Use of Machine Learning Methods to Obtain a Reliable Predictive Model for Resilient Modulus of Subgrade Soil

Khoshnevisan, Sara; Norouzi, Mehdi; Sadik, Laith

doi:10.5703/1288284317768

i

Use of Machine Learning Methods to Obtain a Reliable Predictive Model for Resilient Modulus of Subgrade Soil

2024-08-01
By Khoshnevisan, Sara ; Norouzi, Mehdi ; Sadik, Laith

File Language:

English

Details

Creators:

Khoshnevisan, Sara ; Norouzi, Mehdi ; Sadik, Laith
Corporate Creators:

University of Cincinnati ; Purdue University. Joint Transportation Research Program
Corporate Contributors:

Indiana Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:
Publication/ Report Number:

FHWA/IN/JTRP-2024/27
DOI:

https://doi.org/10.5703/1288284317768
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

Purdue University. Joint Transportation Research Program
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.
Format:

PDF
Funding:

SPR-4714
Download URL:

https://rosap.ntl.bts.gov/view/dot/83076/dot_83076_DS1.pdf
File Type:

[PDF - 9.46 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; US Transportation Collection
Main Document Checksum:

urn:sha-512:70cf6bd484a9c9a73dc9e61a545359345b787d4aceccd71e9d1a66424703fa825dde8b0aaebfc4e8dcd814cdfd0e412c0e37d830dbd4ef48fd5009bf16bba4b8

File Language:

English

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