Purpose -This paper aims to present a new non-destructive method of brick wall dampness testing in real building structures. The electrical impedance tomography (EIT) method makes it possible to obtain a distribution of wall dampness. The paper aims to give basic information about the measuring system, including prototype equipment. The setup was used to determine the dampness of test brick walls on a specially built laboratory test rig. The paper seeks to compare test results obtained by the non-destructive impedance tomography method with the results obtained by the conventional destructive dry-weight method. Design/methodology/approach -The paper adopts a tomography approach to control humidity inside of the brick walls. In case of brick walls other nondestructive methods fields, for example, the ultrasonic tomography is useless. On the other hand the most popular dry-weight method is strictly forbidden for historical buildings. As a forward solver, functionally graded material boundary element method was used. Findings -The paper proves that diffuse tomography could provide reliable results with respect of humidity content inside the brick walls. This method could provide 3D humidity distribution inside of the brick walls. Research limitations/implications -It is expected that the technique's impact will be limited to site inspection of building following floods or to evaluate older damp buildings. Practical implications -The presented technique can eventually lead to much simpler, cheaper and more efficient evaluation of the moisture content in walls. This can revolutionize some procedures in civil engineering. Social implications -The application has commercial potential and could result in more cost-effective repair of old buildings, which has an economic impact on society. Originality/value -The authors propose application of the diffuse tomography for nondestructive investigation of brick walls. According to the authors' best knowledge this is a novel approach.
The flow control and optimization system based on industrial tomography is presented in this publication. Multiphase flow measurement technologies are still being built and improved. There is a clear tendency in the industry to implement more optimally related functions with an emphasis on active inspection and monitoring. Control methods include issues related to the processing of data obtained from various sensors located in nodes. Monitoring takes place within the scope of acquired and processed data and parameter automation. The main purpose of this work is to design a system for data acquisition and analysis by image reconstruction for various tomographic methods (resistive, capacitive, ultrasonic). The practical application of ultrasonic flow measurement to study gas emissions in the heat and power station chimney is presented
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.
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