The goal of the condition assessment of concrete structures is to gain an insight into current condition of concrete and the existence of defects, which decrease durability and usability of the structure. Defects are quite difficult to detect using infrared thermography when concrete elements cannot be thermally excited with the Sun, together with unfavorable thermophysical properties of concrete structures. In this paper, principal component thermography (PCT) is applied as a post-processing method to a sequence of thermograms in order to enhance defect detectability in concrete structures. Defects are detected by analyzing a set of empirical orthogonal functions (EOFs), which were acquired by applying principal component analysis to a sequence of thermograms. The research was performed using concrete samples containing known defects, which were tested using a step heating thermography setup. The results of presented research show that PCT is an effective post-processing method to improve defect detection in concrete structures. By effectively improving the defect detection, PCT has a potential to improve the non-destructive testing (NDT) accuracy of using infrared thermography (IRT) on concrete structures, especially in shaded areas of such structures. The research also shows the defect detectability depending on concrete type thermal excitation setup and defect geometry.

This paper proposes an alternative experimental procedure that uses infrared thermography (IRT) for measuring the surface temperature of building elements, through which it is possible to approximate the thermal transmittance or the U-value. The literature review showed that all authors used similar procedures that require semi-stationary heat transfer conditions, which, in most cases, could not be achieved. The dynamic and the average methods that are given in ISO 9869 were also used with the IRT and the heat flux method (HFM). The dynamic method (DYNM) shows a higher level of accuracy compared to the average method (AVGM). Since the algorithm of the DYNM is more complicated than that of the AVGM, Microsoft Excel VBA was used to implement the algorithm of the DYNM. Using the procedure given in this paper, the U-value could be approximated within 0–30% of the design U-value. The use of IRT, in combination with the DYNM, could be used in-situ since the DYNM does not require stable boundary conditions. Furthermore, the procedure given in this paper could be used for relatively fast and inexpensive U-value approximation without the use of expensive equipment (e.g., heat flux sensors).

With the increasing number of nearly zero-energy buildings (NZEB) due to increase of global awareness on climate change, the new concepts of design and control must be developed because of great NZEB dependency on detailing and multidisciplinary approach. This paper proposes a three-level gateway control method for NZEB project delivery by using digital representation of the building in building information modeling (BIM) environment. These controls (C1, C2 and C3) are introduced before three main phases of any project delivery—design phase, construction phase and handover. The proposed project control procedure uses black-box building energy modeling within the BIM environment, so the paper explores the reliability of one tool for direct energy modeling within the BIM-authoring software. The paper shows two types of validation tests with satisfactory results. This leads to conclusion that analyzed tool for energy simulation within BIM environment can be used in a way that is described in a proposed project control procedure. For further research it is proposed to explore reliability of tools for energy simulation connected to other BIM-authoring software, so this project control procedure could be independent of BIM-authoring software used in the paper.

This paper proposes an improvement of the conventional Lightweight Steel Frame (LSF) wall structure suitable for the design of high-performance modular buildings. A mobile module, named MUZA, is used as a case study building to analyse the performance of such LSF structures in terms of their thermal bridging effect on the U-value of the opaque envelope elements, linear heat losses at junctions, and moisture condensation risk, as well as thermal bridging effect on the overall energy performance of the building. The study included an additional climate- and orientation-dependent analysis that examined the performance of MUZA under various conditions. The main conclusion is that the steel studs increase the U-value from 28.4% to 41.6% compared to cases without the studs, which consequently increases transmission losses through opaque elements. Thanks to the continuous covering of the metal studs with thermal insulation, the thermal bridges at the element junctions are minimized, and in almost all cases, the Ψ-values are well below 0.1 W/(m·K) and are free from moisture condensation. The overall impact of thermal bridges on heating energy demand is significant, while the impact on cooling energy is less pronounced. The designed module with the proposed LSF wall structure can meet the Croatian requirements for Nearly Zero-Energy Buildings (NZEB), but the shading devices and photovoltaics orientation must be optimized depending on the climatic conditions and the orientation of the large transparent openings. MUZA can be a promising solution for post-disaster housing, providing better indoor environmental quality, healthy living conditions, and low energy bills for the affected people. In addition, it can also be used for permanent housing when a fast and robust modular construction is required which is also energy efficient and sustainable.

In order to maintain the quality of construction for nearly zero energy buildings and to reduce the pressure on construction workers with the addition of the need for faster and simpler structures, the use of cavity-insulated LSF (lightweight steel frame) panels is increasing. Requirements for performance quality, quality of life, and low energy consumption have led to the need for closer examination of heat transfer through building elements. Due to the impact on increased heat losses, thermal bridges can cause structural damage due to the increased risk of water vapor condensation on the interior surface. In this paper, numerical heat transfer analysis with the optimization of thermal bridges for LSF cavity insulated walls was made in order to reduce the overall transmission heat losses. The effects of different cavity insulation materials (mineral wool and polyurethane foam) on overall heat transferred through the building elements were analyzed. Additionally, in order to reduce the effect of thermal bridges caused by the steel frame structure, the PVC spacers between the steel and sheathing panels are introduced into calculation models. Lastly, additional layers of insulation were added on the internal and external sides of the LFS panels in order to minimize the effect of thermal bridges and maximize air tightness. Combinations of all three setups were made for wall–window, ceiling–wall, wall–floor joints for the numerical calculation. For each setup, the temperature distribution and overall heat transferred through the building elements were calculated. Different thermal bridge designs have a significant influence on the overall heat transfer, and by choosing the optimal design, the transmission heat losses can be reduced by up to 67%.

Transmission losses through the building envelope account for a large proportion of building energy balance. One of the most important parameters for determining transmission losses is thermal transmittance. Although thermal transmittance does not take into account dynamic parameters, it is traditionally the most commonly used estimation of transmission losses due to its simplicity and efficiency. It is challenging to estimate the thermal transmittance of an existing building element because thermal properties are commonly unknown or not all the layers that make up the element can be found due to technical-drawing information loss. In such cases, experimental methods are essential, the most common of which is the heat-flux method (HFM). One of the main drawbacks of the HFM is the long measurement duration. This research presents the application of deep learning on HFM results by applying long-short term memory units on temperature difference and measured heat flux. This deep-learning regression problem predicts heat flux after the applied model is properly trained on temperature-difference input, which is backpropagated by measured heat flux. The paper shows the performance of the developed procedure on real-size walls under the simulated environmental conditions, while the possibility of practical application is shown in pilot in-situ measurements.