Development of a Relationship between Pavement Condition Index and International Roughness Index in Rural Road Network

Abstract

The main objective of this paper was to investigate the relationship between PCI and IRI values of the rural road network. To this end, 6000 pavement sections of the rural road network in Iran were selected. Road surface images and roughness linear profiles were collected using an automated car to calculate PCI and IRI, respectively. Three exponential regression models were developed and validated in three different IRI intervals. Analysis of the results indicated that exponential regression was the best model to relate IRI and PCI. In these models, R2 values were found to be acceptable, equal to 0.75, 0.76, and 0.59 for roads with good, fair, and very poor qualities, respectively, indicating a good relationship between IRI and PCI. Moreover, validation results showed that the model had a high accuracy. Also, the relation between IRI and PCI became weaker as a result of increasing the level of road surface roughness, which can be caused by the increase in the number and severity of failures. Furthermore, two failures of rail R.C. and rutting were rarely observed in the studied roads. Therefore, the proposed model is more applicable for roads without the mentioned failures and asphalt-pavement rural road network.

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

“…The IRI is a measure of pavement roughness [3,15]. It is common to view roughness in terms of distortion of the pavement surface, which contributes to an undesirable or uncomfortable ride.…”

“…It can also indirectly reflect the response of vehicles to road conditions [16]. The IRI correlates well with other riding aspects, including safety, vehicle vibration, functioning speed, and fuel consumption [3,17]. The IRI can be measured by calculating the distortion of the pavement surface along the longitudinal profile of the vehicle wheel path [18].…”

“…The IRI can be measured by calculating the distortion of the pavement surface along the longitudinal profile of the vehicle wheel path [18]. Developing IRI-based models for performance prediction is common as pavements deteriorate [3,19,20]. Increasing fuel consumption, greenhouse gas emissions, decreasing vehicle efficiency, and causing traffic accidents are well-correlated with increasing pavement roughness, which collectively costs USD one million every year [18,[21][22][23].…”

“…As a result, the performance of urban roads is often mediocre and necessitates frequent maintenance and rehabilitation. With the typically limited funds allocated for upkeeping urban roads, transportation agencies worldwide face serious financial challenges in the maintenance and rehabilitation of flexible pavement networks [3]. This is of particular concern in developing countries, which have extremely scarce financial resources and relatively limited collected information about road conditions needed for adequate condition detection and due rehabilitation [4,5].…”

The use of IRI data in developing optimal pavement rehabilitation plan for developing countries: Palestine as a case study

“…A sigmoid function was found to be effective in predicting PCI from IRI at very high coefficient of determination (R 2 =0.995). In another study, [33] presented three linear models to estimate PCI from IRI using different IRI ranges with R 2 ranging from 0.59 to 0.76. In general, it is recommended that correlation models similar to the previously outlined ones be developed based on local traffic conditions and material characteristics.…”