Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2024/r/gumushane/14410
  Effect of White-Portland cement containing micro and nano silica on the mechanical and freeze-thaw properties of self compacting mortars

  Etli, Serkan; Yılmaz, Tekin; Hansu, Osman

  This study concentrated to investigate the influences of white-Portland cement (W-PC) containing silica fume (SF: micro-silica (µSi) and nano-silica (nSi)) as replacement (2–4 wt%) on mechanical (strength and durability), physical and microstructural behaviors of self-compacting mortars (SCMs) fabricated at 0.48–0.63 w/b ratios. In keeping with these purposes, besides of carrying out the workability (mini-slump flow: M−SF and V-funnel: V-F) experiments of SCMs, the mechanical and microstructural tests implemented were: unconfined compressive strength (UCS), flexural strength (FS), freeze–thaw (F-T) and scanning electron microscope (SEM) analysis. The outcomes displayed that, in comparison with the control, replacing W-PC by SF (particularly nSi) enhanced the UCS and FS, resisted against F-T durability by lowering the strength losses in UCS and FS. The ameliorated microstructure resulting in a reduction in the number and/or a narrowing in the size of micro-cracks and pores contributed to generate a more compact and consolidated SCM structure for the improving of strength and durability due of pore-filling effect and superior pozzolanic characteristics of µSi and especially nSi, this in turn reduced the permeability of SCM, which has a significant effect on the strength and durability behaviors of the SCM.

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• MakaleAçık Erişim2024/r/gumushane/14343
  Geophysical and numerical approaches to solving the mechanisms of landslides triggered by earthquakes: A case study of Kahramanmaraş (6 February, 2023)

  Sari, Mahmut

  Many landslides occurred in the 11 Turkish provinces affected by the earthquakes centered in Kahramanmaraş in February 2023, and many people lost their lives due to those landslides. To reduce the risks and hazards brought on by landslides induced by earthquakes, it is essential to identify the mechanisms causing landslides to arise in such situations. In this study, the geological strength index and both seismic refraction tomography and electrical resistivity tomography (ERT) geophysical methods were used to determine the failure mechanism of layered sedimentary units defined as flysch (claystone-siltstone-sandstone-marl). The shear strength reduction finite element method (SSR-FEM) was used with the RS2 computer program for model solutions. One of the geophysical methods, ERT, facilitated an interpretation of the cyclic failure mechanism that occurs in flysch, and it was found to be congruent with numerical models. Landslide slip circle depth, water-saturated layers, and resistivity values were determined with the ERT method. Seismic methods were employed to determine the velocity values (Vp, Vs) of the landslip mass. These velocity data, along with the elasticity modulus and Poisson ratios derived from them, were utilized in our numerical model. In the maximum shear strength and horizontal displacement analyses performed on the section line determined before the landslide, the natural state and the situation under the influence of groundwater were examined and the strength reduction factor (SRF) was determined as 1.35 and 1.24, respectively. In an evaluation conducted under the influence of the maximum horizontal ground acceleration (0.22 g) determined by taking into account the seismicity of the study area, it was concluded that instability occurred in the flysches and the SRF value was 0.72. An important finding of this study is that the results of geophysical methods used to determine the lithological properties of sedimentary rock masses and understand the causes of landslides align closely with those of numerical analysis methods, providing high accuracy. These methods also support new approaches, particularly in identifying landslip boundaries and detecting areas with high water saturation. It has been discovered that earthquakes actively contribute to the instability of these kinds of soils.

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• MakaleAçık Erişim2024/r/gumushane/11731
  The impact of implementing backbone architectures on fracture segmentation in X-ray images

  Turk, Salih; Bingol, Ozkan; Coskuncay, Ahmet; Aydin, Tolga

  X-ray imaging is widely utilized for the detection of bone fractures due to its affordability, rapid processing capabilities, broad accessibility, and ease of use. Despite these advantages, the intricate analysis of X-ray images necessitates advanced computational techniques to fully exploit their rich informational content. Notably, accurate segmentation of these images plays a critical role in aiding medical professionals with precise diagnoses and effective treatment planning. This study examines the impact of integrating different backbone architectures for the task of fracture segmentation in X-ray images. Specifically, the research focuses on enhancing the widely-used DeepLabV3 model by incorporating pre-trained networks such as ResNet50, ResNet101, and MobileNetV3 into the encoder component to improve feature extraction and segmentation accuracy. The FracAtlas dataset, which presents unique challenges due to its small size and the diversity of fractures from various anatomical regions, was employed for model evaluation. Data augmentation techniques were implemented to expand the dataset, and an additional subset focusing on cropped images of fracture areas was developed. The models were trained over 50 epochs, and their performance was assessed using metrics such as Intersection over Union (IoU), loss values, and Dice scores. The results indicate that the DeepLabV3 model with ResNet-based backbones achieved IoU values exceeding 0.93 on the original dataset and demonstrated outstanding performance on the augmented and cropped datasets, with AUC values reaching up to 0.99. The study also highlights the computational complexity of the models, with ResNet101 exhibiting the highest time complexity, while MobileNetV3 was the most efficient in terms of processing time and memory consumption.

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