Browsing by Author "Dilay Uncu"

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    Preparation and Performance Testing of SBS Modified\rBitumens Reinforced with Halloysite and Sepiolite\rNanoclays
    (2022) Dilay Uncu; Ali Topal; M.Ozgur Seydibeyoglu
    In this study, halloysite and sepiolite nanoclays were used to reinforce SBS modified\rbitumens. The nanoclays used are different from the materials in the literature and have\rproperties such as easy to find, economical and available from local sources. The mixing\rparameters were determined before production process. The polymer additive SBS was\radded into base bitumen at 3% and 5%, while the nanoclay additives were added into polymer\rmodified bitumen at 2% and 4% ratios. The morphological structures were investigated under\rfluorescence microscope. Physical and rheological properties of the samples were compared.\rThe phase separation cases were investigated by storage stability test. Furthermore, rutting\rperformance of samples was measured with zero shear viscosity (ZSV) and multi stress creep\rrecovery (MSCR) test methods.
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    Shear Capacity Prediction of Extremely-Loaded Box Culvert on Elastic Soil Using Artificial Neural Network
    (2024) yesim tuskan; Dilay Uncu
    A box culvert, buried at shallow depths beneath roadways, may experience deflections caused by the dynamic impact of traffic loading and the vertical pressure exerted by the soil fill. A computational model commonly employed used to various engineering issues, including those in geotechnical applications, is the beam-on-elastic-foundation model. In this context, the Moment Distribution Method (MDM) must be applied to account for the elastic foundation. To achieve this, the internal forces acting on the ends of both exterior and interior walls are transferred to the beam-like bottom slab of the culvert, which rests on an elastic soil bed. Subsequently, the secondary internal forces are determined by refining the structural parameters, taking into account the characteristics of the elastic soil bed. This study presents the development and application of an Artificial Neural Network (ANN) model to predict the shear capacity of box culverts on elastic soil under traffic loading conditions. The proposed model is trained and validated using a comprehensive database of beam on elastic foundation solutions. The input parameters include the geometrical and mechanical properties of the culvert and the soil, as well as the loading conditions. The results of the ANN model show R2 values of 0.9633 and 0.9581 for the training and testing sets, respectively, indicating the model's excellent accuracy. These findings suggest that the ANN model can reliably predict the shear capacity of culverts.