United States Department of Transportation
i
The Use of Artificial Intelligence in Pavement Engineering [supporting dataset]
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2023-11-30
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By Kassem, Emad
[JSON-3.56 KB]
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Alternative Title:Efficient and Data-Driven Pavement Management System using Artificial Intelligence
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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.
The total size of the zip file is 3.7 MB. The .xlsx and .xls file types are Microsoft Excel files, which can be opened with Excel, and other free available spreadsheet software, such as OpenRefine.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2024-01-17. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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