The Concept of Using LSTM to Detect Moisture in Brick Walls by Means of Electrical Impedance Tomography

Grzegorz Kłosowski, Anna Hoła, Tomasz Rymarczyk, Łukasz Skowron, Tomasz Wołowiec and Marcin Kowalski
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Grzegorz Kłosowski: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Anna Hoła: Faculty of Civil Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Tomasz Rymarczyk: Faculty of Transport and Computer Science, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland
Łukasz Skowron: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Tomasz Wołowiec: Institute of Public Administration and Business, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland
Marcin Kowalski: Faculty of Transport and Computer Science, University of Economics and Innovation in Lublin, 20-209 Lublin, Poland

Energies, 2021, vol. 14, issue 22, 1-20

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.

Keywords: electrical tomography; moisture detection; machine learning; neural networks; long short-term memory (LSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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