After the construction of the Keban Dam, some settlements with historical, cultural, and natural value were flooded. A scientific committee consisting of academicians and public authorities decided to the relocation of the buildings including the historical Çelebiağa Mosque. In this study, the seismic soil-structure interaction analysis of the historical Çelebiağa Mosque, which was dismantled and reconstructed in a separate region due to the construction of the Keban Dam was carried out. The analysis of the masonry mosque was performed with the SAP2000 finite element analysis software. The Winkler foundation model was used to idealize the soil environment on which the historical mosque was built. The effects of soil-structure interaction on historical masonry mosque were examined in terms of transmitted acceleration, response spectra, and lateral displacement at various heights of the structure. Depending on the results of the analysis, the effects of soil-structure interaction of a reconstructed historical masonry building were investigated. PGA was obtained as 0.51g at the flag level of the minaret under the Kocaeli earthquake and 0.94g under the Sivrice earthquake. Again, the maximum horizontal displacements of the minaret at the flag level were obtained as 11 cm and 8.5 cm under the Kocaeli and Sivrice earthquakes. The behavior of historical masonry structures under earthquake loads has been interpreted by considering the geological conditions.

This article critically examines the use of land in the development of hydroelectric power plants (HPPs) and provides actionable recommendations for establishing tourism destinations. The study assesses the impacts of these facilities on regional tourism and recreational areas, identifying key factors and motivations driving the region’s utilization for tourism activities. These insights were derived from comprehensive surveys and interviews with both visitors and local residents. The study employs hybrid methodologies, including A’WOT and TOWS analyses, to develop strategies that capitalize on regional strengths, address weaknesses, exploit opportunities, and mitigate potential threats. The outcomes of these methodologies provide a framework for sustainable development by integrating adaptation and conservation strategies to minimize damage in regions impacted by HPPs. The findings underscore the importance of balancing energy production with the promotion of sustainable tourism for local economic development. This research offers valuable recommendations for Turkey and other countries undergoing similar planning processes, emphasizing the role of HPPs in fostering eco-friendly tourism destinations.

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.