Browsing by Author "Kumanlioglu, A"

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    An anticipated shear design method for reinforced concrete beams strengthened with anchoraged carbon fiber-reinforced polymer by using neural network
    Tanarslan, HM; Kumanlioglu, A; Sakar, G
    Using externally bonded carbon fiber-reinforced polymer (FRP) for strengthening has been turned into a popular decision owing to its mechanical leads. Consequently, design guidelines and researchers have established several analytical equations to predict the contribution of FRP to ultimate shear capacity. The developed analytical equations projected the influence of FRP reinforcements within certain limits. However, not mentioned parameters such as the shear span-to-depth ratio and anchorage application influence the ultimate behavior of strengthened specimens. Accordingly, distant predictions between test results and code predictions are observed for the specimens in whom anchorage is applied. As an alternative method, artificial neural network (NN) can be used to predict the contribution of anchoraged carbon FRP to shear strength of deficient reinforced concrete beams. Accordingly, two NN models with back-propagation are developed in this study. Unlike the existing design codes, the model considers the effect of anchorage and the shear span-to-depth ratio at the ultimate state. Artificial NN model is trained, validated and tested using the literature of 79 reinforced concrete beams. Then, NN results are compared with those theoretical' predictions calculated directly from International Federation for Structural Concrete, the American guideline (ACI 440.2R) and the Australian guideline. Within all theoretical predictions of design guidelines, fib14 provided the best predictions according to experimental results. Consequently, 25% of fib14 predictions are within +/- 10% of the experimental results, and also, 65% of the fib14 predictions are within +/- 25% of the measured values. Besides, executed comparisons indicated that the NN model is more exact than the guideline equations with respect to the experimental results and can be applied effectively within the range of parameters covered in this study. Copyright (c) 2014 John Wiley & Sons, Ltd.
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    Estimation groundwater total recharge and discharge using GIS-integrated water level fluctuation method: a case study from the Alasehir alluvial aquifer Western Anatolia, Turkey
    Simsek, C; Demirkesen, AC; Baba, A; Kumanlioglu, A; Durukan, S; Aksoy, N; Demirkiran, Z; Hasözbek, A; Murathan, A; Tayfur, G
    The estimation of groundwater recharge is an essential process for hydrogeological study. Realistic determination approach is crucial for assessing groundwater potential in an aquifer system and estimating of groundwater levels and/or changes in dry periods. Based on these matters, we employ a GIS-integrated groundwater level fluctuation method to determine the groundwater recharge for a hydrological period in the Alasehir alluvial aquifer (W. Anatolia). The method basically takes into account both increasing and decreasing of the groundwater levels due to the recharge and discharge mechanisms in the aquifer. In this study, 16 pumping and monitoring wells were drilled with a total depth of 1300 m, and water level data loggers were installed into the monitoring wells to determine the groundwater level changes. The spatial distribution of the monthly groundwater level change map was multiplied by the aquifer storage distribution map and then the accurate water volume is calculated by using the 3-D spatial analysis. According to our evaluation in the aquifer, positive volume change of the groundwater is 187 hm(3) in a year, which is considered as a recharge value of groundwater. It is concluded that the GIS-integrated water table fluctuation method gave rise to estimate the total recharge amount of the groundwater in the Alasehir aquifer. The total groundwater recharge indicates that total inflow in the aquifer from precipitation, leakage from surface water and irrigation waters. It can be stated that the recharge estimation of groundwater in a surficial aquifer, like the Alasehir aquifer, is fairly easy using the GIS-integrated water table fluctuation method.
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    An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks
    Tanarslan, HM; Secer, M; Kumanlioglu, A
    An artificial neural network model is developed to predict the shear capacity of reinforced concrete (RC) beams, retrofitted in shear by means of externally bonded wrapped and U-jacketed fiber-reinforced polymer (FRP) in this study. However, unlike the existing design codes the model considers the effect of strengthening configurations dissimilarity. In addition model also considers the effect of shear span-to-depth ratio (a/d) ratio at the ultimate state. It is also aimed to develop an efficient and practical artificial neural network (ANN) model. Therefore, mechanical properties of strengthening material and mechanical and dimensional properties of beams are selected as inputs. ANN model is trained, validated and tested using the literature of 84 RC beams. Then neural network results are compared with those 'theoretical' predictions calculated directly from International Federation for Structural Concrete (fib14), the American guideline (ACI 440.2R), the Australian guideline (CIDAR), the Italian National Research Council (CNR-DT 200) and Canadian guideline (CHBDC) for verification. Performed analysis showed that the neural network model is more accurate than the guideline equations with respect to the experimental results and can be applied satisfactorily within the range of parameters covered in this study. (C) 2011 Elsevier Ltd. All rights reserved.