United States Department of Transportation
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Use of Machine Learning Methods to Obtain a Reliable Predictive Model for Resilient Modulus of Subgrade Soil
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2024-08-01
[PDF-9.46 MB]
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Edition:Final Report
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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.
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