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

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.

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.

Article Type: Research Article

https://doi.org/10.29333/jisem/8427

J INFORM SYSTEMS ENG, 2020 - Volume 5 Issue 3, Article No: em0119

Publication date: 30 Jul 2020

Article Views: 64

Article Downloads: 36

Open Access References How to cite this article