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

Ahmed, Kamran; Abu–Lebdeh, Ghassan; Lyles, Richard W

doi:10.1080/10298430500502017

Cited by 16 publications

(6 citation statements)

References 4 publications

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“…The DI value starts at 0, which indicates perfect pavement conditions. Although there is no upper limit for the DI value, the MDOT uses a threshold DI of 50 to indicate the need for pavement rehabilitation or reconstruction (Ahmed et al 2006). The authors used DI to determine the surface roughness of a pavement to calculate direct impact on vehicle energy consumption and environmental impacts (Lee et al 2002).…”

Section: Integrated Life-cycle Assessment and Life-cycle Cost Analysimentioning

confidence: 99%

Network-Level Pavement Asset Management System Integrated with Life-Cycle Analysis and Life-Cycle Optimization

Zhang

Keoleian

Lepech

2013

J. Infrastruct. Syst.

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The authors have developed a new network-level pavement asset management system (PAMS) utilizing life-cycle analysis and optimization methods. Integrated life-cycle assessment and cost analysis expand the scope of the conventional network-level PAMS from raw material extraction to end-of-life management. To aid the decision-making process, the authors applied a life-cycle optimization model to determine the near-optimal preservation strategy for a pavement network. The authors utilized a geographic information system (GIS) model to enhance the network-level PAMS by collecting, managing, and visualizing pavement information data. The network-level pavement asset management system proposed in this paper allows decision makers to preserve a healthy pavement network and minimize life-cycle energy consumption, greenhouse gas (GHG) emissions, or cost as a single objective, and also meet budget constraints and other agency constraints within an analysis period. A case study of a pavement network in Michigan compares the near-optimal preservation strategy to the Michigan DOT's current preservation practice. Compared with the current preservation plan, the optimal preservation strategy reduces life-cycle energy consumption, GHG emissions, and cost by 20, 24, and 10%, respectively. The authors also analyzed the impact of annual preservation budget cuts on total life-cycle cost. A US$3 million annual preservation budget reduction (75% reduction of current annual budget) will significantly increase user cost (caused by congestion and pavement surface deterioration) by US$450 million for a 40-year analysis period.

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Section: Integrated Life-cycle Assessment and Life-cycle Cost Analysimentioning

confidence: 99%

Network-Level Pavement Asset Management System Integrated with Life-Cycle Analysis and Life-Cycle Optimization

Zhang

Keoleian

Lepech

2013

J. Infrastruct. Syst.

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“…Despite significant research on predicting the future pavement condition spanning over 40 years, certain challenges persist, including the need for high predictive power due to varying rates of deterioration influenced by geographical environment, climate, equivalent single axial load (ESAL), vehicle-type distribution, and material formulation characteristics [11][12][13][14][15]. Although some studies adopted the international roughness index (IRI) and the distress index, which can represent various distresses that occur on the road pavement surface [16][17][18], they could not achieve high predictive power due to the impact on material properties (ductility and brittleness) and traffic volume distribution. The NC-DoT (North Carolina Department of Transportation) in the U.S. is actively researching how to effectively maintain the 79,000-mile network in North Carolina through cost-benefit analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Concrete Pavement Condition for Sustainable Management: Unveiling the Development of Distresses through Machine Learning

Jung,

Lee,

Baek

et al. 2024

Sustainability

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This study presents a machine learning model for predicting representative surface distresses (crack, durability, patching, joint spall) in concrete pavements, focusing on South Korean examples. It thoroughly analyzes specific distress types using time series data to understand their development over time, aiming to surpass traditional regression methods in forecasting pavement conditions. The research fills a gap by applying machine learning algorithms to detailed long-term data, enhancing the accuracy of distress progression predictions, which is crucial for efficient pavement management. A notable aspect of this study is the use of particle filtering, recognized for its effective resampling in analyzing time series data. To validate predictions, we compared the results from particle filtering with those from traditional regression models, long short-term memory (LSTM) networks, and Deep Neural Networks (DNNs). The accuracy varied significantly, with differences ranging from 3.32% to 23.64%, indicating particle filtering’s suitability for time-series-based pavement condition predictions. These findings are especially relevant in the context of current image-based machine learning and AI research in pavement distress detection and prediction. This research offers a comprehensive reference that is especially valuable due to the lack of studies using long-term usage data, thereby making a significant contribution to pavement management research and practice.

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“…The order of the auto-regression model will depend on the number of lags used; for example, by using only one lag ( K = 1) in Equation 4 will result in an auto-regression model of order one, by using two lags ( K = 2) will result in an auto-regression model of order two, and so on. The goodness of the auto-regression model is reflected in the capabilities of incorporating an unknown factor that affects pavement deterioration, by including the previous pavement conditions to explain the present value ( 16 ).…”

Section: Pavement Performance Modelsmentioning

confidence: 99%

Method to Reduce Uncertainty in the Prediction of Pavement Condition With a Lower Sample Frequency

Goenaga

Karanam

Underwood

2022

Transportation Research Record: Journal of the Transportation R

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In this paper, the effect of missing pavement condition observations in the predictions of the future state of a road network was evaluated. Real data from North Carolina were used for this purpose. First, the auto-regression method was compared against the most common “family-curve” modeling approach. It was found that the auto-regression method improves the predictive accuracy of predictions, at both project and network levels. By using the auto-regression method over the “family-curve” approach it is possible to reduce, on average, the Mean Absolute Percent Error of the predictions by 40%. Second, this paper evaluates the case in which a reduced survey frequency is unavoidable, and state highway agencies might need to plan the network maintenance based on historical observations and the subsample of the current condition. Observations of the Pavement Condition Rating for years 2013–2019 were used to define four different scenarios of reduced survey frequency: Scenario 1—“business-as-usual,” where the entire network is surveyed every year; Scenario 2—“reduced-sampling,” analyzed the case where the entire network is surveyed every other year; Scenario 3—“halfway-sampling,” evaluates the case where only half of the network is surveyed every year; and Scenario 4—“least-sampling,” considers the case where only a third of the network is monitored every year. Scenario 1 was used as the baseline of comparison, and as expected it was found that whenever possible the network should be monitored annually; however, if that is not feasible the best option, from the ones evaluated in the paper, should be Scenario 3.

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

Cited by 16 publications

References 4 publications

Network-Level Pavement Asset Management System Integrated with Life-Cycle Analysis and Life-Cycle Optimization

Network-Level Pavement Asset Management System Integrated with Life-Cycle Analysis and Life-Cycle Optimization

Predicting Concrete Pavement Condition for Sustainable Management: Unveiling the Development of Distresses through Machine Learning

Method to Reduce Uncertainty in the Prediction of Pavement Condition With a Lower Sample Frequency

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