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- W4380488416 abstract "Pavement condition data is important in providing information regarding the current state of the road network and in determining the needs of maintenance and rehabilitation treatments. However, the condition data is often incomplete due to various reasons such as sensor errors and non-periodic inspection schedules. Missing data, especially data missing systematically, presents loss of information, reduces statistical power, and introduces biased assessment. Existing methods in dealing with missing data usually discard entire data points with missing values or impute through data correlation. In this paper, we used a collective learning-based graph convolutional network, which integrates both features of adjacent sections and dependencies between observed section conditions to learn missing condition values. Unlike other variants of graph neural networks, the proposed approach is able to capture dependent relationship between the conditions of adjacent pavement sections. In the case study, pavement condition data collected from Texas Department of Transportation, Austin District, were used. Experiments show that the proposed model was able to produce promising results in imputing the missing data." @default.
- W4380488416 created "2023-06-14" @default.
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- W4380488416 date "2023-06-13" @default.
- W4380488416 modified "2023-09-27" @default.
- W4380488416 title "Pavement Missing Condition Data Imputation through Collective Learning-Based Graph Neural Networks" @default.
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- W4380488416 doi "https://doi.org/10.1061/9780784484883.037" @default.
- W4380488416 hasPublicationYear "2023" @default.
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