Istanbul Technical University
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An Unsupervised Hybrid Approach for Detection of Damage with Autoencoder and One-Class Support Vector Machine
No full textProgressive deterioration and accumulated damage due to overloading, extreme events, and fatigue necessitate the continuous monitoring of civil infrastructure to ensure serviceability and safety. With advances in sensor technology, data-driven structural health monitoring (SHM) strategies, particularly artificial neural networks (ANNs), have gained prominence for analyzing large datasets and identifying complex patterns. Among these, autoencoders (AEs), a specialized class of ANNs, are well-suited for unsupervised learning tasks, enabling dimensionality reduction and feature extraction. This study employs transmissibility functions (TFs) as training samples for the AE. TFs are directly derived from response measurements without the need to measure input and exhibit local sensitivity to changes in dynamic properties, making them an efficient feature for structural assessment. The reconstruction errors in TFs, quantifying the deviation between the original and AE-reconstructed data, are leveraged as damage-sensitive features for classification using a one-class support vector machine (OC-SVM). The proposed methodology is validated through numerical simulations with noise-contaminated data representing various damage scenarios in a shear-building model, as well as experimental tests on a masonry arch bridge model subjected to progressive damage. Numerical investigations demonstrate improved detection accuracy and robustness of the procedure through the incorporation of nonlinear encoding into the dimensionality reduction process, compared to the classical principal component analysis method.. Experimental results confirm the framework’s effectiveness in detecting and localizing damage using unlabeled field data.https://doi.org/10.3390/app15084098https://doaj.org/article/88d83c0e25eb4c7197e48a51007e9eb
Multi-Sensor Flood Mapping in Urban and Agricultural Landscapes of the Netherlands Using SAR and Optical Data with Random Forest Classifier
No full textFloods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings.https://doi.org/10.3390/rs17152712https://hdl.handle.net/11630/2827
Empowering Communities: Education for Improving Public Awareness and Response Capacity in Earthquake Emergencies
No full texthttps://doi.org/10.1007/978-94-024-2304-4_
Optimization of Rare Earth Elements Recovery from Kızıldag (Karaman-Türkiye) Shale Ore Using Response Surface Methodology
No full textIn this study, the usability of bauxite-related shale ore, rich in Rare Earth Elements (REEs), to produce REEs, which have high added value and critical importance, was investigated. In this context, Kızıldag (Karaman-Türkiye) shale ore, was prepared for tests through size reduction and sampling processes. HCl leaching was performed at different acid concentrations, leaching times, leaching temperatures, and liquid/solid ratios to determine the optimum leaching conditions for the milled ore. The modeling and optimization parameters related to leaching recovery were investigated using the response surface method (RSM). The results were evaluated using analysis of variance, three-dimensional graphs were drawn, and the accuracy of the models was discussed. Accordingly, optimum values were found, and the REE leaching recovery corresponding to these optimum values was 49.13%. It was concluded that the optimization results were compatible with the experimental results, and the average error rate (6.35%) was low.https://doi.org/10.37190/ppmp/20543
Anti-cancer effect of Thymus vulgaris based synthesized gold nanoparticles in giant macroporous silica: impact on MCF-7 breast cancer cells
No full textAbstract Conventional cancer therapies, while effective, are frequently associated with significant adverse effects owing to their lack of selectivity, impacting both malignant and healthy cells. To address these challenges, gold nanoparticles (AuNPs) have emerged as a promising platform for targeted drug delivery. Giant macroporous silica (GMS) is a recently developed material, with its drug delivery potential explored in only a single study to date. In this study, gold nanoparticles (AuNPs) synthesized using Thymus vulgaris (garden thyme) extract were incorporated into GMS, forming GMS-AuNPs. Additionally, AuNPs coated with chitosan (AuNPs@CS) were similarly loaded into GMS, resulting in GMS-AuNPs@CS composites. The synthesized materials were characterized through light microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The anti-cancer effects of GMS-AuNPs and GMS-AuNPs@CS were assessed against breast cancer cells using real-time cell analysis. Notably, no cytotoxic effects were observed on MCF-12 A normal breast epithelial cells at any of the tested concentrations. GMS-AuNPs demonstrated a dose- and time-dependent cytotoxic effect on breast cancer cells. These findings suggest that GMS-AuNPs hold promise as a potential therapeutic strategy for breast cancer treatment.https://doi.org/10.1007/s41779-025-01164-5https://curis.ku.dk/ws/files/513404166/s41779-025-01164-5_1_.pdfhttps://dx.doi.org/10.1007/s41779-025-01164-5https://hdl.handle.net/20.500.12451/13374https://avesis.erciyes.edu.tr/publication/details/8dcbbc7e-2487-42df-aa1f-13b04dbc86b7/oa
Full-scale linear rock cutting tests towards linking cutting performances of button and disc cutters
No full texthttps://doi.org/10.1201/9781003559047-34
GEN-TPRM: An OSINT-Driven Risk Assessment Model for Third-Party Organizations
No full texthttps://doi.org/10.1109/acit65614.2025.1118573
Political ecology imaginaries and possible futures in Turkey
No full textAlthough disasters and political ecology problems continue to grow on a global scale, the states and their stakeholders still make decisions that can escalate risks and vulnerabilities. Within their populist discourse, they use political ecology imaginaries, which shape collectively held future visions and regulate desires and beliefs about values, norms and ways of life. Since imaginaries become an extension of control, it is critical to explore counter and alternative political ecology imaginaries for possible futures especially in disaster-prone countries. This study aims to explore alternative political ecology imaginaries and possible, future makings of contemporary artworks. Through the lens of feminist accounts in political ecology and science and technology studies (STS), this exploratory research is based on the qualitative analysis of five purposefully sampled contemporary artworks from Turkey, a disaster-prone country where political ecology risks and vulnerabilities are high and the imaginaries of the state and its stakeholders are extremely authoritative and popularized. Research methodology of the study mainly consists of a literature review, discourse analysis, field notes and participatory observation. The findings of the study contribute to disclose the transgressions around knowledge claims in political ecology studies. Counter and alternative political imaginaries of contemporary artistic research generate possible futures for more-than-human-worlds.https://doi.org/10.1386/tear_00144_
Düzlem dışı darbe yüklemesi altında yapıştırma bağlantılarının optimizasyonu
No full textThesis (M.Sc.) -- Istanbul Technical University, Graduate School, 2025This thesis investigates the optimization of adhesively bonded metal and composite plates subjected to out of plane impact loading, with the aim of enhancing structural performance and reducing weight. Adhesively bonded joints are increasingly preferred in aerospace applications due to their advantageous mechanical performance and ease of manufacturing compared to conventional fasteners. However, they remain sensitive to impact loads, which can reduce the structural integrity of the assembly. The study is divided into three main parts. In the first part, the thickness of an aluminum alloy plate, AL7050, is optimized under impact loading. Finite element simulations are created using LS-DYNA to generate training data. Kriging surrogate models are built to estimate two objective functions: von Mises stress and total mass. The optimization process is carried out using the multiobjective genetic algorithm, gamultiob, in MATLAB, resulting in a Pareto front of optimal thickness values. This approach enables an effective trade-off between minimizing stress and minimizing mass while maintaining a specified safety factor. The second part of the study focuses on optimizing a woven composite plate, AS4 8552, subjected to impact loading. The primary goal is to maximize absorbed energy while minimizing mass. The stacking sequence and total number of plies are treated as design variables, selected from discrete values, the orientation angle is selected as (0o and 45o) and number of plies vary 8 from 16 for symmetric laminate. LS-DYNA simulations are used to impact model inputs and outputs, and Python scripts automate the generation of stacking sequence combinations. The Hashin failure index is used as a constraint to eliminate fail configurations. Metamodels are created using the Gaussian Process Regression (GPR) method to predict absorbed energy and mass for different laminate designs. These models help estimate the results without running a full simulation each time. Then, a genetic algorithm is used to find the best designs by trying many different combinations. The goal is to find laminate configurations that can absorb a high amount of impact energy while keeping the weight as low as possible. In addition, only the designs that meet the required Hashin failure index limit is accepted. In the third section, the optimization of the adhesively bonded joint is performed. Input and output data are obtained from LS-DYNA simulations. The design parameters include the aluminum plate thickness, the stacking sequence, and the number of plies. The objective functions are the total mass and the maximum von Mises stress on aluminum plate. The constraints are defined by the Hashin failure index for the composite plate. This thesis presents an efficient design approach for adhesively bonded joints by combining finite element analysis with LS-DYNA, metamodels and multiobjective optimization with genetic algorithms. The proposed method helps reduce simulation time while maintaining safety and performance, supporting the use of composite and metal bonding structures in aerospace and automotive applications.M.Sc
Fractal Characterization of Crack Patterns in Intact Rocks Under Triaxial Compression
No full texthttps://doi.org/10.1007/s10706-025-03314-