United States Department of Transportation
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Prediction of Road Condition and Smoothness for Flexible and Rigid Pavements in Louisiana Using Neural Networks: Research Project Capsule [21–1P]
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2020-09-01
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By Wu, Zhong
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Abstract:To achieve the objectives of this study, a neural network based system will be developed, using the data collected by DOTD on regular basis and local weather information. The proposed methodology can be summarized as follows. A review and documentation of the current state of practice by DOTD will evaluate short-term and long-term road condition and maintenance strategies. PMS data for the Louisiana pavements will be obtained from DOTD databases as well as meteorological condition data for the project sites from local agencies. These data will be categorized as per research scope and assessed through statistical measures. The team will also conduct statistical analyses to identify the significant variables from the historic pavement distress and condition data, pavement structure, traffic data, climatic data to predict the pavement distresses, and smoothness. Also, the set of variables that dictates the prediction of pavement condition will be used as an input to the ANN models. These variables can be different for different condition indices in the prediction models. ANN models will also be developed for each performance indicator index using software applications. The models will be validated using an existing dataset. The proposed ANN models will be presented in an implementation ready format through the development of a single windows-based interface that can accommodate all the ANN models. All findings and analyses of this study will then be documented in a detailed report.
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