Pollack Periodica
Volume/Issue:
Volume 17: Issue 1
Creep model to determine rut development by autonomous truck axles on pavement
Authors:
Mohammad Fahad Department of Transport Infrastructure and Water Resources Engineering, Faculty of Civil Engineering, Széchenyi István University, Egyetem tér 1, H-9026 Győr, Hungary

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https://orcid.org/0000-0002-5389-7072
,
Richard Nagy Department of Transport Infrastructure and Water Resources Engineering, Faculty of Civil Engineering, Széchenyi István University, Egyetem tér 1, H-9026 Győr, Hungary

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, and
Peter Fuleki Department of Transport Infrastructure and Water Resources Engineering, Faculty of Civil Engineering, Széchenyi István University, Egyetem tér 1, H-9026 Győr, Hungary

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Pages:
66–71
Online Publication Date:
07 Aug 2021
Publication Date:
25 Mar 2022
Article Category:
Research Article
DOI:
https://doi.org/10.1556/606.2021.00328
Keywords:
autonomous trucks; lateral wander; rutting; ABAQUS
Open access

Abstract

Impacts of autonomous truck’s passes on pavement have been analyzed in this research. Two types of lateral positioning namely zero wander and uniform wander along with a super single wide tire and a dual tire have been analyzed with variable traffic speeds in ABQUS. The study concludes with the results in favor of usage of a super single wide tire under a uniform wander mode. The highest amount of pavement damage in terms of maximum rut depth is caused by the dual wheel assembly moving under a zero-wander mode. The magnitude of rut depth increases by a factor of two when a dual tire assembly is used instead of a wide tire assembly. At a uniform wander mode, rut depth increases by 0.2 mm for every 10 km/h decrease in traffic speed within 90 km/h to 70 km/h range.

Abstract

Impacts of autonomous truck’s passes on pavement have been analyzed in this research. Two types of lateral positioning namely zero wander and uniform wander along with a super single wide tire and a dual tire have been analyzed with variable traffic speeds in ABQUS. The study concludes with the results in favor of usage of a super single wide tire under a uniform wander mode. The highest amount of pavement damage in terms of maximum rut depth is caused by the dual wheel assembly moving under a zero-wander mode. The magnitude of rut depth increases by a factor of two when a dual tire assembly is used instead of a wide tire assembly. At a uniform wander mode, rut depth increases by 0.2 mm for every 10 km/h decrease in traffic speed within 90 km/h to 70 km/h range.

1 Introduction

Autonomous trucks are bound to bring new challenges to the current transport infrastructure.

Transverse wheel wander can affect the shape of transverse profiles of pavements during deformation. Application of wheel wander can reduce the load magnitude at a given point in the pavement. Human-driven vehicles tend to position themselves laterally in a normally distributed path [1–3]. Buiter et al. [4] reported that the wheel wander of vehicles is highly dependent on vehicle types, driving habits, wind speed, mechanical alignment of trailers and pavement condition. Lennie et al. [5] identified that the lateral wandering of vehicles is highly dependent on overtaking vehicles, lane widths, shoulder widths, shoulder types, the effect of line markings, and a class of vehicle. The amount of wander is described using a certain value of standard deviation SD [6, 7]. Effect of wheel wander is more significant for thinner pavements [8].

White et al. [1] incorporated the use of wheel wander as normal distribution and a decrease in rut depth was reported for normally distributed loading.

The latest form of research in terms of understanding of impacts of autonomous trucks on pavement performance is conducted by Chen et al. [9] and Noorvand et al. [3], it was investigated the repeated passes of trucks along with the same positions and integrated the autonomous trucks in the existing human-driven truck traffic, results showed that effect of the lateral wandering of autonomous trucks is significant only if more than 50% of autonomous trucks are used in existing human-driven truck traffic.

3D and 2D Finite Element Methods (FEMs) have been used to predict rutting of asphalt pavements. Sadeghnejad et al. [10] has used 2D FE modeling by incorporating the creep model developed by Hua 2000 [11], to predict the rutting behavior of glasphalt mixtures and the effect of temperature and stress on these mixtures. 3D and 2D FE modeling can be used to predict rutting of asphalt layers. 3D FEM, however, is expensive in terms of computational time [12]. Comparison of results for 2D and 3D FE modeling was done by Hua 2000 [11]. The difference in rut depths for two methods was 2%, which is not significant in terms of rutting prediction of asphalt pavements.

Since asphalt is a rheological material, hence creep model can be used to determine deflection in asphalt layers with inputs such as modulus of elasticity [13]. Dynamic creep test was utilized to study permanent deformation if foamed asphalt mixtures and reported that resulting creep strains accumulations are good indicators of permanent deformations in pavements [14].

Creep power law model in ABAQUS, in Eq. (1), has been successfully employed to investigate the permanent deformation in forms of rutting for various asphalt mixtures in finite element analysis [10, 15, 16]. Power law model is simple yet suitable for determining the rutting behavior of asphalt mixtures [10]. In power law model, the time hardening version is used in this research,
ϵ = A σ n t m ,
where, ϵ is the creep strain rate; σ is the deviatoric stress; t is the total time; A ,   n ,   m are creep parameters, where A > 0 ,   n > 0 ,     − 1 < m < 0 .

However, the mode of loading is cyclic or continuous, it is going have a same effect on predicted strain rate since the whole loading time is the same if time hardening version is used to describe material’s behavior [10, 11].

Method introduced by Uzarowski 2006 [2], in which the number of wheel passes are converted into step loading time fits perfectly with application of creep power law model [17, 18]. As it is shown in Eq. (2), using the equation given in MEPDG code [19], the time of loading can be calculated as follows:
t = L e f f 17.6 V s ,
where t is the time of loading; L e f f is the effective length; V s is the vehicle speed.

2 Research methodology

Since the majority of rut depth occurs in the surface layer of pavements and the effect of wheel wander is significant in thinner asphalt pavements [8, 12]. Hence in this study an asphalt layer of 63 mm resting on a rigid surface (Portland Cement Concrete (PCC) foundation) is considered for modeling purposes. Lane width for the truck axle is kept at 3.5 m. Figures 1 and 2 show the illustrations of a dual tire and a super single wide tire assembly respectively.

Fig. 1.
Fig. 1.

Illustration of a dual tire assembly on the pavement

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Fig. 2.
Fig. 2.

Illustration of a super single wide tire assembly

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

2.1 Wheel loading and configurations

An axle load of 75.6 kN with a nominal tire pressure of 720 kPa has been considered in this study. Two different tire types, a super single wide base tire 455/55R22.5 developed by Michelin and a conventional dual tire G159A-11R22.5 developed by Goodyear are shown in Figs 3 and 4 respectively.

Fig. 3.
Fig. 3.

Digital tire footprint of a super single wide tire

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Fig. 4.
Fig. 4.

Digital tire footprint of a dual tire

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Since the tire contact pressure is not uniformly distributed [2, 14, 20], therefore the effects of varying tire pressure is included to help understand the process of permanent deformation.

As it can be observed from Figs 5 and 6, the magnitude of contact pressure in a wide base tire is less and is distributed in a wider area as compared to that of a dual tire. High concentration of contact stresses would occur as a result of usage of dual tire assembly.

Fig. 5.
Fig. 5.

Footprint details of a Goodyear G159A-11R22.5 dual tire used in simulations

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Fig. 6.
Fig. 6.

Footprint details of a Michelin455/55r22.5 super single wide tire used in simulations

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

2.2 Data preparation for ABAQUS

Following parameters are needed in ABAQUS to perform creep analysis using the power law model as it is shown in Table 1. Uzarowski [3] has developed the modified creep parameters hence the validated elastic and creep parameters are used.

Table 1.

Elastic and creep parameters
Material Material Parameters
Elastic Parameters Creep Parameters (Constant)
HMA Layer Elastic Modulus (kPa) Poisson's Ratio A (× 10 − 8 ) n m
950,000 0.41 41 1.48 −0.63

Total traffic volume is assumed to be 30 million tire passes for a period of 20 years. Vehicle speed used in this study is the nominal speed of heavy goods vehicles, weighing greater than 12 tons can be 90, 80, 60 and 50 km/h.

While considering the zero-wander mode, for an outside tread’s footprint length of 175 mm, the time of loading for the first step is 118,000 s as calculated from Eq. (2). The other areas in footprint are longer by 15 percent; hence for step 2, the extra loading time is 18,000 s. The total loading time for zero wander mode and uniform wander mode has been shown in Tables 2 and 3, respectively.

Table 2.

Loading time for zero wander mode for a 30 million tire passes
Speed (km/h) Loading time (s)
Step 1 Step 2
90 105,000 16,000
50 189,000 29,000
40 236,000 36,000
30 315,000 47,000
Table 3.

Loading time for uniform wander mode for 30 million passes
Speed (km/h) Loading time (s)
Step 1 Step 2 Step 3 Step 4 Step 5 Step 6
90 35,000 5,300 35,000 5,300 35,000 5,300
50 63,000 10,000 63,000 10,000 63,000 10,000
40 79,000 12,000 79,000 12,000 79,000 12,000
30 105,000 15,000 105,000 15,000 105,000 15,000
5 630,000 94,500 630,000 94,500 630,000 94,500
2.5 1,260,000 189,000 1,260,000 189,000 1,260,000 189,000

The total cumulative time calculated from Eq. (2), has been distributed in six steps as shown in Table 3 Steps 1 and 2 are for the first positioning of tire and further steps indicate second and third positioning.

2.3 2D FE model

Since the tire assembly is symmetric, hence only one portion of dual and single wheel assembly is considered in FE analysis. This approach would reduce the computational time and will have no effect on results obtained by performing the analysis on uniform wander and zero wander modes. Figures 7 and 8 show the 2D model for a dual and super single tire assembly respectively.

Fig. 7.
Fig. 7.

2D Model for dual tire assembly

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Fig. 8.
Fig. 8.

2D Model for a super single wide tire assembly

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

The mesh density including the element type and size is same for both models created for dual wheel and wide base tire assembly. The model used is a quadrilateral plain strain hourglass CPE4R consisting of 4,550 linear quadrilateral elements with 4,914 nodes. A screenshot of developed mesh is shown in Fig. 9.

Fig. 9.
Fig. 9.

Mesh formation used in FE analysis

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Figures 10 and 11 represent the boundary conditions for dual tire and super single wide tire respectively.

Fig. 10.
Fig. 10.

Loading and boundary conditions for a dual tire assembly

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Fig. 11.
Fig. 11.

Loading and boundary conditions for a super single wide tire assembly

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

3 Results and discussions

Simulation results for a dual tire assembly under zero and uniform wander mode at 90 km/h are shown, respectively. Simulations were performed for a total traffic of 30 million tire passes over a period of 20 years as shown in Figs 12 and 13, respectively. The deflection values are obtained after 35 increments with final step time of 16,000 s and after 38 increments with final step time of 5,300 s respectively. In the analysis part, the upheaval increment is not included in rutting depth in this study.

Fig. 12.
Fig. 12.

Simulation result for dual wheel zero wander at 90 km/h

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Fig. 13.
Fig. 13.

Simulation result for wide wheel uniform wander at 90 km/h

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Due to low contact pressures and wider distribution of contact stresses for a super single wide tire, the deformation at 90 km/h is 11.8 mm, which is almost half of the deformation of 6 mm caused by a dual tire assembly using a zero-wander mode as shown in Figs 14 and 15, respectively.

Fig. 14.
Fig. 14.

Rut depth for various lateral modes and tire types at 90 km/h

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Fig. 15.
Fig. 15.

Rut depth for various lateral modes and tire types at 30 km/h

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

A significant increase in rut depth happens as the speed is reduced to 30 km/h, the increment is highly significant in case of using a dual tire in a uniform wander mode. When measured at super slow speeds of 5 km/h and 2.5 km/h, an abrupt increase in rut development can be observed as shown in Fig. 16. Permanent deformation of 9.6 mm and 11.8 mm has been accumulated at speeds of 5 km/h and 2.5 km/h respectively.

Fig. 16.
Fig. 16.

Rut depth for uniform wander mode at super slow speeds

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

Condition of the flexible pavement starts to deteriorate once the rut depth reaches 6 mm [8, 21], therefore it is of high concern that the number of passes for each of the tire type along with a lateral mode used should be analyzed until the rut depth reaches 6 mm. Figure 16 shows abrupt increase in rutting depth at super slow speeds.

The rate of increase in rut development is much prominent at lower speeds. As it is shown in Fig. 17, the rut development rate is linear from 90 km/h to 30 km/h.

Fig. 17.
Fig. 17.

Effect of traffic speed on rut depth for super single tire at uniform wander mode

Citation: Pollack Periodica 17, 1; 10.1556/606.2021.00328

4 Findings and conclusions

The key findings in this study are mentioned below:

  • Rut depth increases by a factor of 2 from wide tire to dual tire, the factor slightly increases as the speed gets down to 50 km/h;

  • Effects of uniform wander and zero wander are highly significant when dual tire assembly is used;

  • In case of super single tire, the rut depth decreases by an average of 2 mm if uniform wander is used instead of zero wander mode;

  • The decrease in rut depth from zero wander to uniform wander increases significantly at lower speeds;

  • In case of super single tire, the rut depth increases by 0.2 mm for every 10 km/h decrease in the speed;

  • Wider lane width can facilitate in even more uniform distribution of lateral wander by introducing more paths at fixed distances from center line of the lane, thereby reducing the rutting potential;

  • Dual tires at zero wander only require 3.23 million passes to reach a rut depth of 6 mm, on the other hand, super single tire require 30 million passes to reach a rut depth of 6 mm using zero wander mode;

  • An abrupt increase in rut development start as the vehicle speed gets lower than 30 km/h and the curve tends to go vertical until the speed approaches zero;

  • For a super single wide tire at uniform wander mode, rut develops at a factor of 2 mm at super speed lower than 50 km/h.

Based on the results of this study, the use of autonomous trucks will be highly beneficial only if they are only used on expressways or motorways with a minimum speed of 80 km/h. Although the difference between rut depth generated by a super single wide tire in case of a zero wander and uniform wander is quite minute, the difference increases as the vehicle speed decreases.

References

  • [1]↑

    T. D. White , J. E. Haddock , A. J. T. Hand , and H. Fang , “Contributions of pavement structurallLayers to rutting of hot mix asphalt pavements,” TRB’s National Cooperative Highway Research Program, Report 468, Transportation Research Board, National Research Council, USA, 2002.

    • Search Google Scholar
    • Export Citation
  • [2]↑

    L. Uzarowski , “The development of asphalt mix creep parameters and finite element modelling of asphalt rutting,” PhD Thesis, Purdue University, West Lafayette, USA, 2007.

    • Search Google Scholar
    • Export Citation
  • [3]↑

    H. Noorvand , G. Karnati , and B. S. Underwood , “Autonomous vehicles: Assessment of the implications of truck positioning on flexible pavement performance and design,” Transp. Res. Rec., vol. 2640, no. 1, pp. 21–28, 2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [4]↑

    R. Buiter , W. M. H. Cortenraad , A. C. van Eck , and H. van Rij , “Effects of transverse distribution of heavy vehicles on thickness design of full-depth asphalt pavements,” Transp. Res. Rec., no. 1227, pp. 66–74, 1989.

    • Search Google Scholar
    • Export Citation
  • [5]↑

    S. C. Lennie and J. M. Bunker , “Evaluation of Lateral Position for Multi Combination Vehicles,” in Proceedings on Queensland Main Roads Road System and Engineering Technology Forum, Brisbane, Australia, Aug. 19–20, 2003, 2003, Paper no. 2356.

    • Search Google Scholar
    • Export Citation
  • [6]↑

    L. A. Al-Khateeb , A. Saoud , and M. F. Al-Msouti , “Rutting prediction of flexible pavements using finite element modeling,” Jordan J. Civ. Eng., vol. 5, no. 2, pp. 173–190, 2011.

    • Search Google Scholar
    • Export Citation
  • [7]↑

    “Guide for mechanistic-empirical design of new and rehabilitataed pavement structures,” in National Cooperative Highway Research Program Transportation Research Board National Research Council .USA, 2004.

    • Search Google Scholar
    • Export Citation
  • [8]↑

    R. V. Siddharthan , M. Nasimifar , X. Tan , and E. Y. Hajj , “Investigation of impact of wheel wander on pavement performance,” Road Mater. Pavement Des., vol. 18, no. 2, pp. 390–407, 2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [9]↑

    F. Chen , M. Song , X. Ma , and X. Zhu , “Assess the impacts of different autonomous trucks’ lateral control modes on asphalt pavement performance,” Transp. Res. Part C: Emerg. Technol., vol. 103, pp. 17–29, 2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [10]↑

    M. Sadeghnejad , M. Arabani , and M. Taghipoor , “Predicting the impact of temperature and stress on the glasphalt mixtures’ rutting behavior,” Int. J. Pavement Res. Technol., vol. 11, no. 3, pp. 300–310, 2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [11]↑

    J. Hua , “Finite element modeling and analysis of accelerated pavement testing devices and rutting phenomenon,” PhD Thesis, Purdue University, USA, 2000.

    • Search Google Scholar
    • Export Citation
  • [12]↑

    A. Abed and A. Al-Azzawi , “Evaluation of rutting depth in flexible pavements by using finite element analysis and local empirical model,” Am. J. Eng. Appl. Sci., vol. 5, no. 2, pp. 163–169, 2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [13]↑

    T. Olexa and J. Mandula . "Comparison of complex modulus and elasticity modulus of bitumen bonded materials,” Pollack Period., vol. 11, no. 3, pp. 131–140, 2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [14]↑

    I. L. Al-Qadi , P. J. Yoo , M. A. Elseifi , I. Janajreh , G. Chehab , and A. Collop , “Effects of tire configurations on pavement damage,” Asph. Paving Technol. Assoc. Asph. Paving Technol. Tech. Sess., vol. 74, pp. 921–961, 2005.

    • Search Google Scholar
    • Export Citation
  • [15]↑

    I. L. Al-Qadi , P. J. Yoo , M. A. Elseifi , and S. Nelson , “Creep behavior of hot-mix asphalt due to heavy vehicular tire loading,” J. Eng. Mech., vol. 135, no. 11, pp. 1265–1273, 2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [16]↑

    W. K. Huang , X. N. Zhang , H. L. Rong , and B. Chen , “Finite element method for predicting rutting depth of steel deck asphalt pavement based on accelerated pavement test,” in Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems, Yinchuan, China, Aug. 15–16, 2015, 2015, pp. 935–942.

    • Search Google Scholar
    • Export Citation
  • [17]↑

    R. K. A. Al-Rub , M. K. Darabi , C. W. Huang , E. A. Masad , and D. N. Little , “Comparing finite element and constitutive modelling techniques for predicting rutting of asphalt pavements,” Int. J. Pavement Eng., vol. 13, no. 4, pp. 322–338, 2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [18]↑

    J. Harvey , J. Roesler , N. Coetzee , and C. Monismith , “CALTRANS Accelerated Pavement Test (CAL/APT) Program: Summary Report: Six Year Period: 1994–2000,” Pavement Research Center, Institute of Transportation Studies University of California at Berkeley .USA, pp. 1994–2000, 2000.

    • Search Google Scholar
    • Export Citation
  • [19]↑

    Guide for Mechanistic-Empirical Design of New and Rehabilitated Structures, in Final Report for Project 1-37A, National Cooperative Highway Research Program: Transportation Research Board, National Research Council, Washington DC, USA, 2004.

    • Search Google Scholar
    • Export Citation
  • [20]↑

    A. Gulyas , “Axle load trends and their effects on pavement structural design,” Pollack Period., vol. 7, no. 1, pp. 97–106, 2012.

  • [21]↑

    S. Mecheri , F. Rosey , and R. Lobjois , “The effects of lane width, shoulder width, and road cross-sectional reallocation on drivers’ behavioral adaptations,” Accid. Anal. Prev., vol. 104, pp. 65–73, 2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [1]

    T. D. White , J. E. Haddock , A. J. T. Hand , and H. Fang , “Contributions of pavement structurallLayers to rutting of hot mix asphalt pavements,” TRB’s National Cooperative Highway Research Program, Report 468, Transportation Research Board, National Research Council, USA, 2002.

    • Search Google Scholar
    • Export Citation
  • [2]

    L. Uzarowski , “The development of asphalt mix creep parameters and finite element modelling of asphalt rutting,” PhD Thesis, Purdue University, West Lafayette, USA, 2007.

    • Search Google Scholar
    • Export Citation
  • [3]

    H. Noorvand , G. Karnati , and B. S. Underwood , “Autonomous vehicles: Assessment of the implications of truck positioning on flexible pavement performance and design,” Transp. Res. Rec., vol. 2640, no. 1, pp. 21–28, 2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [4]

    R. Buiter , W. M. H. Cortenraad , A. C. van Eck , and H. van Rij , “Effects of transverse distribution of heavy vehicles on thickness design of full-depth asphalt pavements,” Transp. Res. Rec., no. 1227, pp. 66–74, 1989.

    • Search Google Scholar
    • Export Citation
  • [5]

    S. C. Lennie and J. M. Bunker , “Evaluation of Lateral Position for Multi Combination Vehicles,” in Proceedings on Queensland Main Roads Road System and Engineering Technology Forum, Brisbane, Australia, Aug. 19–20, 2003, 2003, Paper no. 2356.

    • Search Google Scholar
    • Export Citation
  • [6]

    L. A. Al-Khateeb , A. Saoud , and M. F. Al-Msouti , “Rutting prediction of flexible pavements using finite element modeling,” Jordan J. Civ. Eng., vol. 5, no. 2, pp. 173–190, 2011.

    • Search Google Scholar
    • Export Citation
  • [7]

    “Guide for mechanistic-empirical design of new and rehabilitataed pavement structures,” in National Cooperative Highway Research Program Transportation Research Board National Research Council .USA, 2004.

    • Search Google Scholar
    • Export Citation
  • [8]

    R. V. Siddharthan , M. Nasimifar , X. Tan , and E. Y. Hajj , “Investigation of impact of wheel wander on pavement performance,” Road Mater. Pavement Des., vol. 18, no. 2, pp. 390–407, 2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [9]

    F. Chen , M. Song , X. Ma , and X. Zhu , “Assess the impacts of different autonomous trucks’ lateral control modes on asphalt pavement performance,” Transp. Res. Part C: Emerg. Technol., vol. 103, pp. 17–29, 2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [10]

    M. Sadeghnejad , M. Arabani , and M. Taghipoor , “Predicting the impact of temperature and stress on the glasphalt mixtures’ rutting behavior,” Int. J. Pavement Res. Technol., vol. 11, no. 3, pp. 300–310, 2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [11]

    J. Hua , “Finite element modeling and analysis of accelerated pavement testing devices and rutting phenomenon,” PhD Thesis, Purdue University, USA, 2000.

    • Search Google Scholar
    • Export Citation
  • [12]

    A. Abed and A. Al-Azzawi , “Evaluation of rutting depth in flexible pavements by using finite element analysis and local empirical model,” Am. J. Eng. Appl. Sci., vol. 5, no. 2, pp. 163–169, 2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [13]

    T. Olexa and J. Mandula . "Comparison of complex modulus and elasticity modulus of bitumen bonded materials,” Pollack Period., vol. 11, no. 3, pp. 131–140, 2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [14]

    I. L. Al-Qadi , P. J. Yoo , M. A. Elseifi , I. Janajreh , G. Chehab , and A. Collop , “Effects of tire configurations on pavement damage,” Asph. Paving Technol. Assoc. Asph. Paving Technol. Tech. Sess., vol. 74, pp. 921–961, 2005.

    • Search Google Scholar
    • Export Citation
  • [15]

    I. L. Al-Qadi , P. J. Yoo , M. A. Elseifi , and S. Nelson , “Creep behavior of hot-mix asphalt due to heavy vehicular tire loading,” J. Eng. Mech., vol. 135, no. 11, pp. 1265–1273, 2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [16]

    W. K. Huang , X. N. Zhang , H. L. Rong , and B. Chen , “Finite element method for predicting rutting depth of steel deck asphalt pavement based on accelerated pavement test,” in Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems, Yinchuan, China, Aug. 15–16, 2015, 2015, pp. 935–942.

    • Search Google Scholar
    • Export Citation
  • [17]

    R. K. A. Al-Rub , M. K. Darabi , C. W. Huang , E. A. Masad , and D. N. Little , “Comparing finite element and constitutive modelling techniques for predicting rutting of asphalt pavements,” Int. J. Pavement Eng., vol. 13, no. 4, pp. 322–338, 2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [18]

    J. Harvey , J. Roesler , N. Coetzee , and C. Monismith , “CALTRANS Accelerated Pavement Test (CAL/APT) Program: Summary Report: Six Year Period: 1994–2000,” Pavement Research Center, Institute of Transportation Studies University of California at Berkeley .USA, pp. 1994–2000, 2000.

    • Search Google Scholar
    • Export Citation
  • [19]

    Guide for Mechanistic-Empirical Design of New and Rehabilitated Structures, in Final Report for Project 1-37A, National Cooperative Highway Research Program: Transportation Research Board, National Research Council, Washington DC, USA, 2004.

    • Search Google Scholar
    • Export Citation
  • [20]

    A. Gulyas , “Axle load trends and their effects on pavement structural design,” Pollack Period., vol. 7, no. 1, pp. 97–106, 2012.

  • [21]

    S. Mecheri , F. Rosey , and R. Lobjois , “The effects of lane width, shoulder width, and road cross-sectional reallocation on drivers’ behavioral adaptations,” Accid. Anal. Prev., vol. 104, pp. 65–73, 2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Cover Pollack Periodica
Pollack Periodica
Print ISSN:
1788-1994
Online ISSN:
1788-3911

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Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

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Miklós M. Iványi

Editorial Board

  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
  • Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
  • Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
  • Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
  • Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
  • Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
  • Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
  • Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
  • Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
  • Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
  • Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
  • Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
  • Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Rita Kiss  (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
  • István Kistelegdi  (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
  • László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
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or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

  • SCOPUS
  • CABELLS Journalytics

 

2024  
Scopus  
CiteScore  
CiteScore rank  
SNIP  
Scimago  
SJR index 0.385
SJR Q rank Q3

2023  
Scopus  
CiteScore 1.5
CiteScore rank Q3 (Civil and Structural Engineering)
SNIP 0.849
Scimago  
SJR index 0.288
SJR Q rank Q3

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2023  
Scopus  
CiteScore 1.5
CiteScore rank Q3 (Civil and Structural Engineering)
SNIP 0.849
Scimago  
SJR index 0.288
SJR Q rank Q3

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Nov 2024 0 11 5
Dec 2024 0 5 5
Jan 2025 0 19 7
Feb 2025 0 27 11
Mar 2025 0 27 6
Apr 2025 0 25 9
May 2025 0 0 0