Abstract
This work presents the application of artificial intelligence for the assessment on the corrosion conditions diagnosis of electric transmission line tower foundations. A literature and patents review and background in relation to corrosion monitoring and application of artificial intelligence relevant to this problem, along with an example of a novel corrosion monitoring of tower legs in the field, based in the close remote procedure are presented. A predictive computational model was developed, under the criteria of artificial neural networks, to obtain and propose a degree of corrosion index in tower leg foundations of electric transmission lines, for the purpose of establishing a decision making process for predictive maintenance programs. Information was gathered in the field, through electrochemical corrosion monitoring of eighty tower legs belonging to twenty transmission line towers under different environmental conditions. A data base was elaborated and with it, the neural network was developed, trained and its performance proved. The configuration 6-5-2 (6 Input, 5 hidden, 2 output neurons) used, presented an excellent agreement (R2=0.9998) between experimental and simulated corrosion data. The sensitivity analysis showed that all studied input variables (soil resistivity, free corrosion potential and electrochemical measurements) have an effect on the estimation of the corrosion level, with the strongest being the corrosion potential and resistance. With the results obtained and presented, an assessment on the corrosion conditions diagnosis for transmission line tower foundations, predictive maintenance programs and actions can be proposed and established.
Keywords: Artificial intelligence, expert systems, neural networks, corrosion monitoring, transmission lines, tower foundations
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