Abstract
Pavement distress, which is exacerbated by structural damage, results in massive carbon-intensity for routine maintenance and transportation safety issues. One promising approach to resolving this issue is to predict structural damage before it occurs. However, structural damage prediction is a complex problem with many influential factors that no single index can precisely solve for in-service pavement. In this study, a new method is adopted that combines all implicit factors into a deep learning neural network (DNN) algorithm to achieve high accuracy for the prediction of pavement structural damage. The training and optimization procedures of the DNN model are introduced explicitly for model repeatability and development. The results show that the state-of-the-art (SOTA) model performed well, with an accuracy rate of 88.46%. The contribution of this research facilitates the development of pavement maintenance decision systems to provide a prediction method for on-site pavement structures. By employing this approach, structural damage can be identified early to prevent further expansion, which will reduce costs and save lives and natural resources.
Published Version
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