Using Generative AI to Explore Termite-Inspired Architectural Forms.
(2025) ASEM01 20251Department of Architecture and Built Environment
- Abstract
- This thesis explores the potential of generative AI to recreate biological spatial systems, using termite mound structures as inspiration. Termite mounds, known for their unique internal networks and passive climate regulation, feature complex spatial logics that challenge traditional modeling techniques. This project investigates whether diffusion-based generative models can be trained to mimic such structures, and how their outputs might be reconstructed into meaningful 3D geometries.
Multiple custom datasets were created from a single termite mound model, capturing sectional views in three formats: open-view, capped surface, and cavities-focused. Each dataset was paired with both simple and descriptive captions to test the impact of... (More) - This thesis explores the potential of generative AI to recreate biological spatial systems, using termite mound structures as inspiration. Termite mounds, known for their unique internal networks and passive climate regulation, feature complex spatial logics that challenge traditional modeling techniques. This project investigates whether diffusion-based generative models can be trained to mimic such structures, and how their outputs might be reconstructed into meaningful 3D geometries.
Multiple custom datasets were created from a single termite mound model, capturing sectional views in three formats: open-view, capped surface, and cavities-focused. Each dataset was paired with both simple and descriptive captions to test the impact of semantic detail on training outcomes. LoRA models were trained using Stable Diffusion 1.5 and Hunyuan Video, and different generation workflows were tested using ComfyUI. Outputs were evaluated based on spatial continuity, form coherence, and biomimetic resemblance.
The results show that clean, abstract datasets paired with well-structured captions improve model performance in producing consistent, spatially connected sequences. Using a reverse-slicing workflow and CT-scan software, selected outputs were reconstructed into 3D meshes and fabricated as physical prototypes. A final comparison of nine trained models revealed key differences in tunnel connectivity, mass-void distribution, and overall architectural logic.
This research demonstrates that AI-driven workflows, when carefully structured, can bridge the gap between 2D generative outputs and 3D architectural constructs. It also highlights current limitations in fully mimicking termite-inspired spatial systems, offering insights for future explorations in generative design and digital fabrication. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9196460
- author
- Daneshvar Kakhki, Shadi LU
- supervisor
- organization
- course
- ASEM01 20251
- year
- 2025
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Generative AI, Diffusion Models, LoRA(Low-Rank Adaptation), LoRA Fine-Tunning, biomimicry in Architecture, Termite Mound Architecture, AI-Driven Design, AI-based Form Finding, Image-to-3D Reconstruction
- language
- English
- id
- 9196460
- date added to LUP
- 2025-06-11 08:56:56
- date last changed
- 2025-06-11 08:56:56
@misc{9196460,
abstract = {{This thesis explores the potential of generative AI to recreate biological spatial systems, using termite mound structures as inspiration. Termite mounds, known for their unique internal networks and passive climate regulation, feature complex spatial logics that challenge traditional modeling techniques. This project investigates whether diffusion-based generative models can be trained to mimic such structures, and how their outputs might be reconstructed into meaningful 3D geometries.
Multiple custom datasets were created from a single termite mound model, capturing sectional views in three formats: open-view, capped surface, and cavities-focused. Each dataset was paired with both simple and descriptive captions to test the impact of semantic detail on training outcomes. LoRA models were trained using Stable Diffusion 1.5 and Hunyuan Video, and different generation workflows were tested using ComfyUI. Outputs were evaluated based on spatial continuity, form coherence, and biomimetic resemblance.
The results show that clean, abstract datasets paired with well-structured captions improve model performance in producing consistent, spatially connected sequences. Using a reverse-slicing workflow and CT-scan software, selected outputs were reconstructed into 3D meshes and fabricated as physical prototypes. A final comparison of nine trained models revealed key differences in tunnel connectivity, mass-void distribution, and overall architectural logic.
This research demonstrates that AI-driven workflows, when carefully structured, can bridge the gap between 2D generative outputs and 3D architectural constructs. It also highlights current limitations in fully mimicking termite-inspired spatial systems, offering insights for future explorations in generative design and digital fabrication.}},
author = {{Daneshvar Kakhki, Shadi}},
language = {{eng}},
note = {{Student Paper}},
title = {{Using Generative AI to Explore Termite-Inspired Architectural Forms.}},
year = {{2025}},
}