Aszfaltkeverékek közvetett húzószilárdságának modellezése mesterséges intelligencia alkalmazásával

Ali Saleh; László Gáspár

doi:10.24228/KTSZ.2024.4.1

Ali Saleh Széchenyi István Egyetem, Építész-, Építő- és Közlekedésmérnöki Kar, Közlekedésépítési és Vízgazdálkodási Tanszék
László Gáspár Széchenyi István Egyetem, Építész-, Építő- és Közlekedésmérnöki Kar, Közlekedésépítési és Vízgazdálkodási Tanszék; KTI Magyar Közlekedéstudományi és Logisztikai Intézet Nonprofit Kft.,

DOI: https://doi.org/10.24228/KTSZ.2024.4.1

Kulcsszavak: habosított bitumen, meleg aszfaltkeverék, neurális hálózat, szupport vektor regresszió (SVR), gépi tanulás

Absztrakt

Az újrahasznosított aszfaltot és habosított bitument tartalmazó aszfaltkeverékek közvetett húzószilárdságának előrebecslése lineáris regressziós és a neurális hálózati modellekkel jó szakmai célkitűzés. A kutatások során a véletlenszerű erdő (random forest) és a neurális hálózat (neural network) modell összehasonlításával igazolták a gépi tanulási technikák ilyen irányú haszmosíthatóságát.

Hivatkozások

L. Gáspár and Zs. Bencze, “Long-Life Pavements - European and American Perspectives”. NBM&CW (New Building Materials & Construction World) 24 (3), 122-135, 2018

T. Baghaee Moghaddam and H. Baaj, “Application of compressible packing model for optimization of asphalt concrete mix design,” Constr Build Mater, vol. 159, 530–539, 2018, DOI: https://doi.org/m42t3

S. B. Cooper, L. N. Mohammad, S. Kabir, and W. King, “Balanced asphalt mixture design through specification modification: Louisiana’s experience,” Trans Res Record, National Research Council, vol. 2447 .92–100, 2014. DOI: https://doi.org/m42v4

S. S. Kar, A. K. Swamy, D. T. Tiwari, and P. K. Jain, “Impact of low viscosity grade bitumen on foaming characteristics,” J of the South Afr Inst of Civil Eng, (60) 2, pp. 40–52, 2018, DOI: https://doi.org/grwjqx5

L. H. Csanyi, “Foamed asphalt in bituminous paving mixtures,” Highw Res Board Bull, 160, 19 57.6. G. Wirtgen, “Wirtgen cold recycling technology.” Wirtgen GmbH Windhagen, Alemania, 2012.

B. A. Williams, A. Copeland, and T. C. Ross, “Asphalt pavement industry survey on recycled materials and warm-mix asphalt usage: 2017,” 2018.8

S. S. Kar, D. Tiwari, A. K. Swamy, and P. K. Jain, “Impact of binder on properties of foamed bituminous mixtures,” Proc of Inst of Civil Eng. Const Mater, (17) 4, 194–204, 2017, DOI: https://doi.org/m42w9

M. M. Iwanski, A. Chomicz-Kowalska and K. Maciejewski, “Resistance to moisture-induced damage of half-warm-mix asphalt concrete with foamed bitumen,” Mat, (13) 3, 2020, DOI: https://doi.org/gmqqbw10

M. Hoy, S. Horpibulsuk and A. Arulrajah, “Strength development of Recycled Asphalt Pavement - Fly ash geopolymer as a road construction material,” Constr Build Mater, vol. 117, 209–219, 2016, DOI: https://doi.org/gqg5ps11

F. Dong, X. Yu, B. Xu, and T. Wang, “Comparison of high temperature perfor-mance and microstructure for foamed WMA and HMA with RAP binder,” Constr Build Mater, vol. 134, 594–601, 2017, DOI: https://https://doi.org/grwjpt

J. Li, W. Fu, and H. Zang, “Design Method for Proportion of Cement-Foamed Asphalt Cold Recycled Mixture,” MATEC Web of Conf, vol. 142, 02002, 2018, DOI: https://doi.org/grwj3k13

Z. Li, P. Hao, H. Liu, and J. Xu, “Effect of cement on the strength and microcosmic characteristics of cold recycled mixtures using foamed asphalt,” J Clean Prod, vol. 230, 956–965, 2019, DOI: https://doi.org/grwjsb14

N. Bala, M. Napiah, and I. Kamaruddin, “Nanosilica composite asphalt mixtures performance-based design and optimisation using response surface methodology,” Int J of Pave Eng, 21 (1), 29–40, 2020, DOI: https://doi.org/m42x15

L. P. F. Abreu, J. R. M. Oliveira, H. M. R. D. Silva, D. Palha, and P. V. Fonseca, “Suitability of different foamed bitumens for warm mix asphalts with increasing recycling rates,” Constr Build Mater, vol. 142, 342–353, Jul. 2017, DOI: https://doi.org/grwjpv16

M. S. Arefin, T. Quasem, M. Nazzal, A. R. Abbas, and Y. AbuHassan, “Effect of short-term and long-term ageing on dynamic modu-lus of foamed warm mix asphalt,” Int J of Pave Eng, (21) 4 , 524–536, 2020, DOI: https://doi.org/grwjs717

S. S. Kar, A. K. Swamy, D. Tiwari, and P. K. Jain, “Impact of Chemical Composition on Foaming Characteristics of Asphalt Binder,” J of Trans Eng, Part B: Pave, (146) 3, 04020045, 2020, DOI: https://doi.org/gqj38q18

B. K. Bairgi, U. A. Mannan, and R. A. Tarefder, “Influence of foaming on tribological and rheological characteristics of foamed asphalt,” Constr Build Mater, vol. 205, 186–195, 2019, DOI: https://doi.org/grwj3619

M. R. Mohd Hasan, Z. You, H. Yin, L. You, and R. Zhang, “Characterizations of foamed asphalt binders prepared using combinations of physical and chemical foaming agents,” Constr Build Mater, vol. 204, 94–104, 2019, DOI: https://doi.org/gqhvmq20

E. A. Taziani, E. Toraldo, F. Giustozzi, and M. Crispino, “Investigation on the combined effect of fibers and cement on the mechanical performance of foamed bitumen mixtures containing 100% RAP,” Adv in Mater Sci and Eng, vol. 2016, 2016, DOI: https://doi.org/f9chdk21

A. Chomicz-Kowalska and P. Ramiaczek, “Comparative Evaluation and Modification of Laboratory Compaction Methods of Road Base Mixtures Manufactured in Low-emission CIR Technology with Foamed Bitumen and Bitumen Emulsion,” in Proc Eng, Elsevier Ltd, 2017, 560–569. DOI: https://doi.org/gmqqb722

Y. Hou, Y. Cai, Z. Zang, Z. Qian, and B. Zhao, “Dynamic Characteristics of Warm Mix Foamed Asphalt Mixture in Seasonal Frozen Area,” Adv in Mater Sci and Eng, vol. 2019, DOI: https://doi.org/grwjrd23

F. V. Guatimosim, K. L. Vasconcelos, L. L. B. Bernucci, and K. J. Jenkins, “Laboratory and field evaluation of cold recycling mixture with foamed asphalt,” Road Mater and Pave Des, (19) 2, 385–399, 2018, DOI: https://doi.org/gqhvk324

N. Karballaeezadeh, D. Mohammadzadeh S, S. Shamshirband, P. Hajikhodaverdikhan, A. Mosavi, and K. wing Chau, “Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road),” Eng Appl of Comp Fluid Mech, (13) 1, 188–198, 2019, DOI: https://doi.org/m43f25

T. Gandhi, W. Rogers, and S. Amirkhanian, “Laboratory evaluation of warm mix asphalt ageing characteristics,” Int J of Pave Eng, (11) 2, 133–142, 2010, DOI: https://doi.org/fb9gtx26

ASTM D692-00 “Standard Specification for Course Aggregate for Bituminous Paving Mixtures”, 2004, 2 p.27.

EN 12591 “Bitumen and bituminous binders— Specifications for paving grade bitumens”, 2009, 36 p.28.

F. Kamran, M. Basavarajappa, N. Bala, and L. Hashemian, “Performance evaluation of stabilized base course using asphalt emulsion and asphaltenes derived from Alberta oil sands,” in Trans Res Rec vol. 2675, no. 10, SAGE Publications Ltd, 2021, 764–775. DOI: https://doi.org/m43m29

V. Vapnik, The nature of statistical learning theory. Springer science & business media, 1999. ISBN 978-1-4419-3160-330

K. Gopalakrishnan, A. M. Asce, and S. Kim, “Support Vector Machines Approach to HMA Stiffness Prediction”, J of Eng Mech (137) 2 DOI: https://doi.org/drkbd331

M. Maalouf, N. Khoury, and T. B. Trafalis, “Support vector regression to predict asphalt mix performance,” Int J Numer Anal Methods Geomech, (32) 16, 1989–1996, 2008, DOI: https://doi.org/c4kk5g32.Y

Zhao, K. Zhang, Y. Zhang, Y. Luo, and S. Wang, “Prediction of air voids of asphalt layers by intelligent algorithm,” Constr Build Mater, vol. 317, p. 125908, 2022. DOI: https://doi.org/m43x33.M

Nazemi and A. Heidaripanah, “Support vector machine to predict the indirect tensile strength of foamed bitumen-stabilised base course materials,” Road Mater and Pave Des, (17) 3, 768–778, 2016, DOI: https://doi.org/m43z34

H. Ziari, M. Maghrebi, J. Ayoubinejad, and S. T. Waller, “Prediction of pavement performance: Application of support vector regression with different kernels,” Transp Res Rec, vol. 2589, 135–145, 2016, DOI: https://doi.org/gnrfzf35

Y. Huang, “Advances in artificial neural networks - Methodological development and application,” Alg, (2) 3, 973–1007, 2009. DOI: https://doi.org/ctbcq236

H. Gong, Y. Sun, X. Shu, and B. Huang, “Use of random forests regression for predicting IRI of asphalt pavements,” Constr Build Mater, vol. 189, 890–897, 2018, DOI: https://doi.org/gk88dm37

A. Fathi, M. Mazari, M. Saghafi, A. Hosseini, and S. Kumar, “Parametric Study of Pavement Deterioration Using Machine Learning Algorithms.” Int Airf and Highw Pave Conf 2019, 12 p. DOI: https://doi.org/gnrfzk38

D. Daneshvar and A. Behnood, “Estimation of the dynamic modulus of asphalt concretes using random forests algorithm,” Int J of Pave Eng, (23) 2, 250–260, 2022, DOI: https://doi.org/gg6rkg39

H. Gong, Y. Sun, W. Hu, P. A. Polaczyk, and B. Huang, “Investigating impacts of asphalt mixture properties on pavement performance using LTPP data through random forests,” Constr Build Mater, vol. 204, 203–212, 2019, DOI: https://doi.org/m43340

Y. Zhan, J. Q. Li, C. Liu, K. C. P. Wang, D. M. Pittenger, and Z. Musharraf, “Effect of aggregate properties on asphalt pavement friction based on random forest analysis,” Constr Build Mater, vol. 292, 2021, DOI: https://doi.org/grtkz541

A. Saleh, Gáspár L., “Különböző újrahasznosított aszfalt (RAP) tartalmú, habosított be-ton kötőanyagú aszfaltkeverékek közvetett húzószilárdságának modellezése”, Útügyi Lapok 12. évf. 19. szám 12 p. 2024. június, DOI: https://doi.org/m75q