Research article Special Issues

Performance of cement-stabilized macadam roads based on aggregate gradation interpolation tests

  • Received: 13 January 2019 Accepted: 28 February 2019 Published: 15 March 2019
  • This study adopted the uniform interpolation method to obtain five gradation types (labeled A, B, C, D, and E, from "coarse-grained" to "fine-grained" types) based on the skeleton dense structure and cement-stabilized macadam (CSM) aggregate gradation range recommended by current specifications. The optimum water content of the CSM exhibited a linear increase with gradation, whereas the maximum dry density exhibited a variation that can be described by a quadratic curve, for which the peak maximum dry density was near the maximum dry density of the Type B gradation. In the CSM structure, the skeleton void effect of the coarse-grained aggregate, the filling effect of the fine-grained aggregate, and the cementation effect of the cement and aggregate exhibited corresponding fluctuations. The ability to resist temperature shrinkage deformation was reduced. Additionally, the optimum values of the compressive strength and compression rebound modulus of the CSM plotted near the curve of the Type D gradation.

    Citation: Zhijun Liu, Xiaobi Wei, Dongquan Wang, Liangliang Wang. Performance of cement-stabilized macadam roads based on aggregate gradation interpolation tests[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2371-2390. doi: 10.3934/mbe.2019119

    Related Papers:

    [1] Lernik Asserian, Susan E. Luczak, I. G. Rosen . Computation of nonparametric, mixed effects, maximum likelihood, biosensor data based-estimators for the distributions of random parameters in an abstract parabolic model for the transdermal transport of alcohol. Mathematical Biosciences and Engineering, 2023, 20(11): 20345-20377. doi: 10.3934/mbe.2023900
    [2] Keenan Hawekotte, Susan E. Luczak, I. G. Rosen . Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach. Mathematical Biosciences and Engineering, 2021, 18(5): 6739-6770. doi: 10.3934/mbe.2021335
    [3] Gigi Thomas, Edward M. Lungu . A two-sex model for the influence of heavy alcohol consumption on the spread of HIV/AIDS. Mathematical Biosciences and Engineering, 2010, 7(4): 871-904. doi: 10.3934/mbe.2010.7.871
    [4] Salih Djillali, Soufiane Bentout, Tarik Mohammed Touaoula, Abdessamad Tridane . Global dynamics of alcoholism epidemic model with distributed delays. Mathematical Biosciences and Engineering, 2021, 18(6): 8245-8256. doi: 10.3934/mbe.2021409
    [5] Hai-Feng Huo, Shuang-Lin Jing, Xun-Yang Wang, Hong Xiang . Modelling and analysis of an alcoholism model with treatment and effect of Twitter. Mathematical Biosciences and Engineering, 2019, 16(5): 3561-3622. doi: 10.3934/mbe.2019179
    [6] Ridouan Bani, Rasheed Hameed, Steve Szymanowski, Priscilla Greenwood, Christopher M. Kribs-Zaleta, Anuj Mubayi . Influence of environmental factors on college alcohol drinking patterns. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1281-1300. doi: 10.3934/mbe.2013.10.1281
    [7] Peixian Zhuang, Xinghao Ding, Jinming Duan . Subspace-based non-blind deconvolution. Mathematical Biosciences and Engineering, 2019, 16(4): 2202-2218. doi: 10.3934/mbe.2019108
    [8] Biyun Hong, Yang Zhang . Research on the influence of attention and emotion of tea drinkers based on artificial neural network. Mathematical Biosciences and Engineering, 2021, 18(4): 3423-3434. doi: 10.3934/mbe.2021171
    [9] Colette Calmelet, John Hotchkiss, Philip Crooke . A mathematical model for antibiotic control of bacteria in peritoneal dialysis associated peritonitis. Mathematical Biosciences and Engineering, 2014, 11(6): 1449-1464. doi: 10.3934/mbe.2014.11.1449
    [10] Piotr Klejment . Application of supervised machine learning as a method for identifying DEM contact law parameters. Mathematical Biosciences and Engineering, 2021, 18(6): 7490-7505. doi: 10.3934/mbe.2021370
  • This study adopted the uniform interpolation method to obtain five gradation types (labeled A, B, C, D, and E, from "coarse-grained" to "fine-grained" types) based on the skeleton dense structure and cement-stabilized macadam (CSM) aggregate gradation range recommended by current specifications. The optimum water content of the CSM exhibited a linear increase with gradation, whereas the maximum dry density exhibited a variation that can be described by a quadratic curve, for which the peak maximum dry density was near the maximum dry density of the Type B gradation. In the CSM structure, the skeleton void effect of the coarse-grained aggregate, the filling effect of the fine-grained aggregate, and the cementation effect of the cement and aggregate exhibited corresponding fluctuations. The ability to resist temperature shrinkage deformation was reduced. Additionally, the optimum values of the compressive strength and compression rebound modulus of the CSM plotted near the curve of the Type D gradation.




    [1] C. Berthelot, D. Podborochynski, B. Marjerison, et al., Mechanistic characterization of cement stabilized marginal granular base materials for road construction, Can. J. Civil Eng., 37 (2010), 1613–1620.
    [2] J. P. Bilodeau, C. O. Plamondon and D. Guy, Estimation of resilient modulus of unbound granular materials used as pavement base: combined effect of grain-size distribution and aggregate source frictional properties, Mater. Struct., 49 (2016), 4363–4373.
    [3] Z. J. Chen, J. S. Xue, F. Cao, et al. Study of strong interlocked skeleton dense gradation for cement-stabilized macadam, Appl. Mech. Mater., 438–439 (2013), 644–648.
    [4] W. S. Guthrie, M. S. Shea and D. L. Eggett, Hydraulic conductivity of cement-treated soils and aggregates after freezing, Proc. Int. Conf. Cold Reg. Eng., 2012 (2012), 93–103.
    [5] Y. Wang, X. Ma and Z. L. Sun, Shrinkage performance of cement-treated macadam base materials, ICTTS, 383 (2010), 1378–1386.
    [6] W. B. Ashraf and M. A. Noor, Performance-evaluation of concrete properties for different combined aggregate gradation approaches, Proc. Eng. 14 (2011), 2627–2634.
    [7] K. A. Davis, L. S. Warr, S. E. Burns, et al., Physical and chemical behavior of four cement-treated aggregates, J. Mat. Civil Eng., 19 (2007), 891–897.
    [8] Z. J. Liu, Experimental research on the engineering characteristics of polyester fiber-reinforced cement-stabilized macadam, J. Mat. Civil Eng., 27 (2015), 04015004.
    [9] Z. J. Liu, Influence of rainfall characteristics on the infiltration moisture field of high-way subgrade, Road Mat. Pavement., 16 (2015), 635‒652.
    [10] I. D. Rey, J. Ayuso, A. Barbudo, et al., Feasibility study of cement-treated 0–8 mm recycled aggregates from construction and demolition waste as road base layer, Road Mat. Pavement., 17 (2016), 678‒692.
    [11] F. Colangelo and R. Cioffi, Mechanical properties and durability of mortar containing fine fraction of demolition wastes produced by selective demolition in South Italy, Composite B Eng., 115 (2017), 43–50.
    [12] F. Colangelo, R. Cioffi, B. Liguori, et al., Recycled polyolefins waste as aggregates for lightweight concrete, Composite B Eng., 106 (2016), 234–241.
    [13] V. Corinaldesi, Mechanical and elastic behaviour of concretes made of recycled-concrete coarse aggregates, Constr. Build. Mat., 24 (2010), 1616–1620.
    [14] W. B. Ashraf and M. A. Noor, Performance-evaluation of concrete properties for different combined aggregate gradation approaches, Proc. Eng., 14 (2011), 2627–2634.
    [15] M. R. Fatmi, B. Islam, M. Rahman, et al., Optimized aggregate gradation for Bangladesh, Appl. Mech. Mat., 71–78 (2011), 4226–4229.
    [16] L. Jin and J. L. Zheng, The analysis about how gradation impact on the performance of cement stabilized macadam base, In: 2012 International Conference on Computer Distributed Control and Intelligent Environmental monitoring, Hunan, China, 2012.
    [17] M. D. Cook, A. Ghaeezadah, and M. T. Ley, Impacts of coarse-aggregate gradation on the workability of slip-formed concrete, J. Mat. Civil Eng., 2018.
    [18] The Ministry of Communications of PRC. Technical guidelines for construction of highway roadbases (JTG/T F20-2015). China Communication Press, Beijing, China, 2015.
    [19] G. H. Hu, Study on the influence of the cement stabilized macadam's gradation to the modulus and strength, Adv. Mat. Res., 1004–1005 (2014), 1579–1584.
    [20] J. X. Liu and B. Li, Optimum design of aggregate gradation for cement stabilized macadam based on the fuzzy orthogonal method, J. Wuhan Univ. Tech., 32 (2010), 60–64.
    [21] Y. Wang, F. J. Ni and W. H. Xuan, Research on dry shrinkage performance of cement-treated base materials, Geohunan Int. Conf., 2009 (2009), 81–86.
    [22] Y. Wang, X. Sun, and Z. X. Li, Research on the reasonable strength of cement-treated macadam base, Adv. Civil Eng. Build. Mat., 2012 (2012), 621–624.
    [23] Y. Wang, W. H. Xuan and X. T. Feng, Studies on fatigue behaviors of cement stabilized macadam mixture, Geohunan Int Conf., 2011. Available from https://ascelibrary.org/doi/pdf/10.1061/47629(408)14.
    [24] Y. J. Jiang, L. W. Li, Y. S. Xu, et al. Performance comparison of cement-stabilized macadam with two skeleton close-grained gradation, Int. Conf. Concrete Pavement Des. Constr. Rehabil., 2011 (2011), 71–75.
    [25] The Ministry of Communications of PRC. Test methods of materials stabilized with inorganic binders for highway engineering (JTG E51-2009). China Communication Press, Beijing, China, 2009.
    [26] P. Zhang, Q. F. Li and H. Wei, Investigation of flexural properties of cement-stabilized macadam reinforced with polypropylene fiber, J. Mater. Civil Eng., 22 (2010), 1282–1287.
    [27] L. J. Zhao, W. Z. Jiang, J. R. Hou, et al., Influence of mixing methods on performance of compressive strength for cement stabilized macadam mixture, China J. Highway Transport., 31 (2018), 151–158.
    [28] Y. B. Yang, H. M. Chen and S. M. Wu, Study on the C80 high-strength rock chips concrete, Key Engineering Materials, Ecol. Environ. Tech. Concrete, 477 (2011), 218–223.
    [29] Y. L. Zhao, Gradation design of the aggregate skeleton in asphalt mixture, J. Test. Eval., 40 (2012), 20120142.
    [30] L. H. Li, P. Huang and D. Liu, Impact on performance of cement stabilized macadam mixtures between gyratory compaction and static compaction methods, J. Chang'an Univ. (Nat. Sci. Ed.), 36 (2016), 17–25.
    [31] D. Luo and C. F. Wu, Influence of gradation and compaction standards to performance of cement stabilized crushed stone base material, Highway, 4 (2014), 187–193.
    [32] T. Ma, X. H. Ding, D. Y. Zhang, et al., Experimental study of recycled asphalt concrete modified by high-modulus agent, Constr. Build Mat., 128 (2016), 128–135.
    [33] X. Yu and X. Wu, The influences of RAP on the performance of cement stabilized crushed stone base, IEEE, 2011 (2011), 6315–6318.
    [34] M. O. Ahmed, A. H. Waddah and K. Manish, Research on the mechanical strength of emulsified asphalt-cement stabilized macadam based on neural network algorithm, Key Eng. Mat., 753 (2017), 326–330.
    [35] A. Muntean, M. Böhm and J. Kropp, Moving carbonation fronts in concrete: A moving-sharp-interface approach, Chem. Eng. Sci., 66 (2011), 538–547.
    [36] I. S. Yoon, O. Çopuroğlu and K. B. Park, Effect of global climatic change on carbonation progress of concrete, Atmos. Environ., 41 (2007), 7274–7285.
  • This article has been cited by:

    1. Yan Wang, Daniel J. Fridberg, Robert F. Leeman, Robert L. Cook, Eric C. Porges, Wrist-worn alcohol biosensors: Strengths, limitations, and future directions, 2019, 81, 07418329, 83, 10.1016/j.alcohol.2018.08.013
    2. John D. Roache, Tara E. Karns-Wright, Martin Goros, Nathalie Hill-Kapturczak, Charles W. Mathias, Donald M. Dougherty, Processing transdermal alcohol concentration (TAC) data to detect low-level drinking, 2019, 81, 07418329, 101, 10.1016/j.alcohol.2018.08.014
    3. Melike Sirlanci, I. Gary Rosen, Tamara L. Wall, Susan E. Luczak, Applying a novel population-based model approach to estimating breath alcohol concentration (BrAC) from transdermal alcohol concentration (TAC) biosensor data, 2019, 81, 07418329, 117, 10.1016/j.alcohol.2018.09.005
    4. Jian Li, Susan E. Luczak, I. G. Rosen, Comparing a distributed parameter model-based system identification technique with more conventional methods for inverse problems, 2019, 27, 0928-0219, 703, 10.1515/jiip-2018-0006
    5. Alastair van Heerden, Mark Tomlinson, Sarah Skeen, Charles Parry, Kendal Bryant, Mary Jane Rotheram-Borus, Innovation at the Intersection of Alcohol and HIV Research, 2017, 21, 1090-7165, 274, 10.1007/s10461-017-1926-z
    6. Melike Sirlanci, Susan Luczak, I. G. Rosen, 2017, Approximation and convergence in the estimation of random parameters in linear holomorphic semigroups generated by regularly dissipative operators, 978-1-5090-5992-8, 3171, 10.23919/ACC.2017.7963435
    7. Kelly Egmond, Cassandra J. C. Wright, Michael Livingston, Emmanuel Kuntsche, Wearable Transdermal Alcohol Monitors: A Systematic Review of Detection Validity, and Relationship Between Transdermal and Breath Alcohol Concentration and Influencing Factors, 2020, 44, 0145-6008, 1918, 10.1111/acer.14432
    8. Christian S. Hendershot, Christina N. Nona, A Review of Developmental Considerations in Human Laboratory Alcohol Research, 2017, 4, 2196-2952, 364, 10.1007/s40429-017-0173-8
    9. Nancy P. Barnett, Mark A. Celio, Jennifer W. Tidey, James G. Murphy, Suzanne M. Colby, Robert M. Swift, A preliminary randomized controlled trial of contingency management for alcohol use reduction using a transdermal alcohol sensor, 2017, 112, 0965-2140, 1025, 10.1111/add.13767
    10. Melike Sirlanci, I G Rosen, Susan E Luczak, Catharine E Fairbairn, Konrad Bresin, Dahyeon Kang, Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data, 2018, 34, 0266-5611, 125006, 10.1088/1361-6420/aae791
    11. Susan E. Luczak, Ashley L. Hawkins, Zheng Dai, Raphael Wichmann, Chunming Wang, I.Gary Rosen, Obtaining continuous BrAC/BAC estimates in the field: A hybrid system integrating transdermal alcohol biosensor, Intellidrink smartphone app, and BrAC Estimator software tools, 2018, 83, 03064603, 48, 10.1016/j.addbeh.2017.11.038
    12. Melike Sirlanci, Susan E. Luczak, Catharine E. Fairbairn, Dahyeon Kang, Ruoxi Pan, Xin Yu, I. Gary Rosen, Estimating the distribution of random parameters in a diffusion equation forward model for a transdermal alcohol biosensor, 2019, 106, 00051098, 101, 10.1016/j.automatica.2019.04.026
    13. Sina Kianersi, Maya Luetke, Jon Agley, Ruth Gassman, Christina Ludema, Molly Rosenberg, Validation of transdermal alcohol concentration data collected using wearable alcohol monitors: A systematic review and meta-analysis, 2020, 216, 03768716, 108304, 10.1016/j.drugalcdep.2020.108304
    14. Catharine E. Fairbairn, I. Gary Rosen, Susan E. Luczak, Walter J. Venerable, Estimating the quantity and time course of alcohol consumption from transdermal alcohol sensor data: A combined laboratory-ambulatory study, 2019, 81, 07418329, 111, 10.1016/j.alcohol.2018.08.015
    15. John D. Clapp, Danielle R. Madden, Sheila Pakdaman, Drinking with Friends: Measuring the Two-week Ecology of Drinking Behaviors, 2022, 46, 1087-3244, 96, 10.5993/AJHB.46.2.1
    16. Clemens Oszkinat, Susan E. Luczak, I. G. Rosen, 2022, Physics-Informed Learning: Distributed Parameter Systems, Hidden Markov Models, and the Viterbi Algorithm, 978-1-6654-5196-3, 266, 10.23919/ACC53348.2022.9867145
    17. Baichen Li, R. Scott Downen, Quan Dong, Nam Tran, Maxine LeSaux, Andrew C. Meltzer, Zhenyu Li, A Discreet Wearable IoT Sensor for Continuous Transdermal Alcohol Monitoring—Challenges and Opportunities, 2021, 21, 1530-437X, 5322, 10.1109/JSEN.2020.3030254
    18. Clemens Oszkinat, Susan E. Luczak, I. Gary Rosen, An abstract parabolic system-based physics-informed long short-term memory network for estimating breath alcohol concentration from transdermal alcohol biosensor data, 2022, 34, 0941-0643, 18933, 10.1007/s00521-022-07505-w
    19. Mengsha Yao, Susan E. Luczak, I. Gary Rosen, Tracking and blind deconvolution of blood alcohol concentration from transdermal alcohol biosensor data: A population model-based LQG approach in Hilbert space, 2023, 147, 00051098, 110699, 10.1016/j.automatica.2022.110699
    20. Keenan Hawekotte, Susan E. Luczak, I. G. Rosen, Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach, 2021, 18, 1551-0018, 6739, 10.3934/mbe.2021335
    21. Clemens Oszkinat, Tianlan Shao, Chunming Wang, I G Rosen, Allison D Rosen, Emily B Saldich, Susan E Luczak, Blood and breath alcohol concentration from transdermal alcohol biosensor data: estimation and uncertainty quantification via forward and inverse filtering for a covariate-dependent, physics-informed, hidden Markov model* , 2022, 38, 0266-5611, 055002, 10.1088/1361-6420/ac5ac7
    22. Mengsha Yao, Susan E. Luczak, Emily B. Saldich, I. Gary Rosen, A population model‐based linear‐quadratic Gaussian compensator for the control of intravenously infused alcohol studies and withdrawal symptom prophylaxis using transdermal sensing, 2022, 0143-2087, 10.1002/oca.2934
    23. Bob M. Lansdorp, Flux-Type versus Concentration-Type Sensors in Transdermal Measurements, 2023, 13, 2079-6374, 845, 10.3390/bios13090845
    24. Kyla-Rose Walden, Emily B. Saldich, Georgia Wong, Haoxing Liu, Chunming Wang, I. Gary Rosen, Susan E. Luczak, 2023, 79, 9780443193866, 271, 10.1016/bs.plm.2023.06.002
    25. Clemens Oszkinat, Susan E. Luczak, I. G. Rosen, Uncertainty Quantification in Estimating Blood Alcohol Concentration From Transdermal Alcohol Level With Physics-Informed Neural Networks, 2023, 34, 2162-237X, 8094, 10.1109/TNNLS.2022.3140726
    26. Lernik Asserian, Susan E. Luczak, I. G. Rosen, Computation of nonparametric, mixed effects, maximum likelihood, biosensor data based-estimators for the distributions of random parameters in an abstract parabolic model for the transdermal transport of alcohol, 2023, 20, 1551-0018, 20345, 10.3934/mbe.2023900
    27. J.M. Maestre, P. Chanfreut, L. Aarons, Constrained numerical deconvolution using orthogonal polynomials, 2024, 24058440, e24762, 10.1016/j.heliyon.2024.e24762
    28. Mengsha Yao, Maria Allayioti, Emily B. Saldich, Georgia Y. Wong, Chunming Wang, Susan E. Luczak, I. G. Rosen, Real-time recursive estimation of, and uncertainty quantification for, breath alcohol concentration via LQ tracking control-based inverse filtering of transdermal alcohol biosensor signals, 2024, 2, 2994-7669, 38, 10.3934/ammc.2024003
    29. Joseph C. Anderson, A new approach to modeling transdermal ethanol kinetics, 2024, 12, 2051-817X, 10.14814/phy2.70070
    30. Mengsha Yao, Gary Rosen, 2025, 10.5772/intechopen.1010428
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5008) PDF downloads(807) Cited by(11)

Article outline

Figures and Tables

Figures(12)  /  Tables(7)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog