The Use of Artificial Intelligence in Pavement Engineering

Mikels, Natalie; Kassem, Emad; Muftah, Ahmed; Sufian, Abu A; University of Idaho; Pacific Northwest Transportation Consortium (PacTrans) (UTC); University of Washington. University Transportation Center for Region 10

doi:://hdl.handle.net/1773/50994

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The Use of Artificial Intelligence in Pavement Engineering

2023-11-01
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By Mikels, Natalie ; Kassem, Emad ; Muftah, Ahmed ; ...

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English

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Creators:

Mikels, Natalie ; Kassem, Emad ; Muftah, Ahmed ; Sufian, Abu A

Mikels, Natalie ; Kassem, Emad ; Muftah, Ahmed ; Sufian, Abu A Less -
Corporate Creators:

University of Idaho ; Pacific Northwest Transportation Consortium (PacTrans) (UTC) ; University of Washington. University Transportation Center for Region 10

University of Idaho ; Pacific Northwest Transportation Consortium (PacTrans) (UTC) ; University of Washington. University Transportation Center for Region 10 Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Artificial Intelligence Pavement Cracking Pavement Maintenance Pavement Performance Pavement Performance Prediction Roughness Rutting
Publication/ Report Number:

2021-M-UAF-2
DOI:

http://hdl.handle.net/1773/50994
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

Pacific Northwest Transportation Consortium (PacTrans) (UTC)
Abstract:

The performance of asphalt pavements decreases with time because of traffic loading and environmental conditions. Performance decay models are needed in pavement management systems to program pavement preservation and rehabilitation treatments to extend the service life and improve the performance of flexible pavements. Many factors affect pavement performance, including the material properties and thickness of each layer, applied traffic, and environmental conditions. Performance models, including those for rutting, cracking, roughness, are often developed and used to forecast the future conditions of pavements. Meanwhile, to develop reliable performance models, numerous variables are needed in such models, and historical performance data are required. This study investigated and developed multiple types of artificial intelligence models to predict pavement performance. The study results demonstrated that random forests regression was best suited for the data utilized in this study. Multiple random forests regression models were developed to predict various indicators of pavement performance, such as the International Roughness Index (IRI), rutting, and cracking. These models utilized a theoretical dataset generated with the Pavement ME software and field data collected from the Long-Term Pavement Performance (LTPP) Program. There were good correlations between all the theoretical and predicted performance indicators. In addition, the predicted performance decay curves were found to closely simulate the measured decay curves. In addition, the results for the models developed with the field dataset demonstrated good correlations between measured and predicted performance indicators for some of the investigated performance indicators.
Format:

PDF
Funding:

69A3551747110
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:ea118c055a2113408deaaa784f248958b8c46c6f94b461c685064181ac5e68fd
Download URL:

https://rosap.ntl.bts.gov/view/dot/73100/dot_73100_DS1.pdf
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The Use of Artificial Intelligence in Pavement Engineering

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