2014Transportation Research Record Journal of the Transportation Research BoardRequires access

Aggregating and Modeling Automated Pavement Condition Survey Data for Flexible Pavements for Use in Pavement Management

Jeremy Lea, John Harvey, Ester Tseng

Open publisher page 9 citations

Abstract

The paper documents part of the initial engineering configuration of the new California pavement management system, known as PaveM. This paper examines the aggregation of flexible pavement cracking data from the automated pavement condition survey to network management sections and the combination of these measurements into condition variables, along with the decision tree that is used to determine treatments. The performance models used to predict condition are detailed, along with the statistical methodology used to develop them. The paper discusses some of the pitfalls of using highly detailed survey data, along with the benefits, and the lessons learned in the process.

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What this paper is about

The paper documents part of the initial engineering configuration of the new California pavement management system, known as PaveM. This paper examines the aggregation of flexible pavement cracking data from the automated pavement condition survey to network management sections and the combination of these measurements into condition variables, along with the decision tree that is used to determine treatments. The performance models used to predict condition are detailed, along with the statistical methodology used to develop them. The paper discusses some of the pitfalls of using highly detailed survey data, along with the benefits, and the lessons learned in the process.

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OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The paper documents part of the initial engineering configuration of the new California pavement management system, known as PaveM. This paper examines the aggregation of flexible pavement cracking data from the automated pavement condition survey to network management sections and the combination of these measurements into condition variables, along with the decision tree that is used to determine treatments. The performance models used to predict condition are detailed, along with the statistical methodology used to develop them. The paper discusses some of the pitfalls of using highly detailed survey data, along with the benefits, and the lessons learned in the process.

Key concepts: Pavement management, Pavement engineering, Computer science, Process (computing), Civil engineering, Engineering, Transport engineering, Geography

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