The optimum design is characterized by structural concrete components that can sustain loads well beyond the yielding stage. This is often accomplished by a fulfilled ductility index, which is greatly influenced by the arrangement of the shear reinforcement. The current study investigates the impact of the shear reinforcement arrangement on the structural response of the deep beams using a variety of parameters, including the type of shear reinforcement, the number of lacing bars, and the lacing arrangement pattern. It was found that lacing reinforcement, as opposed to vertical stirrups, enhanced the overall structural response of deep beams, as evidenced by test results showing increases in ultimate loads, yielding, and cracking of 30.6, 20.8, and 100%, respectively. There was also a 53.6% increase in absorbed energy at the ultimate load. The shear reinforcement arrangement had a greater impact and a significant effect on the structural response than the number of lacing bars. For lacing reinforcement with a phase difference equivalent to the half-lacing cycle (i.e., phase lag lacing), the percentage of improvement under different loading stages was 6.7-27.1% and 20.8-113.3%, respectively. The structural responses are significantly impacted by the lacing arrangement; members with two and three lacing bars, respectively, exhibited improvements in ultimate load of 30.6% and 47%. Beyond the yielding stage, the phase lag lacing specimens deviated from those without phase lag lacing and normal shear stirrups because of the lacing contribution. Phase lag specimens showed more strain than specimens without phase lag lacing, meaning that the lacing reinforcement contributed more to the beam strength. It was found that the first shear cracking load of all the laced reinforced specimens was higher than that of the conventional shear stirrup specimens. Phase lag lacing produced the greatest improvement, with two bars achieving 92.44% and three bars achieving 217.07%. For the aforementioned number of bars, lacing shear reinforcement without phase lag was less successful, with 36.91% and 46.53%, respectively. Doi: 10.28991/CEJ-2025-011-02-019 Full Text: PDF
MO Khudhair, 2020
This paper presents the results of experimental investigation carried out on concrete model piles to study the behaviour of defective piles. This was achieved by employing non-destructive tests using ultrasonic waves. It was found that the reduction in pile stiffness factor is found to be about (26%) when the defect ratio increased from (5%) to (15%). The modulus of elasticity reduction factor as well as the dynamic modulus of elasticity reduction factor increase with the defect ratio
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv
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