Aplicação do método K-Médias para classificação de regiões climáticas de pavimentos asfálticos no Brasil

Autores

DOI:

https://doi.org/10.5902/2179460X87741

Palavras-chave:

Clima, Pavimentos asfálticos, Regiões climáticas, Brasil, Agrupamento K-Médias

Resumo

As condições climáticas impactam significativamente o comportamento e o desempenho dos pavimentos flexíveis. O território brasileiro é conhecido por suas dimensões continentais, o que acarreta enorme variabilidade climática. Com isso, os pavimentos asfálticos nacionais estão sujeitos a diferentes condições climáticas de acordo com cada região em que estão inseridos. Assim, o presente estudo tem como objetivo identificar regiões climáticas com características climáticas homogêneas que afetam o desempenho de pavimentos flexíveis no Brasil, utilizando o método de clusterização K-Médias. A metodologia mostrou-se adequada, dividindo o país em 5 regiões climáticas: a região 1 é caracterizada por altas temperaturas e radiação, e baixa precipitação; a região 2 pela grande amplitude térmica e altas temperaturas; a região 3 por menores amplitudes térmicas, maiores velocidades de vento e alta radiação; a região 4 por temperaturas mais amenas, alta amplitude térmica e precipitação considerável; e a região 5 pelos elevados volumes de precipitação e alta umidade, além de altas temperaturas.

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Biografia do Autor

Cléber Faccin, Universidade Federal de Santa Maria

Doutorado em Engenharia Civil pela Universidade Federal de Santa Maria (UFSM).

Luciano Pivoto Specht, Universidade Federal de Santa Maria

Doutorado em Engenharia Civil pela Universidade Federal do Rio Grande do Sul (UFRGS) e professor associado da Universidade Federal de Santa Maria (UFSM).

Pedro Orlando Borges Junior, Universidade Federal de Santa Maria

Doutorado em Engenharia Civil pela Universidade Federal de Santa Maria (UFSM).

Silvio Lisboa Schuster, Universidade Federal de Santa Maria

Doutorado em Engenharia Civil pela Universidade Federal de Santa Maria (UFSM) e Professor Adjunto.

Pablo Menezes Vestena, North Carolina State University

Graduação e mestrado em Engenharia Civil pela Universidade Federal de Santa Maria (UFSM). Estudante de doutorado na North Carolina State University.

Lucas Dotto Bueno, Universidade Federal de Santa Maria

Doutorado em Engenharia Civil pela Universidade Federal de Santa Maria (UFSM) e Professor Adjunto.

Deividi da Silva Pereira, Universidade Federal de Santa Maria

Doutorado em Engenharia de Transportes pela Universidade de São Paulo (USP). Atualmente é professor titular na Universidade Federal de Santa Maria (UFSM).

Referências

Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433–459. https://doi.org/10.1002/WICS.101

Alvares, C. A., Stape, J. L., Sentelhas, P. C., de Moraes Gonçalves, J. L., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 711–728. https://doi.org/10.1127/0941-2948/2013/0507

Aprilina, K., Sopaheluwakan, A., Susandi, A., Hadi, T. W., Trilaksono, N. J., Lubis, A., Dayantolis, W., Permana, D. S., Nuryanto, D. E., Anggraeni, R., Komalasari, K. E., Fajariana, Y., Yuliyanti, M. S., Haryoko, U., Hidayanto, N., & Linarka, U. A. (2023). The comparison of relationship between climate variables and rice productivity in the clustering area on Java Island, Indonesia. IOP Conference Series: Earth and Environmental Science, 1167(1), 012016. https://doi.org/10.1088/1755-1315/1167/1/012016

Araujo, C. E. S. de. (2020). Statistical classification of homogenous surface temperature regions in Santa Catarina, Brazil. Ciência e Natura, 42, e10–e10. https://doi.org/10.5902/2179460X55311

Balushi, A., Metwally, M., & Al-rashdi, M. H. (2020). Development of Oman Performance Grade Paving Map for Superpave Asphalt Mix Design. The 19th Annual International Conference on Highway, Airports Pavement Engineering, Infrastructures & Asphalt Technology. https://doi.org/doi 10.1515ijpeat-2016-0033

Brondani, C., Faccin, C., Specht, L. P., Nummer, A. V., Pereira, D. da S., Vestena, P. M., & Baroni, M. (2022). Evaluation of Moisture Susceptibility of Asphalt Mixtures: Influence of Aggregates, Visual Analysis, and Mechanical Tests. Journal of Materials in Civil Engineering, 35(2), 04022433. https://doi.org/10.1061/(ASCE)MT.1943-5533.0004603

Brondani, C., Menezes Vestena, P., Faccin, C., Lisboa Schuster, S., Pivoto Specht, L., & da Silva Pereira, D. (2022). Moisture susceptibility of asphalt mixtures: 2S2P1D rheological model approach and new index based on dynamic modulus master curve changes. Construction and Building Materials, 331, 127316. https://doi.org/10.1016/J.CONBUILDMAT.2022.127316

Cunha, M. B., Escalante Zegarra, J. R., & Fernandes Júnior, J. L. (2007). Revisão da seleção do grau de desempenho (PG) de ligantes asfálticos por estados do Brasil.

de Souza, A., Abreu, M. C., de Oliveira-Júnior, J. F., Aristone, F., Fernandes, W. A., Aviv-Sharon, E., & Graf, R. (2022). Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature. Brazilian Archives of Biology and Technology, 65, e22210331. https://doi.org/10.1590/1678-4324-2022210331

Delgadillo, R., Arteaga, L., Wahr, C., & Alcafuz, R. (2018). The influence of climate change in Superpave binder selection for Chile. Road Materials and Pavement Design, 21(3), 607–622. https://doi.org/10.1080/14680629.2018.1509803

Delgadillo, R., Segovia, M., Wahr, C., & Thenoux, G. (2017). Superpave zoning for Chile. Revista Ingenieria de Construccion, 32(1), 25–35. https://doi.org/10.4067/s0718-50732017000100003

Doan, Q. Van, Amagasa, T., Pham, T. H., Sato, T., Chen, F., & Kusaka, H. (2023). Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data. Geoscientific Model Development, 16(8), 2215–2233. https://doi.org/10.5194/GMD-16-2215-2023

Dong, Q., Chen, X., Dong, S., & Zhang, J. (2021). Classification of pavement climatic regions through unsupervised and supervised machine learnings. Journal of Infrastructure Preservation and Resilience 2021 2:1, 2(1), 1–15. https://doi.org/10.1186/S43065-021-00020-7

Faccin, C., Schuster, S. L., Orlando Borges de Almeida Junior, P., Menezes Vestena, P., Pivoto Specht, L., Dotto Bueno, L., & Figueiredo Mathias Leite, L. (2021). Mapas de Grau de Desempenho (PG) de ligantes asfálticos para o Brasil. 35o Congresso de Pesquisa e Ensino Em Transportes.

Faccin, C., Specht, L. P., Schuster, S. L., Boeira, F. D., Bueno, L. D., Brondani, C., Pereira, D. da S., & Nascimento, L. A. H. do. (2021). Flow Number parameter as a performance criteria for asphalt mixtures rutting: evaluation to mixes applied in Brazil Southern region. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2021.1880580

Finkler, N. R., Bortolin, T. A., Cocconi, J., Mendes, L. A., & Schneider, V. E. (2016). Avaliação espaço-temporal da qualidade da água utilizando técnicas estatísticas multivariadas. Ciência e Natura, 38(2), 577–587. https://doi.org/10.5902/2179460X18168

Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., … Zhao, B. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1

Gopisetti, P., Ceylan, H., Cetin, B., & Kim, S. (2021). Assessment of satellite-based MERRA climate data in AASHTOWare pavement mechanistic-empirical design. Road Materials and Pavement Design. https://doi.org/10.1080/14680629.2021.2009010

Gubler, R., Partl, M. N., Canestrari, F., & Grilli, A. (2005). Influence of water and temperature on mechanical properties of selected asphalt pavements. Materials and Structures 2005 38:5, 38(5), 523–532. https://doi.org/10.1007/BF02479543

Hasan, M. R. M., Hiller, J. E., & You, Z. (2015). Effects of mean annual temperature and mean annual precipitation on the performance of flexible pavement using ME design. Road Materials and Pavement Design, 17(7), 647–658. https://doi.org/10.1080/10298436.201.1019504

Haslett, K. E., Knott, J. F., Stoner, A. M. K., Sias, J. E., Dave, E. V., Jacobs, J. M., Mo, W., & Hayhoe, K. (2021). Climate change impacts on flexible pavement design and rehabilitation practices. Road Materials and Pavement Design, 22(9), 2098–2112. https://doi.org/10.1080/14680629.2021.1880468

Hollander, M., Wolfe, D. A., & Chicken, E. (2015). Nonparametric statistical methods. Nonparametric Statistical Methods, 1–819. https://doi.org/10.1002/9781119196037

Hongyu, K., Lúcia, V., Sandanielo, M., & Jorge De Oliveira Junior, G. (2016). Análise de Componentes Principais: Resumo Teórico, Aplicação e Interpretação. E&S Engineering and Science, 5(1), 83–90. https://doi.org/10.18607/ES201653398

INMET. (2021). INMET. https://bdmep.inmet.gov.br

J. F. Hair, BLACK, Rolph E. Anderson, & Ronald L. Tatham. (2005). Análise Multivariada de Dados. Bookman.

Júnior, J. L. O. L., Babadopulos, L. F. A. L., & Soares, J. B. (2019). Moisture-induced damage resistance, stiffness and fatigue life of asphalt mixtures with different aggregate-binder adhesion properties. In Construction and Building Materials (Vol. 216, pp. 166–175). Elsevier Ltd. https://doi.org/10.1016/j.conbuildmat.2019.04.241

Júnior, P. O. B. de A., Schuster, S. L., Faccin, C., Vestena, P. M., Ilha, P. S., Pires, G. M., Müller, E. I., Pereira, D. da S., & Specht, L. P. (2023). Rheological, permanent deformation and fatigue analysis for calibration of the recovery process of bitumens in the rotary evaporator. Road Materials and Pavement Design, 1–26. https://doi.org/10.1080/14680629.2023.2224434

Kakar, M. R., Hamzah, M. O., & Valentin, J. (2015). A review on moisture damages of hot and warm mix asphalt and related investigations. In Journal of Cleaner Production (Vol. 99, pp. 39–58). Elsevier Ltd. https://doi.org/10.1016/j.jclepro.2015.03.028

Kim, Y. R. (2009). Modeling of asphalt concrete.

Kruskal, W. H., & Wallis, W. A. (1952). Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47(260), 583. https://doi.org/10.2307/2280779

Lee, J. S., Kim, J. H., Kwon, O. S., & Lee, B. D. (2018). Asphalt binder performance grading of North Korea for Superpave asphalt mix-design. International Journal of Pavement Research and Technology, 11(6), 647–654. https://doi.org/10.1016/j.ijprt.2018.06.004

Leite, L. F. M.; Tonial, I. A. (1994). Qualidade dos cimentos asfálticos brasileiros segundo as especificações SHRP. 12o Encontro Do Asfalto Do Instituto Brasileiro de Petróleo.

Li, Y., & Wu, H. (2012). A Clustering Method Based on K-Means Algorithm. Physics Procedia, 25, 1104–1109. https://doi.org/10.1016/J.PHPRO.2012.03.206

Lüdecke, D., Ben-Shachar, M. S., Patil, I., & Makowski, D. (2020). Extracting, Computing and Exploring the Parameters of Statistical Models using R. Journal of Open Source Software, 5(53), 2445. https://doi.org/10.21105/JOSS.02445

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Https://Doi.Org/, 281–297.

Moreno-Navarro, F., Rubio-Gámez, M. C., Miró, R., & Pérez-Jiménez, F. (2015). The influence of temperature on the fatigue behaviour of bituminous materials for pavement rehabilitation. Road Materials and Pavement Design, 16, 300–313. https://doi.org/10.1080/14680629.2015.1029676

Mundt, A. K. F. (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses.

Omar, H. A., Yusoff, N. I. M., Mubaraki, M., & Ceylan, H. (2020). Effects of moisture damage on asphalt mixtures. In Journal of Traffic and Transportation Engineering (English Edition) (Vol. 7, Issue 5, pp. 600–628). Chang’an University. https://doi.org/10.1016/j.jtte.2020.07.001

Qiao, Y., Santos, J., Stoner, A. M. K., & Flinstch, G. (2020). Climate change impacts on asphalt road pavement construction and maintenance: An economic life cycle assessment of adaptation measures in the State of Virginia, United States. Journal of Industrial Ecology, 24(2), 342–355. https://doi.org/10.1111/JIEC.12936

R Core Team. (2021). R: The R Project for Statistical Computing. https://www.r-project.org/

Ramadhan, R. H., & Al-Abdul Wahhab, H. I. (1997). Temperature variation of flexible and rigid pavements in Eastern Saudi Arabia. Building and Environment, 32(4), 367–373. https://doi.org/10.1016/S0360-1323(96)00072-8

Rencher, A. C. (2002). Methods of Multivariate Analysis. Methods of Multivariate Analysis. https://doi.org/10.1002/0471271357

Rokitowski, P., Bzówka, J., & Grygierek, M. (2021). Influence of high moisture content on road pavement structure: A Polish case study. Case Studies in Construction Materials, 15, e00594. https://doi.org/10.1016/J.CSCM.2021.E00594

Saevarsdottir, T., & Erlingsson, S. (2013). Effect of moisture content on pavement behaviour in a heavy vehicle simulator test. Road Materials and Pavement Design, 14(SUPPL.1), 274–286. https://doi.org/10.1080/14680629.2013.774762

Schwartz, C. W., Elkins, G. E., Li, R., Visintine, B. A. B., Forman, G. R. R., & Groeger, J. L. (2015). FHWA-HRT-15-019- Evaluation of Long-Term Pavement Performance (LTPP) Climatic Data for Use in Mechanistic-Empirical Pavement Design Guide (MEPDG) Calibration and Other Pavement Analysis.

Seidel, E. J., De Jesus, F., Júnior, M., Ansuj, A. P., Rosane, M., & Noal, C. (2008). Comparação Entre o Método Ward e o Método k-médias no Agrupamento de Produtores de Leite. Ciência e Natura, 30(1), 07–15. https://doi.org/10.5902/2179460X9737

Shapiro, S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(3/4), 591. https://doi.org/10.2307/2333709

Titus-Glover, L. (2021). Reassessment of climate zones for high-level pavement analysis using machine learning algorithms and NASA MERRA-2 data. Advanced Engineering Informatics, 50, 101435. https://doi.org/10.1016/J.AEI.2021.101435

Tripathi, S., Tripathi, S., Bhardwaj, A., & E, P. (2018). Approaches to Clustering in Customer Segmentation. International Journal of Engineering & Technology, 7(3.12), 802–807. https://doi.org/10.14419/ijet.v7i3.12.16505

Wang, C., Zhou, X., Gao, G., Wang, C., Zhou, X., & Gao, G. (2019). Study on Climate Impacts on Asphalt Pavement in Tibet, China. Journal of Geoscience and Environment Protection, 7(10), 49–59. https://doi.org/10.4236/GEP.2019.710004

Wang, W., Wang, L., Xiong, H., & Luo, R. (2019). A review and perspective for research on moisture damage in asphalt pavement induced by dynamic pore water pressure. In Construction and Building Materials (Vol. 204, pp. 631–642). Elsevier Ltd. https://doi.org/10.1016/j.conbuildmat.2019.01.167

Wistuba, M. P., & Walther, A. (2013). Consideration of climate change in the mechanistic pavement design. Road Materials and Pavement Design, 14(SUPPL.1), 227–241. https://doi.org/10.1080/14680629.2013.774759

Wu, J. (2012). Cluster Analysis and K-means Clustering: An Introduction. 1–16. https://doi.org/10.1007/978-3-642-29807-3_1

Yang, X., You, Z., Hiller, J., & Watkins, D. (2018). Pavement performance zone based on mechanistic-empirical design and temperature indices. Transportmetrica A: Transport Science , 15(1), 91–113. https://doi.org/10.1080/23249935.2018.1457734

Yang, Y., Qian, B., Xu, Q., & Yang, Y. (2020). Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm. Advances in Civil Engineering, 2020. https://doi.org/10.1155/2020/6917243

Zhao, K., Ma, X., Zhang, H., & Dong, Z. (2022). Performance zoning method of asphalt pavement in cold regions based on climate Indexes: A case study of Inner Mongolia, China. Construction and Building Materials, 361, 129650. https://doi.org/10.1016/J.CONBUILDMAT.2022.129650

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2026-04-16

Como Citar

Faccin, C., Specht, L. P., Borges Junior, P. O., Schuster, S. L., Vestena, P. M., Bueno, L. D., & Pereira, D. da S. (2026). Aplicação do método K-Médias para classificação de regiões climáticas de pavimentos asfálticos no Brasil. Ciência E Natura, 48, e87741. https://doi.org/10.5902/2179460X87741

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Meio Ambiente