This paper presents a new and improved design procedure in shear for reinforced concrete normal members without shear reinforcement (stirrups) using the techniques of dimensional analysis and multiple regression analysis. A total of 334 data sets have been obtained from existing sources of reinforced concrete shear test results covering a wide range of beam properties and test methods. The proposed equation is applied to existing test data for these reinforced concrete normal beams (shear span to depth ratio (a/d) greater than or equals to 2.0) and the results are compared with those predicated by ACI and BS codes. It can be also noted that the test results are in better agreement with the proposed cracking shear strength equation because of the excellent correlation between experimental results and theoretical values.
Slurry infiltrated fiber concrete (SIFCON) is a relatively new high performance material and can be considered a special type of fiber concrete (FRC) with high fiber content. The matrix consists of a flowing mortar or cement slurry that must penetrate well through the network of fibers placed in the mold. SIFCON has excellent mechanical properties combined with high ductility and toughness values. SIFCON a relatively new material, is composed of mud (cement or cement and sand), water, a plasticizer (water reducer), and fibers. All previous studies have used waste steel fibers, steel fibers and other fibers, but in this study, plastic fibers were made from polyethylene terephthalate (PET) by cutting carbonated beverage bottles. The main objectives of this study are: Determination the effect of the waste plastic fiber volume ratio on the strength and deformation of (SIFCON) samples under the influence of bending loads. Both flexural strength and toughness properties were determined by testing samples (100×100×400) mm at 28 and 56 days of age. The results obtained from these tests were compared with those performed on conventional tests. Aspect Ratio equal to (36.8) and three volume ratios (3%, 5% and 7%) of the total volume of the concrete mixture were used to add fibers with different volume ratios. A conventional concrete mix was created as a reference for comparison. Bending strength and fresh concrete tests were performed. And compared with the reference mixture and according to the analysis of the results. The results showed an improvement in bending strength .It was found through the flexural examination that the flexural strength of the mixture containing fiber percentage (7%) achieved the highest strength compared to the rest of the ratios used, compared with the reference mixture (Ref.) by (32.25, 27.5)% for ages (28, 56), respectively.
Scour around bridge piers is a well-known threat to bridge stability worldwide. It can cause losses in lives and the economy, especially during floods. Therefore, an artificial intelligence approach called artificial neural network (ANN) was used to predict the scour depth around bridge piers. The ANN model was trained with laboratory data, including pier width, flow velocity, particle diameter, sediment critical velocity, flow depth, and scour depth. The data was divided into 70% for training, 15 for validation, and 15% for testing. Besides, the ANN model was trained using various training algrthins and a single hidden layer with 20 neurons in the hidden layer. The results showed that the ANN model with Bayesian regularization backpropagation training algorithm provides a better predicted scour depth with a correlation coefficient (R) equal to 0. 9692 and 0.926 for training and test stages, respectively. Besides, it showed a low mean squared error (MSE), which was 0.0034 for training and 0.0066 for the test. These results were slightly better than the ANN with Levenberg-Marquardt backpropagation with R training equals 0.9552 (MSE training = 0.0047), and R test equals 0.838 (MSE test = 0.007).On the other hand, the ANN model with a scaled conjugate gradient backpropagation training algorithm showed worse predictions (R training = 0.7407 and R test = 0.6409). Besides, the ANN model shows better outcomes than the linear regression model. Finally, the sensitivity analysis has shown that the pier width is the most crucial parameter for estimating scour depth using the ANN model.