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Article

Evaluation of Resilience Parameters of Soybean Oil-Modified and Unmodified Warm-Mix Asphalts—A Way Forward towards Sustainable Pavements

1
Department of Civil Engineering, The University of Lahore, Lahore 54000, Pakistan
2
Department of Transportation Engineering and Management, University of Engineering and Technology Lahore, Lahore 54890, Pakistan
3
Department of Civil Engineering, University of Engineering and Technology Lahore, Lahore 54890, Pakistan
4
Department of Civil Engineering, Prince Sattam Bin Abdul Aziz University, Al-Kharj 16273, Saudi Arabia
5
Department of Civil Engineering, College of Engineering, Mansoura University, Mansoura 35516, Egypt
6
Department of Architecture, Faculty of Engineering and Technology, Future University, Cairo 11835, Egypt
7
Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
8
Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(14), 8832; https://doi.org/10.3390/su14148832
Submission received: 5 May 2022 / Revised: 17 June 2022 / Accepted: 19 June 2022 / Published: 20 July 2022
(This article belongs to the Special Issue Sustainability of Transport Infrastructures)

Abstract

:
The sustainable design and construction of highways is indispensable for the economic growth and progress of any region. Highway pavements are one of the core transportation infrastructures that require energy efficient materials with durability and an optimized lifecycle. Recent research has proven that warm-mix asphalt pavements prepared with renewable bio-binders are less susceptible to distresses. This study aims to investigate the resilience characteristics (load time, deformation time) of soybean oil modified and unmodified warm-mix asphalts. Aggregates, asphalt binders and asphalt mixes were characterized in accordance with the Superpave Mix Design Criteria. The resilient modulus tests were performed as per ASTM D7369. The test results indicated that the soybean-modified warm asphalt mix samples showed a 20% to 32% reduction in load-carrying capacity than unmodified warm asphalt mixes. The values of the horizontal and vertical recoverable deformations observed in the soybean-modified mixes were found to be 3% to 7% more than in the unmodified mixes. A slight variability (up to 7%) was also observed in the time-response spectra, i.e., peak load, unload and rest periods, in the soybean-modified mixes compared with the unmodified mixes. The Pearson correlation coefficient showed a significant trend between the resilient modulus test parameters for the soybean-modified warm asphalt mixes, i.e., load deformation, load time and deformation time. Soybean oil showed sustainable behavior as a bio-binder, particularly in the deformation-time response for the warm asphalt mixes. However, the effect of soybean in terms of the reduction of the load-carrying capacity from a sustainability perspective needs to be investigated.

1. Introduction

Highway infrastructures have a significant influence on the socioeconomic development of countries [1]; therefore, the investment in these infrastructures provides opportunities for the economic growth [2,3] and development of a region [4,5]. The lack of transport infrastructure in developing countries is one of the major hinderances to accessing international markets [6], which highlights the global significance of transport infrastructure [7]. Non-conventional and environmentally friendly materials are beneficial for sustainable construction in the highway industry [8]. The use of these materials enhances the quality of environmental control measures and the development of durable transport infrastructures [9,10]. The sustainable construction of highways is indispensable for the transportation of people and goods [11]. Researchers have taken the motivation of using renewable resource-derived materials and utilized it in the modification of asphalt binders. The asphalt mixes produced using these modified binders exhibit merits over unmodified binders, such as emerging cost, environmental issues and the short supply of materials based on nonrenewable resources [12,13]. The properties of asphalt binders have a considerable effect on the performance of asphalt mixes [14]; therefore, to cope with the evolving issues related to pavement distresses, the modification of asphalt binders is indispensable. The utilization of bio-oils in asphalt binders reduces the stiffness of asphalt mixes, and thereby lessens the cracks that develop in the pavements [15,16,17]. Soybean-derived oil-based asphalt modification improves the mechanical properties of the asphalt binders [18,19,20,21,22].
The asphalt mixes (wearing and base) used in pavement surfacing primarily comprise asphalt binder and aggregates [23,24,25,26,27,28]. These asphalt mixes, termed as warm asphalt mixes, are generally prepared between temperatures of 140 °C and 160 °C [18]. The key objective of the warm asphalt mix design is to obtain the optimum combination of different constituents of the mix [29]. The asphalt mixes exhibit viscoelastic, viscoplastic, and time- and stress-dependent behavior when subjected to repeated loadings [23,30,31,32,33,34]. Therefore, pavement surface courses face different distresses during their service life, such as rutting, fatigue and thermal cracking. For the assessment of the viscoelastic behavior of asphalt mixes, the resilient modulus test can be performed [35,36].
This study aims to evaluate the effects of soybean as a bio-binder on the resilient modulus of warm asphalt mixes. The objectives of this research were: (1) to determine the effects of soybean oil on the load time and deformation time behavior of warm-mix asphalt during resilient modulus tests, (2) to compare the resilient modulus of soybean-modified and unmodified warm asphalt mixes, and (3) to assess the correlation dependency trends of different parameters (on each other) obtained in soybean-modified warm-mix asphalts’ resilient modulus tests and compare these with unmodified warm-mix asphalt trends.

2. Materials and Methods

Commercially available soybean was processed to extract the soybean oil used in this study. The unmodified and soybean oil-based asphalt binders were selected in accordance with the details reported by Tarar et al. [37]. Two unmodified asphalt binders, PG 64-16 and PG 64-22, were labeled as A and B, whereas the two soybean oil (5% by weight of binder)-modified asphalt binders, PG 52-22 and PG 52-28, were categorized as Ao and Bo, respectively. The binders’ characteristics such as high and low temperatures, performance grade, viscosity, mass change, penetration, softening point, ductility, flash and fire point were evaluated in laboratory based on respective American Association of State Highway and Transportation Officials (AASHTO)/American Society for Testing and Materials (ASTM) standards.
Two crushed aggregate sources, i.e., Sargodha (S) and Margalla (M), were used. The properties of the aggregates such as soundness, water absorption, Los Angeles abrasion (C131), elongation and flakiness index, fractured faces, uncompacted voids and sand equivalent were determined in the laboratory as per prevailing ASTM standards.
The Superpave (Sup-1 and Sup-2) and National Highway Authority (NH-A and NH-B) gradations were used. The wearing and base course mixes were designated as W1 to W24 and B1 to B8, respectively. The test matrix of the mixes is summarized in Table 1.
To determine the optimum binder contents (OBC), the mixes were tested according to the Marshall Mix test (ASTM D6926). The mixing and compaction temperatures of the binders were determined using a rotational viscometer (RV) test at 135 °C to 165 °C before the mix preparation. The binders were mixed with aggregate in a controlled mechanical mixer at 145 °C. The Superpave gyratory compactor (SGC) was used to compact the samples while keeping the air voids at 7 ± 0.5%. The indirect tensile strength and modulus of resilience test specimens were fabricated at 101.6 mm (4 inches) in diameter and 63.5 mm (2.5 inches) in thickness.
Modulus of resilience (MR) describes the mechanical properties of asphalt mixes subjected to dynamic (traffic) loading. The asphalt mixes were tested according to ASTM D6931 for indirect tensile strength determination before MR testing. The MR tests were performed according to ASTM D7369 using an environmentally controlled universal testing machine: Cooper Research Technology HYD25 II.
The test temperature was set at 25 °C. The load was applied in the form of a haversine shape, i.e., ( 1 cos   θ ) /2, as shown in Figure 1.
The instantaneous deformation, total deformation, Poisson’s ratio and MR were calculated according to the equations below.
Y = a + bx ,
where Y is deformation value, x is time and a and b are regression constants.
Y = a + b x
where Y is deformation value, x is time and a and b are regression constants.
μ = I 4 I 1 × ( δ v δ h ) I 3 I 2 × ( δ v δ h )     ,  
where μ is Poisson’s ratio, I 1 , I 2   I 3 and I 4 are constants and δ v and δ h are vertical and horizontal recoverable deformations, respectively.
M R = P Cyclic δ h t ( I 1 I 2 δ )   ,  
where MR is resilient modulus,   P Cyclic is the cyclic load applied to the specimen and t is the thickness of the specimen.

3. Results and Discussion

The physical properties of the soybean oil are summarized in Table 2a. The properties of the asphalt binders are shown in Table 2b.
By the addition of soybean oil in binders A and B, few properties showed a decrease, i.e., high and low temperatures, viscosity at 135 °C and softening point, while others showed an increase, i.e., viscosity at 165 °C, mass change, penetration, flash and fire point, viscosity temperature susceptibility (VTS). The performance grade after the addition of soybean oil altered from 64–16 to 52–22 in one sample and 64–22 to 52–28 in another. However, overall, the penetration grade of the asphalt binder remained unchanged with the addition of the soybean oil to the asphalt binders. Soybean oil blended into the asphalt binder proved to have significant potential as a bio-binder.
The physical properties of the aggregates are summarized in Table 3.
The aggregates S and M have water absorption values of 0.95% and 0.93% respectively. The water content in the aggregates affects the performance of the asphalt mixes [38,39,40,41]. The optimum binder content is affected by the higher water absorption of the aggregates [42]. The soundness values of S and M are 3.8 and 4.5, respectively. The soundness value signifies the resistance of the aggregates against weathering. The Los Angeles abrasion values of S and M are 23 and 24.5, respectively, which specifies that the M aggregate source has higher abrasive resistance than S. The long-term performance of the pavement exposed to traffic loadings depends upon the abrasion resistance of the aggregates [43,44,45]. The elongation indices values of S and M are 7 and 3. The flakiness index values of S and M are 9 and 5. Researchers have reported that higher values of elongated and flaky particles reduce the strength of asphalt mixes [42,46,47]. The morphological properties of the aggregates affect the performance of asphalt mixes [48,49,50,51]. The aggregate gradation can also affect the modulus of resilience [52]. The uncompacted voids of S and M are 45 and 44, and the sand equivalents are 71 and 74, respectively. The engineering properties of both M and S aggregates qualify the acceptable limits for possible use in warm asphalt mixes. The consistency in the engineering properties of the aggregates is desirable, as it influences the resilient modulus of sustainable pavement structures [53,54]. The resilient modulus value affects the service life of the material and its resistance against pavement damage [55,56]. The energy absorption of soybean-modified mixes can be calculated based on the hysteresis loop response of samples under repeated loads [57]. This can be used as a potential advantage of soybean-modified mixes by researchers in the future.
The results in Table 4 show a summary of the different parameters obtained in the resilient modulus test, as illustrated in Figure 1 and Figure 2.
It is evident that the soybean-modified mixes took lesser loads (20% to 32%) than unmodified mixes in both wearing and base-course samples. In addition, the peak load time (Tm) was observed to be higher (2% to 7%) in the soybean-modified mixes than in the unmodified mixes. The straight portion of the unloading path T1 and T2 values were lower (2% to 7%) in the unmodified samples than in the soybean-modified samples. The time spectra of the rest periods (Tc, T55, Td, Te and Tf) were also noted to be higher (2% to 7%) in the soybean-modified samples than in the unmodified mixes. The soybean-modified mixes exhibited improved horizontal (3% to 6%) and vertical (6% to 7%) recoverable deformations in comparison to the unmodified mixes.
The M R values for all wearing and base-course mixes were determined using Equation (4), as shown in Figure 3, Figure 4 and Figure 5.
The MR value of S for the Superpave and NH gradations was higher than for M. Figure 3 shows that the MR values of S and M for the Superpave gradation and asphalt binder A were 7049 MPa and 6802 MPa, respectively, while the soybean oil-modified asphalt binders with Superpave gradations showed MR values of 5063 MPa and 4751 MPa, respectively. The NH gradation exhibited an MR value for the asphalt binder A and S and M of 7350–7224 MPa. On the other hand, the MR values for the Ao and NH gradation were 5086–4850 MPa.
Figure 4 indicates that the MR values of S and M for the Superpave gradation and B asphalt binder were 6344 and 6129 MPa, respectively, while the Bo asphalt binders with Superpave gradations showed MR values of 4823 and 4665 MPa, respectively. The NH gradation exhibited MR values for the B asphalt binder and S and M of 6708 and 6512 MPa, respectively. On the other hand, the MR values for the Bo asphalt binders and NH gradation were 4538 and 4349 MPa, respectively.
Figure 5 shows that the MR values of M for the Superpave gradation and B asphalt binder were in the range of 3068 MPa, while the soybean oil-modified asphalt binders with Superpave gradations exhibited MR values in the range of 2545 MPa. The NH gradation exhibited an MR value for the asphalt binder B and M of 2911 MPa. On the other hand, the MR value of the Bo asphalt binder with NH gradation was shown to be 2619 MPa.
Figure 3, Figure 4 and Figure 5 show that the addition of soybean oil decreased the M R   values of both the wearing and base-course asphalt mixes.
Table 5 shows a summary of the statistical analysis carried out using the Origin software from OriginLab®. The different parameters (load, Tm, T1, T2,…) obtained in the MR tests were correlated with each other to assess the trend and possible dependency. The Pearson correlation and the respective significance values are summarized in Table 5. The values of the Pearson correlation indicate the strength of the relationship (linear) between the different variables. A positive Pearson correlation value indicates that two parameters have a direct relationship—if one parameter increases, then the other increases, and vice versa, while a negative Pearson correlation value indicates that both of the parameters have an inverse relationship—if one parameter increases, then other decreases, and vice versa. It can be seen from Table 5 that the load deformation, load time and deformation time showed reasonable significance (shaded regions) for both the modified and unmodified mixes, in line with typical trends, as shown in Figure 1 and Figure 2.
Soybean oil showed sustainable behavior as bio-binder, particularly in the deformation-time response for warm asphalt mixes. However, the effect of soybean in the reduction of the load-carrying capacity from a sustainability perspective needs to be investigated. The minimal requirement of MR for asphalt mixes was reported in ASTM 7369. An MR obtained with the 5% addition of soybean as an asphalt binder falls well within the optimal acceptable stiffness range, especially for pavements subjected to light to medium traffic loading.

4. Conclusions

In this study, the effect of soybean oil on the resilient modulus of asphalt mixes was evaluated using the ASTM D7369 procedure. The statistical analysis was performed to check the correlations between the different parameters obtained in the MR tests. The following conclusions can be drawn from the above findings:
  • The soybean-modified warm asphalt mixes showed a 20% to 32% reduction in load-carrying capacity, i.e., for the resilient modulus than the unmodified warm asphalt mixes.
  • The values of the horizontal and vertical recoverable deformations remained comparable (3% to 7%) in both the soybean-modified and unmodified warm asphalt mixes.
  • A slight variability (2% to 7%) was observed in the time-response spectra, i.e., peak, unload, rest periods of loads and deformations during the resilient modulus tests performed on the soybean-modified and unmodified warm asphalt mixes.
  • Each parameter obtained in the soybean-modified warm-mix asphalt resilient modulus test showed a reasonable correlation trend with the others, as depicted by the Pearson coefficient. Hence, the trends of the soybean-modified and unmodified warm-mix asphalt in resilient modulus tests are comparable.
  • Soybean oil showed sustainable behavior as bio-binder, particularly in the deformation-time response for warm asphalt mixes. However, the effect of soybean in the reduction of the load-carrying capacity from a sustainability perspective needs to be investigated.

Author Contributions

Conceptualization, A.H.K.; Data curation, M.A.T. and A.H.K.; Formal analysis, M.A.T., Z.u.R., W.A., A.A., E.A. and M.M.S.; Funding acquisition, A.H.K.; Investigation, M.A.T.; Methodology, M.A.T.; Project administration, A.H.K. and Z.u.R.; Resources, A.H.K., W.A., A.A., E.A., M.M.S. and M.A.; Supervision, A.H.K. and Z.u.R.; Writing—original draft, M.A.T.; Writing—review & editing, A.H.K., Z.u.R., W.A., A.A., E.A., M.M.S. and M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Higher Education Commission of Pakistan, grant number NRPU 9639.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rahman, I.; Sharma, B.P.; Fetuu, E.; Yousaf, M. Do Roads Enhance Regional Trade? Evidence Based on China’s Provincial Data. J. Asian Financ. Econ. Bus. 2020, 7, 657–664. [Google Scholar] [CrossRef]
  2. Javid, M. Public and Private Infrastructure Investment and Economic Growth in Pakistan: An Aggregate and Disaggregate Analysis. Sustainability 2019, 11, 3359. [Google Scholar] [CrossRef] [Green Version]
  3. Holl, A. Transport Infrastructure, Agglomeration Economies, and Firm Birth: Empirical Evidence from Portugal. J. Reg. Sci. 2004, 44, 693–712. [Google Scholar] [CrossRef]
  4. Gibbons, S.; Lyytikäinen, T.; Overman, H.; Sanchis-Guarner, R. New road infrastructure: The effects on Arms. J. Urban Econ. 2019, 110, 35–50. [Google Scholar] [CrossRef]
  5. Lopez, M.A.G.; Holl, A.; Marsal, E.V. Suburbanization and highways in Spain when the Romans and the Bourbons still shape its cities. J. Urban Econ. 2015, 85, 52–67. [Google Scholar] [CrossRef]
  6. Coşar, A.K.; Demir, B. Domestic road infrastructure and international trade: Evidence from Turkey. J. Dev. Econ. 2016, 118, 232–244. [Google Scholar] [CrossRef]
  7. Ghani, E.; Goswami, A.G.; Kerr, W.R. Highway to Success: The Impact of the Golden Quadrilateral Project for the Location and Performance of Indian Manufacturing. Econ. J. 2015, 126, 317–357. [Google Scholar] [CrossRef]
  8. Thom, N.; Dawson, A. Sustainable Road Design: Promoting Recycling and Non-Conventional Materials. Sustainability 2019, 11, 6106. [Google Scholar] [CrossRef] [Green Version]
  9. Zhao, Y.; Goulias, D.; Peterson, D. Recycled Asphalt Pavement Materials in Transport Pavement Infrastructure: Sustainability Analysis & Metrics. Sustainability 2021, 13, 8071. [Google Scholar] [CrossRef]
  10. Lee, J.; Edil, T.B.; Benson, C.H.; Tinjum, J. Building Environmentally and Economically Sustainable Transportation Infrastructure: Green Highway Rating System. J. Constr. Eng. Manag. 2013, 139, A4013006. [Google Scholar] [CrossRef]
  11. Ibrahim, A.H.; Shaker, M.A. Sustainability index for highway construction projects. Alex. Eng. J. 2019, 58, 1399–1411. [Google Scholar] [CrossRef]
  12. Yang, X.; You, Z.; Dia, Q.; Beale, J.M. Mechanical performance of asphalt mixtures modified by bio-oils derived from waste wood resources. Constr. Build. Mater. 2014, 51, 424–431. [Google Scholar] [CrossRef]
  13. Williams, R.C.; Peralta, J.; Puga, K.L.N. Development of Non-Petroleum-Based Binders for Use in Flexible Pavements—Phase II (Report No. IHRB Project TR-650); Iowa Department of Transportation, Iowa State University: Ames, IA, USA, 2015. [Google Scholar]
  14. Saravanan, U. On the use of linear viscoelastic constitutive relations to model asphalt. Int. J. Pavement Eng. 2012, 13, 360–373. [Google Scholar] [CrossRef]
  15. Elseifi, M.A.; Mohammad, L.N.; Cooper, S.B., III. Laboratory evaluation of asphalt mixtures containing sustainable technologies. J. Assoc. Asph. Paving Technol. 2011, 80, 227–254. [Google Scholar]
  16. Hajj, E.; Souliman, M.; Alavi, M.; Loría Salazar, L. Influence of hydro green bio asphalt on viscoelastic properties of reclaimed asphalt mixtures. Transp. Res. Rec. J. Transp. Res. Board 2013, 2371, 13–22. [Google Scholar] [CrossRef]
  17. Zaumanis, M.; Mallick, R.B.; Frank, R. Evaluation of Rejuvenator’s Effectiveness with Conventional Mix Testing for 100% Reclaimed Asphalt Pavement Mixtures. Transp. Res. Rec. J. Transp. Res. Board 2013, 2370, 17–25. [Google Scholar] [CrossRef] [Green Version]
  18. Podolsky, J.H.; Buss, A.; Williams, R.C.; Cochran, E.W. Effect of bio-derived/chemical additives on warm mix asphalt compaction and mix performance at low temperature. Cold Reg. Sci. Technol. 2017, 136, 52–61. [Google Scholar] [CrossRef]
  19. Elkashef, M.; Podolsky, J.; Williams, R.C.; Cochran, E.W. Introducing a soybean oil-derived material as a potential rejuvenator of asphalt through rheology, mix characterization and Fourier Transform Infrared analysis. Road Mater. Pavement Des. 2017, 19, 1–21. [Google Scholar] [CrossRef]
  20. Podolsky, J.H.; Williams, R.C.; Cochran, E. Effect of corn and soybean oil derived additives on polymer-modified HMA and WMA master curve construction and dynamic modulus performance. Int. J. Pavement Res. Technol. 2018, 11, 541–552. [Google Scholar] [CrossRef]
  21. Podolsky, J.H.; Chen, C.; Buss, A.F.; Williams, R.C.; Cochran, E.W. Effect of bio-derived/chemical additives on HMA and WMA compaction and dynamic modulus performance. Int. J. Pavement Eng. 2019, 22, 613–624. [Google Scholar] [CrossRef]
  22. Tarar, M.A.; Khan, A.H.; Rehman, Z.U. Evaluation of effects of soybean derived oil and aggregate petrology on the performance of asphalt mixes. Road Mater. Pavement Des. 2020, 23, 308–334. [Google Scholar] [CrossRef]
  23. Swamy, A.K.; Daniel, J.S. Effect of Mode of Loading on Viscoelastic and Damage Properties of Asphalt Concrete. Transp. Res. Rec. J. Transp. Res. Board 2012, 2296, 144–152. [Google Scholar] [CrossRef]
  24. Nejad, F.M.; Azarhoosh, A.R.; Hamedi, G.H. Laboratory Evaluation of Using Recycled Marble Aggregates on the Mechanical Properties of Hot Mix Asphalt. J. Mater. Civ. Eng. 2013, 25, 741–746. [Google Scholar] [CrossRef]
  25. Wen, H.; Bhusal, S.; Wen, B. Laboratory evaluation of waste cooking oil-based bio asphalt as an alternative binder for hot mix asphalt. J. Mater. Civ. Eng. 2013, 25, 1432–1437. [Google Scholar] [CrossRef]
  26. Feng, H.; Pettinari, M.; Hofko, B.; Stang, H. Study of the internal mechanical response of an asphalt mixture by 3-D discrete element modeling. Constr. Build. Mater. 2015, 77, 187–196. [Google Scholar] [CrossRef]
  27. Pan, P.; Kuang, Y.; Hu, X.; Zhang, X. A Comprehensive Evaluation of Rejuvenator on Mechanical Properties, Durability, and Dynamic Characteristics of Artificially Aged Asphalt Mixture. Materials 2018, 11, 1554. [Google Scholar] [CrossRef] [Green Version]
  28. Islam, R.; Kalevela, S.A.; Mendel, G. How the Mix Factors Affect the Dynamic Modulus of Hot-Mix Asphalt. J. Compos. Sci. 2019, 3, 72. [Google Scholar] [CrossRef] [Green Version]
  29. Kim, Y.; Lee, J.; Baek, C.; Yang, S.; Kwon, S.; Suh, Y. Performance Evaluation of Warm- and Hot-Mix Asphalt Mixtures Based on Laboratory and Accelerated Pavement Tests. Adv. Mater. Sci. Eng. 2012, 2012, 1–9. [Google Scholar] [CrossRef] [Green Version]
  30. Ouf, M.S.; Abdolsamed, A.A. Controlling Rutting Performance of Hot Mix Asphalt. Int. J. Sci. Eng. Res. 2016, 6, 2229–5518. [Google Scholar]
  31. Al-Qadi, I.L.; Yoo, P.J.; Elseifi, M.A.; Nelson, S. Creep Behavior of Hot-Mix Asphalt due to Heavy Vehicular Tire Loading. J. Eng. Mech. 2009, 135, 1265–1273. [Google Scholar] [CrossRef]
  32. Ahmad, J.; Rahman, M.Y.A.; Hainin, M.R. Rutting Evaluation of Dense Graded Hot Mix Asphalt Mixture. Int. J. Eng. Technol. 2011, 11, 48–52. [Google Scholar]
  33. Huang, Y.; Wang, X.; Liu, Z.; Li, S. Dynamic modulus test and master curve analysis of asphalt mix with trapezoid beam method. Road Mater. Pavement Des. 2017, 18, 1–11. [Google Scholar] [CrossRef]
  34. Rahman, A.A.S.M.; Islam, M.R.; Tarefder, R.A. Assessment and modification of nationally-calibrated dynamic modulus predictive model for the implementation of Mechanistic-Empirical design. Int. J. Pavement Res. Technol. 2018, 11, 502–508. [Google Scholar] [CrossRef]
  35. Khedr, S.A.; Breakah, T.M. Rutting parameters for asphalt concrete for different aggregate structures. Int. J. Pavement Eng. 2011, 12, 13–23. [Google Scholar] [CrossRef]
  36. Ezzat, H.; El-Badawy, S.; Gabr, A.; Zaki, S.; Breakah, T. Predicted performance of hot mix asphalt modified with nano-montmorillonite and nano-silicon dioxide based on Egyptian conditions. Int. J. Pavement Eng. 2018, 21, 642–652. [Google Scholar] [CrossRef]
  37. Tarar, M.A.; Khan, A.H.; Rehman, Z.; Inam, A. Changes in the rheological characteristics of asphalt binders modified with soybean-derived materials. Int. J. Pavement Eng. 2019, 22, 233–248. [Google Scholar] [CrossRef]
  38. Airey, G.D.; Choi, Y. State of the Art Report on Moisture Sensitivity Test Methods for Bituminous Pavement Materials. Road Mater. Pavement Des. 2002, 3, 355–372. [Google Scholar] [CrossRef]
  39. Apeagyei, A.K.; Grenfell, J.R.A.; Airey, G.D. Moisture-induced strength degradation of aggregate–asphalt mastic bonds. Road Mater. Pavement Des. 2014, 15, 239–262. [Google Scholar] [CrossRef] [Green Version]
  40. Apeagyei, A.K.; Grenfell, J.R.A.; Airey, G.D. Influence of aggregate absorption and diffusion properties on moisture damage in asphalt mixtures. Road Mater. Pavement Des. 2015, 16, 404–422. [Google Scholar] [CrossRef]
  41. Goel, G.; Sachdeva, S.N. Stripping Phenomenon in Bituminous Mixes: An Overview. Int. J. Math. Sci. Appl. 2016, 6, 353–360. [Google Scholar]
  42. El-Tahan, D.; Gabr, A.; El-Badawy, S.; Shetawy, M. Evaluation of recycled concrete aggregate in asphalt mixes. Innov. Infrastruct. Solut. 2018, 3, 20. [Google Scholar] [CrossRef]
  43. Hamzah, M.O.; Hasan, M.R.M.; Ismail, M.R.; Shahadan, Z. Effects of Temperature on Abrasion Loss of Porous and Dense Asphalt Mixes. Eur. J. Sci. Res. 2010, 40, 589–597. [Google Scholar]
  44. Mohajerani, A.; Nguyen, B.T.; Tanriverdi, Y.; Chandrawanka, K. A new practical method for determining the LA abrasion value for aggregates. Soils Found. 2017, 57, 840–848. [Google Scholar] [CrossRef]
  45. Wua, J.; Hou, Y.; Wang, L.; Guo, M.; Meng, L.; Xiong, H. Haocheng Xiong a Analysis of coarse aggregate performance based on the modified Micro Deval abrasion test. Int. J. Pavement Res. Technol. 2018, 11, 185–194. [Google Scholar] [CrossRef]
  46. Mahmud, M.Z.H.; Yaacobb, H.; Jayab, R.P.; Hassanb, N.A. Laboratory investigation on the effects of flaky aggregates on dynamic creep and resilient modulus of asphalt mixtures. J. Teknol. (Sci. Eng.) 2014, 70, 107–110. [Google Scholar] [CrossRef] [Green Version]
  47. Tahmoorian, F.; Samali, B. Laboratory investigations on the utilization of RCA in asphalt mixtures. Int. J. Pavement Res. Technol. 2018, 11, 627–638. [Google Scholar] [CrossRef]
  48. Al-Rousan, T.; Masad, E.; Tutumluer, E.; Pan, T. Evaluation of image analysis techniques for quantifying aggregate shape characteristics. Constr. Build. Mater. 2007, 21, 978–990. [Google Scholar] [CrossRef]
  49. Arasan, S.; Hasiloglu, S.A.; Akbulut, S. Shape Properties of Natural and Crushed Aggregate using Image Analysis. Int. J. Civ. Struct. Eng. 2010, 1, 221–233. [Google Scholar]
  50. Wang, H.; Bu, Y.; Wang, Y.; Yang, X.; You, Z. The Effect of Morphological Characteristic of Coarse Aggregates Measured with Fractal Dimension on Asphalt Mixture’s High-Temperature Performance. Adv. Mater. Sci. Eng. 2016, 2016, 1–9. [Google Scholar] [CrossRef] [Green Version]
  51. Galan, J.; Silva, L.; Pasandín, A.; Pérez, I. Evaluation of the Resilient Modulus of Hot-Mix Asphalt Made with Recycled Concrete Aggregates from Construction and Demolition Waste. Sustainability 2020, 12, 8551. [Google Scholar] [CrossRef]
  52. Rizvi, M.A.; Khan, A.H.; Rehman, Z.U.; Inam, A.; Masoud, Z. Evaluation of Linear Deformation and Unloading Stiffness Characteristics of Asphalt Mixtures Incorporating Various Aggregate Gradations. Sustainability 2021, 13, 8865. [Google Scholar] [CrossRef]
  53. Mackiewicz, P.; Szydło, A. Viscoelastic Parameters of Asphalt Mixtures Identified in Static and Dynamic Tests. Materials 2019, 12, 2084. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. White, G. A Synthesis on the Effects of Two Commercial Recycled Plastics on the Properties of Bitumen and Asphalt. Sustainability 2020, 12, 8594. [Google Scholar] [CrossRef]
  55. Sun, Y.; Gu, B.; Gao, L.; Li, L.; Guo, R.; Yue, Q.; Wang, J. Viscoelastic Mechanical Responses of HMAP under Moving Load. Materials 2018, 11, 2490. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Czech, K.R.; Gardziejczyk, W. Dynamic Stiffness of Bituminous Mixtures for the Wearing Course of the Road Pavement—A Proposed Method of Measurement. Materials 2020, 13, 1973. [Google Scholar] [CrossRef] [Green Version]
  57. Arulrajah, A.; Naeini, M.; Mohammadinia, A.; Horpibulsuk, S.; Leong, M. Recovered plastic and demolition waste blends as railway capping materials. Transp. Geotech. 2020, 22, 100320. [Google Scholar] [CrossRef]
Figure 1. Typical load-time cycles with rest periods during MR tests.
Figure 1. Typical load-time cycles with rest periods during MR tests.
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Figure 2. Typical load-time and deformation-time plots for a single cycle with time parameter explanation during MR tests, as per ASTM D7369.
Figure 2. Typical load-time and deformation-time plots for a single cycle with time parameter explanation during MR tests, as per ASTM D7369.
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Figure 3. Resilient modulus of modified and unmodified asphalt mixes (W1–W8).
Figure 3. Resilient modulus of modified and unmodified asphalt mixes (W1–W8).
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Figure 4. Resilient modulus of modified and unmodified asphalt mixes (W9–W16).
Figure 4. Resilient modulus of modified and unmodified asphalt mixes (W9–W16).
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Figure 5. Resilient modulus of modified and unmodified asphalt mixes (B1–B4).
Figure 5. Resilient modulus of modified and unmodified asphalt mixes (B1–B4).
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Table 1. Summary of the test matrix of warm asphalt mixes.
Table 1. Summary of the test matrix of warm asphalt mixes.
Mix IDAsphalt BindersAggregatesGradations
AAoSM
W1--SUP-1
W2--
W3--
W4--
W5--NH-A
W6--
W7--
W8--
BBoSM
W9--SUP-1
W10--
W11--
W12--
W13--NH-A
W14--
W15--
W16--
BBoSM
B1--SUP-2
B2--
B3--NH-A
B4---
Note: W = wearing course, B = base course, S = Sargodha aggregate, M = Margalla aggregate, Sup = Superpave, NH = National Highway Authority.
Table 2. (a) Soybean oil physical properties [22]. (b) Summary of unmodified and soybean oil-modified asphalt binders’ properties [22].
Table 2. (a) Soybean oil physical properties [22]. (b) Summary of unmodified and soybean oil-modified asphalt binders’ properties [22].
(a)
DescriptionSoybean Oil
Flash point (°C), ASTM D93320
Fire point (°C), ASTM D93354
Carbon residue (%), ASTM D1890.37
Dynamic viscosity @ 25 °C(Pa.S), AASHTO T-3160.062
Cloud point (°C), ASTM D5551−9
Melting point (°C), ASTM D54400.5
(b)
Test DescriptionType of Asphalt Binder
AAoBBo
Original asphalt binder (high temperature °C)AASHTO T31568.954.165.353.6
BBR (low temperature), AASHTO T313−17−24−23−29
Performance grades (PG), AASHTO M32064–1652–2264–2252–28
Viscosity (Pa.s) at 135 °C, AASHTO T3160.4620.2500.4450.242
Viscosity (Pa.s) at 165 °C, AASHTO T3160.1160.1250.1100.115
VTS−3.557−1.890−3.381−2.053
Mass change (%), AASHTO T2400.0780.0830.0560.068
Penetration (1/10th mm), ASTM D5 43496568
Softening point (°C), ASTM D365447.14845.6
Ductility (cm), ASTM D113100+100+100+100+
Flash and fire point (°C), ASTM D113300317307315
Table 3. Summary of aggregate physical properties [11].
Table 3. Summary of aggregate physical properties [11].
DescriptionType of AggregateStandards
SM
Water absorption (%)0.950.93ASTM C 127
Soundness (fine) (%)3.84.5ASTM C 88
Soundness (coarse) (%)4.656.98ASTM C 88
Los Angeles aberration (%)2324.5ASTM C 131
Elongation index (%)73ASTM D 4791
Flakiness index (%)95ASTM D 4791
Fractured faces (%)100100ASTM D 5821
Uncompacted voids (fine) (%)4544ASTM C 1252
Sand equivalent (%)7174ASTM D 2419
Note: S = Sargodha aggregate, M = Margalla aggregate.
Table 4. Summary of load, deformation and their corresponding time parameters obtained during the resilient modulus tests.
Table 4. Summary of load, deformation and their corresponding time parameters obtained during the resilient modulus tests.
Mix IDLoad (kN)Tm (s)T1 (s)T2 (s)Tc (s)T55 (s)TD (s)Te (s)Tf (s)δh (mm)δv (mm)δtotal (mm)
W11198.290.110000.099000.092400.066000.052800.023100.027500.018700.001400.085500.08690
W21165.230.111000.099800.093100.066500.053200.023300.027700.018800.001410.086200.08760
W3939.720.119000.107000.099700.071200.057000.024900.029700.020200.001450.092300.09380
W4907.750.119000.107000.100000.071600.057300.025100.029800.020300.001460.092800.09430
W51222.130.111000.099800.093100.066500.053200.023300.027700.018800.001390.086200.08760
W61210.350.111000.099900.093200.066600.053300.023300.027700.018900.001400.086300.08770
W7954.650.112000.101000.094200.067300.053900.023600.028100.019100.001440.087200.08860
W8923.150.116000.104000.097000.069300.055400.024300.028900.019600.001450.089800.09120
W91096.390.116000.104000.097100.069400.055500.024300.028900.019700.001430.089900.09130
W101067.330.116000.105000.097900.069900.055900.024500.029100.019800.001440.090600.09200
W11913.520.119000.107000.099800.071300.057000.024900.029700.020200.001470.092400.09390
W12889.450.119000.107000.100000.071400.057100.025000.029800.020200.001480.092500.09400
W131132.230.111000.099900.093200.066600.053300.023300.027700.018900.001400.086300.08770
W141109.350.117000.105000.097900.070000.056000.024500.029200.019800.001410.090700.09210
W15852.650.116000.104000.097000.069300.055400.024300.028900.019600.001440.089800.09120
W16823.150.117000.105000.097900.070000.056000.024500.029200.019800.001450.090700.09210
B1584.450.116000.104000.097000.069300.055400.024300.028900.019600.001460.090600.09200
B2494.870.119000.107000.099800.071300.057000.024900.029700.020200.001490.092500.09400
B3623.250.117000.105000.097900.070000.056000.024500.029200.019800.001470.087200.08860
B4514.340.117000.105000.097900.070000.056000.024500.029200.019800.001510.086100.08760
Note: peak load time (Tm), straight portion of unloading path between points T1 and T2, 40% rest period (Tc), 55% rest period (T55), 90% rest period (Td), time for 85% rest period (Te), time for 95% rest period (Tf) in measurement units of second (s).
Table 5. Summary of the statistical analysis on MR test parameters using Origin software from OriginLab®.
Table 5. Summary of the statistical analysis on MR test parameters using Origin software from OriginLab®.
Load (kN)Tm (s)T1 (s)T2 (s)Tc (s)T55 (s)TD (s)Te (s)Tf (s)δh (mm)δv (mm)δtota (mm)
Load(kN)Pearson Corr.1−0.37007−0.35666−0.35665−0.35684−0.35421−0.36084−0.36444−0.34458−0.88334−0.34865−0.35739
Sig.--0.026310.032740.032750.032650.034050.030620.028870.039599.88321 × 10−130.037160.03236
Tm (s)Pearson Corr.−0.3700710.995950.996120.996260.99570.995070.996790.992670.552340.99610.99579
Sig.0.02631--00000004.78096 × 10−400
T1 (s)Pearson Corr.−0.356660.9959510.99940.998930.998540.997480.998760.997330.548570.998850.99881
Sig.0.032740--0000005.32052 × 10−400
T2 (s)Pearson Corr.−0.356650.996120.999410.99970.999440.998760.99920.99820.553960.999670.99966
Sig.0.0327500--000004.5638 × 10−400
Tc (s)Pearson Corr.−0.356840.996260.998930.999710.999820.999220.999320.998560.551770.999870.99987
Sig.0.03265000--00004.85904 × 10−400
T55 (s)Pearson Corr.−0.354210.99570.998540.999440.9998210.999070.999190.998950.548640.999780.99978
Sig.0.034050000--0005.3103 × 10−400
TD (s)Pearson Corr.−0.360840.995070.997480.998760.999220.9990710.998820.997280.555040.999160.99893
Sig.0.0306200000--004.42388 × 10−400
Te (s)Pearson Corr.−0.364440.996790.998760.99920.999320.999190.9988210.997250.555630.999080.99898
Sig.0.02887000000--04.34876 × 10−400
Tf (s)Pearson Corr.−0.344580.992670.997330.99820.998560.998950.997280.9972510.540360.998560.9986
Sig.0.039590000000--6.69006 × 10−400
δh (mm)Pearson Corr.−0.883340.552340.548570.553960.551770.548640.555040.555630.5403610.544370.55288
Sig.9.88321 × 10−134.78096 × 10−45.32052 × 10−44.5638 × 10−44.85904 × 10−45.3103 × 10−44.42388 × 10−44.34876 × 10−46.69006 × 10−4--5.98648 × 10−44.70652 × 10−4
δv (mm)Pearson Corr.−0.348650.99610.998850.999670.999870.999780.999160.999080.998560.5443710.99982
Sig.0.03716000000005.98648 × 10−4--0
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Tarar, M.A.; Khan, A.H.; Rehman, Z.u.; Abbass, W.; Ahmed, A.; Ali, E.; Sayed, M.M.; Aziz, M. Evaluation of Resilience Parameters of Soybean Oil-Modified and Unmodified Warm-Mix Asphalts—A Way Forward towards Sustainable Pavements. Sustainability 2022, 14, 8832. https://doi.org/10.3390/su14148832

AMA Style

Tarar MA, Khan AH, Rehman Zu, Abbass W, Ahmed A, Ali E, Sayed MM, Aziz M. Evaluation of Resilience Parameters of Soybean Oil-Modified and Unmodified Warm-Mix Asphalts—A Way Forward towards Sustainable Pavements. Sustainability. 2022; 14(14):8832. https://doi.org/10.3390/su14148832

Chicago/Turabian Style

Tarar, Muhammad Akhtar, Ammad Hassan Khan, Zia ur Rehman, Wasim Abbass, Ali Ahmed, Elimam Ali, Mohamed Mahmoud Sayed, and Mubashir Aziz. 2022. "Evaluation of Resilience Parameters of Soybean Oil-Modified and Unmodified Warm-Mix Asphalts—A Way Forward towards Sustainable Pavements" Sustainability 14, no. 14: 8832. https://doi.org/10.3390/su14148832

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