Development of Multiple-Choice Default Load Spectra Inputs Based on Road Types for the Texas Mechanistic-Empirical Flexible Pavement Design System
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 148, Issue 2
Abstract
Traffic loading is one of the key factors considered in pavement design. It is anticipated that the use of axle load spectra provides more accurate traffic-loading inputs than the traditional equivalent single-axle load (ESAL) value. Previous studies were carried out to develop the statewide default axle load spectra or propose theoretical methods for modeling axle load spectra. Different from previous studies, this paper determined multiple-choice default load spectra inputs for different types of roads with use of traffic data collected from a large number of permanent Weigh-In-Motion (WIM) stations and portable WIM stations across the state of Texas. Types of roads include interstate highways (IH), US/SH highways, farm-to-market (FM) roads, and roads in energy sectors. Characteristics of traffic data for different types of roads were investigated. It is found that there are substantial differences in the axle load distributions between different types of roads. Sensitivity analysis results indicate that significant differences exist in the axle load distributions and vehicle class distributions for IH and US/SH roads based on the WIM data. Multiple-choice default load spectra inputs based on road types are determined and implemented in the Texas Mechanistic-Empirical Flexible Pavement Design System. It is demonstrated that the developed multiple-choice default load spectra inputs have the advantages of considering various characteristics of axle load distribution (ALD) and vehicle class distribution (VCD) for different types of roads and allowing more accurate pavement design than the traditional ESAL input.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The types of data available include laboratory test data and analysis results presented in the figures.
Acknowledgments
The authors acknowledge the financial support provided by Texas Department of Transportation (TxDOT).
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part B: Pavements
Volume 148 • Issue 2 • June 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 1, 2021
Accepted: Sep 16, 2021
Published online: Jan 27, 2022
Published in print: Jun 1, 2022
Discussion open until: Jun 27, 2022
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