Experimental work has been performed on three capillary tubes of different lengths and diameters using R-12 and R-134a. The test also studies the effect of discharge and speed of evaporator fan. The results clearly showed that refrigerant type and discharge significantly influence the temperature drop across the capillary tube. While the speed of evaporator fan has small effect. Experimental results showed that the temperature gradient for the two refrigerants are the same, but after approximatly one meter the temperature gradient of R-134a  is steeper than R-12.

 

In this study, three strengthening techniques, near-surface mounted NSM-CRFP, NSM-CFRP with externally bonding EB-CFRP, and hybrid CFRP with circularization were studied to increase the seismic performance of existing RC slender columns under lateral loads. Experimentally, 1:3 scale RC models were studied and subjected to both lateral static load and seismic excitation. In the dynamic test, a model was subjected to El Centro 1940 NS earthquake excitation by using a shaking table. According to the test results, the strengthening techniques showed a significant increase in load carrying capacity, of about 86.6%, and 46.6%, for circularization and NSM-CFRP respectively, of the reference unstrengthened columns. On the other hand, column

Columns subjected to pure axial load rarely exist in practice. Reinforced concrete columns are usually subjected to combination of axial and lateral actions and  deformations, caused by  spatially‐complex loading patterns as during earthquakes causes lateral deflection that in turn affects the horizontal stiffness. In this study, a numerical model was developed in threedimensional nonlinear finite element and then validated against experimental results reported in the literatures,
to investigate the behavior of conventionally RC columns subjected to axial load and  . lateral reversal cyclic loading. To achieve this goal, numerical analysis was conducted by using finite element program ABAQUS/Explicit. The variables co

In this paper, fire resistance and residual capacity tests were carried out on encased pultruded glass fiber-reinforced polymer (GFRP) I-beams with high-strength concrete beams. The specimens were loaded concurrently under 25% of the ultimate load and fire exposure (an increase in temperature of 700 °C) for 70 min. Subsequently, the fire-damaged specimens were allowed to cool and then were loaded statically until failure to explore the residual behaviors. The effects of using shear connectors and web stiffeners on the residual behavior were investigated. Finite Element (FE) analysis was developed to simulate the encased pultruded GFRP I-beams under the effect of fire loading. The thermal analyses were performed using the general-pu

To investigate the flexural behavior of self-consolidating hybrid fiber-reinforced concrete beams containing voids experimentally, six RC beams were tested, one solid without fiber and the others containing hooked-steel and macro-polypropylene fibers with a volume fraction of 1 and 0.5%, respectively. One of the five fibrous beams was solid; two contain a series of recycled plastic balls of diameters 110 and 120 mm, and another two contain a single longitudinal circular void created by PVC pipes of diameters 90 and 110 mm. The flexural behavior of the beams was assessed depending on the load-deflection curve, load-strain curve, ductility, toughness, stiffness, and crack patterns. The experimental outcomes showed that all the tested

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class