Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025/r/gumushane/14168
  Supervised classification-based framework for rock mass discontinuity identification using point cloud data

  Günen, Mehmet Akif; Öztürk, Kaşif Furkan; Aliyazıcıoğlu, Şener

  Mapping and evaluating rock mass discontinuities using point clouds is a critical task in mining, civil, and geological engineering. Rock discontinuities can significantly impact the integrity, strength, and stability of rock masses. The orientation of these discontinuities is also a key characteristic of the rock mass. Accurate orientation estimation from point clouds enables more precise predictions of rock mass behavior, leading to improved safety, more efficient excavation processes, reduced operational costs, and significant time savings. In this context, a supervised classification-based framework is proposed for calculating orientation parameters from point cloud. Supervised classification plays a crucial role in tasks where a model learns complex patterns from labeled data to accurately predict previously unseen instances. The proposed method consists of eight-steps, including: data collection, pre-processing (data filtering), adaptive neighborhood size selection (omnivariance-based), feature extraction (geometric features), feature selection (Minimum Redundancy Maximum Relevance method), classification (Support Vector Machine), clustering (connected component labeling), and plane fitting to calculate orientation parameters (dip angle and dip direction). The framework was applied to two real-world datasets and one synthetic dataset, which was tested in two different subsampled forms (random and uniform subsampling). The results statistically demonstrated that the technique was effective in detecting and characterizing rock mass discontinuities with high Accuracy, Recall, Precision, and F-Score values ranging from 94.64% to 99.57%. The deviations of the method in the measurements of the dip angle and the dip direction, compared to the manual measurements, range from 1% to 4%, indicating strong agreement with the manual measurements and the existing studies.

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• MakaleAçık Erişim2024/r/gumushane/11772
  Damage assessment of Siverek Castle during the Kahramanmaraş Earthquakes (Mw 7.7 and Mw 7.6) on 06 February 2023: Remediation and strengthening proposals

  Nasery, Mohammad Manzoor; Çelik, Muhammet; Şadoğlu, Erol

  On February 6, 2023, two significant earthquakes with magnitudes (Mw) of 7.7 and 7.6 struck Turkey, occurring nine hours apart. In addition to the tragic loss of over 50,000 lives in the earthquakes centered in Kahramanmaraş, hundreds of thousands of engineering structures, such as residences, schools, hospitals, historical landmarks, highways, and more, were severely damaged. This study assesses the damages and risk scenario following the Kahramanmaraş earthquakes concerning Siverek Castle. In addition, remediation and strengthening proposals, required to eliminate the damage and the possible risk, have been developed. The initial stage involved observational damage assessments on the castle and surrounding slopes as part of field studies, identifying five different types of damage and potential risks. Subsequently, a precise 3D digital model of the damaged castle and its slopes was generated using the digital photogrammetry method. Additionally, geological and geophysical studies were conducted in the field to determine the characteristics of the mound structure, historical castle walls, soil and rock on the slope. Non-destructive, geophysical methods consisting Vertical Electrical Sounding (VES), Seismic Refraction Method (SRM) and Multichannel Analysis of Surface Waves (MASW) measurements were specifically employed in the area with historical remnants. To verify the obtained data, five boreholes were drilled in the lower parts of the slope, and experimental studies were conducted to determine the soil and rock material properties of the slope. In the numerical studies, a total of 54 2D stability analyses were performed under static, long-term static, and dynamic conditions. Additionally, 1000 different probabilistic rockfall analyses were conducted, both in 2D and 3D, to calculate the run-out distance, bounce height, velocity, and kinetic energies of the blocks that fell or were about to fall during the earthquake. In the final stage of the study, remediation and strengthening recommendations were prepared for the strengthening of the fortification walls and slopes where failures occurred, and stability analyses were conducted. Consequently, a design proposal recommending five distinct approaches to remediate and strengthen the castle and slopes impacted by the Kahramanmaraş earthquakes was endorsed by the relevant authorities, and construction has commenced. When the remediation and strengthening works are completed, the security of the cultural heritage will be ensured, and it is planned to be opened to visitors.

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• MakaleYalnız künye2019/r/gumushane/12659
  Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data

  Comert, Resul; Avdan, Ugur; Gorum, Tolga; Nefeslioglu, Hakan A.

  The Black Sea Region of Turkey is one of the most landslide prone areas due to its high slope topography, heavy rainfall, and highly weathered hillslope material conditions. Preparation of landslide inventory maps is the first step in producing landslide susceptibility maps. Ground-based methods for mapping landslide occurrences are time-consuming and expensive. Additionally, landslide mapping based on satellite imageries and aerial photographs has some limitations, including climatic conditions, cost, and limited repetitive measurement capacity. Visual interpretation-based landslide mapping, which is based on satellite imageries and aerial photographs, is a time-consuming procedure that requires an experience-based expert opinion. Therefore, the data acquisition based on unmanned aerial vehicle (UAV) and landslide event inventory maps using an object-based classification approach can be superior to other methods in terms of speed and cost. In this study, we developed a semiautomatic model using object-based image analyses for rapid mapping of shallow landslides from the data obtained from UAVs after major landslide events in the Black Sea Region of Turkey. For this purpose, two test sites—Kurucasile (Bartin) and Cayeli (Rize)—were selected. Landslide mapping models were developed in the investigation sites, and the performance of the models was evaluated. The landslides' data obtained with the developed models were compared to the landslides' data produced by the experts. The comparison process revealed that landslides mapped by using UAV data have an accuracy rate higher than 86% according to the number of landslides and 83% according to the landslide area.

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• MakaleYayıncıda açık erişim2016/r/gumushane/15222
  Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming

  Alemdag, S.; Gurocak, Z.; Cevik, A.; Cabalar, A.F.; Gokceoglu, C.

  This paper investigates a series of experimental results and numerical simulations employed to estimate the deformation modulus of a stratified rock mass. The deformation modulus of rock mass has a significant importance for some applications in engineering geology and geotechnical projects including foundation, slope, and tunnel designs. Deformation modulus of a rock mass can be determined using large scale in-situ tests. This large scale sophisticated in-situ testing equipments are sometimes difficult to install, plus time consuming to be employed in the field. Therefore, this study aims to estimate indirectly the deformation modulus values via empirical methods such as the neural network, neuro fuzzy and genetic programming approaches. A series of analyses have been developed for correlating various relationships between the deformation modulus of rock mass, rock mass rating, rock quality designation, uniaxial compressive strength, and elasticity modulus of intact rock parameters. The performance capacities of proposed models are assessed and found as quite satisfactory. At the completion of a comparative study on the accuracy of models, in the results, it is seen that overall genetic programming models yielded more precise results than neural network and neuro fuzzy models.

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• MakaleAçık Erişim2012/r/gumushane/13177
  New considerations for empirical estimation of tensile strength of rocks

  Gurocak, Zulfu; Solanki, Pranshoo; Alemdag, Selcuk; Zaman, Musharraf M.

  In this paper, a combined laboratory and modeling study was conducted to develop a database for predicting tensile strength of rocks. Six hundred eighty six rock samples from 24 different sites throughout eastern Turkey were collected and tested for the development of this database and evaluation of models. A total of 512 samples were used for developing the models and the remaining 174 samples were used as control dataset. The material parameters selected in the development of the models include tensile strength (σ t), point load index (Is (50)), Schmidt rebound number (N) and unit weight (γ). A total of four models, two regression models, namely, simple linear regression and multiple regression, and two feed forward-type artificial neural network (ANN) models, namely, radial basis function network (RBFN) and multi-layer perceptron network (MLPN) are developed. A commercial software, Statistica 8.0, is used to develop these models. The strengths and weaknesses of the developed models were assessed by comparing the predicted σ t values with the experimental values with respect to the R 2 values. Overall, the MLPN model was found to be the best model for the present development and evaluation datasets. As a result of these analyses, an equation was suggested based on ANN model to estimate the tensile strength of rocks.

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• MakaleYalnız künye2008/r/gumushane/16258
  The use of cation packing index for characterizing the weathering degree of granitic rocks

  Ceryan, S.; Zorlu, K.; Gokceoglu, C.; Temel, A.

  The weathering state and weatherability of rocks are highly important for engineering geology projects and the use of rocks as building stones. The weathering state of rocks can be described by various chemical and petrographical weathering indices. To determine weathering state of a rock, considerable amount of chemical analyses is required. Weatherability of rocks depends on not only their mineral composition but also the number of cation replaceable with hydrogen in a mineral. In this study, the use of the k-value (cation packing index is defined as the number of cation in a mole) for characterizing the degree of weathering of granitic rocks is investigated. For the purpose of the study, the Kurtun granitic rocks are selected as the material of the study. The study contains three main stages and these are field investigations, laboratory studies and regression analyses. During the field studies, in-situ weathering descriptions and sampling are carried out. The samples are subjected to chemical and petrographic analyses and physical and mechanical tests. At the final stage of the study, a series of regression analyses between the cation packing index and other properties of the samples are performed. The results show that the k-value can be used as a weathering indicator. When compared with the other chemical weathering indices, the k-value can be used as a weathering index without chemical analyses, because it can be determined by mineralogical analyses. (C) 2008 Elsevier B.V. All rights reserved.

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