Enhancing Network-Level Decision Making through the use of a Structural Capacity Index

Abstract

The objective of this study was to show potential applications of a network-level structural index developed for evaluation of flexible pavements. First, several potential applications for implementation of network-level structural measures were identified, and then data from the state of Virginia were used to modify the proposed applications for the index and show examples of them. Several applications were validated with data from network-level deflection testing with the falling weight deflectometer on Interstate highways in Virginia and data from the Virginia Department of Transportation Pavement Management System. The results of the research indicate that including the structural index in the network-level decision process can facilitate a greater understanding of the behavior of the performance of a pavement. Furthermore, the index that was proposed for network-level evaluation of flexible pavements in Virginia was used to develop enhanced deterioration models for particular pavement treatments and to demonstrate the dependence of pavement performance on its structural capacity. It was shown that the functional characteristics of a pavement alone were not adequate to describe the structural condition of the pavement. Therefore, the structural condition, for example, as based on the results of deflection testing, should be considered when network-level pavement management decisions are made.

Research Trends in Pavement Management during the First Years of the 21st Century: A Bibliometric Analysis during the 2000–2013 Period

“…Therefore, different decisions are taken if only functional condition parameters are employed, or if deflection tests are included in the decision process [88]. Hence, the introduction of a structural capacity index is suggested in network-level decisions to find the most cost-effective rehabilitation option [89]. On this basis, simplified deflection-based analytical techniques [90] and a new index related to the Structural Capacity Index (SCI) [91] have been introduced.…”

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

“…In most Pavement Management Systems (PMSs), nonstructural indices such as Pavement Condition Index (PCI) are used as pavement indicators to select treatments [3,5] while ignoring the structural conditions of pavement [6]. It has recently been proven that there is a statistical relationship between functional and structural conditions [7]. Hence in recent years, various agencies around the world have attempted to use indices of structural capacity in PMS and decision-making processes [6].…”

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

“…In most Pavement Management Systems (PMSs), non-structural indices such as Pavement Condition Index (PCI) are used as pavement indicators to select treatments [3,5] while ignoring the structural conditions of pavement [6]. It has recently been proven that there is a statistical relationship between functional and structural conditions [7]. Hence in recent years, various agencies around the world have attempted to use indices of structural capacity in PMS and decision-making processes [6].…”