Modeling Pavement Performance Based on LTPP Database for Flexible Pavements

Radwan, Mostafa A.; Abo-Hashema, Mostafa A.; Hashem, Moatafa D.; Faheem, Hamdy

doi:10.18400/tekderg.476606

Cited by 15 publications

(18 citation statements)

References 11 publications

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“…Temperatures range from an average minimum of 6.60 • C in the winter to an average maximum of 36.9 • C in the summer. Beni-Suef receives fewer than eighty millimeters of precipitation annually in most areas [28,29]. The near-surface soil in this area is classified as Typic Torripsamments or Lithic Torripsamments [30].…”

Section: Location and Climatementioning

confidence: 99%

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

Radwan,

Mousa,

Zahran

2024

Sustainability

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Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets.

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Section: Location and Climatementioning

confidence: 99%

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

Radwan,

Mousa,

Zahran

2024

Sustainability

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“…In the observation category, the data collection is conducted using visual inspections by equipped modes of transport, e.g., automobile, bicycle [11][12][13][14][15][16], and intelligent monitoring techniques [17,18]. In addition, many pavement prediction models used the long-term pavement performance (LTPP) or short-term pavement performance (STPP) dataset to predict the future pavement performance [19][20][21].…”

Section: Data Sourcementioning

confidence: 99%

An Overview of Pavement Degradation Prediction Models

Shtayat

Moridpour

Best

et al. 2022

Journal of Advanced Transportation

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Pavement management systems (PMSs) have a primary role in determining pavement condition monitoring and maintenance strategies. Moreover, many researchers have focused on pavement condition evaluation tools, starting with data collection, followed by processing, analyzing, and ultimately reaching practical conclusions regarding pavement condition. The analysis step is considered an essential part of the pavement condition evaluation process, as it focuses on the tools used to find the most accurate results. On the other hand, prediction models are important tools used in pavement condition evaluation to determine the current and future performance of the road pavement. Therefore, pavement condition prediction has an effective and significant role in identifying the appropriate maintenance techniques and treatment processes. Moreover, pavement performance indices are commonly used as key indicators to describe the condition of pavement surfaces and the level of pavement degradation. This paper systematically summarizes the existing performance prediction models conducted to predict the condition of asphalt pavement degradation using pavement condition indexes (PCI) and the international roughness index (IRI). These performance indices are commonly used in pavement monitoring to accurately evaluate the health status of pavement. The paper also identifies and summarizes the most influencing parameters in road pavement condition prediction models and presents the strength and weaknesses of each prediction model. The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems.

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“…According to this paper achievement, it is observed from obtained result that the proposed neural network model for rutting depth has shown good agreement with experimental data. Radwan et al ( 22 ) developed ANN models for predicting fatigue and rutting distresses for wet and dry non-freeze climatic zones. When compared to the created statistical models, the results showed that the ANN technique can predict both distresses with high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Road Pavement Rutting Using Artificial Neural Network and Conventional Measurements

Shatnawi

Obaidat

Al-Sharideah

2022

Transportation Research Record: Journal of the Transportation R

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Rutting leads hydroplaning, accidents, poor riding quality, and significant maintenance costs. This study assists the development of statistical and Artificial pavement rutting models. The proposed methodology is reliable, time-saving, cost-saving, and comfortable. The suggested technique to anticipate rutting considers traffic volumes, pavement, and geometrical parameters such as lane and shoulder widths. This research modeled 33 main highways' ruts. Most of these roads have serious de-stressing problems with rutted pavement. The developed rutting prediction models demonstrated a medium to high correlation between rut depth and independent variables including annual average daily traffic, truck fleet percentage, pavement thickness, and number of lanes. The correlation coefficients such as R2 were found to be moderate for most of the developed models. The linear models of rutting prediction were statistically significant, with R2 values averaging around 66%, whereas the logistic regression model was the best developed rutting model, with an R2 value of 67%, when all variables, including traffic, pavement, and geometry, were considered. Nonlinear models with an R2 value of 57% were used to get similar findings. The artificial neural network (ANN) has been used in this study to model rut depth with same independent variables and gave higher results with R2 value of 82%. The findings showed that an ANN outperformed regression modeling in predicting the depth of a rut.

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Modeling Pavement Performance Based on LTPP Database for Flexible Pavements

Cited by 15 publications

References 11 publications

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

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

An Overview of Pavement Degradation Prediction Models

Modeling Road Pavement Rutting Using Artificial Neural Network and Conventional Measurements

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