Under the current Turkish Building Earthquake Code (TBDY-2018), the 'earthquake zone' concept from the previously applied Code on Structures to be Built in Earthquake Zones (DBYBHY-2007) has been abandoned, and acceleration effects based on the building's location have been taken into account. Previously, structures within the same earthquake zone were subjected to the same seismic loads; after this change, even nearby structures are subjected to different seismic loads. In this study, to examine the impact of code changes on structures in Kocaeli Province, a commonly used, typical reinforced concrete structure was selected, and seismic analyses were conducted on this structure assuming it was located in 12 different districts and on various soil types within the province. The analyses revealed that, across all districts, for soil classes ZA/Z1, the base shear forces obtained according to the 2018 earthquake code were lower than those obtained under the 2007 code, whereas those for soil classes ZD/Z3 and ZE/Z4 were higher.

This study investigates the combined influence of forward directivity (FD), site class, and damping ratio on the seismic input energy demand of linear single-degree-of-freedom (SDOF) systems using an energy-balance framework. Four near-fault ground-motion sets with FD and non-forward directivity (NFD) were compiled for ZC and ZD soil classes, and elastic input energy spectra were computed for damping ratios of 2%, 5%, and 20%. Analysis of the earthquake records showed that increasing the damping ratio from 2% to 20% reduced the peak average input energy by approximately 35-40% for the selected records. The effect of forward directivity in the selected ground motion is that the average input energy increases by approximately 35-50% in the ZC soil class, while this increase is almost three times greater in the ZD soil class. Overall, increasing damping systematically reduces peak mean input energy, while FD effects markedly amplify energy demand, especially on soft soils. These findings underline that near-fault pulse-like characteristics and site conditions should be jointly considered when interpreting energy-based seismic demand.

Energy efficiency in buildings is of central importance in Türkiye's energy strategies in terms of reducing external dependence and achieving sustainability goals. This study examines the effect of the external infill wall material (horizontal perforated brick, pumice block, and aerated concrete), a fundamental component of the building envelope, on the energy performance class (A-G) in six different climate zones of Türkiye, according to the TS 825 standards updated in 2024. For the analyses, a five-story apartment building model with ten apartments and a total floor area of 1440 m² was used as a reference, and calculations were performed using the Energy Performance of Buildings Software Program (BEP-TR 2). As a result of the simulations, it was observed that a decrease in the thermal conductivity (λ) of the wall material significantly reduced annual heating consumption, and the use of aerated concrete enabled the achievement of energy class "B" in all zones. These findings highlight the critical role of material selection in the process of complying with the Nearly Zero-Energy Buildings (nSEB) criteria, which become mandatory as of 2025.

This study proposes a source-study-aware validation framework to estimate the shear capacity of stirrup-free steel-fiber-reinforced concrete corbels and assess the reliability and generalizability of machine-learning models in structural engineering. A database of 108 specimens, compiled from seven independent studies, was analyzed using linear regression, ridge regression, and hyperparameter-optimized XGBoost. The evaluation combined standard specimen-level five-fold cross-validation, predefined source-study holdout evaluation, exhaustive partition-sensitivity analysis, and specimen-level out-of-bag bootstrap uncertainty analysis. In the predefined source-study hold-out evaluation, with hyperparameters selected exclusively from the training portion via an inner group-aware search, the models achieved test R² values of 0.842, 0.866, and 0.912, respectively, with XGBoost providing the highest point estimate of performance. However, an exhaustive sensitivity analysis of source-study partitions, in which hyperparameters were re-selected independently within the training portion of each partition, indicated that this ranking was not maintained: Linear Regression achieved the highest mean and median R² and the narrowest interquartile range, whereas XGBoost exhibited the lowest mean and median R², the widest interquartile range, and the highest incidence of negative R² values and large prediction errors among the three models; this reflects instability introduced by re-tuning under limited, group-diverse training data. The specimen-level out-of-bag bootstrap uncertainty analysis also favored the linear models in terms of mean performance and confidence-interval width. Standardized coefficients and SHAP analyses consistently identified the shear span-to-effective-depth ratio, the longitudinal reinforcement ratio, and the fiber ratio as the dominant predictors. This study provides a transparent and uncertainty-aware framework for determining when predictive performance is transferable across independent experimental studies and when caution is required in engineering applications.