Accurate building physics performance analysis requires time-consuming, detailed modeling, and calculation time requirement. This paper evaluates the impact of model simplifications on thermal and visual comfort as well as energy performance. In the framework of dynamic zonal thermal simulation, a case study of a residential building in hot climate is investigated. A detailed model is created and simplified through four scenarios, by incrementally reducing the number of thermal zones from modeling every space as a separate zone to modeling the building as a single zone. The differences of total energy and comfort performance in the detailed and simplified models are analyzed to evaluate the grade of the simplifications’ accuracy. The results indicate that all simplification scenarios present a marginal average deviation in total energy demand and thermal comfort by less than 20%. Combining rooms with similar thermal features into a zone presents the optimal scenario, while the worst scenario is the single-zone model. Results showed that thermal zone merging as a simulation simplification method has its limitations as well, whereas a too intensive simplification can lead to undesired error rates. The method is well applicable in further early-stage design and development tasks, specifically in large-scale projects.

The energy consumption of residential buildings represents the majority part of energy usage in Egypt. The aim of this study is to assess the energy performance of a dwelling in New Minia City, Egypt through different orientations. The energy consumption of the case study building is 52560 kWh annually. However, this energy demand could be reduced by 5.8% if the building is oriented to the north direction. The difference of the energy consumption between the best and the worst building orientations, North and South, respectively, reached up to 7.5%. The location of the actual building has a noticeable effect on the energy consumption.

As urbanization continues to advance rapidly, the emergence of biophilic design offers a positive perspective to address the alienation between humans and nature, becoming a hot research topic in areas related to human living environments. Biophilic design, as a design concept inspired by nature, has positive significance in promoting the development of ecological diversity and improving human physical and mental health. This paper makes a comparative analysis of two of China’s residential communities in the same high-density environment through the main influencing factors of plot ratio, greening rate, external facades environments, and internal living space. Starting from the five senses of the human body, namely, sight, hearing, touch, smell, and taste, this paper aims to investigate the design of living spaces through the lens of biophilic design, and proposes a biophilic design model, along with strategies and recommendations tailored to high-density urban environments, in the hope of serving as a valuable reference and source of inspiration for future healthy dwelling design.

Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid machine learning that copes with the drawbacks of single methods. In this work, an optimized version of a popular machine learning model, namely feed-forward neural network (FFNN) is used for simultaneously predicting annual thermal energy demand (ATED) and annual weighted average discomfort degree-hours (WADDH) by analyzing eleven input factors that represent the building circumstances. The optimization task is carried out by a multi-tracker optimization algorithm (MTOA) which is a powerful metaheuristic algorithm. Moreover, three benchmark algorithms including the slime mould algorithm (SMA), seeker optimization algorithm (SOA), and vortex search algorithm (VSA) perform the same task for comparison purposes. The accuracy of the models is assessed using error and correlation indicators. Based on the results, the MTOA (with root mean square errors 2.48 and 5.88, along with Pearson correlation coefficients 0.995 and 0.998 for the ATED and WADHH, respectively) outperformed the benchmark techniques in learning the energy behavior of the building. This algorithm could optimize 100 internal variables of the FFNN and acquire the trend of ATED and WADHH with excellent accuracy. Despite different rankings of the four algorithms in the prediction phase, the MTOA (with root mean square errors 9.84 and 95.96, along with Pearson correlation coefficients 0.972 and 0.997 for the ATED and WADHH, respectively) was still among the best, and altogether, the hybrid of FFNN-MTOA is recommended for promising applications of building energy analysis in real-world projects.

The first part of the paper is about to introduce the principles of "placemaking" as an innovation and important tool of the cities in the 21st century. The process helps designers to transform the spaces of "nobody" to a community-based space for supporting the connection among humans. The second part of the paper shows the process of the used principles by the author for designing the new image of the existing shopping mall in Csepel, Hungary. This work was selected as one of the best design ideas for renewing the existing underutilized space.

Historic urban cores face escalating pressures from climate change, rapid urbanization, and uncoordinated redevelopment, which often threaten their cultural identity and social cohesion, demanding innovative solutions that balance heritage conservation with contemporary housing needs. This study introduces an integrated evaluation framework encompassing 18 criteria across architectural, urban, and green dimensions to assess adaptive housing interventions in urban heritage contexts. Building on resilience theory, urban living, and sustainable urban futures, the paper traces the historical and contemporary design influences that have shaped urban housing design in Damascus, and investigates strategies to maintain prospective housing identity by applying the methodology of the developed framework to three representative dwellings in Damascus’s UNESCO-listed city. Considering the heritage-specific indicators, social place memory, and the cultural significance—with environmental performance and socio-economic viability—the developed compass-like tool in this research visualizes multi-criteria scores to identify leverage points for resilience. Results highlight priority zones for intervention and suggested policy incentives. Through the provision of a flexible, clear tool grounded in adaptive housing concepts, this study empowers planners, conservationists, and communities to develop sustainable, forward-thinking approaches for historic urban environments globally.