Architecture Engineers Award

The accelerating global transition toward carbon neutrality demands rapid and reliable energy prediction tools for innovative building systems such as Building-Integrated Photovoltaic (BIPV) modular buildings. Conventional physics-based simulation methods, while accurate, are computationally intensive and unsuitable for real-time design optimization. This study proposes a novel machine learning-based rapid energy prediction methodology tailored specifically to the thermal and geometric characteristics of modular BIPV-integrated buildings. Feature Engineering for Modular Building Representation A comprehensive feature engineering framework was developed to capture the distinctive attributes of modular construction. The approach incorporates six-surface thermal property encoding, geometric parameters, and detailed solar irradiance calculations to represent envelope exposure and inter-module interactions. This structured encoding ensures that key thermal behaviors and photovoltaic infl...

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...

  Generative design has rapidly become a cornerstone of modern architectural and engineering innovation, enabling automated exploration of spatial layouts through computational intelligence. However, while these models efficiently generate diverse design options, they often lack integrated systems for evaluating performance metrics such as energy use, carbon impact, and spatial efficiency. This study bridges that gap by introducing a fully automated, image-based performance evaluation framework that accelerates the transition from concept to simulation-ready layouts. The approach redefines the workflow of early-stage design, providing instant sustainability insights that support low-carbon and high-performance outcomes. Image-Based Performance Evaluation Framework Traditional performance evaluation methods rely heavily on manual modeling, which is both time-consuming and prone to human error. To overcome this, the research introduces an automated image-to-simulation (Image2Sim)...