Generative optimization of building blocks for density, solar and structural performance

This study addresses the challenge of performance-informed building blocks generation by developing a generative design framework that simultaneously optimizes building massing, density distribution, and solar and structural performance. As energy consumption, carbon emissions, and material efficiency become increasingly critical in building engineering, there is a growing need for integrated methodologies that combine architectural form exploration with quantifiable performance objectives. The aim of this research is to formulate and validate a modular, cell-based algorithm that generates building configurations optimized for solar gain, thermal comfort, and structural efficiency. The methodology employs parametric design tools, including Grasshopper and Python, alongside simulation engines such as Ladybug for solar radiation analysis and Karamba for finite element structural evaluation. Multi-objective optimization is conducted using the Octopus application to identify Pareto-optimal solutions across competing criteria. The proposed approach is validated using a mid-rise residential block case in Tehran, demonstrating its effectiveness under real-world regulatory and climatic constraints. Findings show significant improvements in seasonal solar performance and reductions in structural deflection, with up to 248% more winter solar gain and 4.6% lower displacement compared to conventional designs. The key contribution of this research lies in its integration of environmental and structural simulation within an automated generative workflow that ensures both design adaptability and engineering feasibility. The novelty of the study is in bridging early-stage form generation with detailed performance feedback, providing a scalable method for sustainable and structurally sound building design. The proposed framework is adaptable to various site contexts and can inform future advances in computational building engineering. • Generative design applied to optimize massing, density, and solar performance • Integrated solar and structural analysis in early-stage form generation • Adaptive building forms tested under real-world design constraints

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Publication Details

Journal
Journal of Building Engineering
Published
2025-06-27
DOI
https://doi.org/10.1016/j.jobe.2025.113307
Citations
1
Primary Topic
Building Energy and Comfort Optimization
Type
article
Field-Weighted Citation Impact
1.01
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Generative optimization of building blocks for density, solar and structural performance

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article

Generative optimization of building blocks for density, solar and structural performance

Dan Luo, Saeed Banihashemi, Saba Fattahi Tabasi, Khuong Le Nguyen, Hamid Reza Rafizadeh
article en
1 citations

Abstract

This study addresses the challenge of performance-informed building blocks generation by developing a generative design framework that simultaneously optimizes building massing, density distribution, and solar and structural performance. As energy consumption, carbon emissions, and material efficiency become increasingly critical in building engineering, there is a growing need for integrated methodologies that combine architectural form exploration with quantifiable performance objectives. The aim of this research is to formulate and validate a modular, cell-based algorithm that generates building configurations optimized for solar gain, thermal comfort, and structural efficiency. The methodology employs parametric design tools, including Grasshopper and Python, alongside simulation engines such as Ladybug for solar radiation analysis and Karamba for finite element structural evaluation. Multi-objective optimization is conducted using the Octopus application to identify Pareto-optimal solutions across competing criteria. The proposed approach is validated using a mid-rise residential block case in Tehran, demonstrating its effectiveness under real-world regulatory and climatic constraints. Findings show significant improvements in seasonal solar performance and reductions in structural deflection, with up to 248% more winter solar gain and 4.6% lower displacement compared to conventional designs. The key contribution of this research lies in its integration of environmental and structural simulation within an automated generative workflow that ensures both design adaptability and engineering feasibility. The novelty of the study is in bridging early-stage form generation with detailed performance feedback, providing a scalable method for sustainable and structurally sound building design. The proposed framework is adaptable to various site contexts and can inform future advances in computational building engineering. • Generative design applied to optimize massing, density, and solar performance • Integrated solar and structural analysis in early-stage form generation • Adaptive building forms tested under real-world design constraints

Journal of Building EngineeringVol. 111
University of Technology Sydney (AU), Queensland University of Technology (AU), University of Canberra (AU), Queensland Department of Environment and Science (AU), Faculty of Design (SI)
Sustainable cities and communities
Openalex Percentile: Top 23%
Building Energy and Comfort Optimization
1.01
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