Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/21167

TitleEvolutionary intelligence in asphalt pavement modeling and quality-of-information
Author(s)Neves, José
Ribeiro, Jorge
Pereira, Paulo A. A.
Alves, Victor
Machado, José Manuel
Abelha, António
Novais, Paulo
Analide, César
Santos, Manuel
Fernández-Delgado, Manuel
KeywordsEvolutionary intelligence
Extended logic programming
Knowledge representation and reasoning
Quality-of-information
An answer degree-of-confidence
Asphalt pavement modeling
Issue dateJan-2012
PublisherSpringer
JournalProgress in Artificial Intelligence
Abstract(s)he analysis and development of a novel approach to asphalt pavement modeling, able to attend the need to predict failure according to technical and non- technical criteria in a highway, is a hard task, namely in terms of the huge amount of possible scenarios. Indeed, the current state-of-the-art for service-life prediction is at empiric and empiric-mechanistic levels, and do not provide any suitable answer even for one single failure criteria. Consequently, it is imperative to achieve qualified models and qualitative reasoning methods, in particular due to the need to have first-class environments at our disposal where defective information is at hand. In order to fulfill this goal, this paper presents a dynamic and formal model oriented to fulfill the task of making predictions for multi-failure criteria, in particular in scenarios with incomplete information; it is an intelligence tool that advances according to the Quality-of- Information of the extensions of the predicates that model the universe of discourse. On the other hand, it is also considered the Degree-of-Confidence factor, a parameter that measures one`s confidence on the list of characteristics presented by an asphalt pavement, set in terms of the attributes or variables that make the argument of the predicates referred to above.
TypeArticle
URIhttp://hdl.handle.net/1822/21167
DOI10.1007/s13748-011-0003-5
ISSN2192-6352
Publisher versionhttp://download.springer.com/static/pdf/344/art%253A10.1007%252Fs13748-011-0003-5.pdf?auth66=1354820151_02620f4f7471deb353c4becb6c4e3a6c&ext=.pdf
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:DI/CCTC - Artigos (papers)

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