Urban street quality directly affects the daily lives of residents and the experiences of tourists, playing a crucial role in the sustainable development of cities. However, most studies either focus on a single demographic or lack interpretable data analysis. To address this, we propose a framework integrating deep learning, elastic net regression, and SHapley Additive exPlanations (SHAPs). Using street view images, we quantitatively assess street quality in Xi’an’s Mingcheng District, considering the perspectives of both residents and tourists. The framework assesses comfort, convenience, safety, and culture to determine street quality and explores influencing factors. The results indicate that high-quality streets are primarily located near major urban roads, tourist attractions, and commercial areas, while older residential areas in historic districts exhibit widespread low-quality streets. Building density significantly and negatively impacts street quality, whereas visibility of the sky and green coverage positively influences street quality. SHAP reveals that greenery can mitigate the negative effects of high building density and enhance street quality. This study provides actionable insights for enhancing urban street quality through data-driven, human-centered approaches, directly contributing to the Sustainable Development Goal 11 (Sustainable Cities and Communities) by promoting more livable, safe, inclusive, and sustainable urban environments.

Traditional timber assembly, especially dougong assembly, involves strict sequential dependencies, complex spatial relationships, and long-horizon execution. This study aims to develop and evaluate BRAC-ACT, a task-structured robotic assembly framework that translates Song-style dougong tectonic logic into learnable, executable, and evaluable robotic tasks. A preliminary whole-task pilot was first conducted to examine complete-sequence ACT execution and informed the subsequent task-structured design. The formal experiment used seven task-specific ACT policies trained from 50 demonstrations per task on a bimanual robotic platform with 1:10 scale 3D-printed, magnet-assisted specimens. Across 300,000 training steps, all seven policies showed progressive stabilization of the recorded training-loss and gradient-norm metrics. Across ten evaluation rounds, six completed the full Task 1–7 sequence, yielding a 60.0% round-level completion rate under a retry-enabled procedure in which individual tasks could be attempted repeatedly before progression or round termination. Task-wise analysis identified Tasks 6 and 7 as the main bottlenecks, with failures concentrated in placement and spatial alignment rather than grasping. The study demonstrates the feasibility of structuring traditional architectural assembly knowledge into task-specific imitation-learning policies, chained execution, and process-level diagnostic evaluation, providing a methodological basis for translating complex tectonic knowledge into robotic assembly workflows. The results are limited to scaled magnet-assisted specimens in a controlled workspace and should not be interpreted as intervention-free autonomous or full-scale timber assembly performance.