Yuling Chen

The building sector is a major source of global carbon emissions, with embodied carbon playing an increasingly critical role. This study quantitatively compares the life-cycle embodied carbon of three residential building design patterns (cast-in situ, CIS; design for disassembly, DfD; and skeleton–infill, SI) under a unified scenario of a 90-year service life with functional renovations every 30 years. A total of 36 cases, derived from twelve prototypical residential designs implemented in each pattern, were evaluated via life-cycle assessment following the EN 15978 standard. The results show that the SI pattern reduces embodied carbon by 45–55% compared to CIS, while the DfD pattern achieves a 35–45% reduction (with SI pattern consistently performing best). Structural system selection also influences outcomes, with shear wall structures reducing emissions by 7–14% compared to frame systems. Plan layout effects were marginal. The analysis indicates that the design pattern is the dominant factor influencing embodied carbon outcomes. SI pattern yields the largest carbon reduction by pairing a long-lived structural frame with flexible infill that extends service life and adaptability, while DfD lowers material demand via component reuse. These findings highlight the substantial benefits of circular design strategies and support a shift toward more adaptable, long-lifespan, and low-carbon residential design.

Although functional identifiability represents a key aspect for promoting visual connotation and sustainable usability in historic building groups, there is still no consensus on how to quantitatively describe its identification basis at a large scale. The recent emergence of the potentiality of deep learning and computer vision has provided an alternative to traditional empirical-based judgment, which is limited by its subjective bias and high traversal costs. To address these challenges, this study aims to build a workflow for a visual analysis of function and facade to extract the different contributions that facade elements provide to functional expression. The approach is demonstrated with an experiment on a section of the Chinese Eastern Railway (CER) where large-scale historical buildings images were categorized to identify functions using deep learning, together with activation and substance for visual calculations. First, the dataset aggregated with images of historic buildings along the CER was used to identify functional categories using SE-DenseNet merging channel attention. The results of the model visualized using t-SNE and Grad-CAM were then used to analyze the relationships of facade features across functional categories and differences in elemental feature representation across functional prototypes. The results show the following: (1) SE-Densenet can more efficiently identify building functions from the closely linked facade images of historic building groups, with the average accuracy reaching 85.84%. (2) Urban–rural differences exist not only in the count of spatial distributions among the CER’s historic building groups, but also in a significant visual divergence between functions related to urban life and those involved in the military, industry, and railways. (3) Windows and walls occupy areas with more characteristics, but their decorative elements have a higher intensity of features. The findings could enhance the objective understanding and deeper characteristics of the historical building group system, contributing to integrated conservation and characteristic sustainability.