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Öğe An experimental study on strengthening of vulnerable RC frames with RC wing walls(TECHNO-PRESS, 2012) Kaltakci, M. Yasar; Yavuz, GunnurOne of the most popular and commonly used strengthening techniques to protect against earthquakes is to infill the holes in reinforced concrete (RC) frames with fully reinforced concrete infills. In some cases, windows and door openings are left inside infill walls for architectural or functional reasons during the strengthening of reinforced concrete-framed buildings. However, the seismic performance of multistory, multibay, reinforced concrete frames that are strengthened by reinforced concrete wing walls is not well known. The main purpose of this study is to investigate the experimental behavior of vulnerable multistory, multibay, reinforced concrete frames that were strengthened by introducing wing walls under a lateral load. For this purpose, three 2-story, 2-bay, 1/3-scale test specimens were constructed and tested under reversed cyclic lateral loading. The total shear wall (including the column and wing walls) length and the location of the bent beam bars were the main parameters of the experimental study. According to the test results, the addition of wing walls to reinforced concrete frames provided significantly higher ultimate lateral load strength and higher initial stiffness than the bare frames did. While the total shear wall length was increased, the lateral load carrying capacity and stiffiiess increased significantly.Öğe The seismic improvement and control of weak concrete frames with partial concrete shear walls(SAGE PUBLICATIONS LTD, 2014) Kaltakci, M. Yasar; Yavuz, GunnurControlling the lateral movement of buildings under earthquake effects is very important to prevent total collapse and therefore reinforced concrete (RC) shear walls are used to obtain the lateral stability of such buildings. In this study, the usability of partial RC shear walls to strengthen buildings with weak earthquake behavior was investigated. The basic parameters of the study were determined to be the ratio of shear wall height to shear wall length (H-w/L-w) and whether the upper inflection point of the bent bar, a steel bar commonly used in the beams of RC buildings in Turkey, is integrated into or bent outside of the partial shear wall section. Three units of two-story, double-span weak RC frames were produced at a one-third scale. Two of these three frames were then strengthened by partial shear walls integrated on both sides of the central column. Strengthened and non-strengthened specimens were tested under reversed-cyclic lateral loading. The frame systems strengthened via partial RC shear walls showed significant improvement in strength and stiffness. No debonding was observed in the anchorage rods used in the shear wall-foundation connection.Öğe Shear strength estimation of RC deep beams using the ANN and strut-and-tie approaches(TECHNO-PRESS, 2016) Yavuz, GunnurReinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum R-2 approximate to 0.97). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.Öğe Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks(WALTER DE GRUYTER GMBH, 2014) Yavuz, Gunnur; Arslan, Musa Hakan; Baykan, Omer KaanIn this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R-2 approximate to 0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.
