Untitled
Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/2802
Browse
Recent Submissions
Article Katkı Malzemelerinin Fazla Kullanılmasının Betona Etkisi: Vaka Analizi(2020) İlgün Abdülkerim; Türker İlker Yılmaz; Müsevitoğlu Abdullah; Çöğürcü, Mustafa TolgaDünya’da beton katkı malzemeleri 1870’li yıllarda araştırılmaya başlanmıştır. 1930’lı yılların başından itibaren su azaltıcı katkılar ile ilgili araştırmalar yapılmaya başlamıştır. Bu tür katkılar 1940-1960 yılları arasında beton karışımlarında kullanılmış olsa da nasıl bir etki yarattığı tam olarak bilinmediği için yaygınlaşmamıştır. Yapılan araştırmalar; beton karışımına eklenen katkı malzemelerinin belirlenmiş oranlarda ilave edilmemesinin, betonun dayanım, dayanıklılık gibi mekanik özelliklerinin yanı sıra, priz süresi, çökme değeri gibi işlenebilirlik özelliklerini de olumsuz etkilediğini göstermektedir. Bu kapsamda inşaat halindeki bir binada inceleme yapılmış ve yapıdaki hasarlar tespit edilmiştir. Tespit edilen hasarlara bağlı olarak yapıda onarım çalışmaları önerilerek takibi yapılmıştır.Article Citation - Scopus: 4Life Cycle Assessment of Microbial Electrolysis Cells for Hydrogen Generation Using Traci Methodology(Sakarya University, 2022) Tutar Öksüz, SeçilBio electrochemical systems (BESs) use electrochemically active microorganisms to convert the chemical energy of organic matter into electrical energy, hydrogen, or other useful products through redox reactions. Microbial electrolysis cell (MEC) is one of the most common BESs which are able to convert organic substrate into energy (such as hydrogen and methane) through the catalytic action of electrochemically active bacteria in the presence of electric current and absence of oxygen. In the past decades, BESs have gained growing attention because of their potential, but there is still a limited amount of research is done for the environmental effects of BESs. This study initially provides an update review for MECs including general historical advancement, design properties, and operation mechanisms. Later, a life cycle assessment (LCA) study was conducted using a midpoint approach, which is TRACI methodology with EIO-LCA model to identify the potential impacts to the environment whether adverse or beneficial using the MECs to produce hydrogen with domestic wastewater as a substrate. The results show that the cumulative negative impacts were substantially larger than the positive impacts by contrast with the expectations, and the cumulative output data show that human health non-cancer impact provides the highest environmental effects than others mainly because of the inorganic chemicals, pumping and wastewater recycling equipment step. In addition, global warming potential and smog creation potential are also elevated mainly due to electricity usage, inorganic chemical and glassware reactor production. Later we are externally normalized each impact category to compare the results at the normalization level, and we again found that human health (cancer or non-cancer) potential provides the most negative impact on the environment in the MEC system originates on human health indicators. © 2022, Sakarya University. All rights reserved.Article Application of an Artificial Neural Network for Predicting Compressive and Flexural Strength of Basalt Fiber Added Lightweight(Tulpar Academic Publishing, 2021) Calis, G.; Yıldızel, S.A.; Keskin, U. S.Concrete is known as one of the fundamental materials in construction with its high amount of use. Lightweight concrete (LWC) can be a good alternative in reducing the environmental effect of concrete by decreasing the self-weight and dimensions of the structure. In order to reduce self-weight of concrete artificial aggregates, some of which are produced from waste materials, are utilized, and it also contributes to de-velop a sustainable material Artificial neural networks have been the focus of many scholars for long time with the purpose of analyzing and predicting the lightweight concrete compressive and flexural strengths. The artificial neural network is more powerful method in terms of providing explanation and prediction in engineering studies. It is proved that the error rate of ANN is smaller than regression method. Furthermore, ANN has superior performance over nonlinear regression model. In this paper, an ANN based system is proposed in order to provide a better understand-ing of basalt fiber reinforced lightweight concrete. In the regression analysis pre-dicted vs. experimental flexural strength, R-sqr is determined to be 86%. The most important strength contributing factors were analyzed within the scope of this study. © 2021, Tulpar Academic Publishing. All rights reserved.
