A HYBRID LEARNING FRAMEWORK FOR POST-DISASTER DAMAGE ASSESSMENT AND SHELTER DECISION-MAKING: INCORPORATING 3D-PRINTED MODULAR ARCHITECTURE IN THE KAHRAMANMARAŞ, TÜRKİYE CONTEXT

Authors : Tuğba Erdil Dinçel

Pages : 429-447

Doi:10.46519/ij3dptdi.1711752

View : 78 | Download : 180

Publication Date : 2025-12-28

Article Type : Research Paper

Abstract :The growing frequency and severity of natural disasters have highlighted the urgent need for adaptive, efficient, and sustainable temporary housing strategies. This study introduces a hybrid computational framework that integrates parametric design, Bayesian networks, fuzzy logic, and weakly supervised learning to enhance post-disaster temporary housing decisions. Using high-resolution aerial imagery from the 2023 Türkiye Earthquake dataset, the system extracts multi-layered spatial and structural features to classify damage levels and inform shelter typology. In addition to damage assessment and decision support, the framework incorporates fabrication-aware modules for 3D-printed modular architecture, enabling rapid, locally manufacturable shelter components tailored to site-specific needs. This integration improves deployment speed, supports modular adaptability, and aligns with Industry 4.0 principles for automated construction. The proposed SEHRNet-based architecture combines deep learning with probabilistic graphical models to accommodate both quantitative and qualitative uncertainty. A hybrid decision-making mechanism integrating TOPSIS, PROMETHEE, and Simulated Annealing enables evaluation of shelter alternatives under multiple constraints such as cost, modularity, climate compatibility, and cultural adaptability. A feedback loop based on Multi-Time-Step Rolling with MPC allows for real-time updates and adaptive planning. The results demonstrate improved decision accuracy and provide a fabrication-aware, computationally scalable solution for disaster-responsive shelter planning.
Keywords : Post-Disaster Architecture, 3D-Printed Modular Housing, Additive Manufacturing, Hybrid Learning Models, Decision Support Systems.

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