The ability of deep learning has been tested to learn graphical features for building-plan generation. However, whether the deeper space allocation strategies can be obtained and thus reduce energy consumption has still not been investigated. In the present study, we aimed to train a neural network by employing a characterized sample set to generate a residential building floor plan (RBFP) for achieving energy reduction effects. The network is based on Pix2Pix, including two sub-models: functional segmentation layout (FSL) generation and building floor plan (BFP) generation. To better characterize the energy efficiency, 98 screened floor plans of Solar Decathlon (SD) entries were labeled as the sample set. The data augmentation method was adopted to improve the performance of the FSL sub-model after the preliminary testing. Three existing residential buildings were used as cases to observe whether the network-generated RBFP gained the effect of decreasing energy consumption with decent space allocation. The results showed that, under the same simulation settings and building exterior profile (BEP) conditions, the function arrangement of the generated scheme was more reasonable compared to the original scheme in each case. The annual total energy consumption was reduced by 13.38%, 12.74%, and 7.47%, respectively. In conclusion, trained by the sample set that characterizes energy efficiency, the RBFP generation network has a positive effect in both optimizing the space allocation and reducing energy consumption. The implemented data augmentation method can significantly improve the network’s training results with a small sample size.

Industrial heritage creative industrial parks are an organic component of historical neighborhood buildings. According to several field visits and surveys of creative industrial parks, it has been found that the external space of some creative industrial parks does not meet the needs of the people. This research primarily investigates the critical factors influencing the usability of external spaces in these revitalization projects. Spatial syntax, a field that has seen considerable advancement in recent years, offers a more scientific approach to understanding spatial relationships. Therefore, this paper employs spatial syntax as its principal methodology. It conducts a quantitative analysis of the external spatial morphology of four creative industrial parks of industrial heritage in Tianjin. The study critically evaluates the syntactic values and identifies issues in the external spatial morphology, including a dearth of cultural elements and limited environmental richness. Furthermore, it provides recommendations for the enhancement of these external spaces. The aim is to furnish data support and strategic insights to invigorate the revitalization efforts of Tianjin’s industrial heritage creative industrial parks.

In the context of urban stock renewal, the spatial arrangement of public cultural facilities (PCFs) should follow the principles of equity and efficiency to ensure that residents have equitable access to and quality of public cultural services. The aim of this article is to study the spatial distribution of PCFs and the coupling of supply and demand of cultural resources in Tianjin’s central area. By building a supply-demand coupling coordination model and other methods, the equalization of the spatial distribution of PCFs is measured from various perspectives, and the results suggest that more than half of the sub-districts are in a situation of supply and demand imbalance. To fulfill the purpose of meeting residents’ actual needs, balancing supply and demand for cultural resources, and coordinating the increase in stock, these sub-districts’ facilities enter the step of optimization. Depending on the circumstances, the quality and scale of these facilities are optimized, or new facility points are added based on the maximized coverage model. The optimization is shown to be beneficial in terms of updating design and coverage quantity using two real-world cases. Finally, the coverage of facilities in the study area is maximized, facility utilization is made more efficient, and residents’ needs for public cultural services are satisfied.

The elderly community day care model is an emergent solution to the aging problem influenced by the Eastern perspective of family in China. Due to the structural problem of spatial disorder in most of China’s urban built-up areas, the planning and construction of elderly day care centers (EDCCs) is facing great challenges. This study aims to comprehensively compare the spatial distribution and accessibility measurement methods for elderly residents and EDCCs in typical Chinese urban built-up areas based on the accessibility theory and spatial analysis methods from the community living circle perspective. The results show that different spatial distribution analysis methods have their own emphases and limitations, requiring comprehensive application in practice. The potential model method is most suitable for the accessibility measurement in this scenario. The threshold setting of service distance for the urban built-up areas public service facilities in the current Chinese standard needs to be further optimized. The existing EDCCs suffer from serious quantity deficiencies and misplaced supplies in the region. These findings can reveal the EDCCs distribution characteristics of typical Chinese urban built-up areas and provide new insights for urban planners and policy makers who are assessing the equity and efficiency of public service facilities.

In recent years, as machine learning has been widely studied in the field of architecture, scholars have demonstrated that computers can be used to learn the graphical features of building façade generation. However, existing deep learning in façade generation has yet to generate only a single façade, without comprehensive generation of five façades including the roof. Moreover, most of the existing literature has utilized the Pix2Pix algorithm for façade generation experiments, failing to attempt to replace the original generator in Pix2Pix with a different generator for experiments. This study addresses the above issues by collecting and filtering entries from the international Solar Decathlon (SD competition) to obtain a data set. Subsequently, a low-rise residential building façade generation model based on the Pix2Pix neural network was constructed for training and testing. At the same time, the original U-net generator in Pix2Pix was replaced with three different generators, U-net++, HRNet and AttU-net, for training and test results were obtained. The results were evaluated from both subjective and objective aspects and it was found that the AttU-net generative network showed the best comprehensive generation performance for such façades. HRNet is acceptable if there is a need for fast training and generation