@Article{cmes.2026.079302,
AUTHOR = {Chih-Yu Liu, Cheng-Yu Ku, Ting-Yuan Wu},
TITLE = {AI-Enhanced Soil Classification Using Machine Learning Models within the AASHTO Framework},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {146},
YEAR = {2026},
NUMBER = {3},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v146n3/66822},
ISSN = {1526-1506},
ABSTRACT = {Accurate soil classification is essential for pavement design; however, the traditional American Association of State Highway and Transportation Officials (AASHTO) classification system relies on extensive laboratory testing and subjective judgment. This study presents an artificial intelligence (AI) enhanced framework for AASHTO soil classification. A synthetic dataset of 349,015 samples was generated using parameter ranges for five AASHTO input variables to support model development. Four machine learning models were trained, analyzed, and compared where the random forest (RF) consistently achieved the highest accuracy of 100% among the four models in predicting AASHTO soil groups. Feature importance analysis indicates that percent passing the No. 200 sieve is the most influential factor, and under missing input scenarios. Additionally, the models remain reliable under partial input loss, though accuracy is most sensitive to the absence of percent passing the No. 200 sieve, dropping to 85.8%, while all other variables maintain accuracies of at least 93.1%. Prediction uncertainty using Monte Carlo simulations shows model performance within a 95% confidence interval. Overall, the proposed AI models can accurately and efficiently predict AASHTO soil groups using incomplete datasets for geotechnical engineering.},
DOI = {10.32604/cmes.2026.079302}
}