Predicting the volumetric behavior of crumb rubber asphalt mixtures via machine learning
SCIENTIFIC REPORTS, cilt.1, sa.1, ss.1-34, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 1 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1038/s41598-026-74358-x
- Dergi Adı: SCIENTIFIC REPORTS
- Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Scopus, Science Citation Index Expanded (SCI-EXPANDED), BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record
- Sayfa Sayıları: ss.1-34
- Erciyes Üniversitesi Adresli: Evet
Özet
This study examined advanced machine learning models to predict key volumetric parameters of stone mastic asphalt (SMA) mixtures incorporating waste tire rubber. The input parameters were rubber aggregate and rubber modifier. The target outputs were air void, voids filled with asphalt, voids in mineral aggregate, and density. A total of 216 asphalt samples were prepared, representing 72 distinct mixture combinations, as three replicate samples were produced for each condition. Five machine learning techniques—Gaussian Process Regression, Support Vector Machine Regression (SVR), Adaptive Neuro-Fuzzy Inference System, Multivariate Adaptive Regression Splines, and Least Squares Support Vector Machines (LS-SVR) were implemented, and models were evaluated using the 5-fold cross-validation method. Model performance was assessed using mean absolute error, root mean square error, determination coefficient, Wilmott’s refined index, NSE (Nash–Sutcliffe efficiency coefficient), overall index, CA (Combined Accuracy), and RRMSE (relative root mean square error) indices. The LS-SVR model was found to achieve the best predictive performance across all four outputs, with an RRMSE value below 10%, an NSE value greater than 0.9, and a CA value closest to zero. Furthermore, the results show that LS-SVR provides the closest fit to the experimental data and the lowest error levels. In contrast, the standard SVR model produced the weakest predictions, with the widest error distributions and highest deviations. Transforming SVR into LS-SVR significantly improved accuracy. However, the use of only two input variables in this study represents a deliberate simplification that limits the generalizability of the model to broad applications. Consequently, this research is primarily positioned as a proof-of-concept investigation to demonstrate the applicability of ML to a controlled laboratory dataset. Furthermore, the lack of external (independent) validation prevents the developed models from being interpreted as universal predictive tools in practical asphalt mix design. Therefore, the results show that the models successfully capture the relationships between rubber content and volumetric properties within the narrow range of laboratory parameters examined in this study. For broader applications, the inclusion of additional inputs and testing with independent datasets are necessary; these steps will allow for stronger inferences about the actual generalization performance of the models.