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The paper presents the structure and indices of the annual energy consumption in educational buildings subject to comprehensive thermal retrofitting. Seven buildings were analyzed; the energy consumption for heating and ventilation, hot water preparation, and built-in lighting was analyzed in each of them and, in the case of one structure, also cooling. The indices of the usable, final, and primary energy consumption were analyzed. The values calculated were compared to the requirements of the energy standards in force in Poland. The percentage shares of the above-mentioned energy demands of each of the buildings investigated are given in the total energy performance. Within the investigation, we evaluated the shares of the particular building services in the total energy consumption and determined that even after the thermal retrofitting, the energy demands for heating together with lighting are still the most significant compared to the other demands.

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. [ABSTRACT FROM AUTHOR]

The flexural analysis of doubly curved functionally graded porous conoids was performed in the present paper. The porosities inside functionally graded materials (FGMs) can occur during the fabrication and lead to the occurrence of micro-voids in the materials. The mathematical model includes expansion of Taylor’s series up to the third degree in thickness coordinate and normal curvatures in in-plane displacement fields. Since there is a parabolic variation in transverse shear strain deformation across the thickness coordinate, the shear correction factor is not necessary. The condition of zero-transverse shear strain at upper and lower surface of conoidal shell is implemented in the present model. The improvement in the 2D mathematical model enables to solve problems of moderately thick FGM porous conoids. The distinguishing feature of the present shell from the other shells is that maximum transverse deflection does not occur at its centre. The improved mathematical model was implemented in finite element code written in FORTRAN. The obtained numerical results were compared with the results available in the literature. Once validated, the current model was employed to study the effect of porosity, boundary condition, volume fraction index, loading pattern and others geometric parameters.

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. [ABSTRACT FROM AUTHOR]

Featured Application: The solutions presented in the work, based on the use of a multi-sensor matrix and the analysis of multidimensional data, can be used in practice for assessing the mycological risk of buildings, detecting the presence of mold in rooms and evaluating the risk of the sick building syndrome. Paper is in the scope of moisture-related problems which are connected with mold threat in buildings, sick building syndrome (SBS) as well as application of electronic nose for evaluation of different building envelopes and building materials. The machine learning methods used to analyze multidimensional signals are important components of the e-nose system. These multidimensional signals are derived from a gas sensor array, which, together with instrumentation, constitute the hardware of this system. The accuracy of the classification and the correctness of the classification of mold threat in buildings largely depend on the appropriate selection of the data analysis methods used. This paper proposes a method of data analysis using Principal Component Analysis, metric multidimensional scaling and Kohonen self-organizing map, which are unsupervised machine learning methods, to visualize and reduce the dimensionality of the data. For the final classification of observations and the identification of datasets from gas sensor arrays analyzing air from buildings threatened by mold, as well as from other reference materials, supervised learning methods such as hierarchical cluster analysis, MLP neural network and the random forest method were used. [ABSTRACT FROM AUTHOR]

The problem of water supply pipe’s failures is one of the most onerous for water distribution network’s (WDN) operators. Multiple actions undertaken to detect, minimise and eliminate the phenomenon of water losses are mainly focused on financial aspect of lost water. However, at some point, the further minimisation of water losses is unprofitable due to reaching the economic level of water leakages. In WDNs failures analysis the biggest emphasis is put on damages of main pipes and distribution pipes, while failures of household water connections are often neglected. That is because the most typical water losses in household connections are hard-to-detect background leakages with relatively small water outflow. Still, in a long-time perspective, such leakages can cause significant water losses. The aim of this paper was to evaluate the parameters of water lost through damaged household water connection (volume, velocity, suffosion conditions). The basis for the analysis were laboratory investigations and a numerical simulation of a pipe failure using the FEFLOW v. 5.3 software. The results of the simulation, compared with laboratory tests results, enabled to state that, contrary to the widespread practice, leakages from household connections cannot be ignored.

The paper presents experimental measurements and numerical simulation of thermal environment in naturally ventilated room by a fresh air valve. For the aim of Computer Fluid Dynamics (CFD) simulations, a model room was created. The fresh air valve is located in an occupied space, at the external wall. It has a major effect on mixing indoor and outdoor air, temperature profiles, thermal condition and indoor air quality of the rooms during the heating period. To determine the thermal condition of a naturally ventilated building, PN-EN 15251:2012 standard was used. According to the standard, using PMV/PPD is suitable for evaluating the thermal environment. In the naturally ventilated buildings, the following criteria are very important for local thermal discomfort: draught, radiant temperature asymmetry and vertical air temperature differences. To compare the simulation results, real air temperatures were measured by the thermocouples in a day room having the same geometry. A series of simulations has been carried out to determine the profiles of temperature and velocity of indoor air. Obtained results prove correlation with calculations of profiles of indoor air temperature, estimated using the thermocouples.