Estimation of IRI from PASER using ANN based on k-means and fuzzy c-means clustering techniques: a case study

Barzegaran, Jalal; Dezfoulian, Reza Shahni; Fakhri, Mansour

doi:10.1080/10298436.2021.2000988

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…The IRI is not only used for roughness assessment; it is an important parameter of road surface quality that affects driving safety and fuel consumption. A number of studies have explored pavement assessment and pavement index applicability in developing countries [16,17]. For example, Barzegaran et al [16] investigated 507 km of asphalt pavement in Kermanshah, Iran, using the IRI and the Pavement Surface and Evaluation Rating (PASER) as a quick and cost-effective indicator.…”

Section: Z U )mentioning

confidence: 99%

“…A number of studies have explored pavement assessment and pavement index applicability in developing countries [16,17]. For example, Barzegaran et al [16] investigated 507 km of asphalt pavement in Kermanshah, Iran, using the IRI and the Pavement Surface and Evaluation Rating (PASER) as a quick and cost-effective indicator. Particularly in European and American countries, the IRI has been widely used in road design, acceptance, management, and maintenance [18][19][20].…”

Section: Z U )mentioning

confidence: 99%

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Evaluation of Pavement Roughness by the International Roughness Index for Sustainable Pavement Construction in New Taipei City

et al. 2022

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Good road quality is an indicator of urban progress. In August 1998, the New Taipei City government implemented the “Road Leveling Project” on roads within its jurisdiction to improve the quality of public transportation. This paper aimed to analyze the International Roughness Index (IRI) of 152 asphalt concrete pavement sections in New Taipei City before and after road leveling. An intelligent pavement inspection vehicle that met the requirements of E 950 Class I was used to detect the IRI values of the road sections before and after maintenance. The statistical analysis showed that the implementation of the Road Leveling Project had a significant effect on improving the road roughness of roads under its jurisdiction. Moreover, it became possible to arrange a budget for roads with IRI values above a threshold or to estimate the budget needed to improve roads with an IRI higher than a certain value.

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Section: Z U )mentioning

confidence: 99%

Section: Z U )mentioning

confidence: 99%

Evaluation of Pavement Roughness by the International Roughness Index for Sustainable Pavement Construction in New Taipei City

et al. 2022

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“…Lima et al (2019) assessed the abilities of (Gopalakrishnan et al, 2017;Hanandeh et al, 2020aHanandeh et al, , 2020bYou et al, 2020;Piryonesi and El-Diraby, 2021;Ye et al, 2021;Zuhair Murad, 2021;Al Bodour et al, 2022;Hanandeh, 2022). Barzegaran et al (2021) used IRI prediction models from pavement distresses which could be an alternative solution. In this study, 507 km of asphalt pavements in Kermanshah, Iran, were studied utilizing IRI and pavement surface and evaluation rating (PASER) as a quick and cost-effective index.…”

Section: Iri ( P )mentioning

confidence: 99%

Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling

Hanandeh

Hanandeh²,

Alhiary³

et al. 2022

Front. Built Environ.

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The pavement management system is recognized as an assertive discipline that works on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithms and artificial intelligence to propose a modern generation of pavement indices for road networks in Jordan. The datasets used in this study were collected from multiple roads in Jordan, and 128 data points were used in this study. The input variables are the pavement condition index (PCI) and the international roughness index (IRI) in the artificial neural network (ANN) and gene expression programming (GEP) models. The output variable is the pavement serviceability rate (PSR). The results show an efficient performance benefit of using these techniques. In addition, the ANN and GEP models were able to predict the output variable with a reasonable accuracy, where the ANN model has an R2 value of 0.95, 0.87, and 0.98 for the PCI, IRI, and PSR, respectively. The (R2) values of the GEP model are 0.94, 0.89, and 0.99 for PCI, IRI, and PSR, respectively.

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“…A two-layer recurrent NN along with decision tree support vector classier was used to model pavement PSI as a function of material and structural properties, traffic and maintenance history, and time [38]. Data pre-processing of IRI into clusters using k-mean and fuzzy c-mean was shown improve ANN model performance significantly [39]. The IRI model was a function of traffic and pavement structural variables.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks

et al. 2022

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The traditional manual approach of pavement condition evaluation is being replaced by more sophisticated automated vehicle systems. Although these automated systems have eased and hastened pavement management processes, research is ongoing to further improve their performances. An average state road agency handles thousands of kilometers of the road network, most of which have multiple lanes. Yet, for practical reasons, these automated systems are designed to evaluate road networks one lane at a time. This requires time, energy, and possibly more equipment and manpower. Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN) were employed to examine the feasibility of modeling and predicting pavement distresses of multiple lanes as functions of pavement distresses of a single adjacent lane. The successful implementation of this technique has the potential to cut the energy and time requirement at the condition evaluation stage by at least half, for a uniform multi-lane highway. Results showed promising model performances that indicate the possibility of evaluating a multi-lane highway pavement condition (PC) by single lane inspection. Traffic direction parameters, location, and lane matching parameters contributed significantly to the performance of the ANN PC prediction models.

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Estimation of IRI from PASER using ANN based on k-means and fuzzy c-means clustering techniques: a case study

Cited by 9 publications

References 45 publications

Evaluation of Pavement Roughness by the International Roughness Index for Sustainable Pavement Construction in New Taipei City

Evaluation of Pavement Roughness by the International Roughness Index for Sustainable Pavement Construction in New Taipei City

Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling

Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks

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