Architecture Engineers Award

Climate change has intensified the challenges associated with maintaining indoor–outdoor thermal comfort, prompting the need for advanced, data-driven design approaches. This study introduces an integrated framework combining Multiobjective optimization (MOO) and explainable machine learning (ML) to analyze how spatial morphology affects indoor–outdoor thermal comfort (IOTC). By merging global optimization capability with transparent interpretability, the framework supports climate-responsive architectural decision-making and delivers insights that improve both design quality and environmental performance. Multiobjective Optimization for Thermal Comfort The framework employs a genetic algorithm (GA)–based MOO model to optimize nine morphological parameters related to building and courtyard forms. These parameters serve as decision variables for the simultaneous optimization of predicted mean vote (PMV) and the universal thermal climate index (UTCI) during contrasting seasonal conditi...

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 ...