Abstract
This project explores the development and optimization of predictive models for the resilient modulus (MR) of subgrade soil using advanced machine learning techniques. Comprehensive data from INDOT spanning several years was analyzed to enhance the accuracy of MR predictions. The study not only refined the modeling approach through statistical methods and validation but also identified crucial soil properties that significantly impact MR values. Recommendations for future data collection were made to further improve the models. The developed models and these recommendations will be used to guide INDOT in making informed decisions for pavement design and maintenance, which will ultimately lead to more efficient and cost-effective engineering practices.
Keywords
clustering, machine learning, model validation, random forest, resilient modulus, XGBoost, regression, repeated load triaxial test, resilient modulus
Report Number
FHWA/IN/JTRP-2024/27
SPR Number
4714
Sponsoring Organization
Indiana Department of Transportation
Performing Organization
Joint Transportation Research Program
Publisher Place
West Lafayette, Indiana
Date of Version
2024
DOI
10.5703/1288284317768
Recommended Citation
Khoshnevisan, S., Norouzi, M., & Sadik, L. (2024). Use of machine learning methods to obtain a reliable predictive model for resilient modulus of subgrade soil (Joint Transportation Research Program Publication No. FHWA/IN/JTRP-2024/27). West Lafayette, IN: Purdue University. https://doi.org/10.5703/1288284317768
MR Main Code
XGBoost for k values.ipynb (764 kB)
Calibrated_constitutive_model_blackbox
k1.xlsx (776 kB)
Closedform_k1_universal
K2.xlsx (685 kB)
Closedform_k2_universal
k3.xlsx (691 kB)
Closedform_k3_universal
INDOT_data_final.csv (175 kB)
Cleaned Dataset
INDOT MR explicit models.xlsx (10 kB)
Linear and Polynomial Models Excel File
SPR-4714 Technical Summary.pdf (332 kB)
Technical Summary