AI-Integrated Self-Help Rural Housing and Livelihood Resilience System: A Bottom-Up Approach for Climate-Vulnerable Communities in Southwest Bangladesh

Liasa Yeasmin Lia; Shayer Ghafur; Dipannita Nandi

doi:10.38027/smart.v3n1-9

Authors

Liasa Yeasmin Lia Department of Architecture, Faculty of Architecture, Bangladesh University of Engineering & Technology (BUET), Dhaka, Bangladesh Author https://orcid.org/0009-0009-7792-4651
Shayer Ghafur Department of Architecture, Faculty of Architecture, Bangladesh University of Engineering & Technology (BUET), Dhaka, Bangladesh Author https://orcid.org/0000-0002-1239-4794
Dipannita Nandi Department of Architecture, Faculty of Architecture, Bangladesh University of Engineering & Technology (BUET), Dhaka, Bangladesh Author https://orcid.org/0009-0008-7779-3746

DOI:

https://doi.org/10.38027/smart.v3n1-9

Keywords:

AI-driven generative design, Climate-resilient rural housing, Vernacular architecture, Cyclone resilience, Digital resilience model

Abstract

This study investigates the conceptual potential of AI-driven generative design as an advanced design-policy tool for developing personalized, climate-resilient rural housing in the disaster-prone southwest region of Bangladesh. Centered on Nalian village in Dacope, Khulna, the research integrates field survey data, vernacular housing analysis, functional layout extraction, and climate-responsive design principles to propose a hypothetical digital resilience model for self-help housing adaptation. The model applies Graph Neural Network (GNN) and Convolutional Neural Network (CNN) principles for plan-generation logic, incorporates aerodynamic form considerations for cyclone-responsive three-dimensional massing, and establishes context-based material selection criteria. The academic contribution of this research is the integration of artificial intelligence, vernacular spatial knowledge, and community-based housing resilience within a unified conceptual framework. The study's originality is demonstrated by positioning AI not only as a form-generating tool but also as a decision-support mechanism that translates local needs, climatic risks, and construction practices into adaptable housing strategies. This research expands the scope of smart design policies by illustrating how digital design systems can facilitate participatory, scalable, and context-sensitive housing decision-making for climate-vulnerable communities. The findings indicate that integrating AI-assisted design with local knowledge can enhance access to resilient housing solutions, reinforce disaster preparedness strategies, and inform future policy frameworks for sustainable rural settlement planning in Bangladesh and similar global contexts.

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