Weatherability Optimization for Ice-Shell Architecture Using Explainable Surrogate Models
Ice-shell architecture faces significant challenges due to its sensitivity to environmental conditions, as weatherability directly influences structural safety, lifespan, and industrial viability. Existing methods to enhance weatherability tend to be expensive, data-heavy, or heavily dependent on expert experience. This research introduces a cost-effective, early-stage optimization methodology using explainable surrogate models to support design decision-making. By integrating computational tools with interpretable AI techniques, the study aims to improve performance prediction, streamline workflows, and increase automation in the architectural design of ice-shell structures. Weatherability and Structural Reliability of Ice-Shell Architecture The climatic vulnerability of ice-shell buildings is a major factor restricting their large-scale application, especially in cold regions such as northeastern China. Extreme temperature fluctuations, solar radiation, and wind loads contribute to ...