As renovating existing residential buildings shifts towards more detailed methodologies, conducting comprehensive diagnostic assessments before renovation is crucial for achieving successful outcomes. This research introduces an innovative large-scale diagnostic assessment method for existing residential buildings, addressing the inefficiencies, redundancies, and subjective biases present in traditional diagnostic processes through intelligent assistance modeling. The proposed method focuses on five key elements: construction year, exterior walls, windows, balconies, and shading devices, categorizing assessment levels into four grades (A, B, C, D) based on varying renovation intensities. Evaluation criteria are established for service life, thermal performance, degradation, and aesthetic quality. An intelligent assistance model, constructed using training datasets, enables rapid large-scale assessments, significantly reducing the evaluation time while maintaining an accuracy rate of over 95%. Empirical testing on residential buildings in Shenzhen confirmed the model’s effectiveness, demonstrating its superior accuracy and efficiency compared to traditional methods. A weighted analysis revealed that the impact of each factor on the building’s condition was as follows: exterior wall thickness (0.38), exterior finish deterioration (0.35), window deterioration (0.29), balcony deterioration (0.28), and construction year (0.23). Additionally, an interactive software application integrating the intelligent assessment model was developed. This study employed an interdisciplinary approach, combining machine learning and big data, offering a new perspective on building assessment and providing significant reference value for future residential renovation and urban planning.

As China intensifies its efforts to renovate older residential areas, the renewal of housing built between 1980 and 2000 has reached a critical juncture, necessitating a shift from broad-brush approaches to more refined, precision-oriented renovation strategies. However, standardized diagnostic evaluation criteria and decision-making frameworks for pre-renovation assessment remain underdeveloped. This study builds upon existing research in building regeneration, constructing a diagnostic evaluation process for existing residential buildings from an architectural perspective. The process comprises three phases: data collection and integration, visual model construction, and diagnostic evaluation and analysis. Focusing on key residential components—roofs, walls, exterior windows, and shading elements—the research integrates Building Information Modeling (BIM) technology to propose a diagnostic grading system based on renovation intensity. It employs a comprehensive assessment method combining qualitative diagnostics with quantitative indicator analysis, culminating in the development of a diagnostic evaluation template. The methodology is then applied to two typical residential buildings in Shenzhen, encompassing the collection, integration, and diagnosis of key element information, followed by further evaluation based on diagnostic conclusions to guide renovation design. This research provides scientific guidance and decision-making direction for the refined renewal of existing housing and the formulation of related standards and guidelines, enhancing decision-making efficiency with advanced demonstrative effects and forward-looking insights.

As urbanization accelerates in China, existing multi-story residential buildings constructed between 1980 and 2000 face increasing fire risks due to high occupant density and outdated fire safety codes. This study develops a multi-factor coupling simulation framework integrating BIM, PyroSim (2020), and Pathfinder (2020) to evaluate fire evacuation safety. Results suggest that kitchens located farther from stairwell entrances tend to impose greater smoke temperature and visibility hazards. Alternating evacuations may outperform uniform platform-based strategies. Concentrating slow-moving occupants on lower floors may improve evacuation efficiency, while counterflow and returning behaviors tend to cause the greatest obstruction on middle floors (5th–7th stories). Multi-factor coupling analysis indicates nonlinear thresholds: maximum safe occupancy decreases from 6.0 to below 4.0 persons per household as building height increases from seven to nine stories; exit widths beyond 0.95 m yield diminishing returns; and critical stairwell obstacle sizes decrease from 0.6 m to 0.3 m as household density increases. Three retrofit strategies were examined: adding outdoor stairs achieved a safety margin of 71.2 s; adding evacuation platforms raised safe occupancy from 3.5 to 4.5 persons per household; and installing kitchen sprinklers reduced the smoke concentration in stairwells. These findings may provide a useful reference for fire safety retrofitting in existing multi-story residential buildings.