International Roughness Index prediction model for flexible pavements

Abdelaziz, Nader; El-Hakim, Ragaa T. Abd; El-Badawy, Sherif M.; Afify, Hafez

doi:10.1080/10298436.2018.1441414

Cited by 134 publications

(61 citation statements)

References 20 publications

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“…It solves, in parallel, to the inputs presented to it during the training period. Some authors regard the ANN as a "black box", since the results are produced without establishing causal relationships between input and output [43,44].…”

Section: Types Of Pavement Deterioration Modelsmentioning

confidence: 99%

“…Subjective models were discarded because it was not wanted to provide a biased model with detailed information available. Moreover, although ANN models can achieve a better coefficient of determination (R 2 ) [44], they were discarded because they act as a black box, not providing information about the influence and importance of each parameter or predictor in the final result. Bayesian models are not necessary because there is no need a model to handle with scarce data.…”

Section: Types Of Pavement Deterioration Modelsmentioning

confidence: 99%

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IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work

et al. 2020

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Pavement performance models play a vital role in any pavement management system. The Regional Government of Biscay (RGB) (Spain) manages a 1200 km road network and conducts pavement data collections, including the International Roughness Index (IRI) values. The aim of the paper is to develop an IRI performance model for two-lane roads with flexible pavement until the first maintenance and/or rehabilitation activity is performed. Due to the huge amount of available information, a deterministic model was selected. A literature review of deterministic models showed that, apart from age and traffic volumes, the pavement structure is a key factor. Therefore, it was decided to analyze the only road stretches whose entire pavement section was known (surface layer + base + subbase). Various variables related to age, traffic volumes and employed materials were introduced as possible factors. The multiple linear regression model with the highest coefficient of determination and all the variables significant included the real pavement age, the cumulated heavy traffic and the total thickness of bituminous layers. As the material employed in the surface layer could affect roughness progression, a qualitative variable was introduced to consider various surface materials. The model improved its accuracy, indicating that the surface layer material is also an influencing factor on IRI evolution.

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Section: Types Of Pavement Deterioration Modelsmentioning

confidence: 99%

Section: Types Of Pavement Deterioration Modelsmentioning

confidence: 99%

IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work

et al. 2020

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“…The developed models were IRI prediction as a function of age, IRI initial, transverse cracking (all severities), alligator fatigue cracking (all severities), and standard deviation of the rut depth (SDRUT). The R 2 of regression analysis was 0.57, while the R 2 was 0.75 of the ANNs model (Abdelaziz et al, 2017, Abdelaziz et al, 2020.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index

Abed¹

2020

jcoeng

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Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of each observed distress, the pavement condition surveys were conducted by actually walking through all the sections. Using these data, PCI was calculated utilizing Micro PAVER software. Dynatest Road Surface Profiler (RSP) was used to collect IRI data of all the sections. Using the SPSS software, linear and nonlinear regressions have been used for developing two models between PCI and IRI based on the collected data. These models have the coefficients of determination (R2) equal to 0.715 and 0.722 for linear and quadratic models. Finally, the results indicate the linear and quadratic models are acceptable to predict PCI from IRI directly.

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“…Pavement deterioration can be attributed to different factors such as traffic loads, pavement age, material properties, environment condition, subgrade properties, and pavement strength, which influence the pavement's mechanical characteristics [8]. Many highway agencies worldwide deal with the initial IRI value as a quality assertion norm and the terminal IRI as an index of required pavement maintenance and rehabilitation activities [22].…”

Section: Introductionmentioning

confidence: 99%

Development of Roughness Prediction Models for Laos National Road Network

Gharieb

Nishikawa

2021

CivilEng

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The International Roughness Index (IRI) has been accepted globally as an essential indicator for assessing pavement condition. The Laos Road Management System (RMS) utilizes a default Highway Development and Management (HDM-4) IRI prediction model. However, developed IRI values have shown the need to calibrate the IRI prediction model. Data records are not fully available for Laos yet, making it difficult to calibrate IRI for the local conditions. This paper aims to develop an IRI prediction model for the National Road Network (NRN) based on the available Laos RMS database. The Multiple Linear Regression (MLR) analysis technique was applied to develop two new IRI prediction models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections. The final database consisted of 83 sections with 269 observations over a 1850 km length of DBST NRN and 29 sections with 122 observations over a 718 km length of AC NRN. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The model’s parameter analysis confirmed their significance, and R2 values were 0.89 and 0.84 for DBST and AC models, respectively. It can be concluded that the developed models can serve as a useful tool for engineers maintaining paved NRN.

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International Roughness Index prediction model for flexible pavements

Cited by 134 publications

References 20 publications

IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work

IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work

Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index

Development of Roughness Prediction Models for Laos National Road Network

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