The impedance-based structural health monitoring technique uses measured signatures changes to identify incipient damages in structures. The purpose is to perform a correlation of these changes with the physical phenomena. However, since electromechanical coupling exists, some environmental influences such as temperature changes may lead to false decision regarding the condition of the structure. As a result, innovative machine learning tools have been extensively investigated to avoid errors in structural prognosis and, in this sense, recent applications of convolutional neural networks (CNN) have emerged within the scope of SHM research, focusing mainly on vibration analysis. However, studies that aim to combine neural architectures with intelligent materials for structural monitoring purposes have been poorly evaluated. Consequently, its integration with the electromechanical impedance method is still considered as being a new application of CNN. Thus, in order to contribute to the SHM area, this work presents a combination of the CNN architecture and the EMI methodology. In the present contribution, three aluminum beams subjected to three different steady temperature levels (0 °C, 10 °C and 20 °C) were studied. For this aim, a test chamber was used for humidity and temperature control. Artificial damages such as mass addition were taken into account so that impedance signatures related to both pristine and damaged conditions can be analyzed. Thus, a one-dimensional Convolutional Neural Network (1D CNN) was designed, trained and used for damage prediction purposes. In this context, a temperature robust model that is able to identify damage independently of environmental condition was developed.

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This chapter presents some basic concepts about some fundamental Deep Learning techniques currently used in the data processing. Next, the use of these techniques to aid decision-making in Electromechanical Impedance-based Structural Health Monitoring (ISHM) is presented. Initially, using a CNN to classify structural damage in specimens is evaluated, eliminating the need for temperature compensation. Then, an LSTM network prediction model of the evolution of an accelerated corrosive process (HCl acid) in specimens is presented. Finally, a model based on CNN is carried out as a case study of thickness loss in a real fuel storage tank plate.

Steel structures undergo loads and stresses during service life, subject to structural damages such as fatigue, corrosion, cracks, and plastic deformations. Therefore, to detect damage, the dynamic responses of the structures are used, comparing two states: with and without damage. These dynamic responses are obtained from a signal representing the structure's electromechanical impedance. Thus, these impedance signatures must accurately represent the analyzed structure. By comparing the impedance signatures of the low-cost device used at UFG/UFCAT with the SySHM system developed by LMEst/UFU, it can be observed that the low-cost equipment requires calibration in its impedance measurements. This work proposes a method based on the Least-Squares approach to determine a mathematical model to convert the signals acquired by the low-cost device into signals suitable for analysis. In conclusion, it was feasible to demonstrate the utilization potential of the cost-effective device under impedance-based monitoring conditions.