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Open AccessArticle

A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings

1
University of Economics and Innovation in Lublin, Research & Development Centre Netrix S.A., 20-209 Lublin, Poland
2
Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2285; https://doi.org/10.3390/s18072285
Received: 15 May 2018 / Revised: 25 June 2018 / Accepted: 12 July 2018 / Published: 14 July 2018
This article presents the results of research on a new method of spatial analysis of walls and buildings moisture. Due to the fact that destructive methods are not suitable for historical buildings of great architectural significance, a non-destructive method based on electrical tomography has been adopted. A hybrid tomograph with special sensors was developed for the measurements. This device enables the acquisition of data, which are then reconstructed by appropriately developed methods enabling spatial analysis of wet buildings. Special electrodes that ensure good contact with the surface of porous building materials such as bricks and cement were introduced. During the research, a group of algorithms enabling supervised machine learning was analyzed. They have been used in the process of converting input electrical values into conductance depicted by the output image pixels. The conductance values of individual pixels of the output vector made it possible to obtain images of the interior of building walls as both flat intersections (2D) and spatial (3D) images. The presented group of algorithms has a high application value. The main advantages of the new methods are: high accuracy of imaging, low costs, high processing speed, ease of application to walls of various thickness and irregular surface. By comparing the results of tomographic reconstructions, the most efficient algorithms were identified. View Full-Text
Keywords: inverse problem; electrical tomography; moisture inspection; dampness analysis; machine learning; nondestructive evaluation inverse problem; electrical tomography; moisture inspection; dampness analysis; machine learning; nondestructive evaluation
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Rymarczyk, T.; Kłosowski, G.; Kozłowski, E. A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings. Sensors 2018, 18, 2285.

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