Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms

Ricardo Gaussmann; Dennis Coelho; Anita Fernandes; Paul Crocker; Valderi R. Q. Leithardt

doi:10.29333/jisem/8427

Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms

Ricardo Gaussmann ¹ ^* , Dennis Coelho ¹, Anita Fernandes ¹ ², Paul Crocker ³ ⁴, Valderi R. Q. Leithardt ² ⁴ ⁵

More Detail

¹ University of the Itajai Valley, Specialization Course in Big Data, Santa Catarina, BRAZIL
² University of the Itajaí Valley, Master in Applied Computing, Santa Catarina, BRAZIL
³ Telecommunications Institute, IT Branch, Covilha, PORTUGAL
⁴ Department of Informatics, University of Beira Interior, Covilha, PORTUGAL
⁵ COPELABS, Lusophone University of Humanities and Technologies, Lisboa, PORTUGAL
^* Corresponding Author

Full Text (PDF)

Research Article

Journal of Information Systems Engineering and Management, 2020 - Volume 5 Issue 3, Article No: em0119
https://doi.org/10.29333/jisem/8427

Published Online: 30 Jul 2020

Views: 1890 | Downloads: 1190

Open Access

How to cite this article

APA 6th edition

In-text citation: (Gaussmann et al., 2020)
Reference: Gaussmann, R., Coelho, D., Fernandes, A., Crocker, P., & Leithardt, V. R. Q. (2020). Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms. Journal of Information Systems Engineering and Management, 5(3), em0119. https://doi.org/10.29333/jisem/8427

Vancouver

In-text citation: (1), (2), (3), etc.
Reference: Gaussmann R, Coelho D, Fernandes A, Crocker P, Leithardt VRQ. Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms. J INFORM SYSTEMS ENG. 2020;5(3):em0119. https://doi.org/10.29333/jisem/8427

AMA 10th edition

In-text citation: (1), (2), (3), etc.
Reference: Gaussmann R, Coelho D, Fernandes A, Crocker P, Leithardt VRQ. Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms. J INFORM SYSTEMS ENG. 2020;5(3), em0119. https://doi.org/10.29333/jisem/8427

Chicago

In-text citation: (Gaussmann et al., 2020)
Reference: Gaussmann, Ricardo, Dennis Coelho, Anita Fernandes, Paul Crocker, and Valderi R. Q. Leithardt. "Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms". Journal of Information Systems Engineering and Management 2020 5 no. 3 (2020): em0119. https://doi.org/10.29333/jisem/8427

Harvard

In-text citation: (Gaussmann et al., 2020)
Reference: Gaussmann, R., Coelho, D., Fernandes, A., Crocker, P., and Leithardt, V. R. Q. (2020). Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms. Journal of Information Systems Engineering and Management, 5(3), em0119. https://doi.org/10.29333/jisem/8427

MLA

In-text citation: (Gaussmann et al., 2020)
Reference: Gaussmann, Ricardo et al. "Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms". Journal of Information Systems Engineering and Management, vol. 5, no. 3, 2020, em0119. https://doi.org/10.29333/jisem/8427

ABSTRACT

The road infrastructure is considered to be a key prerequisite of social and economic development of any country and therefore solutions that assist in the management and maintenance of this key infrastructure are important. This paper presents the application of Machine Learning algorithms, such as Multilayer Perceptron Neural Network and K-means for estimating the level of services required for highway conservation in Brazil. The data used is from the Federal District highways, recorded in the form of Service Orders in the Road Administration System, as well as the road solutions catalog elaborated from the price table of the Federal District Roads Department. A database was created containing data for routine maintenance history, road solutions catalog and price lists. The machine learning algorithms were applied and evaluated, and it was concluded that the K-means algorithm had the best performance for estimating the maintenance costs of Brazilian highways.

KEYWORDS

k-means multilayer perceptron machine learning road maintenance costs

REFERENCES

Amann, E., Baer, W., Trebat, T. and Villa Lora, J. (2016). Infraestructure and its role in Brazil´s development process. The Quarterly Review of Economics and Finance, 62(November, 2016), 66-73. https://doi.org/10.1016/j.qref.2016.07.007
Barros, L. B., Marcy, M. and Carvalho, M. T. M. (2018). Construction cost estimation of Brazilian highways using artificial neural networks. International Journal of Structural and Civil Engineering Research, 7(3), 283-289. https://doi.org/10.18178/ijscer.7.3.283-289
Bishop, C. M. (2016). Pattern Recognition and Machine Learning. Springer. ISBN-13: 978-0387310732.
Bonaccorso, G. (2017). Machine Learning Algorithms. Packt Publishing, Birmighan. ISBN 1785884514, 9781785884511.
Cao, Y. (2019). Modeling and predicting the variation of US highway construction cost (Doctoral Dissertation), School of Building Construction, Georgia Institute of Technlogy. Available at: http://hdl.handle.net/1853/61237 (Accessed: 3 November 2019).
Cao, Y., Ashuri, B. and Baek, M. (2018). Prediction of unit price bids of resurfacing highway projects through ensemble machine learning. Journal of Computing in Civil Engineering, 32(5). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000788
CEICDATA (2020). Brazil Highways Statistics: Flow of Highways: Flow of Vehicles Index. Available at: https://www.ceicdata.com/en/brazil/highways-statistics-flow-of-highways-flow-of-vehicles-index. (Accessed: 10 July 2020).
Chen, D. and Chen, J. D. (2017) Monte Carlo Simulation Based Statistical Modeling. Springer. https://doi.org/10.1007/978-981-10-3307-0
Chou, J. S. (2009) Web based CBR System applied to early cost budgeting for pavement maintenance project. Expert System with applications, 36(2), 2947-2960. https://doi.org/10.1016/j.eswa.2008.01.025
Elfaki, A. O., Alatawi, S. and Abushandi, E. (2014). Using intelligent techniques in construction project cost estimation: 10 year survey. Advances in Civil Engineering, 2014. Article ID 107926. https://doi.org/10.1155/2014/107926
Gaussmann. R., Coelho, D.K, Fernandes, A., Crocker, P. and Leithardt, V. (2020). Using Machine Learning For Road Maintenance Cost Estimates In Brazil: A Case Study In The Federal District. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), IEEE Xplore, ISBN: 978-989-54659-0-3. https://doi.org/10.23919/CISTI49556.2020.9141148
Ghisolfi, V., Ribeiro, G. M., Chaves, G. L. D. and Orrico Filho, R. D. (2019) Evaluating impacts of overweight in road freight transportation: a case study in Brazil with system dynamics. Sustainability, 11, 3128. https://doi.org/10.3390/su11113128
Hurwitz, J. and Kirsch, D. (2018) Machine Learning for Dummies. Wiley. ISBN 1119245516, 9781119245513.
Huseynov, S., Ribera, L. A. and Palma, M. A. (2018). Predicting the highway costs index with machine learning. Available at: https://www.semanticscholar.org/paper/Predicting-the-Highway-Costs-Index-with-Machine-Huseynov-Ribera/8a278f7ebeea2d58a206302ec05246aa442f8293 (Access: 3 February 2020).
Ivanova, E. and Masarova, J. (2013) Importance of Road Infrastructure in the Economic Development and Competitiveness. Economics and Management, 18(2), 263-274. https://doi.org/10.5755/j01.em.18.2.4253
Kelleher, J. D., Namee, B. M. and D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. The MIT Press, 1st ed. ISBN-13: 978-0262029445.
Martin, L. D. (2015). Survey of the use of pavement management systems in Brazil and case of study (Levatamento da Utilização de Sistemas de Gerência de Pavimentos no Brasil e Estudo de Caso) (Dissertation), UFRJ/COPPE, Brazil, Program of Civil Engineering. Available at: http://www.coc.ufrj.br/pt/documents2/mestrado/2015/2595-martins-ld-tm-15-pdf (Accessed: 9 November 2019).
Mitchell, T. M., Carbonell, J. G. and Michalski, R. S. (2016). Machine Learning. Kluwer Academic Publishers. ISBN-13: 978-0934613095.
Mourougane, A. and Pisu, M. (2011). Promoting Infrastructure Development in Brazil, OECD Economics Department Working Papers, No. 898, 2011. OECD Publishing, Paris. https://doi.org/10.1787/5kg3krfnclr4-en
OECD. (2017). Transport Infrastructure, in Gaps and Governance Standarts of Public Infrastructure in Chile. Infrastrucuture Governance Review, OECD Publisching, Paris. https://doi.org/10.1787/9789264280700-en
Oladele, A. S., Adedmila, A. S. and Egwurube, J. A. (2011). Highway maintenance cost estimation modeling for developing countries: a case study of Nigeria. Botswana Journal of Technology, October 2011. Available at: https://www.researchgate.net/publication/301676255_HIGHWAY_MAINTENANCE_COST_ESTIMATION_MODELING_FOR_DEVELOPING_COUNTRIES_A_CASE_STUDY_OF_NIGERIA (Accessed: 20 December 2019).
Piryonisi, S. M. and El-Diraby, T. E. (2017). A data analytics solution for predicting the condition of roads using the most affordable attributes. Canadian Society of Civil Engineering (CSCE), Vancouver. Available at: https://www.researchgate.net/publication/318788153_A_data_analytics_solution_for_predicting_the_pavement_condition_index_of_roads_using_the_most_affordable_attributes (Accessed: 3 November 2019).
Pivatto, D., Fernandez, R. N., Saulo, H. and Carraro, A. (2017) Estimating the optimal time for a road concession contract in Brazil. International Journal of Economics and Finance, 9(12), 44-53. https://doi.org/10.5539/ijef.v9n12p44
Raschka, S. (2015) Python Machine Learning. Packt Publishing Ltd. ISBN 1783555149, 9781783555147.
Shalev-Shwartz, S. and Ben-David, S. (2014) Understanding Machine Learning: from theory to algorithms. Cambridge University Press. https://doi.org/10.1017/CBO9781107298019
Sodikov, J. (2005). Cost estimation of highway projects in developing countries: artificial neural network approach. Journal of the Estern Asia Society for Transportation Studies, 6, 1036-1047. https://doi.org/10.11175/easts.6.1036
Wilmot, C. G and Mei, B. (2005). Neural Network modeling of highway construction costs. Journal of Construction Engineering and Management, 131(7). https://doi.org/10.1061/(ASCE)0733-9364(2005)131:7(765)
Wolff, M. G. C. and Caldas, M. A. F. (2018). A model for the evaluation of Brazilian road transport: a sustainable perspective. Journal of Advanced Trasportation, 2018, Article ID 5274789. https://doi.org/10.1155/2018/5274789

LICENSE

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.