Designing energy-efficient buildings for reconstruction in disaster areas is essential for mitigating environmental challenges and supporting sustainable recovery. However, in Türkiye, standardized mass housing is often implemented following disasters for rapid reconstruction, frequently overlooking local climatic conditions. The Housing Development Administration of Türkiye (TOKİ) has initiated standardized mass housing projects across 11 provinces in the disaster area of the 2023 Türkiye Kahramanmaraş earthquake. This study conducts a comparative analysis of heating and cooling energy consumption and CO₂ emissions for a reference TOKİ housing using DesignBuilder simulations across the 11 impacted provinces, to show the environmental impact of standardized material selection. The evaluated archetype features a rectangular geometry with a symmetrical floor plan, comprising 20 dwelling units and a fixed façade configuration governed by orientation. The façade consists of reinforced concrete shear walls, vertically perforated brick walls, and PVC-framed double-glazed windows. The analysis utilized the updated climate zoning of the Turkish Standard for Thermal Insulation in Buildings (TS 825), which recently expanded from four to six climate zones. Findings indicate significant variations: intra-zone annual heating and cooling energy consumption differed by up to 184.85% and 52.84%, respectively. CO₂ emissions showed relatively limited differences, with a maximum of 12.35% intra-zone. These findings show that energy consumption varies significantly both between and within climate zones, highlighting the limitations of relying solely on generalized climate zone data for building design and material selection. Moreover, an evaluation of the latest TS 825 revision shows that the updated classification does not yield consistent heating and cooling energy outcomes for certain provinces. The study concludes that province-specific climatic data should be integrated into post-disaster housing design, particularly during material selection, to improve energy efficiency, reduce emissions, and support sustainable recovery.

The increasing pace of urbanization and climate change necessitate a holistic assessment of building energy performance during the early design phase. This study proposes an Evolutionary Field Optimization (EFO)-based multi-input multi-output artificial neural network (MIMO-ANN) model to simultaneously predict the heating load, cooling load, CO₂ emissions, and lighting energy consumption of smart buildings. The model’s dataset consists of 7963 observations generated via EnergyPlus building energy simulations of standardized TOKİ residential units constructed post-earthquake in Türkiye. No operational or physically measured building energy consumption data were used in the model development process. For the validation setting, the simulation-generated dataset was split into training (60%), validation (10%), and test (30%) subsets. The EFO algorithm was employed to automatically optimize the ANN architecture by dynamically determining the optimal number of hidden layers and neurons. The optimization process demonstrated strong global search capability and fast convergence, reducing the objective function by approximately 86% within 10 iterations. Experimental results on the test subset showed exceptional predictive accuracy for simulation data, with test R² values ranging from 0.9996 to 0.9998 across all four outputs, indicating that the optimized network topology effectively avoided overfitting. While the model’s performance under real-world operational uncertainties and varying occupant behaviors remains to be fully investigated, the proposed EFO-ANN framework provides a computationally efficient and highly accurate analytical core for early-stage design. It serves as a strategic decision-support tool intended for architects and engineers designing post-disaster housing, public authorities forming national energy efficiency policies, and developers building predictive engines for digital twin-enabled smart building systems.