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Structural faults on anchor rods due to corrosive processes are among the major natural causes worldwide for the collapse of guyed towers. As such towers are widely used on both power transmission lines and antenna systems, their collapse may cause unexpected interruptions of telecommunications and power delivery services. Despite being of utmost importance, underground corrosion monitoring is still a technological challenge, because it is a highly nonlinear problem, influenced by several soil characteristics and its complex iterations with the metallic structure of the rods. Some techniques have already been proposed to locate such faults; however, either they require previous knowledge about the medium or they need the interpretation of the acquired signal by a human specialist. These requirements make it difficult to use such solutions in automatic monitoring as well as to make their integration with Internet of Things (IoT) systems. In this article, a novel technique is proposed to automatically locate structural faults on anchor rods, based on a two-step machine learning (ML) solution. The system is fed by a vector composed of frequency-domain samples of the input impedance electromagnetic (EM) signals measured on the rods through a dedicated designed connector. Experimental results have demonstrated that the proposed combination of EM signal acquisition and ML data processing was able to perform the fault location on anchor rods accurately and with a total independence of any additional human analysis.

This paper introduces a new strategy to determine the soil permittivity and its loss tangent which uses simulation and measurement result for comparison. The setup of the measurement system includes a high frequency connector and a two-rod transmission line (TRTL) where one of the rods is embedded in an unknown-permittivity soil. In order to validate the simulation model by optimizing the complex permittivity of the soil from the measurement result a smart searching tool performed by the PSO (Particle Swarm Optimization) algorithm is applied. The measurements were carried out by a VNA (Vector Network Analyzer) to obtain the reflected signals from the TRTL for a non-parameterized soil. The effectiveness of the PSO algorithm is demonstrated by equalizing both simulated and measurement results of the soil complex permittivity in the simulation model.

Network parameters are capable of conveying information about the constitution of a medium in which a highfrequency wave propagates. In this paper, this characteristic is exploited in order to design a nondestructive system to detect corrosion in anchor rods of guyed towers. A compound database is built with the simulated and measured parameters return loss, input impedance, and voltage standing wave ratio for normal and faulty rods examples. Artificial neural networks are used to capture underlying characteristics of data and establish relationships between these parameters and the presence of corrosion in the rods, without the need for physical models. Experimental results show that the innovative use of a multiparameter strategy achieves a high accuracy and enhances the detection capacity of the system.