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The shop/house - the building combining commercial/retail uses and dwellings - appears over many periods of history in most cities in the world. This book combines architectural history, cross-cultural understandings and accounts of contemporary policy and building practice to provide a comprehensive account of this common but overlooked building. The merchant's house in northern European cities, the Asian shophouse, the apartment building on New York avenues, typical apartment buildings in Rome and in Paris - this variety of shop/houses along with the commonality of attributes that form them, mean that the hybrid phenomenon is as much a social and economic one as it is an architectural one. Professionals, city officials and developers are taking a new look at buildings that allow for higher densities and mixed-use. Describing exemplary contemporary projects and issues pertaining to their implementation as well as the background, cultural variety and urban attributes, this book will benefit designers dealing with mixed-use buildings as well as academics and students.

The performance of air-type PVT and BIPVT collectors has been extensively studied. As a system that generates heat and power, PVT collector testing has some particularities especially when using air as a heat recovery fluid and a building-integrated design (BIPVT). The electrical and thermal experimental performance of such collectors are currently being evaluated using in-house methods or PV and/or solar thermal collector standards. The use of a wide range of methods, testing conditions and experimental setups makes it difficult not only to compare the performance of different designs, but also to have confidence in the results obtained. This study evaluates the performance of an air-type BIPVT collector with in-channel perforated baffle plates for heat transfer enhancement designed for a building-integrated façade. As part of a joint research project between Korea and Canada, the proposed collector’s performance was evaluated through indoor (Canada) and outdoor experiments (Korea). Limited comparison of the results obtained with the two testing methods could be performed due to differences in environmental testing conditions, BIPVT collector area and experimental setup. Nevertheless, the limited measurement points under comparable testing conditions indicate that the results from the indoor and outdoor experiments have a similar trend. A comparison between the studied collector having a full PV absorber and a BIPVT collector with a hybrid PV/solar thermal collector absorber using a similar indoor experimental setup and testing conditions was performed. It showed that under still air conditions, for an irradiance level of approximately 820 W/m2 and with a low flow rate, the BIPVT collector with a hybrid PV/solar thermal absorber has a thermal and electrical efficiency of 25.1% and 5.9%, respectively. Under similar conditions, the BIPVT collector with a full PV absorber has a thermal efficiency of 23.9% and an electrical efficiency of 13.5%. At higher flowrates, both units have similar thermal efficiencies, however, the BIPVT collector with a PV absorber remains with an electrical efficiency that is more than double that of the unit with a hybrid PV/solar thermal absorber.

Achieving climate neutrality requires reducing energy consumption and CO2 emissions in the building sector, which has prompted increasing attention towards nearly zero energy, zero energy, and positive energy communities of buildings; there is a need to determine how individual buildings up to communities of buildings can become more energy efficient. This study addresses the scientific problem of optimizing energy efficiency strategies in building areas and identifies gaps in existing theories related to passive design strategies, active energy systems, and renewable energy integration. This study delineates boundaries at the building and community scales to examine the challenges of attaining energy efficiency goals and to emphasize the intricate processes of selecting, integrating, and optimizing energy systems in buildings. The four boundaries describe: (B1) energy flows through the building envelope; (B2) energy flows through heating, ventilation, air conditioning and energy systems; (B3) energy flows through individual buildings; (B4) energy flows through a community of buildings. Current theories often treat these elements in isolation, and significant gaps exist in interdisciplinary integration, scalable frameworks, and the consideration of behavioral and socioeconomic factors. Achieving nearly zero energy, zero energy, and positive energy communities requires seamless integration of renewable energy sources, energy storage systems, and energy management systems. The proposed boundaries B1–B4 can help not only in analyzing the various challenges for achieving high energy efficiency in building communities but also in defining and evaluating these communities and establishing fair methods for energy distribution within them. The results demonstrate that these boundaries provide a comprehensive framework for energy-efficient designs, constructions, and operational practices across multiple buildings, ensuring equitable energy distribution and optimized performance. In addition, the definition of boundaries as B1-B4 contributes to providing an interface for energy-efficient designs, constructions and operational practices across multiple buildings.

Heritage buildings are a crucial aspect of a country’s cultural heritage, serving as a means of preserving and passing down its history and traditions to future generations. The heritage buildings in southern China possess significant conservation, utilization, and research value. However, research is lacking on the spatial distribution characteristics and subdivision types of these buildings in the region. This study aimed to investigate the spatial agglomeration and distribution characteristics of heritage buildings in southern China, as well as the factors contributing to the formation of these spatial distribution patterns. This article focused on the protection of 981 heritage buildings in southern China since the founding of China. The study examined the buildings’ spatial agglomeration and distribution characteristics from various dynasties and subdivided types. It utilized the average nearest neighbor analysis, unbalance index, and kernel density estimation to analyze this distribution. Additionally, this study also investigated the primary factors influencing the spatial distribution and differentiation of these buildings. The results demonstrated the following: (1) In general, the spatial distribution of heritage buildings in southern China is characterized by unevenness and clustering, with a concentration in the eastern coastal and Sichuan provinces. (2) In terms of temporal dimension, the spatial distribution of heritage buildings exhibits unique characteristics in various dynastic zones. (3) In the type dimension, the number of different types of heritage buildings varies greatly. (4) Further analysis of the distribution and types of heritage buildings indicates that quantitative differences are primarily influenced by natural, human, and socio-economic factors. This research was unique as it explored the geospatial distribution characteristics and determinants of heritage buildings. It offers a valuable perspective on the spatial distribution of heritage buildings and can serve as a reference for future studies on the preservation and protection of such buildings in China. Additionally, the findings can provide guidance for the management and rational use of heritage buildings in southern China.

In order to address the growing urgency of energy-related carbon emission reduction and improve the construction of the existing public building carbon emission database model, this study constructs a public building carbon emission database model based on energy activity data by collecting the energy consumption data of relevant buildings in the region and classifying the building types, aiming to quantitatively analyze the carbon emission characteristics of different types of public buildings and provide data support for the national and local governments, enterprises, universities and research institutions, and the power industry. This study is divided into three phases: The first stage is the mapping of carbon emission benchmarks. The second stage is the analysis of multi-dimensional-building carbon emission characteristics. The third stage is to evaluate the design optimization plan and propose technical improvement suggestions. At present, this research is in the first stage: collecting and analyzing information data such as the energy consumption of different types of buildings, building a carbon emission database model, and extracting and analyzing the carbon emission benchmarks and characteristics of each building type from the data of 184 public buildings in a given area. Moreover, preliminary exploration of the second phase has been conducted, focusing on identifying key influencing factors of carbon emissions during the operational phase of public buildings. Office buildings have been selected as representative samples to carry out baseline modeling and variable selection using linear regression analysis. The results of this study are of great significance in the energy field, providing data support for public building energy management, energy policy formulation, and carbon trading mechanisms.

Quantifying the energy consumption of buildings is a complex and multi-scale task, with the entire process dependent on input data and urban surroundings. However, most urban energy models do not account for the urban environment. This paper employs a physical-based, bottom-up method to predict urban building operating energy consumption, using imported topography to consider shading effects on buildings. This method has proven to be feasible and aligned well with the benchmark. Research also suggests that commercial and transport buildings have the highest energy use intensity, significantly more than residential and office buildings. Specifically, cooling demands far outweigh heating demands for these building types. Therefore, buildings in the commercial and transportation sectors would receive greater consideration for energy efficiency and improvements to the cooling system would be a priority. Additionally, the method developed for predicting building energy demand at an urban scale can also be replicated in practice.

This article addresses the question of mapping building functions jointly using both aerial and street view images via deep learning techniques. One of the central challenges here is determining a data fusion strategy that can cope with heterogeneous image modalities. We demonstrate that geometric combinations of the features of such two types of images, especially in an early stage of the convolutional layers, often lead to a destructive effect due to the spatial misalignment of the features. Therefore, we address this problem through a decision-level fusion of a diverse ensemble of models trained from each image type independently. In this way, the significant differences in appearance of aerial and street view images are taken into account. Compared to the common multi-stream end-to-end fusion approaches proposed in the literature, we are able to increase the precision scores from 68% to 76%. Another challenge is that sophisticated classification schemes needed for real applications are highly overlapping and not very well defined without sharp boundaries. As a consequence, classification using machine learning becomes significantly harder. In this work, we choose a highly compact classification scheme with four classes, commercial, residential, public, and industrial because such a classification has a very high value to urban geography being correlated with socio-demographic parameters such as population density and income.

During a rescue excavation in the industrial area of Müllendorf (Burgenland, Austria), the southeastern part of an extensive settlement was investigated over an area of about 2.8 ha. Among the features were several storage pits, 55 reconstructed buildings and a ditch that partially enclosed the settlement. The bronze artefacts, which were recovered from the settlement area date from the early to the late Tumulus period (Bz B1–C2). The distribution of the bronze objects and the almost uniform northwest-southeast orientation of the buildings led to the assumption that most of the settlement structures date to the Middle Bronze Age. Comparison of the buildings from Müllendorf to those of other Bronze Age settlements shows that the building types follow the Bronze Age constructional traditions. There are connections to the Březno type, which has been present since the Early Bronze Age, as well as to the compact rectangular and more variable building types of the Urnfield period. Bei einer Rettungsgrabung im Industriegebiet von Müllendorf (Burgenland) wurde auf einer Fläche von etwa 2,8 ha der südöstliche Teil eines weitläufigen Siedlungsareals untersucht. Unter den Befunden sind mehrere Speichergruben, 55 rekonstruierte Gebäudegrundrisse und ein die Siedlung teilweise einfassender Graben hervorzuheben. Das vorgelegte Buntmetallinventar des Siedlungsareals hat eine Laufzeit von der älteren bis zur jüngeren Hügelgräberzeit (Bz B1–C2) und verteilt sich über die gesamte bebaute Fläche. Das restliche Fundmaterial wartet noch auf eine Bearbeitung. Die Verteilung der Buntmetallobjekte und die fast einheitliche Nordwest-Südost-Orientierung der Gebäude könnten für eine mittelbronzezeitliche Datierung der meisten Siedlungsstrukturen sprechen. Aus dem Vergleich der Gebäudegrundrisse mit jenen von anderen bronzezeitlichen Siedlungen geht hervor, dass sich die Gebäudetypen von Müllendorf gut in die bronzezeitliche Bautradition einfügen. Es zeigen sich sowohl Verbindungen zu dem seit der Frühbronzezeit vorkommenden Gebäudetyp Březno als auch zu den gedrungen rechteckigen und typenreicheren Gebäuden der Urnenfelderzeit.

By challenging the traditional, typological, approach to the history of town halls, we aim to place Italian civic architecture at the center of a cross-disciplinary study focused, on one hand, on the uses and functions of these buildings, and on the other, on their cultural and identity meanings. The papers gathered in this special collection do not present a history of architectural models and persistencies, but rather one of continuous transformations, conversions, and adaptations, shaped by the material and symbolic functions that public buildings fulfilled, and often continue to fulfill, in the places where they were built.

Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R2 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario.