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Impedance spectroscopy – comparison of dielectric model with experimental results
(IOP Publishing, 2023-08-25) Kusák, Ivo; Luňák, Miroslav; Mizerová, Cecílie; Rovnaník, Pavel
Impedance measurements of building materials have been gaining popularity especially in the last twenty years. No electrical component has only resistance, capacitance or inductance, as there is an interplay of these parameters. This is compounded in the case of building materials, which contain a significant number of different phases that vary in chemical composition, crystalline structure and properties. It is, therefore, necessary to choose a connection and measurement system that provides the most accurate information about the building material. This information is primarily meant to include the complex impedance, its components and the quantities derived from them. The derived quantities are electrical resistance or electrical capacitance. Using these quantities we can point out the composition of the material, its conductivity and identify the percolation threshold or describe its sensory properties in more detail. For measurements, an alternating electric field is crucial, and the range of frequencies depends on the instruments used. For materials characterization, the most used frequency range is 100 Hz to 100 kHz; however, we can measure down to 1 MHz.
Non-Destructive Characterization of Cured-in-Place Pipe Defects
(MDPI, 2023-12-08) Dvořák, Richard; Jakubka, Luboš; Topolář, Libor; Rabenda, Martyna; Wirowski, Artur; Puchýř, Jan; Kusák, Ivo; Pazdera, Luboš
Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements.
The Effect of Specimen Size on Acoustic Emission Parameters and Approximate Position of Defects Obtained during Destructive Testing of Cementitious and Alkali-Activated Degraded Fine-Grained Materials
(MDPI, 2023-05-04) Topolář, Libor; Kocáb, Dalibor; Hrubý, Petr; Jakubka, Luboš; Hoduláková, Michaela; Halamová, Romana
Two sizes of test samples were selected to investigate the effect of size on the level of degradation. The smaller test specimens had dimensions of 40 × 40 × 160 mm, and the larger ones had dimensions of 100 × 100 × 400 mm. Both sizes of test specimens were always made of the same mortar. In one case, Blast Furnace Cement was chosen as the binder. In the other case, it was an alkali-activated material as a possibly more environmentally economical substitute. Both types of material were deposited in three degrading solutions: magnesium sulphate, ammonium nitrate and acetic acid. The reference set was stored in a water bath. After six months in the degradation solutions, a static elastic modulus was determined for the specimens during this test, and the acoustic emission was measured. Acoustic emission parameters were evaluated: the number of hits, the amplitude magnitude and a slope from the amplitude magnitude versus time (this slope should correspond to the Kaiser effect). For most of the parameters studied, the size effect was more evident for the more degraded specimens, i.e., those placed in aggressive solutions. The approximate location of emerging defects was also determined using linear localisation for smaller specimens where the degradation effect was more significant. In more aggressive environments (acetic acid, ammonium nitrate), the higher resistance of materials based on alkaline-activated slag was more evident, even in the case of larger test bodies. The experiments show that the acoustic emission results agree with the results of the static modulus of elasticity.
Classification of Thermally Degraded Concrete by Acoustic Resonance Method and Image Analysis via Machine Learning
(MDPI, 2023-01-22) Dvořák, Richard; Chobola, Zdeněk; Plšková, Iveta; Hela, Rudolf; Bodnárová, Lenka
The study of the resistance of plain concrete to high temperatures is a current topic across the field of civil engineering diagnostics. It is a type of damage that affects all components in a complex way, and there are many ways to describe and diagnose this degradation process and the resulting condition of the concrete. With regard to resistance to high temperatures, phenomena such as explosive spalling or partial creep of the material may occur. The resulting condition of thermally degraded concrete can be assessed by a number of destructive and nondestructive methods based on either physical or chemical principles. The aim of this paper is to present a comparison of nondestructive testing of selected concrete mixtures and the subsequent classification of the condition after thermal degradation. In this sense, a classification model based on supervised machine learning principles is proposed, in which the thermal degradation of the selected test specimens are known classes. The whole test set was divided into five mixtures, each with seven temperature classes in 200 °C steps from 200 °C up to 1200 °C. The output of the paper is a comparison of the different settings of the classification model and validation algorithm in relation to the observed parameters and the resulting model accuracy. The classification is done by using parameters obtained by the acoustic NDT Impact-Echo method and image-processing tools.
Database 3D Surfaces for Evaluation of Joint Rock Coefficients
(Elsevier, 2016-06-13) Ficker, Tomáš; Martišek, Dalibor
Surfaces of rock joints were classified by means of the computerized procedure utilizing three-dimensional reliefs. These standard reliefs were formed as fractal objects using scaling properties of rock joints. Applicability of the computational scheme was tested and verified.

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