The provision of openings in serviceable reinforced concrete beams may result in a substantial decline in the beam's capacity and integrity, indicating the necessity of opening strengthening. The present study investigates the experimental response of reinforced concrete T-beams with multiple web-strengthened openings disposed in shear span to static and impact loads. Fourteen RC T-beams were tested in two groups, each of seven beams. The first group was tested under static loading up to failure, while the second group was tested under repeated impact loading until the width of shear cracks reached 0.3 mm. The residual static strengths of the beams subjected to impact loading were then determined. The test variables considered were
... Show MoreThe provision of openings in serviceable reinforced concrete beams may result in a substantial decline in the beam's capacity and integrity, indicating the necessity of opening strengthening. The present study investigates the experimental response of reinforced concrete T-beams with multiple web-strengthened openings disposed in shear span to static and impact loads. Fourteen RC T-beams were tested in two groups, each of seven beams. The first group was tested under static loading up to failure, while the second group was tested under repeated impact loading until the width of shear cracks reached 0.3 mm. The residual static strengths of the beams subjected to impact loading were then determined. The test variables considered were
... Show MoreThe drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the