Development of Pavement Performance Models for Riyadh Street Network

Al‐Mansour, Abdullah I.; Al-Swailmi, S; Al-Swailem, Swailem

doi:10.3141/1655-04

Cited by 10 publications

(7 citation statements)

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“…This index is calculated using the type, severity, and quantity of each distress in a specific sample. The pavement section can be qualified based on UDI using the following ratings [1]:…”

Section: Traffic Levelmentioning

confidence: 99%

“…The city area expanded from 75 km 2 to more than 1600 km 2 . The General Directorate of Operation and Maintenance of Riyadh Municipality is the agency responsible for maintaining a street network exceeding in length 10,000 km [1]. It was not possible to maintain the roads of a city with this huge street network without a pavement maintenance management system (PMMS).…”

Section: Introduction 1backgroundmentioning

confidence: 99%

“…Based on these models, the future pavement condition can be forecasted and can be used as an input to the maintenance policy to predict the required maintenance and rehabilitation (M&R) activities. Therefore, the PMMS assists top management in finding optimum strategies for providing and maintaining pavement in a serviceable condition [1].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Prediction of Pavement Maintenance Performance Using an Expert System

2022

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The pavement experiences deterioration due to traffic and environment, i.e., unsatisfactory riding quality and structural inadequacy, over time. Thus, predicting pavement performance over time is one of the key elements of any pavement maintenance management system (PMMS). It can be used as an efficient tool to program/schedule the maintenance applications and expenditures, and thus the necessary funds can be allocated. Using a combination of independent variables for any selected pavement section can generate section-wise condition assessment and prediction models. Moreover, these models can be used to select the most cost-effective maintenance alternative to be applied to that pavement section. The present study developed an expert system based on pavement performance models which combines the available maintenance data with the knowledge acquired from the experts of the General Administration of Operation and Maintenance in Riyadh, Saudi Arabia. Eight regression models were first developed for four maintenance and rehabilitation (M&R) strategies, i.e., no maintenance, routine maintenance, overlay, and reconstruction for low and high traffic. Then, a practical expert system was developed to aid pavement maintenance engineers in finding the most effective and efficient M&R strategies and suitable time for the application. The regression models revealed that the effect of routine maintenance and reconstruction is greater in low traffic than in high traffic, while the effect of overlay is greater in high traffic than in low traffic. Based on this initial system, another improved one can be developed using the machine learning technique.

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“…This index is calculated using the type, severity, and quantity of each distress in a specific sample. The pavement section can be qualified based on UDI using the following ratings [1]:…”

Section: Traffic Levelmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

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Prediction of Pavement Maintenance Performance Using an Expert System

2022

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“…Amado (2000) predicted the pavement serviceability rating (PSR) value on the basis of historical data after dividing the data into homogenous groups based on pavement type. Al-Mansour et al (1999) developed a pavement performance model for the Riyadh street network in Saudi Arabia. He categorized pavement sections into groups based on drainage, traffic and maintenance type and predicted the urban distress index, UDI:…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of pavement distress index with limited data on causal factors: an auto-regression approach

Ahmed¹,

Abu–Lebdeh²,

Lyles³

2006

International Journal of Pavement Engineering

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“…With ever-increasing traffic volumes and loads, the need for funding to maintain the serviceability of infrastructures has escalated. Preventive maintenance (PM) treatments have been employed by many highway agencies to retard further deterioration of the pavement surface and restore pavement serviceability (1,2). PM mainly focuses on alleviating pavement distresses, rather than improving the pavement structure, thereby minimizing maintenance costs (3).…”

mentioning

confidence: 99%

Nomogram for Predicting Asphalt Pavement Roughness After Preventive Maintenance Based on Long-Term Pavement Performance Data

Zhang

Gong

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Relative effectiveness has been used as the primary criterion when choosing pavement preventive maintenance (PM) treatments; however, the most effective treatment is not necessarily the most cost-effective treatment because of the higher cost. Cost-effectiveness should be determined by predictive models of PM-treated pavement performance, which has rarely been investigated. In this study, a predictive model for post-treatment pavement roughness—as defined by international roughness index (IRI)—was established utilizing the data from the Long-Term Pavement Performance Program (LTPP) Specific Pavement Study 3 (SPS-3). The generalized least squares (GLS) model was employed to improve the predictive performance by exploiting the characteristics of LTPP panel data. A nomogram was also provided to help highway agencies manually obtain the predicted post-treatment IRI values. Results show that post-treatment IRI was significantly higher in dry and non-freeze areas than in other climate areas. The effect of pavement structure on post-treatment IRI was time-dependent; it was insignificant at the beginning and gradually increased after 4 years. Although post-treatment IRI was affected by pavement structures and climate, the relative effectiveness of different PM treatments was only related to the pre-treatment IRI. Thin overlay significantly improved the pavement IRI, and when pre-treatment IRI was 2.0 m/km, the post-treatment IRI of the thin overlay would be reduced to 0.6 times that of the control. However, there was no significant difference in pavement IRI between different seal treatments.

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Development of Pavement Performance Models for Riyadh Street Network

Cited by 10 publications

References 0 publications

Prediction of Pavement Maintenance Performance Using an Expert System

Prediction of Pavement Maintenance Performance Using an Expert System

Prediction of pavement distress index with limited data on causal factors: an auto-regression approach

Nomogram for Predicting Asphalt Pavement Roughness After Preventive Maintenance Based on Long-Term Pavement Performance Data

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