Topological Characterization and Typical Topologies of Disruption Aggregates in Asphalt Mixture
Publication: Journal of Materials in Civil Engineering
Volume 36, Issue 7
Abstract
Mesoscale contact networks for asphalt mixtures play a crucial role in load resistance. However, there is a lack of quantitative characterization methods for contact networks. Topology can describe the contact relationships between multiple objects through points and lines. In this study, aggregate information was extracted by industrial computed tomography (CT) and digital image processing technology (DIP). The classification of disruption aggregates was determined by a two-dimensional (2D) topological contact model. Then, the correlation mechanism between topological indices and topologies was established based on the topological matrix. The algebraic connectivity (La) of nine different asphalt mixture disruption aggregates was analyzed. The results revealed that each type of asphalt mixture disruption aggregate was dominated by a certain La with higher frequency. As the nominal maximum size (NMAS) increased, there was a shift in the dominant topologies of different asphalt mixture disruption aggregates from those associated with lower La to those linked to higher La. Based on the higher frequency of La, there were 8 topologies with frequencies exceeding 20%; 5 with frequencies surpassing 10%; and 14 and 17 with frequencies greater than 5% and 2%, respectively. Furthermore, it was observed that the topologies of disruption aggregates predominantly consisted of simple topologies. Additionally, the frequency of complex topologies decreased as the number of nodes and connectivity increased.
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Data Availability Statement
Some or all data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
National Key Research and Development Program of China (2022YFB2602600). Joint Funds of the National Natural Science Foundation of China Regional Innovation Development (U20A20315). Natural Science Foundation of Heilongjiang Province of China (YQ2022E037).
References
Aboufoul, M., A. Chiarelli, I. Triguero, and A. Garcia. 2019. “Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads.” Powder Technol. 352 (Jun): 294–304. https://doi.org/10.1016/j.powtec.2019.04.072.
Arevalo, R., L. A. Pugnaloni, I. Zuriguel, and D. Maza. 2013. “Contact network topology in tapped granular media.” Phys. Rev. E 87 (2): 022203. https://doi.org/10.1103/PhysRevE.87.022203.
Cai, X., and D. Wang. 2013. “Evaluation of rutting performance of asphalt mixture based on the granular media theory and aggregate contact characteristics.” Road Mater. Pavement Des. 14 (2): 325–340. https://doi.org/10.1080/14680629.2013.794368.
Dimitrov, D., C. Knauer, K. Kriegel, and G. Rote. 2009. “Bounds on the quality of the PCA bounding boxes.” Comput. Geom. Theory Appl. 42 (8): 772–789. https://doi.org/10.1016/j.comgeo.2008.02.007.
Du, S., Q. Wang, and L. Guo. 2010. “Modeling the scale dependences of topological relations between lines and regions induced by reduction of attributes.” Int. J. Geogr. Inf. Sci. 24 (11): 1649–1686. https://doi.org/10.1080/13658811003591672.
Garcia, A., M. Aboufoul, F. Asamoah, and D. Jing. 2019. “Study the influence of the air void topology on porous asphalt clogging.” Constr. Build. Mater. 227 (Dec): 116791. https://doi.org/10.1016/j.conbuildmat.2019.116791.
Hua, X., H. Y. Hong, J. G. Liu, and Y. Shi. 2020. “A novel unified method for the fast computation of discrete image moments on grayscale images.” J. Real-Time Image Process. 17 (5): 1239–1253. https://doi.org/10.1007/s11554-019-00878-7.
Kennedy, J., and R. Mendes. 2006. “Neighborhood topologies in fully informed and best-of-neighborhood particle swarms.” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 36 (4): 515–519. https://doi.org/10.1109/TSMCC.2006.875410.
Kusumawardani, D. M., and Y. D. Wong. 2020. “The influence of aggregate shape properties on aggregate packing in porous asphalt mixture (PAM).” Constr. Build. Mater. 255 (Sep): 119379. https://doi.org/10.1016/j.conbuildmat.2020.119379.
Li, T., P. Liu, C. Du, M. Schnittcher, J. Hu, D. Wang, and M. Oeser. 2022a. “Microstructural analysis of the effects of compaction on fatigue properties of asphalt mixtures.” Int. J. Pavement Eng. 23 (1): 9–20. https://doi.org/10.1080/10298436.2020.1728532.
Li, W., B. Sun, Y. Huang, and S. Mahmoodi. 2022b. “Adaptive complex network topology with fitness distance correlation framework for particle swarm optimization.” Int. J. Intell. Syst. 37 (8): 5217–5247. https://doi.org/10.1002/int.22790.
Liang, Z., C. Xing, H. Xu, Y. Tan, T. Qiu, B. Chai, J. Li, and T. Liu. 2023. “Asphalt pavement compaction and vehicle speed monitoring using intelligent aggregate.” IEEE Trans. Intell. Transp. Syst. 24 (9): 10177–10185. https://doi.org/10.1109/TITS.2023.3273598.
Lin, M. C., W. Zhou, J. Y. Liu, G. Ma, and X. X. Cao. 2022. “A topological view on microscopic structural evolution for granular material under loading and unloading path.” Comput. Geotech. 141 (Jan): 104530. https://doi.org/10.1016/j.compgeo.2021.104530.
Liu, L., J. Wu, and S. Meng. 2019. “Analysis and improvement of neighborhood topology of particle swarm optimization.” J. Comput. Methods Sci. Eng. 19 (4): 955–968. https://doi.org/10.3233/JCM-190003.
Manrique-Sanchez, L., S. Caro, N. Estrada, D. Castillo, and A. E. Alvarez. 2022. “Random generation of 2D PFC microstructures through DEM gravimetric methods.” Road Mater. Pavement Des. 23 (4): 925–941. https://doi.org/10.1080/14680629.2020.1860804.
Martin-Hernandez, J., H. Wang, P. Van Mieghem, and G. D’Agostino. 2014. “Algebraic connectivity of interdependent networks.” Physica A 404 (Jun): 92–105. https://doi.org/10.1016/j.physa.2014.02.043.
Mcgennis, R. B., R. Anderson, T. W. Kennedy, and M. Solaimanian. 1995. Background of Superpave asphalt mixture design and analysis. McLean, VA: Federal Highway Administration. Office of Technology Applications.
Milanovic, J. V., and W. Zhu. 2018. “Modeling of interconnected critical infrastructure systems using complex network theory.” IEEE Trans. Smart Grid 9 (5): 4637–4648. https://doi.org/10.1109/TSG.2017.2665646.
Pouranian, M. R., M. Shishehbor, and J. E. Haddock. 2020. “Impact of the coarse aggregate shape parameters on compaction characteristics of asphalt mixtures.” Powder Technol. 363 (Mar): 369–386. https://doi.org/10.1016/j.powtec.2020.01.014.
Reff, N., and L. J. Rusnak. 2012. “An oriented hypergraphic approach to algebraic graph theory.” Linear Algebra Appl. 437 (9): 2262–2270. https://doi.org/10.1016/j.laa.2012.06.011.
Sanfilippo, D., A. Garcia-Hernandez, A. Alexiadis, and B. Ghiassi. 2022. “Effect of freeze–thaw cycles on the void topologies and mechanical properties of asphalt.” Constr. Build. Mater. 344 (Aug): 128085. https://doi.org/10.1016/j.conbuildmat.2022.128085.
Schneider, M., and T. Behr. 2005. “Topological relationships between complex lines and complex regions.” In Proc., Conceptual Modeling–ER 2005: 24th Int. Conf. on Conceptual Modeling, Klagenfurt, Austria, October 24–28, 2005, 483–496. Berlin: Springer.
Schuck, B., T. Teutsch, S. Alber, W. Ressel, H. Steeb, and M. Ruf. 2021. “Study of air void topology of asphalt with focus on air void constrictions—A review and research approach.” Road Mater. Pavement Des. 22 (Jun): S425–S443. https://doi.org/10.1080/14680629.2021.1907215.
Shi, L., X. Xiao, X. Wang, H. Liang, and D. Wang. 2022. “Mesostructural characteristics and evaluation of asphalt mixture contact chain complex networks.” Constr. Build. Mater. 340 (Jul): 127753. https://doi.org/10.1016/j.conbuildmat.2022.127753.
Sun, P., K. Zhang, S. Han, Z. Liang, W. Kong, and X. Zhan. 2022. “Method for the evaluation of the homogeneity of asphalt mixtures by 2-dimensional image analysis.” Materials 15 (12): 4265. https://doi.org/10.3390/ma15124265.
Tan, Y., Z. Liang, H. Xu, and C. Xing. 2022. “Internal deformation monitoring of granular material using intelligent aggregate.” Autom. Constr. 139 (Jul): 104265. https://doi.org/10.1016/j.autcon.2022.104265.
Walker, D. M., and A. Tordesillas. 2010. “Topological evolution in dense granular materials: A complex networks perspective.” Int. J. Solids Struct. 47 (5): 624–639. https://doi.org/10.1016/j.ijsolstr.2009.10.025.
Wang, N., F. Chen, T. Ma, Y. C. Luan, and J. Q. Zhu. 2022. “Compaction performance of cold recycled asphalt mixture using SmartRock sensor.” Autom. Constr. 140 (Aug): 104377. https://doi.org/10.1016/j.autcon.2022.104377.
Xie, S., J. Yi, H. Wang, S.-H. Yang, M. Xu, and D. Feng. 2022. “Mechanical response analysis of transverse crack treatment of asphalt pavement based on DEM.” Int. J. Pavement Eng. 23 (7): 2206–2226. https://doi.org/10.1080/10298436.2020.1849687.
Xing, C., Z. Liang, Y. Tan, D. Wang, and C. Zhai. 2021. “Skeleton filling system evaluation method of asphalt mixture based on compressible packing model.” J. Transp. Eng. Part B. Pavements 147 (4): 04021062. https://doi.org/10.1061/JPEODX.0000320.
Xing, C., B. Liu, Z. Sun, Y. Tan, X. Liu, and C. Zhou. 2022. “DEM-based stress transmission in asphalt mixture skeleton filling system.” Constr. Build. Mater. 351 (Oct): 128956. https://doi.org/10.1016/j.conbuildmat.2022.128956.
Xing, C., B. Liu, Z. Sun, H. Xu, D. Wang, and Y. Tan. 2023. “The effect of particle shape on the meso-structure of mixture skeleton based on DIP and 3D DEM.” Constr. Build. Mater. 384 (Jun): 131445. https://doi.org/10.1016/j.conbuildmat.2023.131445.
Xing, C., H. N. Xu, Y. Q. Tan, X. Y. Liu, C. H. Zhou, and T. Scarpas. 2019. “Gradation measurement of asphalt mixture by X-Ray CT images and digital image processing methods.” Measurement 132 (Jan): 377–386. https://doi.org/10.1016/j.measurement.2018.09.066.
Xing, C., L. Zhang, K. Anupam, Y. Tan, D. Wang, and C. Zhai. 2020. “Particle distribution around the damage area of asphalt mixture based on digital image correlation.” Powder Technol. 375 (Sep): 11–19. https://doi.org/10.1016/j.powtec.2020.07.090.
Zhang, L., et al. 2022. “Study on the extraction of CT images with non-uniform illumination for the microstructure of asphalt mixture.” Materials 15 (20): 7364. https://doi.org/10.3390/ma15207364.
Zhang, L., M. Shan, C. Xing, Y. Cui, P. Wang, and M. Liu. 2023. “Mechanism of physical hardening on the fracture characteristics of polymer-modified asphalt binder.” Constr. Build. Mater. 409 (Dec): 134091. https://doi.org/10.1016/j.conbuildmat.2023.134091.
Zhao, Y. J., X. W. Wang, J. W. Jiang, and L. Zhou. 2019. “Characterization of interconnectivity, size distribution and uniformity of air voids in porous asphalt concrete using X-ray CT scanning images.” Constr. Build. Mater. 213 (Jul): 182–193. https://doi.org/10.1016/j.conbuildmat.2019.04.056.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 36 • Issue 7 • July 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jul 8, 2023
Accepted: Dec 12, 2023
Published online: Apr 18, 2024
Published in print: Jul 1, 2024
Discussion open until: Sep 18, 2024
ASCE Technical Topics:
- Aggregates
- Business management
- Computing in civil engineering
- Design (by type)
- Engineering fundamentals
- Industries
- Infrastructure
- Load and resistance factor design
- Load factors
- Materials characterization
- Materials engineering
- Mixtures
- Models (by type)
- Organizations
- Pavements
- Practice and Profession
- Structural design
- Transportation engineering
- Two-dimensional models
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