Development of fuzzy models for asphalt pavement performance

The objective of this paper was to develop fuzzy models for asphalt pavement performance. The fuzzy logic can convert linguistic or qualitative variables into quantitative values. This feature makes it possible to gather experts’ experience about the knowledge they have on factors that affect the pa...

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Main Authors: Sérgio Pacífico Soncim, Igor Castro Sá de Oliveira, Felipe Brandão Santos
Format: Article
Language:English
Published: Universidade Estadual de Maringá 2019-05-01
Series:Acta Scientiarum: Technology
Online Access:http://periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/35626
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spelling doaj-1148347a93c94c04a90ed237fda074832020-11-24T21:55:22ZengUniversidade Estadual de MaringáActa Scientiarum: Technology1807-86642019-05-01411e35626e3562610.4025/actascitechnol.v41i1.3562619002Development of fuzzy models for asphalt pavement performanceSérgio Pacífico SoncimIgor Castro Sá de OliveiraFelipe Brandão SantosThe objective of this paper was to develop fuzzy models for asphalt pavement performance. The fuzzy logic can convert linguistic or qualitative variables into quantitative values. This feature makes it possible to gather experts’ experience about the knowledge they have on factors that affect the pavement performance and its state condition. Forms developed in an organized way were applied for acquiring the knowledge from experts on pavement construction and maintenance. The variables pavement age, traffic, International Roughness Index (IRI) and Flexible Pavement Condition Index (FPCI) were associated with numerical scales and linguistic concepts such as new, old, light, heavy, good, fair, and poor. From the information obtained through the application of forms, variables were modeled with the aid of software InFuzzy and fuzzy models were developed for IRI and FPCI. For validating the model, a straight line adjustment was used to relate the predicted to the observed data. Also, the corresponding correlation coefficient (r) was calculated and residuals were analyzed. The developed models fitted to observed data and correlation coefficient r = 0.71 and 0.70, respectively.http://periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/35626
collection DOAJ
language English
format Article
sources DOAJ
author Sérgio Pacífico Soncim
Igor Castro Sá de Oliveira
Felipe Brandão Santos
spellingShingle Sérgio Pacífico Soncim
Igor Castro Sá de Oliveira
Felipe Brandão Santos
Development of fuzzy models for asphalt pavement performance
Acta Scientiarum: Technology
author_facet Sérgio Pacífico Soncim
Igor Castro Sá de Oliveira
Felipe Brandão Santos
author_sort Sérgio Pacífico Soncim
title Development of fuzzy models for asphalt pavement performance
title_short Development of fuzzy models for asphalt pavement performance
title_full Development of fuzzy models for asphalt pavement performance
title_fullStr Development of fuzzy models for asphalt pavement performance
title_full_unstemmed Development of fuzzy models for asphalt pavement performance
title_sort development of fuzzy models for asphalt pavement performance
publisher Universidade Estadual de Maringá
series Acta Scientiarum: Technology
issn 1807-8664
publishDate 2019-05-01
description The objective of this paper was to develop fuzzy models for asphalt pavement performance. The fuzzy logic can convert linguistic or qualitative variables into quantitative values. This feature makes it possible to gather experts’ experience about the knowledge they have on factors that affect the pavement performance and its state condition. Forms developed in an organized way were applied for acquiring the knowledge from experts on pavement construction and maintenance. The variables pavement age, traffic, International Roughness Index (IRI) and Flexible Pavement Condition Index (FPCI) were associated with numerical scales and linguistic concepts such as new, old, light, heavy, good, fair, and poor. From the information obtained through the application of forms, variables were modeled with the aid of software InFuzzy and fuzzy models were developed for IRI and FPCI. For validating the model, a straight line adjustment was used to relate the predicted to the observed data. Also, the corresponding correlation coefficient (r) was calculated and residuals were analyzed. The developed models fitted to observed data and correlation coefficient r = 0.71 and 0.70, respectively.
url http://periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/35626
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AT igorcastrosadeoliveira developmentoffuzzymodelsforasphaltpavementperformance
AT felipebrandaosantos developmentoffuzzymodelsforasphaltpavementperformance
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