ASENN: attention-based selective embedding neural networks for road distress prediction

ASENN: attention-based selective embedding neural networks for road distress prediction

Abstract This study proposes an innovative neural network framework, ASENN (Attention-based Selective Embedding Neural Network), for the prediction of pavement deterioration. Considering the complexity and uncertainty associated with the pavement deterioration process, two fundamental frameworks, SE...

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Bibliographic Details
Main Authors: Babitha Philip, Zhenyu Xu, Hamad AlJassmi, Qieshi Zhang, Luqman Ali
Format: Article
Language:English
Published: SpringerOpen 2023-10-01
Series:Journal of Big Data
Subjects:
Pavement deterioration
Tabular data
Road distress parameters
Prediction models
Deep learning
Online Access:https://doi.org/10.1186/s40537-023-00845-x

Internet

https://doi.org/10.1186/s40537-023-00845-x

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