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 9 publications

(4 citation statements)

References 16 publications

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“…When factoring in these costs, the total expenses for JPCP amount to EUR 910,800, demonstrating that a critical analysis of life cycle costs provides a more nuanced understanding of the economic implications. The CRCP, on the other hand, only requires a punchout repair after the 40th year of service, as well as overlaying the concrete pavement with a SMA [64,65]. The costs for this amount to EUR 129,443.75.…”

Section: Agency Costmentioning

confidence: 99%

An Evaluation of the Economic Viability and Accessibility of CRCP and JPCP: A Comparative Analysis

Moharekpour,

Shokri,

Wellerdick

et al. 2024

Sustainability

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Road infrastructure serves as a foundational driver of a nation’s economic and cultural growth. Incorporating life cycle cost analysis (LCCA), as well as considerations of availability and environmental impact, enables policymakers to make strategic decisions that not only enhance fiscal efficiency but also support sustainable progress. This paper centers on an in-depth examination of two prevalent pavement technologies: continuously reinforced concrete pavements (CRCP) and jointed plain concrete pavements (JPCP). It specifically delineates the application of these methods to a hypothetical one-kilometer motorway construction in Germany. Employing LCCA for concrete pavements, the paper evaluates long-term fiscal prudence among alternative investment opportunities, factoring in resource utilization—both materials and machinery—and long-term care and upkeep obligations over the pavements’ operational lifespans. The analysis extends to appraise agency expenditures associated with the pair of pavement strategies and estimates the concomitant delay durations and costs relevant to the exemplar project. Central to this research is the investigation of road availability and its quantifiable influence on traffic efficacy, parsing through metrics such as the tally of days roads are out of service and the subsequent repercussions on vehicular flow. The investigation also proposes strategies for the reduction of embodied carbon in CRCP and JPCP systems. While accounting for variances in functional performance and vehicular comfort levels, this study contributes scientifically by tackling pragmatic engineering dilemmas involved in pavement selection, with a spotlight on minimizing costs, curtailing traffic interruptions, and mitigating ecological impacts for the duration of the pavement’s life cycle.

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Section: Agency Costmentioning

confidence: 99%

An Evaluation of the Economic Viability and Accessibility of CRCP and JPCP: A Comparative Analysis

Moharekpour,

Shokri,

Wellerdick

et al. 2024

Sustainability

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

Cited by 9 publications

References 16 publications

An Evaluation of the Economic Viability and Accessibility of CRCP and JPCP: A Comparative Analysis

An Evaluation of the Economic Viability and Accessibility of CRCP and JPCP: A Comparative Analysis

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

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