Contracting for Pavement Distress Data Collection

Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed.

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“…The use of Bayesian methodology is not new. Smith et al [36] developed a model relating pavement distresses with various design variables.…”

Section: Bayesian Networkmentioning

confidence: 99%

Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

2021

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“…Although the comparison with results from the manual survey was still questionable, the ability to detect and measure pavement distress was conclusively shown. The most recent comparison was completed in Washington State (5). One of the conclusions of this latest study was that better techniques needed to be developed to evaluate automated distress measurement equipment.…”

Section: Variability Of Distress Data Collectionmentioning

confidence: 99%

Structured Approach to Managing Quality of Pavement Distress Data: Virginia Department of Transportation Experience

Larson

Sami

Luhr

2000

Transportation Research Record: Journal of the Transportation R

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The Virginia Department of Transportation (VDOT) Pavement Management Program has examined and improved the quality of condition data to carry out pavement performance analysis and multiyear work planning. In 1995 the agency changed the way it gathered pavement condition data, moving from subjective windshield surveys to using automated condition survey equipment. In 1997 the agency acquired the staff and resources to examine these data and respond to concerns of district personnel that the data were inconsistent and not representative of actual conditions. At that time, VDOT acknowledged the need for a formal and large-scale quality assurance program for its pavement condition data. It was shown in 1998 that standardization of test methods and calibration of equipment for roughness measurement yielded data of much higher quality than that in previous years. Quality data are crucial to the sound functioning of a pavement management system. It has been found that ( a) distress data quality is a serious problem and the data can potentially be bad enough to be completely useless and ( b) improving or reengineering distress data quality requires a significant effort. A structured approach to develop and implement a pavement distress data quality program at VDOT is described, including the processes, statistical details, and a clear vision of needs. Most important, this reengineering effort involves attending to the data collection process by building controls at critical junctures during the project in order to deliver a quality data product in time and on budget.

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“…in Washington and Oregon to define the accuracy of data needed by the agencies with an evaluation of certain participating vendors using semiautomated data collection methods is described in one of the studies (8). Initial guidelines about quality control and quality assurance are provided based on discussion with some agencies contracting for pavement distress data collection.…”

mentioning

confidence: 99%

Effects of Comprehensive Quality Assurance/Quality Control Plan on Pavement Management

Shekharan

Frith²,

Chowdhury

et al. 2007

Transportation Research Record: Journal of the Transportation R

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Highway agencies have intuitively known for years about the need for high-quality data in their pavement management systems. Until now, however, it has been difficult to arrive at the value of good data. In Virginia, pavement condition data for a large network of roads have been obtained from digital continuous videos and interpreted by using a semiautomated software process. A detailed quality assurance process has been developed and applied to achieve the desired high-quality data. The project includes quality assurance that has been carried out since the inception of the project, including the application of necessary adjustments in the data collection process, to ensure that quality data conforming to predefined standards are obtained. Further, during production the data are continually monitored with the goal of attaining high-quality data. When the volume of data is large as in the present case, continual application of a quality assurance process is vital not only to prevent major changes at any stage but also to provide data that are usable as they are available. The effects of a complete and comprehensive quality monitoring plan, including quality control, quality assurance, and an independent validation and verification, on pavement management data and the resulting budgetary estimates are quantified. Pre- and postindependent validation and verification results were analyzed to determine the effects of a comprehensive quality monitoring plan on pavement distress data collection.

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Contracting for Pavement Distress Data Collection

Cited by 8 publications

References 1 publication

Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

Structured Approach to Managing Quality of Pavement Distress Data: Virginia Department of Transportation Experience

Effects of Comprehensive Quality Assurance/Quality Control Plan on Pavement Management

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