Part 1: Pavement Management: Analysis of Long-Term Effectiveness of Thin Hot-Mix Asphaltic Concrete Overlay Treatments

Labi, Samuel; Lamptey, Geoffrey; Konduri, Sravanthi; Sinha, Kumares C.

doi:10.3141/1940-01

Cited by 13 publications

(4 citation statements)

References 2 publications

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“…where x = log (1 + SV) and SV = 2.2704 IRI2. Labi, Lamptey, Konduri, and Sinha (2005) Based on the aforementioned arguments and to be consistent with other studies and the experience of road authorities, this study adopts the IRI as the key indicator of road performance.…”

Section: Road Pavement Deterioration and Performance Criteriamentioning

confidence: 81%

Measuring pavement maintenance effectiveness using Markov Chains analysis

Mandiartha

Duffield

Thompson

et al. 2016

Structure and Infrastructure Engineering

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This study assesses the effectiveness of road network pavement maintenance using Markov Chain analysis based on historical costs and road roughness progression data. The analysis is based on a database developed by the State of Victoria, Australia, consisting of 2,197 road sections. The analysis measures maintenance effectiveness using the criterion of whether road sections remain in the same condition state or move to the next worst state based on a predefined roughness value. Principal inputs for the stochastic models, such as the development of transition probability matrices and associated cost functions, are discussed. Results show that, within the current budget envelop and only undertaking routine maintenance, the probability of road sections remaining in the same condition state, which is a determinant of maintenance effectiveness, exhibits a declining tendency from good to worst condition states. This finding prompts the discussion on when to increase maintenance expenditure such that the whole of life outcome is optimized. The method discussed in this paper provides tools for road authorities to select the appropriate maintenance action for each condition state of pavements based on the comparison analysis of the results of Markov Chain for different types of maintenance actions.

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Section: Road Pavement Deterioration and Performance Criteriamentioning

confidence: 81%

Measuring pavement maintenance effectiveness using Markov Chains analysis

Mandiartha

Duffield

Thompson

et al. 2016

Structure and Infrastructure Engineering

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“…Each distress type within the above-named categories of surface distresses can constitute a performance indicator for purposes of pavement condition evaluation. For example, rutting is a common performance indicator that has been used to evaluate pavement condition in a significant number of studies (Hall, Correa, & Simpson, 2003;Irfan, Khurshid, & Labi, 2009;Labi, Lamptey, Konduri, & Sinha, 2005). However, given all the different types of distresses, choosing one type of distress to evaluate overall pavement condition may not be appropriate for making rehabilitation decisions.…”

Section: -30mentioning

confidence: 99%

Integration and Evaluation of Automated Pavement Distress Data in INDOT’s Pavement Management System

Alinizzi¹,

Qiao²,

Kandil³

et al. 2018

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This study was in two parts. The first part established and demonstrated a framework for pavement data integration. This is critical for fulfilling QC/QA needs of INDOT's pavement management system, because the precision of the physical location references is a prerequisite for the reliable collection and interpretation of pavement data. Such consistency is often jeopardized because the data are collected at different years, and are affected by changes in the vendor, inventory, or referencing system or reference points. This study therefore developed a "lining-up" methodology to address this issue. The applicability of the developed methodology was demonstrated using 2012-2014 data from Indiana's highway network. The results showed that the errors in the unlined up data are significant as they mischaracterize the true pavement condition. This could lead to the reporting of unreliable information of road network condition to the decision makers, ultimately leading to inappropriate condition assessments and prescriptions. Benefits of the methodology reverberate throughout the management functions and processes associated with highway pavements in Indiana, including pavement performance modeling, optimal timing of maintenance, rehabilitation, and reconstruction (MRR), and assessment of the effectiveness of MRR treatments and schedules. The second part of the study developed correlations for the different types of pavement distresses using machine learning algorithms. That way, the severity of any one type of distress can be estimated based on known severity of other distresses at that location. The 2012-2014 data were from I-70, US-41, and US-52, and the distress types considered are cracking, rutting, faulting, and roughness. Models were developed to relate surface roughness (IRI) to pavement cracks, and between the different crack types, with resulting degrees of confidence that varied across the different crack types and road functional classes. In addition, for each functional class and for each crack type, models were built to relate crack depth to crack width. The concept can be applied to other distress types, such as spalling, bleeding, raveling, depression, shoving, stripping, potholes, and joint distresses, when appropriate data are available.

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“…The previous maintenance data could be another approach to establish a systematic maintenance program. For instance, IRI threshold was acquired from the mean of past IRI values before the treatment, as seen in [8] - [11]. However, it could not clarify if the data was collected based only on engineering aspects or biased by politics, which could preempt the treatment decision.…”

Section: Literature Reviewmentioning

confidence: 99%

Determining the Effectiveness of Asphalt Concrete Overlays on Rigid Pavement Using Discrete-Event Simulation

Athigakunagorn¹

2017

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Abstract. The budget for preserving highway pavement conditions is increasing every year, as well as the impulse to seek transparency of maintenance decisions. This paper presents a new paradigm in applying discrete-event simulation (DES) to capture an indeterministic process of rigid pavement deterioration with an implementation of rehabilitation treatment, namely, asphalt concrete (AC) overlays. This paradigm should facilitate decision makers in establishing the most appropriate maintenance procedure. In this study, the deterioration model was constructed and run over 50 simulated years with various numbers of International Roughness Index (IRI) to determine the rehabilitation cost and average IRI over the analysis period. The result shows that when focusing only on the treatment cost, the best treatment type is Minimal Surface Preparation with 4" AC overlay (MSP). If the treatment performance is considered as the objective for decision making, Crack/Break and Seat section with 8" AC overlay (CB&S8) provides the best pavement condition. Nevertheless, when both objectives are simultaneously considered, Crack/Break and Seat section with 4" AC overlay (CB&S4) outperforms the other rehabilitation types, specifically when the treatment is implemented on a good condition pavement with a relatively low IRI. MSP is, however, the most suitable when a pavement is in a less preferable condition with a relatively high IRI.

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Part 1: Pavement Management: Analysis of Long-Term Effectiveness of Thin Hot-Mix Asphaltic Concrete Overlay Treatments

Cited by 13 publications

References 2 publications

Measuring pavement maintenance effectiveness using Markov Chains analysis

Measuring pavement maintenance effectiveness using Markov Chains analysis

Integration and Evaluation of Automated Pavement Distress Data in INDOT’s Pavement Management System

Determining the Effectiveness of Asphalt Concrete Overlays on Rigid Pavement Using Discrete-Event Simulation

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