Evaluation of Design Procedure and Performance of Continuously Reinforced Concrete Pavement According to AASHTO Design Methods

Moharekpour, Milad; Liu, Pengfei; Schmidt, Joshua H.; Oeser, Markus; Jing, Ruxin

doi:10.3390/ma15062252

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…The mechanical response of various pavements under the practical traffic load is obtained and analyzed. In the late 1950s, experimental roads of American Association of State Highway (AASHTO) were paved, which still had an influence on the development of pavement technology worldwide (Metcalf, 2016;Milad et al, 2022). The MnROAD project in Minnesota, United States, provided a foundational platform for analyzing and comparing the service performance of various pavement structures (Burnham et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Characteristics and analysis of static strain response on typical asphalt pavement using fiber Bragg grating sensing technology

Liu,

Chen,

Cao

et al. 2024

Front. Built Environ.

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To study the real internal strain response of asphalt pavement and provide crucial data for optimizing pavement design. By burying the fiber grating sensors on site, the strain tests of four asphalt pavement structures under different working conditions were carried out, and the results showed that the static strain time curve is viscoelastic and conforms well to the Bugers model, and the fitting coefficient of determination is 0.98. The strain response of the asphalt surface courses of the four pavement structures under static load shows a double hump variation with the transverse position, with peaks occurring directly beneath the wheel load center. The transverse strain fluctuated between tension and compression, mirroring changes in the lateral position. While longitudinal strains, always tensile, were symmetrically aligned with the centerline of the longitudinal sensors, this pattern differed notably from that of the transverse sensors. In the base layers, the strain profile typically presented a single peak, located at the wheel gap, underscoring a critical area of stress concentration. Numerically, the peak strain of asphalt surface course is larger than that of base course. The most unfavorable loading position of the base course occurs at the wheel gap of the lower base course. The most adverse loading position of the surface course appears at the wheel load at the bottom of the upper or middle course. The research results can provide data support for improving the design method of asphalt pavement.

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Section: Introductionmentioning

confidence: 99%

Characteristics and analysis of static strain response on typical asphalt pavement using fiber Bragg grating sensing technology

Liu,

Chen,

Cao

et al. 2024

Front. Built Environ.

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“…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%

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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“…In this study using AASHTO 1993 (American Association of State Highway and Transportation Officials) and MDPJ 2017 (Pavement Design Manual) as methods to produce pavement thickness (Abdollahi et al, 2022;Moharekpour et al, 2022;Nasution et al, 2019;Syuhada et al, 2022). And able to be passed by ODOL (Over Dimension Over Load) vehicles on the design of rigid pavement (Rigid Pavement) in Penungkal Abab Lematang Ilir Regency.…”

Section: Introductionmentioning

confidence: 99%

Planning of Rigid Pavement Thickness in The Construction of The Sp. Door - Sp. Benakat Penungkal Regency Abab Lematang Ilir

Nisumanti

Julian

Qubroh

2022

JAETS

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The access section to Benakat Village, Pali Regency is a special road made by the company, with the condition of the existing land being unpaved with a length of 1,375 meters and a width of 7 meters, and has severe damage triggered by vehicles with excess capacity or ODOL (Over Dimension Over Load)) that pass through that path. The purpose of this study was to determine the results of the calculation of rigid pavement thickness planning by comparing the 1993 AASHTO and 2017 MDPJ methods so that the road Sp. Doors – Sp. Benakat is able to accommodate ODOL vehicles. This study uses AASHTO 1993 and MDPJ 2017 as methods to produce rigid pavement thickness (Rigid Pavement) so that ODOL vehicles can pass. Based on the results of data analysis and calculations, the input indicators in each method are different for each method so that AASHTO 1993 produces a thickness of 35 cm, and in MDPJ it produces a thickness of 28.5 cm

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Evaluation of Design Procedure and Performance of Continuously Reinforced Concrete Pavement According to AASHTO Design Methods

Cited by 7 publications

References 15 publications

Characteristics and analysis of static strain response on typical asphalt pavement using fiber Bragg grating sensing technology

Characteristics and analysis of static strain response on typical asphalt pavement using fiber Bragg grating sensing technology

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

Planning of Rigid Pavement Thickness in The Construction of The Sp. Door - Sp. Benakat Penungkal Regency Abab Lematang Ilir

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