Development of network-level pavement deterioration curves using the linear empirical Bayes approach

Pantuso, Antonio; Flintsch, Gerardo W.; Katicha, Samer W.; Loprencipe, Giuseppe

doi:10.1080/10298436.2019.1646912

Cited by 31 publications

(16 citation statements)

References 17 publications

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“…Long and short-term planning of maintenance and rehabilitation activities is the major tool for maximizing proper network conditions at the lowest possible cost and requires accurate and robust deterioration models for pavement networks. A deterioration model (DM) predicts future pavement conditions and helps agencies identify the most effective maintenance and rehabilitation activities [9,10], and such planning and optimization become more critical when agencies face budget reduction or are otherwise budget constrained [3].…”

Section: Introductionmentioning

confidence: 99%

“…Markov probabilistic modeling uses samples of probabilistic models, with the transition process represented by a pavement-performance curve [12]. Using information from the pavement's "before" state, the Markov process predicts the "after" state [9]. The Markov transition method is useful in network-level applications where neither historical data nor good regression equations are available [16].…”

Section: Introductionmentioning

confidence: 99%

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Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

2020

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This paper describes the process and outcome of deterioration modeling for three different pavement types (asphalt, concrete, and composite) in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). In the state of Iowa, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the long/short-term memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that the LSTM model achieved a higher prediction accuracy over time for all different pavement types.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

2020

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“…The condition of road pavement is vulnerable to the impact of uncertain environmental factors and traffic loads, resulting in pavement deterioration over time (Chou & Le, 2011;Elhadidy et al, 2015;Neal & Pro, 2020;Pantuso et al, 2019). The deteriorating pavement is prone to cause severe structural damage and shorten the service life (U.S. Department of Transportation, Federal Highway Administration, 2020; Biondini & Frangopol, 2016;Roads & Bridges, 2020;Kim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Multi-Objectives Optimization Approach on Modelling Pavement Maintenance Strategies for Pavement Sustainability

Xue

Wang

et al. 2020

Journal of Civil Engineering and Management

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Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability.

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“…Pavement deterioration is recognized to be uncertain due to the inherent variability in the material properties and the big challenge of collecting good quality data for performance modeling. To account for this uncertainty, researchers have developed various probabilistic models to simulate the deterioration process and forecast future conditions [4][5][6][7]. e uncertainty of pavement deterioration also leads to the variability in both M&R effectiveness and timing.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Maintenance Probability and Resurfacing Thickness Based on the Pavement Management System

Jiang

2020

Advances in Civil Engineering

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In order to investigate the key factors and analyze their effects on maintenance and rehabilitation (M&R) strategies, data for 2495 pavement sections were collected from the pavement management system (PMS), including pavement performance data, traffic data, material property data, and M&R record data. Logistic regression was first employed to explore the influential factors on maintenance probability. Afterward, the classification tree model was established to find out the key factors on resurfacing thickness. Results showed that road sections with higher IRI, rutting depth (RD), deterioration rate of surface friction coefficient (DRSFC), pavement patching ratio (PPR), and transverse cracking severity index (TCSI) before treatment had significantly higher maintenance probability, which could be quantified by the developed logistic model. Moreover, treatments implemented on bridge decks tended to have greater resurfacing thickness. For pavement M&R projects, with the tensile strength ratio (TSR) of top layer materials higher than 88.7% and pretreatment SFC higher than 49, the resurfacing thickness would be thinner. For bridge M&R projects, middle layer TSR higher than 88.3% led to thinner overlays, and much thinner resurfacing thickness can be observed if pretreatment RD was less than 8.72 mm. When middle layer TSR was lower than 88.3% and pretreatment IRI was higher than 2.383 m/km with larger AESAL, the resurfacing thickness would probably be the thickest. The two models built in this paper provided probabilistic estimation of maintenance probability and explored key factors together with their critical split points for resurfacing thickness, which could be regarded as an alternative decision-making tool for pavement engineers.

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Development of network-level pavement deterioration curves using the linear empirical Bayes approach

Cited by 31 publications

References 17 publications

Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

An Integrated Multi-Objectives Optimization Approach on Modelling Pavement Maintenance Strategies for Pavement Sustainability

Factors Affecting Maintenance Probability and Resurfacing Thickness Based on the Pavement Management System

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