Corrosion Grade on Anchor Rods of Guyed Transmission Towers Applying Machine Committee / Grau de Corrosão em Hastes de Âncora de Torres de Transmissão Guiadas Comitê de Aplicação de Máquinas

Authors

  • Assiel. A. Adada Brazilian Journals Publicações de Periódicos, São José dos Pinhais, Paraná
  • Tiago S. de Matos
  • Mariana D´Orey Gaivão Portella Bragança
  • Luiz A. de Lacerda
  • Larissa M. de Almeida

DOI:

https://doi.org/10.34117/bjdv6n10-654

Keywords:

Artificial neural networks, Machine committee, Guyed transmission tower, Corrosion grade, Anchor rods.

Abstract

The use of guyed structures in electric power transmission lines is a growing practice because of their cost efficiency. However, the anchor systems are subject to corrosion, which can lead to their rupture and loss of tower support. Monitoring the evolution of the corrosion of the anchor rods by visual inspection is a destructive and costly method; therefore, there is considerable interest in developing methods and tools that are capable of generating a maintenance diagnosis of the system. This work aimed to propose a classification tool for guyed towers in terms of the corrosion degree by a machine committee with neural networks and applied it to the Paraiso-Açu line located in Rio Grande do Norte in Brazil. Thirty-eight samples were collected and 33 variables related to the soil corrosion along the line were analyzed. The targets for training the networks were obtained from the inspection of anchor rods taken from the field. A simplification of the problem's dimension was proposed by principal component analysis, describing the phenomenon with 6 variables instead of 33, simplifying the practical application by massively reducing the requirements for data sampling in the field. Several network typologies were trained and the best ones in terms of their generalist and specialist capacities were combined in a machine committee for the final proposal of this work. The classification obtained by the application of the committee for 10 towers was compared with the classification from non-destructive impulse reflectometry tests and showed an 80% correlation.

References

Gerhardus HK, Michiel B P H, Thompson NG (2002) Corrosion costs and preventive strategies in the United States.

Syrett B, Gorman J, Arey M, Koch G (2001) Cost of corrosion in the Electric power industry, Electric Power Research Institute (EPRI), Palo Alto, California.

Juchniewicz R (1961) The influence of Alternating Current on the anodic behavior of metals, in 1st International Congress on Metallic Corrosion, London.

Adedeji KB, Ponnle AA, Abe BT, Jimoh AA, Abu-Mahfouz AM, Hamam Y (2018) A Review of the Effect of AC/DC Interference on Corrosion and Cathodic Protection Potentials of Pipelines. International Review of Electrical Engineering. https://doi.org/10.15866/iree.v13i6.15766.

Alamilla JL, Espinosa-Medina MA, Sosa E (2009) Modelling Steel Corrosion damage in soil environment. Corros. Sci. https://doi.org/10.1016/j.corsci.2009.06.052

Andrade C, Garcés P, Martínez I (2008) Galvanic currents and corrosion rates of reinforcements measured in cells simulating different pitting areas caused by chloride attack in sodium hydroxide. Corros. Sci. https://doi.org/10.1016/j.corsci.2008.07.013

Cole IS, D. Marney D (2012) The science of pit corrosion: A review of the literature on the corrosion of ferrous metals in soils. Corros. Sci. https://doi.org/10.1016/j.corsci.2011.12.001

Lazzari L. (2017) Corrosion in Water, Soil and Air. In: Engineering Tools for Corrosion. http://doi.org/10.1016/B978-0-08-102424-9.00005-7

Berenguer RA, Oliveira Junior ER, Silva MWP, Souza GB, Helene P, Monteiro ECB (2016) Guy Structure with Galvanic Corrosion: Case Study. Journal of Civil Engineering and Architecture. http://doi: 10.17265/1934-7359/2016.07.007

Mariano FCMQ, Lima RR, Alvarenga RR, Rodrigues PB, Lacerda WS. (2014) Neural network committee to predict the AMEn of poultry feedstuffs. Neural Comput & Applic. http://doi: 10.1007/s00521-014-1680-3

Barzegar R, Sattarpour M, Deo R, Fijani E, Adamowski J (2019) An ensemble tree-based machine learning model for predicting the uniaxial compressive strength of travertine rocks. Neural Comput & Applic. http://doi: 10.1007/s00521-019-04418-z

Rumelhart D, Hinton G, Williams R (1986) Learning representation by back-propagating errors. Nature. https://doi.org/10.1038/323533a0

Plunkett K, Elman J (1997) Exercises in rethinking Innateness MIT Press

I. Goodfellow I, Bengio Y, e A. Courville A (2016) Deep Learning. http://www.deeplearningbook.org/. Accessed 28 December 2018.

Haykin S (1999) Neural Networks - A Compare Found. Prentice Hall International Inc, Ontario

Stott JFD, John G, Abdullahi AA (2018) Corrosion in Soil. In: Reference Module in Materials Science and Materials Engineering. http://doi.org/ 10.1016/B978-0-12-803581-8.10524-7

Downloads

Published

2020-10-29

How to Cite

Adada, A. A., Matos, T. S. de, Bragança, M. D. G. P., Lacerda, L. A. de, & Almeida, L. M. de. (2020). Corrosion Grade on Anchor Rods of Guyed Transmission Towers Applying Machine Committee / Grau de Corrosão em Hastes de Âncora de Torres de Transmissão Guiadas Comitê de Aplicação de Máquinas. Brazilian Journal of Development, 6(10), 82988–83002. https://doi.org/10.34117/bjdv6n10-654

Issue

Section

Original Papers