This paper presents influence of treatment and mixing methods on recycled aggregate concretes (RAC) designed regarding various techniques. Absolute Volume Method (AVM) according to TS 802, Equivalent Mortar Volume Method (EMV), silica fume (SF) as a mineral addition were considered in the design of concretes. In total, four groups of concretes were produced in the laboratory: (1) natural aggregate concrete (NAC) designed with AVM as control concrete, (2) RAC designed with AVM as control RAC, (3) RAC with SF as a mineral addition designed with AVM as treated RAC and (4) RAC designed with EMV as treated RAC. The tests were performed at 28th days and the statistical analysis were made on the test results. According to the results, EMV and SF increased the compressive strength of concretes and this resulted an increase in the strength class of concrete. A significant statistical difference between the concretes were determined. According to multiple comparison analysis, it was found that especially there was a significant relationship among NAC, RAC and RAC-EMV. In addition, it was recommended that EMV and AVM with 5% SF could be used in the design of RAC rather than AVM only to achieve the target strength class C30/37.

In this study, a method has been developed to reduce the negative effects of superabsorbent polymers on concrete mechanical properties. The method involves concrete mixing and curing, with the concrete mixture being designed using a decision tree algorithm. Instead of the standard water curing approach, air curing conditions were used during the curing process. In addition, heat treatment was applied to reduce any possible negative effects of the polymers on the concrete's mechanical properties and to enhance their performance. The details of all these stages are presented in this method. Various experimental studies were conducted to demonstrate the validity of this method, which proved to be effective in reducing the negative effects of superabsorbent polymers on concrete mechanical properties.

Clustering analysis primarily highlights the in homogeneity of data and can be utilized in structural engineering to demonstrate strength irregularity. It is well-known that strength irregularity between neighboring floors within a structure or among structural elements can lead to non-holistic behavior. Therefore, the clustering of compressive strength holds significant importance. Despite the relevance, only a few studies have addressed the clustering of compressive strength in recycled aggregate concrete (RAC) and proposed potential solutions for clustering issues. This paper aims to investigate the clustering of compressive strength in RAC and explore viable solutions. In this experimental study, four concrete groups were produced under standard conditions. The first group included natural aggregate concretes (NAC) designed with the Absolute Volume Method (AVM) as control concretes. The second group, comprised of RAC, was designed with the equivalent mortar volume method (EVM) as the control RAC. The third group consisted of RAC treated with silica fume (SF) and designed using AVM, while the fourth group included RAC designed with EVM. Statistical analyses were conducted on the 28-day compressive strength test results. The results indicated that the strength class of compressive strength clusters varied among the four groups. The clustering of test results was influenced by the type of concrete components used and the design method employed. Additionally, using silica fume and adopting the Absolute Volume Method reduced strength fluctuation and regulated the strength class of clusters by bringing them closer together. In contrast, the Equivalent Mortar Volume Method resulted in a greater dispersion of strength classes. The clustering effect of recycled aggregate (RA) was more pronounced than that of natural aggregate (NA). Given these findings, it is essential to implement measures when utilizing RAC in sustainable structures to address potential clustering issues.

In this study, a hybrid model combining discrete wavelet transforms (WTs) and artificial neural networks (ANNs) is used to estimate the monthly streamflow. The WT-ANN hybrid model was developed using the Daubechies main wavelet to predict the streamflow for three gauging stations on the Çoruh river basin one month in advance, with different combinations of air temperature, precipitation, and streamflow variables, and their wavelet transformations. Four different hybrid WT-ANN models were generated and compared with four different conventional ANN models. The dataset was chronologically divided into training, validation, and testing data. The results indicated that the WT-ANN hybrid models performed better than the traditional ANN models for all three stations. Furthermore, the chronologically divided dataset was used to examine the effects of changes in hydrological data over time on model performance. In conclusion, model performances in the training period deteriorated during the validation and testing periods due to structural changes in the hydrological data.

Araştırmanın amacını, alışveriş merkezlerini tercih eden tüketicilerin ürün algısı (product percetion) ve tüketim alışkanlıklarına (consumer habits) yönelik davranış desenleri incelemek, bu desenler arasında ilişkiyi ortaya koyarak modellemektir. İstanbul (Avrupa) içi alışveriş merkezlerini tercih eden tüketiciler çalışmanın birimlerini oluşturmuşlardır. Tüketicilerin ürün algısı ile tüketim alışkanlıklarını ölçmek, harita üzerinde incelemek amacıyla uyum analizi kullanılmıştır. Daha sonra robust regresyon tekniği ile bu ilişki modellenmiştir. Uyum analizi sonuları ana iki kümenin oluştuğu görülmüş ve bu ana iki küme robust regresyon analizi ile desteklenmiştir. Araştırmaya katılan tüketicilerde, ürünü için garanti şartını arayanlar, garanti şartıyla beraber tavsiye, reklam etkisi gibi kendisine yakın olan değişkenlerle beraber hareket ettiği görüldü. Diğer yandan, bilinçli alışverişi tercih eden tüketicilerin ise ürünlerin bütçe dostu, kaliteli ve ucuz olması gibi etkenlerden etkilenerek hareket ettiği saptanmıştır. Robust regresyon yönteminde ürün algısı ile tüketim alışkanlıkları arasındaki ilişki anlamlı olduğu belirlenmiştir.