Performance models for hot mix asphalt pavements in urban roads

Kırbaş, Ufuk; Karaşahin, Mustafa

doi:10.1016/j.conbuildmat.2016.04.118

Cited by 72 publications

(26 citation statements)

References 25 publications

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“…Various input variables and a NN algorithm were also utilized to predict cracking of the road pavement, rutting depth, IRI, the condition rating index (CRI), visual condition index (VCI), and the present serviceability index (PSI) [23][24][25][26][27][28][29]. In addition, it was found that the prediction performance when using the NN algorithm was higher than that achieved using the conventional statistical analysis [30,31].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Choi

2019

Electronics

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In Korea, data on pavement conditions, such as cracks, rutting depth, and the international roughness index, are obtained using automatic pavement condition investigation equipment, such as ARAN and KRISS, for the same sections of national highways annually to manage their pavement conditions. This study predicts the deterioration of road pavement by using monitoring data from the Korean National Highway Pavement Management System and a recurrent neural network algorithm. The constructed algorithm predicts the pavement condition index for each section of the road network for one year by learning from the time series data for the preceding 10 years. Because pavement type, traffic load, and environmental characteristics differed by section, the sequence lengths (SQL) necessary to optimize each section were also different. The results of minimizing the root-mean-square error, according to the SQL by section and pavement condition index, showed that the error was reduced by 58.3–68.2% with a SQL value of 1, while pavement deterioration in each section could be predicted with a high coefficient of determination of 0.71–0.87. The accurate prediction of maintenance timing for pavement in this study will help optimize the life cycle of road pavement by increasing its life expectancy and reducing its maintenance budget.

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Section: Literature Reviewmentioning

confidence: 99%

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Choi

2019

Electronics

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“…IRI (international roughness index) and rut depth data are not recommended to be collected annually on county roads for lower tra c volumes [2]. IRI is widely accepted and used for evaluating pavement condition, establishing performance models and performing cost-e ectiveness analysis of maintenance strategies [3][4][5][6]. However, it is proved that IRI cannot be a unique predictor of pavement condition ratings with 41% of the variation in PCI (pavement condition index) remains unaccounted by IRI [7].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, systematic and random errors can highly distort some output parameters of PMS (pavement management system), even in error ranges that may be considered acceptable in practice [9]. IRI and PCI are two common used performance indices to characterize pavement performance at both network-level and project-level for both urban roads and highways [5,6,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Comparing of Data Collection for Network Level Pavement Management of Urban Roads and Highways

Wang

Yue

2020

Journal of Advanced Transportation

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Pavement condition data are collected by agencies to support pavement management system (PMS) for decision-making purpose as well as to construct performance model. The cost of pavement data collection increases with the increase of survey frequencies. However, a lower monitoring frequency could lead to unreliable maintenance decisions. It is necessary to understand the influence of monitoring frequencies on maintenance decision by considering the reliability of performance prediction models. Because of different maintenance conditions of urban roads and highways, their performance show different trends. In this paper, the influence of pavement monitoring frequency on the pavement performance models was investigated. The results indicate that low collection frequencies may result in delay in maintenance action by overestimating pavement performance. The collection frequency for Pavement Condition Index (PCI) can be reduced without compromising the accuracy of performance model, more work should be done to ensure the PCI data quality, thus to guarantee the rationality of maintenance decisions. Effect of frequency reduction on pavement performance (IRI) models of urban roads seems greater than on pavement performance (IRI) models of highways, which may lead to heavier monitoring work for urban roads management. This paper provided an example which demonstrated how a comparative analysis can be performed to determine whether the current data collection plan can provide sufficient data for time series analysis.

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“…e abovementioned black box effect could somehow be associated also with the relatively low attention paid in the civil engineering literature to the mathematical equations behind the artificial neural networks. Actually, several literature papers are simply devoted to the use of such computational tools in a broad variety of engineering applications, but without a proper discussion of the mathematical framework [24][25][26][27][28][29]. Moreover, in such papers, the discussion is often limited to the evaluation of the quality of the training and testing phases of the ANN; just few researchers [20,30,31] have at least presented the predictive analytical equations elaborated by means of the ANN.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks

Baldo

Manthos

Pasetto

2018

Advances in Civil Engineering

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The current paper deals with the numerical prediction of the mechanical response of asphalt concretes for road pavements, using artificial neural networks (ANNs). The asphalt concrete mixes considered in this study have been prepared with a diabase aggregate skeleton and two different types of bitumen, namely, a conventional bituminous binder and a polymer-modified one. The asphalt concretes were produced both in a road materials laboratory and in an asphalt concrete production plant. The mechanical behaviour of the mixes was investigated in terms of Marshall stability, flow, quotient, and moreover by the stiffness modulus. The artificial neural networks used for the numerical analysis of the experimental data, of the feedforward type, were characterized by one hidden layer and 10 artificial neurons. The results have been extremely satisfactory, with coefficients of correlation in the testing phase within the range 0.98798–0.91024, depending on the considered model, thus demonstrating the feasibility to apply ANN modelization to predict the mechanical and performance response of the asphalt concretes investigated. Furthermore, a closed-form equation has been provided for each of the four ANN models developed, assuming as input parameters the production process, the bitumen type and content, the filler/bitumen ratio, and the volumetric properties of the mixes. Such equations allow any other researcher to predict the mechanical parameter of interest, within the framework of the present study.

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Performance models for hot mix asphalt pavements in urban roads

Cited by 72 publications

References 25 publications

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Comparing of Data Collection for Network Level Pavement Management of Urban Roads and Highways

Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks

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