A review on empirical methods of pavement performance modeling
This paper conducts a comprehensive review of empirical methods of pavement performance modeling. The paper firstly analyzes performance measures used in existing pavement performance modeling. Then, based on whether pavement performance models can be automatically updated with newly observed data, this paper classifies the empirical methods into traditional modeling methods and adaptive modeling methods and discusses their main features, strengths, and weaknesses. It is found that, in the traditional modeling methods, comprehensive modeling methods typically can well incorporate both temporal and spatial characteristics of pavement performance but the interpretation capability of performance models based on machine learning techniques needs to be enhanced; the adaptive modeling methods describe the deterioration behavior of pavement performance in a more accurate way but need to improve the setting of updating conditions and the evaluation of updating effects. Finally, recommendations are made for future studies.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09500618
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Hu, Aihui
- Bai, Qiang
- Chen, Lin
- Meng, Siyuan
- Li, Qihui
- Xu, Zhiman
- Publication Date: 2022-8-1
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 127968
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Serial:
- Construction and Building Materials
- Volume: 342
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0950-0618
- Serial URL: http://www.sciencedirect.com/science/journal/09500618?sdc=1
Subject/Index Terms
- TRT Terms: Data models; Empirical methods; Machine learning; Pavement performance
- Subject Areas: Data and Information Technology; Highways; Pavements;
Filing Info
- Accession Number: 01853097
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 26 2022 1:21PM