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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

This paper refers to an original concept of tomographic measurement of brick wall humidity using an algorithm based on long short-term memory (LSTM) neural networks. The measurement vector was treated as a data sequence with a single time step in the presented study. This approach enabled the use of an algorithm utilising a recurrent deep neural network of the LSTM type as a system for converting the measurement vector into output images. A prototype electrical impedance tomograph was used in the research. The LSTM network, which is often employed for time series classification, was used to tackle the inverse problem. The task of the LSTM network was to convert 448 voltage measurements into spatial images of a selected section of a historical building’s brick wall. The 3D tomographic image mesh consisted of 11,297 finite elements. A novelty is using the measurement vector as a single time step sequence consisting of 448 features (channels). Through the appropriate selection of network parameters and the training algorithm, it was possible to obtain an LSTM network that reconstructs images of damp brick walls with high accuracy. Additionally, the reconstruction times are very short.

Details

Title
The Concept of Using LSTM to Detect Moisture in Brick Walls by Means of Electrical Impedance Tomography
Author
Kłosowski, Grzegorz 1   VIAFID ORCID Logo  ; Hoła, Anna 2   VIAFID ORCID Logo  ; Rymarczyk, Tomasz 3   VIAFID ORCID Logo  ; Skowron, Łukasz 1   VIAFID ORCID Logo  ; Wołowiec, Tomasz 4 ; Kowalski, Marcin 5 

 Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland; [email protected] 
 Faculty of Civil Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland; [email protected] 
 Faculty of Transport and Computer Science, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland; [email protected] (T.R.); [email protected] (M.K.); Research & Development Centre Netrix S.A., 20-704 Lublin, Poland 
 Institute of Public Administration and Business, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland; [email protected] 
 Faculty of Transport and Computer Science, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland; [email protected] (T.R.); [email protected] (M.K.) 
First page
7617
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
19961073
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2602042512
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.