Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Comparative Analysis of the Seismic Risk of Tunceli-Ovacık Rural Building Stock Using Different Rapid Screening Methods
Polat, Berivan Yılmazer; Ayhan, Ezgi
Located at the intersection of the Yedisu Segment, a seismic gap, and the active Ovacık Fault, the Tunceli-Ovacık district is a region with a high earthquake risk but an unclear level of structural fragility. This study aims to assess the seismic safety of the rural building stock located in a seismically critical area. To this end, the Turkish Rapid Visual Screening (RVS), based on local parameters, and the Canadian Seismic Survey Method, widely used in international literature, were applied to determine the current condition of the buildings; the earthquake risk of the region was analyzed based on the results obtained. Furthermore, similarities and differences between the methods were comparatively examined to ensure scientific integrity. Within the scope of the study, 209 buildings representing the building typology in the region, 88% of which are masonry-mixed systems, were analyzed using both rapid assessment algorithms. While the overall risk distribution ratios were similar, building-level matching analyses showed a medium level of agreement between the methods. This indicates that the local and universal methods, rather than definitively verifying each other, provide complementary data by focusing on different parameters in the seismic assessment of the rural building stock. This study demonstrates that an integrated risk mapping approach, where different methodologies complement each other, can offer a rapid and reliable decision support mechanism for urban transformation strategies in cities with high earthquake risk, such as Tunceli.
- MakaleYalnız künye2024Scopus SJRQ1Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches
Khatti, Jitendra; Polat, Berivan Yılmazer
This investigation introduces the optimal performance models for predicting the compressive strength (CS) and pozzolanic activity index (PAI) by comparing the machine learning models. The machine learning models, i.e., multilinear regression (MLR), support vector machine (SVM), gaussian process regression (GPR), decision tree (DT), random forest (RF), and gene expression programming (GEP) have been trained (TRN) and tested (TST) by 28 and 7 data points. For the first time, the SiO2, Al2O3, Fe2O3, SiO2 +Al2O3 +Fe2O3, reactive SiO2, Blaine specific surface area, and specific gravity have been used as input variables to compute the CS, and 28 days PAI (28PAI), and 90 days PAI (90PAI) of the natural pozzolans. The multicollinearity analysis showed the SiO2, Al2O3, Fe2O3, SiO2 +Al2O3 +Fe2O3, reactive SiO2, and specific gravity have problematic multicollinearity (variance inflation factor – VIF > 10). Therefore, the root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), performance index (PI), and variance accounted for (VAF) metrics have been implemented to evaluate the model's performance and multicollinearity impact. From the comparison of models, it has been recorded that model GPR outperformed the MLR, SVM, DT, RF, and GEP models in predicting CS (PI = 1.29, VAF = 71.31, R = 0.8473, MAE = 0.9390 MPa), 28PAI (PI = 1.87, VAF = 94.88, R = 0.9744, MAE = 0.7295 %), and 90PAI (PI = 1.72, VAF = 88.11, R = 0.9393, MAE = 1.2444 %) in the TST phase, close to ideal values. The score, generalizability. Wilcoxon test, uncertainty analysis, Anderson-daring test, and accuracy metrics have confirmed the superiority of GPR models in predicting CS, 28PAI, and 90PAI of natural pozzolans.
AtıfScopus28WoS25OpenAlex28
İncele - MakaleAçık Erişim2023Experimental investigation and numeric modelling of the impact performance of basalt fiber reinforced geopolymer concrete
Polat, Berivan Yılmazer; Güngör, Hiranur
Geopolymer concrete (GC) has been found to have better results than Portland cement concrete (PCC), including lower carbon dioxide emissions, higher compressive strength, and greater durability. The study aims to determine the effects of basalt fiber on GC due to the impact caused by the sudden loading effect. Samples were prepared with different mixing ratios for the experimental process, with PCC used as the control sample. Basalt fibers were added to the samples in three different ratios based on optimal mixing ratios determined through physical and mechanical tests, such as UPV (Ultrasonic Pulse Velocity), compressive strength, and flexural strength. Subsequently, fibrous samples were analyzed using SEM (scanning electron microscopy) and EDS (energy dispersive spectroscopy). A drop-weight experiment was conducted on GC plates measuring 50×50×5 cm, which contained two different fiber ratios that yielded the best mechanical and physical properties. The results were analyzed using numerical modeling methods. No changes in content were made. The sample with the highest impact resistance was found to be the 2% basalt fiber-reinforced GC, with a displacement value 21.21% lower than PCC. The language used is clear, concise, and objective, with a formal register and precise word choice. The text follows a logical structure with causal connections between statements and adheres to conventional academic formatting and citation styles. The ANSYS modeling results indicated an 89.63% similarity with the experimental data regarding the displacement values. Additionally, the study demonstrated that the inclusion of basalt fiber additives enhances the impact resistance of geopolymer concrete. Furthermore, numerical modeling can predict the impact behavior of concrete to a significant extent, eliminating the need for experimental processes.
- MakaleAçık Erişim2023Numerical Analysis of Double Geopolymer Concrete Layer with Different Properties
Polat, Berivan Yılmazer
Researchers have been working to find alternatives to conventionally produced concrete in recent years. Geopolymer concretes are also the focus of these studies. There are not enough studies on modelling geopolymer concretes, which are the subject of many experimental studies. In this study, which is believed to close the gap in the literature, stress and strain analyses of geopolymer concrete with two different properties under load in two different systems were performed. The locations of the concrete with different properties were also changed in the model, and the results obtained in both cases were compared. It was observed that when the geopolymer concrete with a lower modulus of elasticity and 100% slag content was on top, the stress values decreased, and the strain rates increased.
