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- W4293764903 abstract "Efficient and well-maintained pavement systems are crucial to ensure appropriate conditions for the road networks. If timely maintenance and rehabilitation (M&R) is not performed, the pavement deterioration may lead to poor conditions that affect the comfort and safety of road users. The effectiveness of any M&R actions essentially depends on the time of treatment. This paper presents the development of pavement roughness models using the artificial neural networks (ANNs) approach for composite pavements using the Long-Term Performance Pavement (LTPP) program database for the wet, non-freeze climate region. A total of 49 composite pavement sections with 353 data points were analyzed. The use of an M&R variable in the model development resulted in more realistic and accurate models to predict future pavement conditions, identify M&R actions, and simulate interventions for future years. The developed models could be used by transportation agencies as a valuable tool for more effective M&R scheduling prioritizing worst condition pavement sections." @default.
- W4293764903 created "2022-08-31" @default.
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- W4293764903 date "2022-08-31" @default.
- W4293764903 modified "2023-09-27" @default.
- W4293764903 title "Pavement Performance Modeling Considering Maintenance and Rehabilitation for Composite Pavements in the LTPP Wet Non-Freeze Region Using Neural Networks" @default.
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- W4293764903 doi "https://doi.org/10.1061/9780784484357.004" @default.
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