AASHTO Flexible Pavement Design Equation Study

Baus, R L; Fogg, Jeth A.

doi:10.1061/(asce)0733-947x(1989)115:5(559)

Cited by 5 publications

(5 citation statements)

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“…However, this may be intrinsically related to the chosen design reliability level, and thus, the greater thicknesses obtained. It is evident that other factors may have a greater impact on thickness, such as the resilient modulus of the subgrade [10] which is clear from simulation results. Even the growth rate factor, which is one of the most uncertain parameters to be estimated, has a significant influence on the pavement layer thicknesses because it obviously relates to an increase in the total heavy vehicle traffic.…”

Section: Discussionmentioning

confidence: 79%

“…Among them, semi-empirical or mechanistic empirical methods such as those based on the AASHTO guides [6,7], also based on multilayer elastic system analysis (see [8]), are used in practice. Several studies have been conducted with the aim of assessing the influence of the different inputs on the outcomes of such methods [9][10][11]. As a result of the sensitivity analyses, the important role of some factors was highlighted (for example, the determination of the subgrade resilient modulus, see [10]).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have been conducted with the aim of assessing the influence of the different inputs on the outcomes of such methods [9][10][11]. As a result of the sensitivity analyses, the important role of some factors was highlighted (for example, the determination of the subgrade resilient modulus, see [10]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Impact of Heavy Vehicle Traffic Trends on the Overdesign of Flexible Asphalt Pavements

et al. 2020

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Given their environmental impact, the careful design of asphalt pavements is crucial. Previous research has highlighted the influence of several parameters on the outputs of different pavement design methods. In this study, the focus is on heavy vehicle trends, considering both the percentage of heavy vehicles in the average traffic flow and its evolution over time, which is usually included as a growth factor in the design inputs. Since these factors are very often assumed to be based on old estimates, the first aim of this study was to update them by exploring a recent series of continuous data collected on the Italian motorway network and showing how to infer estimates from historical traffic data. Subsequently, the variability of these input factors is introduced in standard pavement design methods to assess their influence on the design process and to quantify the risk of overdesign. While the analysis of historical heavy vehicle traffic data may reveal an overall zero-growth traffic tendency, different scenarios should be considered and assessed in cost-benefit analyses given the not negligible influence of growth factors on pavement thicknesses. This influence is shown here in different simulated design conditions, with different initial traffic volumes, share of heavy vehicles, and resilient moduli.

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Section: Discussionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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The Impact of Heavy Vehicle Traffic Trends on the Overdesign of Flexible Asphalt Pavements

et al. 2020

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“…The AASHTO-93 design method is based on algorithms developed in the AASHO road test, completed in May 1962 and modified in the design guides published on July 1986 (Baus et al, 1989) and1993. The design formula is represented by Equation 1.…”

Section: Aashto-93 Design Methodsmentioning

confidence: 99%

Determinación probabilística del tiempo de servicio de Estructuras de Pavimentos

2016

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In Chile, as in many South American countries, Keywords: Pavement design, asphalt pavements, probabilistic assessment ResumenEn Chile, como en muchos países de Suramérica, AASHTO 93 es el método oficial para el diseño de pavimentos asfálticos. El método entrega un número estimado de ejes equivalentes estándar de 80 kN que el pavimento puede soportar hasta alcanzar un cierto valor de serviciabilidad. Un factor de seguridad es luego aplicado al número estimado de ejes equivalente tomando en cuenta el nivel de confianza y la varianza estimada de los datos de entrada utilizados en el método, con lo cuál se estima la magnitud del ciclo de vida de la estructura. Normalmente, los datos utilizados en el método AASHTO 93 son asumidos como valores deterministas, sin tomar en cuenta su naturaleza aleatoria. Este trabajo presenta una evaluación probabilística del método de diseño AASHTO 93 de pavimentos bajo condiciones chilenas, el cuáll proporciona un conjunto de funciones de densidad de probabilidad, que permiten realizar la estimación probabilística del ciclo de vida del pavimento en términos del valor del número de ejes equivalente. Para alcanzar el objetivo se desarrollo un modelo de simulación a partir de una muestra de 13 grupos de carreteras ubicados a largo de Chile.Palabras clave: Diseño de pavimentos, pavimentos en asfalto, evaluación probabilística

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“…FWD data was gathered by SCDOT during the design stages of the project, utilizing South Carolina’s traditional procedure given in Flexible Pavement Overlay Design by Dynamic Deflections ( 18 ). This procedure utilizes a two-layer closed-form solution, avoiding the need for matching basin deflections and allowing for a more time-efficient evaluation procedure through the reduction of coring and testing locations.…”

Section: Comparisons With Fwd Datamentioning

confidence: 99%

Use of Traffic Speed Deflectometer Data in Project-Level Pavement Rehabilitation Design

Weitzel,

Pierce,

Carroll

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Current traffic speed deflectometer (TSD) analyses have focused on the application of pavement structural-condition data at the network level. Pavement management systems use the deflection data to adjust the treatment as determined from the surface deflections and ride quality, resulting in better treatment selection. However, TSD data can also be used at the project level to inform pavement design decisions, though this has yet to be well documented in the U.S. A pilot study was conducted in 2021 to develop a methodology to analyze the TSD data for the South Carolina Department of Transportation (SCDOT) based on the AASHTO 1993 overlay design methodology. The TSD analysis methodology determines the effective structural number for each TSD datapoint. The results generated from this analysis were used for an ongoing construction project to assess the structural condition of the pavement within the project limits. Grade restrictions on the project presented a challenge in determining limits of pavement reconstruction and pavement overlay. The results of the TSD analysis were used to characterize the effective structural number at a 52 ft interval, allowing for optimization of the limits of pavement reconstruction. This paper presents the TSD analysis methodology used and how SCDOT incorporated the TSD results at the project level to perform rehabilitation designs of an ongoing construction project.

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AASHTO Flexible Pavement Design Equation Study

Cited by 5 publications

References 0 publications

The Impact of Heavy Vehicle Traffic Trends on the Overdesign of Flexible Asphalt Pavements

The Impact of Heavy Vehicle Traffic Trends on the Overdesign of Flexible Asphalt Pavements

Determinación probabilística del tiempo de servicio de Estructuras de Pavimentos

Use of Traffic Speed Deflectometer Data in Project-Level Pavement Rehabilitation Design

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