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The construction industry is a key driver of environmental change due to its extensive use of resources and high emissions, thus significantly burdening global efforts towards sustainable development targets. A large portion of the environmental footprint of buildings results from the energy required to sustain indoor comfort levels. Thus, enhancing the energy efficiency of existing buildings becomes critical in reducing their environmental impact. This study explores the impact of thermal performance improvements on the heating demand, employing numerical modeling and two energy performance methodologies, PHPP and Mc001-2022, across various climatic datasets and case studies in Romania. The results show substantial variability in heating demand predictions: Mc001-2022 predicts up to 27.2% higher continuous heating demands and 21.0% higher intermittent demands compared to PHPP in one case study. In the second case study, the differences range from 8.1% higher to 6.9% lower for continuous heating and from 3.3% higher to 9.9% lower for intermittent heating, depending on the scenario. These findings underscore the importance of the methodological choice and localized climatic data in heating demand assessments, highlighting the need for a tailored, context-specific approach to energy performance assessment, integrating multiple energy efficiency measures suited to the unique characteristics of each building. [ABSTRACT FROM AUTHOR]

Urban digital twins and realistic three-dimensional (3D) scenes have further enhanced the ever-increasing demand for building models. Airborne Laser Scanning (ALS) point clouds are the predominant data source for 3D building reconstruction owing to their highly detailed spatial features. However, information on the reconstruction of compound buildings (i.e. buildings with diverse plane structures or various roof structures) is lacking. Existing methods often produce building models that suffer from topological inconsistencies and/or geometric errors. To address these issues, this study proposes an automatic building reconstruction approach based on ALS point cloud that offers both modeling flexibility and geometric consistency. Specifically, a structure-aware partitioning algorithm that partitions a complex building roof into several regular roof patches is incorporated. Subsequently, a primitive model library comprising representative primitives suitable for building reconstruction is created. Suitable primitive model selection and reconstruction optimization are employed to ensure the accuracy and compactness in the final model. To comprehensively evaluate the performance, two typical ALS point cloud datasets are tested. Experiments demonstrate the generated models can achieve a boundary consistency of 92% while ensuring Root Mean Square Error below 0.09 m. Comparative experiments with state-of-the-art methods further validate the effectiveness of the proposed approach in compound building reconstruction.

The construction industry is a key driver of environmental change due to its extensive use of resources and high emissions, thus significantly burdening global efforts towards sustainable development targets. A large portion of the environmental footprint of buildings results from the energy required to sustain indoor comfort levels. Thus, enhancing the energy efficiency of existing buildings becomes critical in reducing their environmental impact. This study explores the impact of thermal performance improvements on the heating demand, employing numerical modeling and two energy performance methodologies, PHPP and Mc001-2022, across various climatic datasets and case studies in Romania. The results show substantial variability in heating demand predictions: Mc001-2022 predicts up to 27.2% higher continuous heating demands and 21.0% higher intermittent demands compared to PHPP in one case study. In the second case study, the differences range from 8.1% higher to 6.9% lower for continuous heating and from 3.3% higher to 9.9% lower for intermittent heating, depending on the scenario. These findings underscore the importance of the methodological choice and localized climatic data in heating demand assessments, highlighting the need for a tailored, context-specific approach to energy performance assessment, integrating multiple energy efficiency measures suited to the unique characteristics of each building.

This publication devises a method which formulates a national strategy for the renovation of single family houses by treating each era of initial construction independent of all the others, and applies a universally adoptable procedure to evaluate the cost efficiency of carbon emission reduction. A multi-objective optimization was conducted which varied the building envelope and energy systems while optimizing for annual cost and carbon emissions. The optimization was carried out in oemof.solph and PyGMO on typical German building stocks from 11 different construction eras between 1860 and 2020. The buildings were modeled in TEASER using TABULA building stock data. The results indicate that post-war era construction has the greatest improvement potential by saving over 16 tons of CO2/yr with respect to its business as usual case. The recommended solutions for each construction era have an investment cost to emission reduction ratio which is 25% better than those of the current efficiency subsidies.

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO 2 concentration. The validation results show that the model can predict room temperature and CO 2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller. [ABSTRACT FROM AUTHOR]

It is important to have practical methods for constructing and learning a good mathematical model for a building's thermal system in the presence of unmeasured disturbances and using data from closed loop operation. With this goal in mind, this paper presents a mathematical framework that explains the asymptotic behavior of an estimated model under those conditions and that can aid in learning an accurate model. Some analytic results from the literature of system identification are extended and interpreted for building systems. A new identification approach for determining an accurate thermal network (RC) model for a multi-zone building is developed based on the analytic result, and its superior performance over a conventional grey-box modeling approach is demonstrated experimentally.

The classical planar-wavefront-based TomoSAR imaging model suffers from the problem that the effective integration interval is not enough to cover the target distribution region in the low-altitude airborne case. It will lead to a deterioration of the performance of tomogram reconstruction and inaccuracy of estimated scatterers. This paper reviews the exact and approximate forms of the aforementioned inaccurate model based on planar wavefront and points out the problem with the conventional model. To solve this problem, we propose spherical wavefront models with the exact form or an approximate form of the slant range formula. The estimated variable for the scatterer's location is converted from elevation to off-nadir angle, and the effective integration interval has been extended. In addition, we explore relationships between the exact form of the conventional model and the exact form of the proposed model, and the relationship between the approximate form of the conventional model and the approximate form of the proposed model. This provides a basis for modifying the inversion algorithm that is designed based on the conventional model to adapt to the low-altitude airborne case. Eventually, through experiments based on simulated data and measured data, the imprecise reconstructions obtained with the conventional model are demonstrated, and the correctness of spherical wavefront models and the effectiveness of transformation between models are proved. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO2 concentration. The validation results show that the model can predict room temperature and CO2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller.

This study aims to investigate the impact of the presence of buildings on the radiation characteristics of MF broadcast antennas. Two different antennas are considered: a monopole operating at 1494 kHz and a two-element linear array radiating at 1008 kHz. The buildings were modeled as wire-grids and the total electric field intensity was calculated as the sum of the scattered field by the wire-grid and the field radiated from the antenna in free space. The radiation pattern of the antennas, when one or two buildings were situated in their vicinity, were the end result of the analysis, and they were compared to the corresponding patterns in free space. The results demonstrate that the radiation characteristics of antennas are mostly affected by the heights of buildings. If these heights are less than a critical value, the buildings do not significantly obstruct the operation of the antenna, despite the value of other parameters, such as the length and the width of the buildings, as well as their distance from the antenna. [ABSTRACT FROM AUTHOR]

Optimal controllers can enhance buildings’ energy efficiency by taking forecast and uncertainties into account (e.g., weather and occupancy). This practice results in energy savings by making better use of energy systems within the buildings. Even though the benefits of advanced optimal controllers have been demonstrated in several research studies and some demonstration cases, the adoption of these techniques in the built environment remains somewhat limited. One of the main reasons is that these novel control algorithms continue to be evaluated individually. This hampers the identification of best practices to deploy optimal control widely in the building sector. This paper implements and compares variations of model predictive control (MPC), reinforcement learning (RL), and reinforced model predictive control (RL-MPC) in the same optimal control problem for building energy management. Particularly, variations of the controllers’ hyperparameters like the control step, the prediction horizon, the state-action spaces, the learning algorithm, or the network architecture of the value function are investigated. The building optimization testing (BOPTEST) framework is used as the simulation benchmark to carry out the study as it offers standardized testing scenarios. The results reveal that, contrary to what is stated in previous literature, model-free RL approaches poorly perform when tested in building environments with realistic system dynamics. Even when a model is available and simulation-based RL can be implemented, MPC outperforms RL for an equivalent formulation of the optimal control problem. The performance gap between both controllers reduces when using the RL-MPC algorithm that merges elements from both families of methods.

The classical planar-wavefront-based TomoSAR imaging model suffers from the problem that the effective integration interval is not enough to cover the target distribution region in the low-altitude airborne case. It will lead to a deterioration of the performance of tomogram reconstruction and inaccuracy of estimated scatterers. This paper reviews the exact and approximate forms of the aforementioned inaccurate model based on planar wavefront and points out the problem with the conventional model. To solve this problem, we propose spherical wavefront models with the exact form or an approximate form of the slant range formula. The estimated variable for the scatterer’s location is converted from elevation to off-nadir angle, and the effective integration interval has been extended. In addition, we explore relationships between the exact form of the conventional model and the exact form of the proposed model, and the relationship between the approximate form of the conventional model and the approximate form of the proposed model. This provides a basis for modifying the inversion algorithm that is designed based on the conventional model to adapt to the low-altitude airborne case. Eventually, through experiments based on simulated data and measured data, the imprecise reconstructions obtained with the conventional model are demonstrated, and the correctness of spherical wavefront models and the effectiveness of transformation between models are proved.

Urban scene modeling is a challenging task for the photogrammetry and computer vision community due to its large scale, structural complexity, and topological delicacy. This paper presents an efficient multistep modeling framework for large-scale urban scenes from aerial images. It takes aerial images and a textured 3D mesh model generated by an image-based modeling system as the input and outputs compact polygon models with semantics at different levels of detail (LODs). Based on the key observation that urban buildings usually have piecewise planar rooftops and vertical walls, we propose a segment-based modeling method, which consists of three major stages: scene segmentation, roof contour extraction, and building modeling. By combining the deep neural network predictions with geometric constraints of the 3D mesh, the scene is first segmented into three classes. Then, for each building mesh, the 2D line segments are detected and used to slice the ground into polygon cells, followed by assigning each cell a roof plane via a MRF optimization. Finally, the LOD model is obtained by extruding cells to their corresponding planes. Compared with direct modeling in 3D space, we transform the mesh into a uniform 2D image grid representation and most of the modeling work is performed in 2D space, which has the advantages of low computational complexity and high robustness. In addition, our method doesn’t require any global prior, such as the Manhattan or Atlanta world assumption, making it flexible to model scenes with different characteristics and complexity. Experiments on both single buildings and large-scale urban scenes demonstrate that by combining 2D photometric with 3D geometric information, the proposed algorithm is robust and efficient in urban scene LOD vectorized modeling compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Thermal mass of buildings and domestic hot water tanks represent interesting sources of thermal energy storage readily available in the existing building stock. To exploit them to their full potential, advanced control strategies and a coupling to the power grid with heat pump systems represent the most promising combination. In this paper, model predictive control (MPC) strategies are developed and tested in a semi-virtual environment laboratory setup: a real heat pump is operated from within a controlled climate chamber and coupled with loads of a virtual building, i.e., a detailed dynamic building simulation tool. Different MPC strategies are tested in this laboratory setup, with the goals to minimize either the delivered thermal energy to the building, the operational costs of the heat pump, or the CO2 emissions related to the heat pump use. The results highlight the ability of the MPC controller to perform load-shifting by charging the thermal energy storages at favorable times, and the satisfactory performance of the control strategies is analyzed in terms of different indicators, such as costs, comfort, carbon footprint, and energy flexibility. The practical challenges encountered during the implementation with a real heat pump are also discussed and provide additional valuable insights.

This study aims to investigate the impact of the presence of buildings on the radiation characteristics of MF broadcast antennas. Two different antennas are considered: a monopole operating at 1494 kHz and a two-element linear array radiating at 1008 kHz. The buildings were modeled as wire-grids and the total electric field intensity was calculated as the sum of the scattered field by the wire-grid and the field radiated from the antenna in free space. The radiation pattern of the antennas, when one or two buildings were situated in their vicinity, were the end result of the analysis, and they were compared to the corresponding patterns in free space. The results demonstrate that the radiation characteristics of antennas are mostly affected by the heights of buildings. If these heights are less than a critical value, the buildings do not significantly obstruct the operation of the antenna, despite the value of other parameters, such as the length and the width of the buildings, as well as their distance from the antenna.

The building sector has a strategic role in the clean energy transition towards a fully decarbonized stock by mid-century. This data article investigates the use of different weather datasets in building energy simulations across Europe. It focuses on a standard performing building optimized to a nearly-zero level accounting for climate projections towards 2060. The provided data quantify the building energy performance in the current and future scenarios. The article investigates how heating and cooling loads change depending on the location and climate scenario. Hourly weather datasets frequently used in building energy simulations are analyzed to investigate how climatic conditions have changed over recent decades. The data give insight into the implications of the use of weather datasets on buildings in terms of energy consumption, efficiency measures (envelope, appliances, systems), costs, and renewable production. Due to the ongoing changing climate, basing building energy simulations and design optimization on obsolete weather data may produce inaccurate results and related building designs with an increased energy consumption in the coming decades. Energy efficiency will become more crucial in the future when cooling and overheating will have to be controlled with appropriate measures used in combination with renewable energy sources.

Buildings’ heating and cooling systems account for an important part of total energy consumption. The EU’s directives and engagements motivate building owners and relevant stakeholders in the energy and construction sectors towards net zero energy buildings by maximizing the use of renewable energy sources, ICT, and automation systems. However, the high costs of investment for the renovation of buildings, in situ use of renewable energy production, and installation of expensive ICT infrastructure and automation systems in small–medium range buildings are the main obstacles for the wide adoption of EU building directives in small- and medium-range buildings. On the other hand, the concept of sharing computational and data storage resources among various buildings can be an alternative approach to achieving smart buildings and smart cities where the main control power resides on a server. Unlike other studies that focus on the implementation of AI techniques in a building or separated buildings with local processing resources and data storage, in this work a corporate server was employed to control the heating systems in three building typologies and to examine the potential benefits of controlling existing buildings in a unified energy-savings platform. The key finding of this work is that the AI algorithms incorporated into the proposed system achieved significant energy savings in the order of 20–40% regardless of building typology, building functionality, and type of heating system, despite the COVID-19 measures for frequent ventilation of the buildings, even in cases with older-type heating systems.

This paper introduces a method to generate a three-dimensional (3D) virtual model of an imaginary city from a single street-view image to represent the appearance of the city in a given input photograph. The proposed approach differs from reconstruction approaches, which generate a city model by guessing the city name from the input photograph. In contrast, we use machine learning to identify where to generate the city, what to allocate in the city, and how to arrange the components. We employ generative adversarial networks and convolutional neural networks to create a terrain map and identify the components and styles that represent the virtual city appearance. We demonstrate that our system creates 3D virtual cities that are visually similar in terms of plausibility and naturalness to actual cities corresponding to input photographs from around the world. To the best of our knowledge, this is the first work to generate a city model including all general city components, including streets, buildings, and vegetation, to match the style of a single input image. [ABSTRACT FROM AUTHOR]

We introduce a new approach for procedural modeling. Our main idea is to select shapes using selection-expressions instead of simple string matching used in current state-of-the-art grammars like CGA shape and CGA++. A selection-expression specifies how to select a potentially complex subset of shapes from a shape hierarchy, e.g., ”select all tall windows in the second floor of the main building facade”. This new way of modeling enables us to express modeling ideas in their global context rather than traditional rules that operate only locally. To facilitate selection-based procedural modeling we introduce the procedural modeling language SelEx. An important implication of our work is that enforcing important constraints, such as alignment and same size constraints can be done by construction. Therefore, our procedural descriptions can generate facade and building variations without violating alignment and sizing constraints that plague the current state of the art. While the procedural modeling of architecture is our main application domain, we also demonstrate that our approach nicely extends to other man-made objects. [ABSTRACT FROM AUTHOR]

Smart control systems and new technologies are necessary to reduce the energy consumption in buildings while achieving thermal comfort. In this work, we monitor the thermal evolution inside a scale reduced closed space whose exterior and/ or interior wall faces have been painted with a coating solution. Based on the experimental data obtained under different environmental conditions, a simulator was developed and tuned to reproduce the thermodynamic behavior inside the spaces, with a relative error of less than 3.5%. This simulator lets us also estimate energy savings, temperature, and flux behavior under other conditions. [ABSTRACT FROM AUTHOR]

Urban modeling and visualization are highly useful in the development of smart cities. Buildings are the most prominent features in the urban environment, and are necessary for urban decision support; thus, buildings should be modeled effectively and efficiently in three dimensions (3D). In this study, with the help of Gaofen-7 (GF-7) high-resolution stereo mapping satellite double-line camera (DLC) images and multispectral (MUX) images, the boundary of a building is segmented via a multilevel features fusion network (MFFN). A digital surface model (DSM) is generated to obtain the elevation of buildings. The building vector with height information is processed using a 3D modeling tool to create a white building model. The building model, DSM, and multispectral fused image are then imported into the Unreal Engine 4 (UE4) to complete the urban scene level, vividly rendered with environmental effects for urban visualization. The results of this study show that high accuracy of 95.29% is achieved in building extraction using our proposed method. Based on the extracted building vector and elevation information from the DSM, building 3D models can be efficiently created in Level of Details 1 (LOD1). Finally, the urban scene is produced for realistic 3D visualization. This study shows that high-resolution stereo mapping satellite images are useful in 3D modeling for urban buildings and can support the generation and visualization of urban scenes in a large area for different applications.

In this paper, we propose an automatic modeling method based on constructive grammar for building Chinese Huizhou traditional vernacular houses, which are famous and important examples of Chinese traditional architecture. The proposed method includes two steps: layout generation and feature generation. In the layout generation step, layout guidelines based on 2-D geometric rules are used to evaluate the layout result. In the feature generation step, a probabilistic network is used to generate the features for a single house while feature conflict penalty functions are used to generate the reasonable features for multiple houses. Both the layout generation and feature generation steps use the Metropolis-Hastings algorithm to search for the optimal solution. To increase the computational efficiency of the proposed system, the two steps are performed on a graphics processing unit device using a Monte Carlo sampler. Moreover, a dynamic probability strategy is used to perform the layout perturbation. The proposed method is easy to implement and extend. The experimental results show that the proposed method is practical and efficient. Non-expert users can rapidly generate reasonable Huizhou vernacular houses via simple and intuitive interactions with the system.

Building modeling and consumption analysis are important elements of smart grid applications. The models of residential buildings in Nordic countries, where multi-layer slabs are mandatory to meet energy efficiency requirements, the modeling task becomes particularly challenging. In fact, the complexity of the detailed model rises as the number of layers and thermal zones increases. In some cases, this situation limits the implementation of model-based predictive control where the computation time for execution of the model must be as short as possible to achieve optimized loop time performance. To that end, this paper proposes the development of a modular, accurate and flexible real-time multi-zone and multi-layer building emulation system representing the dynamic thermal-electric behavior of residential buildings. The proposed hardware implementation architecture includes space and water heating systems, which are the main consuming loads in Nordic countries. The emulation system can perform in accelerated simulation or in real-time modes, either for one building or for a virtual park of buildings. Experimental validation using measurement data of occupied Canadian buildings with different insulation characteristics and using a hardware-in-the-loop configuration has permitted to corroborate the usefulness of the proposed emulation system. [ABSTRACT FROM AUTHOR]

Most of the proposed Vapor Intrusion (VI) models are developed assuming a steady indoor environment (i.e., building pressure and air exchange rate). To account for variations in these building conditions, these models are coupled with multizone codes to enable more precise modeling of indoor air pollution. In this paper, semi-empirical VI models are integrated into MATHIS-QAI, a multi-zone ventilation software. This coupled tool allows consideration not only of the impact of the building ventilation system characteristics, airtightness, and foundation type, but also the computation of more realistic pollution scenarios by specifying the lateral separation between the pollution source in the soil and the building. A sensitivity analysis was conducted to quantify the influence of these parameters on the indoor concentration of pollutants. The results showed that the main driving parameter in this event is the source location in the soil. However, a significant impact of the building characteristics and weather conditions on the indoor pollutant concentration was also observed. These characteristics vary significantly from one building to another, necessitating specific and appropriate calculations. The proposed tool, based on nodal modeling, offers an easy-to-use simulation that does not require significant computational resources compared to Computational Fluid Dynamics approaches. This coupling can be utilized for optimal management and reduction of uncertainties in risk assessment. Ultimately, it can serve as a relevant tool in the design and conception of more efficient buildings against VI. • Semi-empirical vapor intrusion (VI) models were integrated in a ventilation code. • A parametric study was conducted considering several VI scenarios. • VOCs source position in the soil remains the key parameter on VI process. • Specifying pollution scenario features increases the quality of the VI modelling. • Higher precision of VI estimations enhances building protection management. [ABSTRACT FROM AUTHOR]

This publication devises a method which formulates a national strategy for the renovation of single family houses by treating each era of initial construction independent of all the others, and applies a universally adoptable procedure to evaluate the cost efficiency of carbon emission reduction. A multi-objective optimization was conducted which varied the building envelope and energy systems while optimizing for annual cost and carbon emissions. The optimization was carried out in oemof.solph and PyGMO on typical German building stocks from 11 different construction eras between 1860 and 2020. The buildings were modeled in TEASER using TABULA building stock data. The results indicate that post-war era construction has the greatest improvement potential by saving over 16 tons of CO 2 /yr with respect to its business as usual case. The recommended solutions for each construction era have an investment cost to emission reduction ratio which is 25% better than those of the current efficiency subsidies. [Display omitted] • Formulates a national renovation strategy which is centered on construction era. • 1950 era homes have the highest renovation potential for emission savings. • Era-specific renovations are at least 25% better than current German strategies. • If the recommended solutions are followed then 90MTon of CO 2 could be saved. • Flexible, renewable thermal systems dominated the recommended renovation solutions. [ABSTRACT FROM AUTHOR]

With more and more data being collected, data-driven modeling methods have been gaining in popularity in recent years. While physically sound, classical gray-box models are often cumbersome to identify and scale, and their accuracy might be hindered by their limited expressiveness. On the other hand, classical black-box methods, typically relying on Neural Networks (NNs) nowadays, often achieve impressive performance, even at scale, by deriving statistical patterns from data. However, they remain completely oblivious to the underlying physical laws, which may lead to potentially catastrophic failures if decisions for real-world physical systems are based on them. Physically Consistent Neural Networks (PCNNs) were recently developed to address these aforementioned issues, ensuring physical consistency while still leveraging NNs to attain state-of-the-art accuracy, and applied to zone temperature modeling. In this work, we scale PCNNs to model the temperature dynamics of buildings with several connected thermal zones and propose a thorough comparison with classical gray-box and black-box methods. More precisely, we design three distinct PCNN extensions with different levels of information sharing between the modeled zones, thereby exemplifying the modularity and flexibility of the architecture, and formally prove their physical consistency. In the presented case study, PCNNs are shown to achieve state-of-the-art accuracy, even outperforming classical NN-based models despite their constrained structure. Our investigations furthermore provide a clear illustration of NNs achieving seemingly good performance while remaining completely physics-agnostic, which can be misleading in practice. While this performance comes at the cost of computational complexity, PCNNs on the other hand show accuracy improvements of 17–35% compared to all other physically consistent methods, paving the way for scalable physically consistent models with state-of-the-art performance. [Display omitted] • Gray-box building models do not scale well and have a limited expressiveness. • Classical black-box methods attain great performance but remain physics-agnostic. • Multi-zone Physically Consistent Neural Networks (PCNNs) are proposed. • PCNNs respect given physical rules by design despite relying on Neural Networks. • They simultaneously achieve state-of-the-art accuracy on a building case study. [ABSTRACT FROM AUTHOR]

In this paper, a primitive-based 3D building roof modeling method, by integrating LiDAR data and aerial imagery, is proposed. The novelty of the proposed modeling method is to represent building roofs by geometric primitives and to construct a cost function by using constraints from both LiDAR data and aerial imagery simultaneously, so that the accuracy potential of the different sensors can be tightly integrated for the building model generation by an integrated primitive’s parameter optimization procedure. To verify the proposed modeling method, both simulated data and real data with simple buildings provided by ISPRS (International Society for Photogrammetry and Remote Sensing), were used in this study. The experimental results were evaluated by the ISPRS, which demonstrate the proposed modeling method can integrate LiDAR data and aerial imagery to generate 3D building models with high accuracy in both the horizontal and vertical directions. The experimental results also show that by adding a component, such as a dormer, to the primitive, a variant of the simple primitive is constructed, and the proposed method can generate a building model with some details.

This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution.

To reduce the energy consumption in buildings is necessary to analyze individual rooms and thermal zones, studying mathematical models and applying new control techniques. In this paper, the design, simulation and experimental evaluation of a sliding mode controller for regulating internal temperature in a thermal zone is presented. We propose an experiment with small physical dimensions, consisting of a closed wooden box with heat internal sources to stimulate temperature gradients through operating and shut down cycles.

Virtual models' production is of high pertinence in research and business fields such as architecture, archeology, or video games, whose requirements might range between expeditious virtual building generation for extensively populating computer-based synthesized environments and hypothesis testing through digital reconstructions. There are some known approaches to achieve the production/reconstruction of virtual models, namely digital settlements and buildings. Manual modeling requires highly-skilled manpower and a considerable amount of time to achieve the desired digital contents, in a process composed by many stages that are typically repeated over time. Both image-based and range scanning approaches are more suitable for digital preservation of well-conserved structures. However, they usually require trained human resources to prepare field operations and manipulate expensive equipment (e.g., 3D scanners) and advanced software tools (e.g., photogrammetric applications). To tackle the issues presented by previous approaches, a class of cost-effective, efficient, and scarce-data-tolerant techniques/methods, known as procedural modeling, has been developed aiming at the semi- or fully-automatic production of virtual environments composed of hollow buildings exclusively represented by outer façades or traversable buildings with interiors, either for expeditious generation or reconstruction. Despite the many achievements of the existing procedural modeling approaches, the production of virtual buildings with both interiors and exteriors composed by non-rectangular shapes (convex or concave n-gons) at the floor-plan level is still seldomly addressed. Therefore, a methodology (and respective system) capable of semi-automatically producing ontology-based traversable buildings composed of arbitrarily-shaped floor-plans has been proposed and continuously developed, and is under analysis in this paper, along with its contributions towards the accomplishment of other virtual reality (VR) and augmented reality (AR) projects/works oriented to digital applications for cultural heritage. Recent roof production-related enhancements resorting to the well-established straight skeleton approach are also addressed, as well as forthcoming challenges. The aim is to consolidate this procedural modeling methodology as a valuable computer graphics work and discuss its future~directions.