Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/105483
DC Field | Value | Language |
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dc.contributor.author | Justo-Silva, Rita | - |
dc.contributor.author | Ferreira, Adelino | - |
dc.contributor.author | Flintsch, Gerardo | - |
dc.date.accessioned | 2023-03-02T09:52:24Z | - |
dc.date.available | 2023-03-02T09:52:24Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2071-1050 | - |
dc.identifier.uri | https://hdl.handle.net/10316/105483 | - |
dc.description.abstract | Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed. | pt |
dc.language.iso | eng | pt |
dc.publisher | MDPI | pt |
dc.relation | MIT-Portugal grant (PD/BD/113721/2015) | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | pavement performance prediction models | pt |
dc.subject | modeling techniques | pt |
dc.subject | machine learning | pt |
dc.title | Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models | pt |
dc.type | article | pt |
degois.publication.firstPage | 5248 | pt |
degois.publication.issue | 9 | pt |
degois.publication.title | Sustainability (Switzerland) | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/su13095248 | - |
degois.publication.volume | 13 | pt |
dc.date.embargo | 2021-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairetype | article | - |
item.fulltext | Com Texto completo | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.researchunit | CITTA - Research Centre for Territory, Transports and Environment | - |
crisitem.author.orcid | 0000-0002-0228-7629 | - |
Appears in Collections: | I&D CITTA - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Review-on-machine-learning-techniques-for-developing-pavement-performance-prediction-modelsSustainability-Switzerland.pdf | 20.62 MB | Adobe PDF | View/Open |
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This item is licensed under a Creative Commons License