS and scanning electron microscopy was conducted. Additionally, feature importance modeling and optimization identified curing age and coarse aggregate as the most influential factors that would impact the model prediction. The contribution of this research lies in the combined modeling and experimental evaluation of a ternary waste concrete system, along with the development of a GUI. This deployable GUI will enhance the industrial applicability of ML-based concrete optimization by reducing material costs, minimizing trial batching, and supporting sustainable mix design practices.
Md. Samsuzzaman Sobuz, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi et al.· Scientific Reports· 0 citations