https://hdl.handle.net/20.500.12573/205 2026-05-08T06:31:01Z https://hdl.handle.net/20.500.12573/2524 Stress and damage distribution analysis of steel reinforced geopolymer concrete beams: Finite element method and experimental comparison under varying design parameters Ozbayrak, Ahmet; Kucukgoncu, Hurmet; Aslanbay, Huseyin Hilmi; Aslanbay, Yuksel Gul Geopolymer concrete (GPC) is a sustainable and eco-friendly alternative to ordinary Portland cement-based concrete (OPC). However, its application in reinforced concrete structures remains limited due to insufficient research on structural performance. This study examines the effects of tensile reinforcement ratio, sodium silicate/sodium hydroxide ratio, and curing method on GPCreinforced concrete (GPC-RC) beams. Experimental and numerical bending tests were performed on GPC and OPC beams with similar tensile reinforcement and strength properties. Load- displacement and moment-curvature relationships were obtained and compared, while stress and stiffness behaviors were analyzed numerically. The results show that curing methods and reinforcement ratios significantly influence GPC beam behavior. In GPC samples, numerical and experimental displacement and load values differed by approximately 10 % at both yield and ultimate points. For OPC, these differences were 35 % and 14 % at the yield point and 17 % and 25 % at the ultimate point. GPC exhibited distinct stress and damage distribution characteristics compared to OPC. The finite element models were statistically validated, confirming their consistency with experimental results. These findings contribute to the understanding of GPC's structural behavior and provide guidance for its design and optimization in reinforced concrete applications. 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2519 Ground failures and foundation performances in Adıyaman-Gölbaşı following the 6 February 2023 Kahramanmaraş-Türkiye Earthquake Sequence Cetin, Kemal Onder; Moug, Diane; Soylemez, Berkan; Ayhan, Bilal Umut; Zarzour, Moutasem; Suhaily, Ahmed Al; Akil, Bulent; Unutmaz, Berna; Firat, Seyhan; Tekin, Erhan; Cakir, Elife; Frost, David; Macedo, Jorge; Bray, Jonathan; Moss, Robb; Bassal, Patrick; Gurbuz, Ayhan; Isik, Nihat Sinan; Akin, Muge; Sahin, Arda; Duman, Emre The 6 February 2023 Kahramanmara & scedil;-T & uuml;rkiye earthquake sequence (M7.8 and M7.6) presents an exceptional opportunity to investigate both the effects of local soil conditions on damage patterns under strong shaking conditions and the performance of building foundations in areas that experienced ground failure. The significant ground failure and structural damage in Ad & imath;yaman-G & ouml;lba & scedil;& imath; triggered an intensive series of detailed reconnaissance and field surveys. This article aims to present the resulting database of observations on ground failures, building, and foundation performances. The field reconnaissance of ground failures and their effects on building performances involved aerial and walk-down surveys, including high-quality photographs taken across the town. In addition, data on building damage statistics compiled by the Ministry of Environment, Urbanization, and Climate Change were accessed and analyzed. The subsurface characteristics of the town were characterized using available data from pre-earthquake site investigation campaigns employed for town planning purposes. It is concluded that the ground failures in the town primarily resulted from soil liquefaction and cyclic softening. Most of the poor building and foundation performances and ground failures were documented in the northern part of Atat & uuml;rk Boulevard, closer to the lake of G & ouml;lba & scedil;& imath;, where soil site characteristics were unfavorable. This revealed once again the significant effects of local soil site conditions, particularly soil liquefaction, on the intensified ground failures, foundation, and structural damage levels. 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2516 Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation Dangayach, Raghav; Jeong, Nohyeong; Demirel, Elif; Uzal, Nigmet; Fung, Victor; Chen, Yongsheng Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process. 2024-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2440 Lifli betonlar için elastisite modülü tahmini Yağmur, Eren Bu çalışmada, farklı ayrık lif tiplerinin betonun elastisite modülü üzerindeki etkileri araştırılmıştır. Bu amaçla 260 adet silindirik basınç deney numunesi derlenmiştir. Dikkate alınan lif tipleri çelik, PVA, polipropilen, polyolefin, bazalt ve olefindir. Çalışma sonuçları tüm lif tipleri için kaba agrega miktarının ince agrega miktarına oranının 1.5’i aşması durumunda beton basınç dayanımının azaldığını göstermiştir. Çelik lifli karışımların lif narinlik oranının 60’dan küçük ve eşit olduğu durumlarda elastisite modülü artış gösterirken 60’dan büyük değerler için elastisite modülünün azaldığı görülmüştür. Dikkate alınan tüm lif tipleri için geçerli olan bir elastisite modülü denklemi önerilmiştir. Önerilen denklem deney sonuçları ile ve literatürde yer alan diğer formüllerle karşılaştırılmış ve farklı durumlar için denklemlerin geçerlilikleri sorgulanmıştır.; In this study, the effects of different discrete fiber types on the elastic modulus of concrete are investigated. For this purpose, 260 cylindrical pressure test specimens are compiled. The fiber types considered are steel, PVA, polypropylene, polyolefin, basalt and olefin. The results of the study are showed that if the ratio of coarse aggregate to fine aggregate exceeds 1.5 for all fiber types, the compressive strength of concrete decreases. It has been observed that the elastic modulus increases in cases where the fiber aspect ratio of the steel fibers is less than and equal to 60, while the elastic modulus decreases for values greater than 60. An elastic modulus equation, which applies to all fiber types considered, is proposed. The proposed equation is compared with the experimental results and the other formulas in the literature and the validity of the equations for different cases are questioned. 2020-01-01T00:00:00Z