Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025Mescitli Formasyonu’na (Gümüşhane, Türkiye) ait kumtaşlarının endüstriyel hammadde olarak kullanım potansiyelinin incelenmesi
Aliyazıcıoğlu, Şener; Dağ, Serhat; Zeybek, Halil İbrahim; Çavuşoğlu, İbrahim; Gücer, Mehmet Ali; Sipahi, Ferkan; Dokuz, Abdurrahman
Doğal zenginliklerin ortaya çıkarılması ve yöresel kayaçların ekonomiye kazandırılarak endüstriyel kullanım alanlarının belirlenmesi günümüzde büyük önem taşımaktadır. Bu çalışmada, Gümüşhane’deki Mescitli Formasyonu kumtaşlarının endüstriyel hammadde olarak kullanım potansiyeli araştırılmıştır. Kumtaşları başlıca monokristalin ve polikristalin kuvars, feldispat, volkanik ve tortul kayaç parçaları, opak mineral, kil matriks ve az oranda bağlayıcı karbonat/silis minerali içermektedir. Grovak (psammit) olarak sınıflandırılan kumtaşlarının kimyasal ayrışma indeksi (CIA) parametresine göre alterasyondan çok fazla etkilenmediği belirlenmiştir. Jeoteknik özellikler açısından, ortalama kuru yoğunluk 2.48 gr/cm3, doygun yoğunluk 2.54 gr/cm3, gözeneklilik %6.63 boşluk oranı %7.11, ultrasonik P-dalga hızı 4793 m/sn, tek eksenli basınç dayanımı 103.09 MPa ve dolaylı çekme dayanımı 5,86 MPa olarak belirlenmiştir. Donma-çözünme deneyi ile don kaybı %1.19 olarak belirlenmiş, 600-800ºC sıcaklıklara kadar ısıtılan kumtaşı numunelerinde ortalama 9.8 gr ve 17.9 gr kütle kaybı gözlenmiştir. Aynı sıcaklık koşullarında kumtaşlarının dayanımının sıra ile %7.64 ve %15.17 oranında azaldığı görülmüştür. Hücre Sitotoksisite analizlerinde toksik etkiye rastlanmamıştır. Sonuçlar, kumtaşlarının günlenmeye maruz kalacak alanlarda kullanım için uygun olmadığı, bina içyapı ve dekoratif amaçlı kaplamalarda kullanılabileceğini göstermiştir. Sıcaklık dayanımı ve hücre sitotoksisite analizleri ise kumtaşlarının ızgara taşı olarak kullanılabileceğini ortaya koymuştur.
- MakaleYayıncıda açık erişim2025Optimizing Visibility of Historical Structures Using Modified Weighted Differential Evolution: Insights from the Kromni Valley, Gümüşhane, Türkiye
Günen, Mehmet Akif; Öztürk, Kaşif Furkan; Aliyazıcıoğlu, Şener
It is very important for historical structures to see each other in order to reveal the historical and cultural identity of a region. Historical structures in the Kromni Valley of Gümüşhane, located near the Sümela Monastery, served as places of worship, communication, trade, and social activity centers during their period of active use. This study analyses the spatial relationships of 38 historic buildings, including churches, chapels and castles, whose 3D models are created by in-situ measurements and point clouds obtained by unmanned aerial vehicles, using a 3D viewshed analysis using geographic information systems and remote sensing data. The research introduces a modified weighted differential evolution-based viewshed analysis (mWDE-WS) to enhance the visibility of these structures. In order to assess the applicability of the proposed method, a statistical comparison was conducted between four different Differential Evolution (DE) algorithms (standard DE, LSHADE, CobiDE, JADE and WDE) and the mWDE. The Wilcoxon signed-rank test indicates that mWDE is a more effective solution than alternative methods for addressing the relevant real-world issues. The study also integrates drainage network analysis to assess flood risks and the relationship between cultural structures and water flow. Findings show that historical structures in the region were built not randomly but within a rational approach and 64% of the study area is visible from structures and 2% of the area is visible from ten or more structures. mWDE-WS analysis revealed that the visible area could increase by 20% to 84.37% if the historic structures were placed in optimal locations. In addition, the historical structures were built away from 3rd order streams to minimize flood risk and humidity, demonstrating the community's awareness of the local topography and hydrology.
- MakaleYalnız künye2025Discontinuities identification from rock outcrop using auto-encoder and point clouds
Günen, Mehmet Akif; Aliyazıcıoğlu, Şener
Rock slopes and geotechnical structures often exhibit discontinuity planes, which significantly influence their mechanical behavior and stability. The precise identification of these discontinuities is critical for ensuring the safety and cost-effectiveness of construction projects. However, previous methods are often limited by subjectivity, low automation, and challenges in inaccessible or hazardous environments. This study introduces a novel seven-step approach utilizing auto-encoders for estimating the orientation parameters of individual discontinuity sets from point cloud data. The steps include: data collection, filtering point clouds to eliminate outliers, determining neighborhood size, feature extraction, training a stacked auto-encoder for feature learning, estimating discontinuity sets using density-based clustering, and calculating individual discontinuities through the least squares method for orientation parameter estimation. In addition, the methodology incorporates a planarity-based approach for estimating neighborhood size. The statistical performance has been validated through evaluations in two real case studies and one synthetic case study. The classification achieves an accuracy exceeding 95%, highlighting the high efficacy of the approach. Results for orientation parameter estimation show consistency with existing methods and in situ measurements, with average orientation values for discontinuities within 5 degrees for individual sets. This framework represents a considerable advancement in rock discontinuity analysis, offering a robust and automated solution that is particularly effective in hazardous and hard-to-reach areas.
- MakaleAçık Erişim2025Supervised 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.
- MakaleAçık Erişim2023Investigation of geometric object and indoor mapping capacity of Apple iPhone 12 Pro LiDAR
Günen, Mehmet Akif; Erkan, İlker; Aliyazıcıoğlu, Şener; Kumaş, Cavit
LiDAR (light detection and ranging) sensors use laser beams to calculate distances in the surroundings. These sensors can be applied to a wide range of tasks, and they are frequently helpful in tasks like building 3D maps, navigating airplanes, robots, conducting mining operations, and automated driving. High-resolution distance measurements are taken by LiDAR sensors, but they also gather environmental data. This information aids in locating, identifying, and quantifying things and their surroundings. The iPhone 12 Pro, which Apple released in 2020, was evaluated for accuracy with various geometric shapes and its capacity to recognize indoor environments. Free of charge 3D Scanner and the Clirio Scan application were employed in this situation. However, it was found that the root mean square error and mean error in indoor mapping were ±1.41 cm and-0.56 cm in 3D Scanner and ±3.94 cm and-0.60 cm in the Clirio Scan application, respectively, despite the findings obtained showing low accuracy in scanning small geometric objects due to the scanning difficulty. Clirio does not reject the null hypothesis in the t-test that was conducted. The accuracy of the LiDAR sensor in indoor mapping has been shown to be more promising than that of small items. In order to evaluate the reliability and reusability of the indoor mapping application according to reference measurements, intraclass correlation test was performed and the results were determined to be reliable.