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International Journal of Pavement Engineering Volume 24, 2023 - Issue 2
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Research Article

Establishment of probabilistic prediction models for pavement deterioration based on Bayesian neural network

Feng Xiaoa Department of Roadway Engineering, Southeast University, Nanjing, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0002-8791-1776View further author information
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Xinyu Chena Department of Roadway Engineering, Southeast University, Nanjing, People’s Republic of ChinaView further author information
,
Jianchuan Chenga Department of Roadway Engineering, Southeast University, Nanjing, People’s Republic of ChinaView further author information
,
Shunxin Yanga Department of Roadway Engineering, Southeast University, Nanjing, People’s Republic of ChinaCorrespondence[email protected]
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Yang Maa Department of Roadway Engineering, Southeast University, Nanjing, People’s Republic of China;b School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, People’s Republic of ChinaView further author information
Article: 2076854 | Received 14 Dec 2021, Accepted 03 May 2022, Published online: 31 May 2022

Citations (5)

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Read on this site (2)

Khaled A. Abaza. (2023) Stochastic-based pavement rehabilitation model at the network level with prediction uncertainty considerations. Road Materials and Pavement Design 24:11, pages 2680-2698.
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Feng Xiao, Xinyu Chen, Shunxin Yang & Jianchuan Cheng. (2023) Bi-objective pavement maintenance and rehabilitation optimization decision-making model incorporating the construction length of preventive maintenance projects. Structure and Infrastructure Engineering 0:0, pages 1-15.
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Articles from other publishers (3)

Ali Taheri & John Sobanjo. (2024) Ensemble Learning Approach for Developing Performance Models of Flexible Pavement. Infrastructures 9:5, pages 78.
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Tisinee Surapunt & Shuliang Wang. (2024) Ensemble Modeling with a Bayesian Maximal Information Coefficient-Based Model of Bayesian Predictions on Uncertainty Data. Information 15:4, pages 228.
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Babitha Philip & Hamad AlJassmi. (2023) Time-series forecasting of road distress parameters using dynamic Bayesian belief networks. Construction Innovation 24:1, pages 317-340.
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