Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 15 May 2018 / Approved: 17 May 2018 / Online: 17 May 2018 (11:33:13 CEST)
Version 2 : Received: 11 June 2018 / Approved: 12 June 2018 / Online: 12 June 2018 (10:58:47 CEST)
Version 3 : Received: 4 July 2018 / Approved: 5 July 2018 / Online: 5 July 2018 (08:17:10 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Sensors 2018, 18, 2285
DOI: 10.3390/s18072285

Abstract

The 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. 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, both flat intersections (2D) and spatial (3D) images. The presented group of methods 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.

Subject Areas

inverse problem; electrical tomography; moisture inspection; dampness analysis; machine learning, nondestructive evaluation

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