Estimated Maintenance Costs of Brazilian Highways Using Machine Learning Algorithms
Gaussmann, R. G
; Coelho, D.C
; Fernandes, A.
; Leithardt, V. L.
journal of information systems engineering & management Vol. 5, Nº 3, pp. em0119 - em0119, July, 2020.
ISSN (print): 2468-4376
ISSN (online): 2468-4376
Journal Impact Factor: (in )
Digital Object Identifier: 10.29333/jisem/8427
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.