Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls

Abstract

This paper presents the results of research on the use of machine learning algorithms and electrical tomography in detecting humidity inside the walls of old buildings and structures. The object of research was a historical building in Wrocław, Poland, built in the first decade of the 19th century. Using the prototype of an electric tomograph of our own design, a number of voltage measurements were made on selected parts of the building. Many algorithmic methods have been preliminarily analyzed. Ultimately, the three models based on machine learning were selected: linear regression with SVM (support vector machine) learner, linear regression with least squares learner, and a multilayer perceptron neural network. The classical Gauss–Newton model was also used in the comparison. Both the experiments based on real measurements and simulation data showed a higher efficiency of machine learning methods than the Gauss–Newton method. The tomographic methods surpassed the point methods in measuring the dampness in the walls because they show a spatial image of the interior and not separate points of the examined cross-section. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. Machine learning has a greater potential to create correct tomographic reconstructions than traditional mathematical methods. In this research, linear regression models performed slightly worse than neural networks.

How the Economic Complexity of a National Economy Affects the Environment

“…The working hypothesis was tested using the STATISTICA software package with the application of correlation and regression analysis methods. In the process of model specification, Environmental Performance Index (EPI) was chosen as the dependent, explanatory variable, while the Economic Complexity Index (ECI) was chosen as the regressor (independent, explanatory variable) (Rymarczyk, Kłosowski, Hoła, Sikora, Wołowiec, Tchórzewski and Skowron, 2021).…”

Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks

“…Electrical impedance tomography [32,33], generally called electrical tomography (ET) [34,35], includes some image reconstruction techniques. The methods available in the literature are electrical resistance tomography (ERT) [36,37], electrical capacitance tomography (ECT) [38][39][40][41] and electrical impedance tomography.…”

Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

“…Electrical capacitance tomography (ECT) [4][5][6][7][8][9][10][11][12][13][14][15][16], electrical impedance tomography (EIT) [17][18][19][20][21][22][23][24], magnetoacoustic tomography [25], ultrasound and radio [26,27], X-ray tomography [28], optical tomography [29,30], and other non-invasive technologies are used to monitor industrial operations. Recently, an increasing number of research papers in the field of industrial system operation have included the use of various computational methods, such as intelligent predictive methods [31], fuzzy logic [32,33], machine learning [34], numerical modelling [35], deep learning [18,36], and binary programming [37].…”