Robust Design Strategies for Multi-Atrium Commercial Buildings in Severe Cold Regions: Environmental Performance and Spatial Quality Under Design-Variable Uncertainty

Nominally optimized atrium designs may lose reliability when geometric and envelope parameters deviate during implementation. Extending a previously established deterministic surrogate-assisted workflow, this study investigates uncertainty-aware design decision-making for a multi-atrium commercial building in Urumqi, China. The severe-cold climate is treated as a fixed environmental boundary condition, while uncertainty analysis is restricted to six implementation-related design variables. A Honeybee–EnergyPlus model supported by short-term field calibration was used to generate a Latin-hypercube dataset, and CatBoost surrogate models were developed for predicted percentage of dissatisfied (PPD), daylight autonomy at 300 lx (DA), and summer–winter solar-radiation difference (D-RAD). Sobol indices and SHAP were used to interpret parameter importance and interaction effects. Monte Carlo propagation of the six uncertain variables was embedded within NSGA-II using mean–variability robust objectives, and TOPSIS was applied to select representative solutions for subsequent space-syntax evaluation. The CatBoost models achieved global holdout test-set R2 values of 0.990–0.994. Compared with the deterministic compromise solution, the robust solution shifted toward a different design configuration while maintaining broadly comparable mean performance. The uncertainty-adjusted DA and D-RAD objectives improved by approximately 2.21% and 6.20%, respectively, whereas the PPD objective deteriorated marginally by approximately 0.25%, reflecting the multi-objective trade-off inherent in the robust formulation. Expanded high-fidelity re-evaluation of ten Pareto-region designs revealed larger local errors for DA and D-RAD than suggested by the global holdout statistics, although the comparative performance ranking was strongly preserved. The results indicate that implementation-related design-variable uncertainty can alter the preferred design region and the balance among competing environmental objectives. Environmental robustness and spatial-configuration quality should therefore be treated as complementary criteria, with space-syntax analysis serving as a post-optimization screening tool rather than a direct measure of user experience.