The effect of axle load spectra from AASHTO method on flexible pavement performance

Santos, Thaís Aparecida dos; Júnior, Carlos Alberto Prado da Silva; Fontenele, Heliana Barbosa

doi:10.4025/actascitechnol.v41i1.35117

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…The resilient modulus of subgrade soil can be calculated using Equation 1. This equation can be changed, where it depends on the testingexperimental method in obtaining the resilient modulus of the subgrade (roadbed soil) [4], [6], [8].…”

Section: Subgrade Layermentioning

confidence: 99%

“…This research will focus on the design of flexible pavement because it is the most used. There are two main methods used for pavement design which are AASHTO 1993 empirical method and Mechanistic Empirical Pavement Design Method Guide (MEPDG) [7], [8]. although AASHTO 1993 empirical method is old and has some limitations and drawbacks but still it is the most used method because it is much simpler and requires less data than MEPDG methods which is more accurate, but it is more sophisticated and requires a lot of data which is not always available for different transportation agencies.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Depth of Flexible Pavement Layers Using Artificial Neural Network

ElSahly¹,

AlQahtani²,

Abdelfatta³

2023

World Congress on Civil, Structural, and Environmental Engineering

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Transportation infrastructure is a vital component in achieving economic growth and nations' development. Pavement structures constitute major component in the infrastructure. The purpose of the study is to provide a model that can estimate the thickness of the flexible pavement layers based on; the estimated number of 18000 lb single axle load application (W18), resilient modulus of the subgrade (Mr), modulus of elasticity of the three layers (EAC, Ebase, and Esubbase) using Artificial Neural Network (ANN). since that the developed standards by AASHTO 1993 of designing flexible pavement do not provide a direct and a simple way in estimating the thickness of the three layers of flexible pavement (asphalt concrete, base, and subbase layers). Although the American Association of State Highway and Transportation Official (AASHTO) 1993 empirical procedure is an old method and has some limitations, it has been used instead of the Mechanistic Empirical Pavement Design Method Guide (MEPDG). Since the it is simpler than the MEPDG, where the MEPDG requires a lot of data in which is not always available for different transportation agencies in most of the developing countries. The results of the ANN model show a decent prediction of the depths of flexible pavement layers, since the R2 value is 0.99 (close to 1.0) and the MSE value is 0.28 (close to zero), which indicates strong correlation, accuracy, and low inconsistency between the observed and predicted thickness of the flexible pavement layers.

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Section: Subgrade Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of the Depth of Flexible Pavement Layers Using Artificial Neural Network

ElSahly¹,

AlQahtani²,

Abdelfatta³

2023

World Congress on Civil, Structural, and Environmental Engineering

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show abstract

“…This defect consists of a progressive degradation of the material properties due to the application of cyclic stresses which value is lower than the material resistance [1]. It is typically caused by elastic strains derived from traffic load repetitions, which leads to failure of the surface course [2]. As each of these layers plays a particular role in the pavement structure and is comprised by a specific type of material, it is necessary to consider their respective elastic stiffness and thicknesses in the distribution of stresses and strains within the pavement.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the mechanistic-empirical method has been increasingly used for pavement design [2]. In the United States, Canada and some South American countries, the pavement are designed by the Mechanist-Empirical Pavement Design Guide (MEPDG) developed by AASHTO [5].…”

Section: Introductionmentioning

confidence: 99%

Use of mechanistic-empirical method of pavement design for performance sensitivity analysis to asphalt pavement fatigue

Silva

Pitanga

et al. 2021

Matéria (Rio J.)

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In the new Brazilian mechanistic-empirical design method of asphalt pavements, MeDiNa, the characterization of permanent deformation (PD) for the selection of soils and gravel is based on tests performed with at least 150,000 loading cycles for each of the nine specimens indicated in the DNIT standard. Despite providing information about the material behavior under a wide range of testing conditions, the experimental program related to these PD characterizations is time consuming and it is believed that it can be optimized. This paper evaluates the influence of the number of loading cycle applications on the characterization of the materials. For this purpose, seven materials were analyzed at their optimum moisture content (OMC) and one of them was also compacted in a condition above the OMC, in a total of eight data sets. Statistical regression analyzes were performed to identify the parameters of the predictive model for different numbers of cycles and the PD predictions for the different materials were compared. From these results, simulations were performed in the MeDiNa software to predict the performance of the materials. Four different N values were evaluated, considering 150,000 cycles as reference: discarding the 500 first cycles, but considering the PD accumulated in that interval; discarding the 500 first cycles and considering the PD accumulated in that interval; final N of 80,000; and final N of 100,000. For the analyzed materials, no significant differences were observed in the PD prediction, even considering tests with 50,000 or 70,000 cycles less than the 150,000 cycles required in the standard. This indicates that, although characterization is recommended following standardized procedures, the experimental program of the current PD standard can possibly be significantly optimized by reducing the number of cycles applied to materials in laboratory tests. This possibility must be analyzed for each material.

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Using mechanistic-empirical simulations and nonparametric testing to explore variability in the expected performance of reference asphalt pavement designs

Moins,

Hernando,

Seghers

et al. 2024

Construction and Building Materials

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The effect of axle load spectra from AASHTO method on flexible pavement performance

Cited by 6 publications

References 13 publications

Estimation of the Depth of Flexible Pavement Layers Using Artificial Neural Network

Estimation of the Depth of Flexible Pavement Layers Using Artificial Neural Network

Use of mechanistic-empirical method of pavement design for performance sensitivity analysis to asphalt pavement fatigue

Using mechanistic-empirical simulations and nonparametric testing to explore variability in the expected performance of reference asphalt pavement designs

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