Journal ArticleDOI
Pavement performance prediction through fuzzy regression
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TLDR
The results demonstrate the capability of the model, which is able to assist road administration units to determine desirable repair actions regarding the predicted pavement conditions by using fuzzy regression to better account the uncertainties of the traditional method.Abstract:
Accurate predictions of future pavement conditions are essential for determining the most cost-effective maintenance strategy. The current methods for assessing pavement conditions involve either equipment measures or visual inspections. Equipment measures are not extensively implemented because of high cost; thus, subjective evaluations by road inspectors are often used as a replacement. Nevertheless, visual inspections could draw in errors and variations due to subjectivity and uncertainty. The present serviceability index (PSI), one of the most common indicators used to evaluate pavement performance, is incapable of transforming one's imprecise judgment into an exact number between 0 (the worst) and 5 (the best). Conventional regression cannot deal with visual inspection data that are linguistic or non-crisp. In contrast, fuzzy regression is capable of handling such fuzzy data. In this paper, pavement conditions are exemplified by five membership functions and estimated by using fuzzy regression to better account the uncertainties of the traditional method. Also, a similarity indicator is applied to measure the goodness of fit. A case study using pavement inspection data is presented to establish estimated fuzzy regression equations. The results demonstrate the capability of the model, which is able to assist road administration units to determine desirable repair actions regarding the predicted pavement conditions.read more
Citations
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Journal ArticleDOI
Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods
TL;DR: In this article, the capabilities of artificial neural networks (ANNs) and group method of data handling (GMDH) methods in predicting flexible pavement conditions were analyzed in three levels: in 1 year, in 2 years and in the pavement life cycle (long term).
Journal ArticleDOI
Fuzzy regression analysis: Systematic review and bibliography
TL;DR: The topic of fuzzy regression analysis is consolidated in order to aid new researchers in this area, focuses the field’s attention on key open questions, and highlights possible directions for future research.
Journal ArticleDOI
Development of distress condition index of asphalt pavements using LTPP data through structural equation modeling
TL;DR: In this paper, the authors proposed an asphalt pavement distress condition index based on various types of distress data collected from the Long-Term Pavement Performance (LTPP) database through Structural Equation Modeling (SEM).
Journal ArticleDOI
Performance models for hot mix asphalt pavements in urban roads
Ufuk Kırbaş,Mustafa Karaşahin +1 more
TL;DR: In this paper, three different deterioration models have been developed that can predict the future performance of pavements in urban HMA paved roads, including deterministic regression analysis, multivariate adaptive regression splines (MARS) and artificial neural networks (ANN).
References
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Journal ArticleDOI
Fuzzy linear regression with fuzzy intervals
TL;DR: A new class of fuzzy linear regression models based on Tanaka's approach, here all training data influence the estimated interval, and an adaptation of the fuzzy regression equation to new data becomes possible.
Journal Article
The Pavement Serviceability-Performance Concept
W N Carey Jr,P E Irick +1 more
TL;DR: In this paper, the AASHO Road Test has been used to evaluate the serviceability of pavements subjectively by a panel made up of men selected to represent many important groups of highway users.
Journal Article
Pavement performance prediction model using the markov process
TL;DR: A pavement performance and prediction model based on the pavement condition index and the age of the pavement has been developed in this article, where a combination of homogeneous and nonhomogeneous Markov chains has been used in the development of the model.
Journal ArticleDOI
Multiobjective fuzzy linear regression analysis for fuzzy input-output data
Masatoshi Sakawa,Hitoshi Yano +1 more
TL;DR: In this article, three types of multiobjective programming problems for obtaining fuzzy linear regression models are formulated corresponding to the three indices, and a linear programming based interactive decision-making method is developed to derive the satisficing solution of the decision maker for the formulated multiobjectives programming problems.
Journal Article
Multiobjective fuzzy linear regression analysis for fuzzy input-output data
Masatoshi Sakawa,Hitoshi Yano +1 more
TL;DR: A linear programming based interactive decision making method to derive the satisficing solution of the decision maker for the formulated multiobjective programming problems is developed.
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