The article presents the application of the time domain reflectometry (TDR) technique for measuring the moisture of porous building materials used in construction. The work is focused on using the potential of artificial intelligence to improve the quality of TDR measurements through a new approach to the interpretation of data obtained from the TDR readings. Machine learning is a data analysis technique, used nowadays in many scientific disciplines. The authors performed a measurement data analysis using the artificial intelligence algorithms to assess moisture of aerated concrete samples tested with a TDR multimeter using two non-invasive sensors which differ in thickness. Data analysis was carried out using supervised machine learning to analyse a series of reflectograms obtained during the measurement. For the data achieved by the classical and machine learning method interpretation, correlation analysis was conducted to confirm the potential of artificial intelligence to improve the quality of TDR measurement. The summary of the work discusses the obtained analytical results and highlights the effectiveness of moisture assessment using the Gaussian Process Regression method, which allowed achieving a level of 0.2 - 0.3% of the RMSE errors value, which is about 10 times lower than the traditional approach.
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W artykule przedstawiono dwie techniki wykrywania wilgoci w porowatych materiałach budowlanych. Treść artykułu obejmuje opis metod detekcji oraz porównanie przykładowych odczytów uzyskanych przy użyciu dwóch rodzajów czujników. Odczyty przedstawiają zależności pomiędzy wilgotnością wyznaczaną grawimetrycznie a wartościami przenikalności elektrycznej wyznaczonymi obiema technikami pomiarowymi. Na podstawie uzyskanych danych ustalono odpowiednie modele kalibracyjne i określono ich jakość. Celem artykułu jest pokazanie potencjału pomiarowego obu technik pomiarowych oraz podkreślenie ich zalet i wad.
EN
This article presents two techniques for detecting moisture in porous building materials. The content of the article includes a description of the detection methods and a comparison of sample readings obtained using two types of sensors. The readings show the relationship between moisture determined using a gravimetric method and the values of permittivity determined by both measurement techniques. Based on the obtained data, appropriate calibration models were established and their quality was determined. The aim of the article is to show the measurement potential of both measurement techniques and highlight their advantages and disadvantages.
The paper presents the models for moisture evaluation using a set of the reflectometric sensors in some types of building materials. The readouts reveal the relationship between the building material moisture, being assessed gravimetrically and the apparent permittivity values obtained by the TDR (Time Domain Reflectometry) method and surface sensors. Based on the readouts, equations describing this relationship were derived. These types of equations function as calibration equations and are used to calibrate the sensors. Most of the equations used to describe the examined relationships are linear regression. These equations very often refer to specific materials and cannot be applied to others that differ in density or chemical composition, which is the cause of many incorrect measurements. In this article, we propose the use of the analysis of covariance method (ANCOVA) for the analysis of reflectometric data. Using this method, it will be possible to determine the moisture content of materials, regardless of their type and construction of the sensor, which can significantly improve moisture measurements using the reflectometric method. For comparative aims data achieved in conducted research were analyzed using both traditional linear regression models and using the analysis of covariance method (ANCOVA). Both types of fitting models are discussed and their quality was compared in terms of accuracy expressed by the Residual Standard Error (RSE), the Root Mean Square Error (RMSE) and the determination coefficient (R2) values. The paper showed that the use of the ANCOVA method allows for improvement the fit of the model in terms of the determination coefficient by 0.0174. Moreover, the average RSE and RMSE value in the ANCOVA models are smaller about 1.24 vol.% and 1.25 vol.% than the ones in the regression model, respectively, which means that the models obtained using ANCOVA more accurately describe the examined relationship.
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Zapewnienie właściwego napowietrzania instalacji kanalizacyjnej jest gwarancją prawidłowego działania całego systemu kanalizacyjnego w budynku. Tradycyjnie dopływ powietrza do instalacji odbywa się przez rurę wywiewną, zainstalowaną ponad dachem budynku lub przez zawory napowietrzające. W budynkach pasywnych, które charakteryzują się zwiększonymi wymaganiami w zakresie minimalizacji strat ciepła, zastosowanie tradycyjnego napowietrzania prowadzi do występowania dodatkowych mostków cieplnych na granicy dachu i rury wywiewnej. Zaproponowany nowatorski system napowietrzania eliminuje konieczność wyprowadzenia pionu ponad dach budynku, tym samym przyczynia się do częściowej minimalizacji tych strat. Zastąpienie rury wywiewnej przy połaci dachowej rurą wywiewną zamontowaną na przyłączu instalacji kanalizacyjnej oraz zakończenie pionu kanalizacyjnego hermetycznym zbiornikiem, którego zadaniem jest magazynowanie powietrza potrzebnego do napowietrzania układów, pozwala na ograniczenie powstawania w instalacji podciśnienia i prowadzi do poprawy charakterystyki pracy instalacji kanalizacyjnej w budynku. W niniejszej pracy zbadano skuteczność działania zaproponowanego systemu w rzeczywistym obiekcie. Przeprowadzono kilka serii badań, na podstawie których ustalono najbardziej korzystne rozwiązanie w zakresie pojemności hermetycznego zbiornika na powietrze, w zależności od ilości spłukiwanej wody.
EN
Ensuring the proper aeration of the sewage installation guarantees proper performance of the entire sewage system in a building. Traditionally, the air is supplied to the installation via an exhaust pipe installed above the roof of the building or through vacuum valves. In passive buildings, with increased restrictions towards the heat loses minimization, the use of traditional aeration runs to the additional thermal bridges between the roof and exhaust pipe. The proposed innovative aeration system eliminates the need to lead a riser above the roof surface of the building, thus contributing to a partial elimination of heat losses. Replacing the exhaust pipe at the roof with an exhaust pipe installed at the sewage connection and the ending the sewage riser with a hermetic tank located at the top, that stores the air needed for system aeration, would allow to reduce the formation of underpressure in the installation and would improve the sewage system performance. In this paper, the effectiveness of the proposed system in a real building was examined. A series of tests was carried out on the basis of which the best solution for capacity of a hermetic air tank was analyzed depending on the quantity of flushed water.
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The article presents the laboratory investigations of the basic thermal and hygric parameters of standard lightweight aggregate-concrete and lightweight aggregate-concrete supplemented with municipal sewage sludge. Both types of concrete are based on light aggregates, commonly used in the Polish building market. In order to improve the hygric parameters of the material, such as water absorptivity, the admixture of water emulsion of reactive polisiloxanes was applied. Within the presented research, together with basic moisture parameters estimation, capillary rise process was monitored using Time Domain Reflectometry (TDR) modified sensors. Hygric parameters were supplemented with the estimation of thermal conductivity coefficient λ determined using stationary method. The analysis of thermal and hygric properties of concrete confirmed the applicability of lightweight aggregate-concrete with sewage sludge supplementation for further production.
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