From Layout to Low-Carbon in 20 Seconds: Advancing Performance-Driven Generative Design
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) framework capable of directly converting flat layout images into simulation-ready models. This innovative process ensures data consistency, reduces human intervention, and establishes a new paradigm where digital images become the foundation for performance simulation. The framework enables seamless connectivity between generative outputs and analytical models, ensuring that design quality and efficiency evolve hand-in-hand.
Geometric Feature Extraction and Representation
A crucial aspect of this research lies in the creation of a novel geometric feature set specifically designed for flat layout representation. Unlike traditional feature engineering that emphasizes basic spatial metrics, this new feature set captures both topological and geometric properties of floor plans, enabling more accurate performance predictions. The enhanced feature representation supports better pattern recognition by machine learning algorithms, ensuring that the complexity of real-world spatial arrangements is effectively encoded into computational models.
The Image2Sim Algorithm: Automation and Efficiency
The Image2Sim algorithm stands as the technological backbone of the proposed workflow. By leveraging computer vision and data-driven modeling, the algorithm automates the translation of layout images into simulation models. When applied to the RPLAN dataset, Image2Sim achieved remarkable improvements—reducing the modeling failure rate by 8% and cutting simulation time from 333 days to just 2 days. This breakthrough demonstrates how automation can drastically enhance both accuracy and productivity, making simulation-based evaluation feasible at the earliest stages of design exploration.
Graph-Aware Extreme Gradient Boosting (GAXGBoost) Model
To enable efficient and intelligent performance evaluation, the study introduces the Graph-Aware Extreme Gradient Boosting (GAXGBoost) surrogate model. Unlike conventional machine learning models such as XGBoost, MARS, GNN, and ANN, GAXGBoost integrates spatial and relational data awareness, allowing it to better capture the interdependencies between design components. Experimental results confirm that GAXGBoost consistently outperforms other models across all accuracy metrics, providing robust and interpretable feedback for generative layout optimization.
Toward Performance-Driven Generative Design
The proposed framework represents a significant leap toward truly performance-driven generative design. By combining automated modeling, advanced feature extraction, and powerful surrogate modeling, it transforms the early design process into a data-informed, sustainability-oriented workflow. Designers can now receive near-instant performance evaluations within seconds, encouraging low-carbon, energy-efficient decision-making from the very start of a project. This research not only enhances computational design intelligence but also lays the foundation for scalable, real-time sustainability assessment in architecture and urban planning.
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