This paper demonstrates an experimental and numerical study aimed at comparing the influence of openings of different configurations on the flexural behavior of reinforced concrete gable roof beams. The experimental program consisted of testing six simply supported gable beams subjected to mid-point concentrated load. The variable which has been investigated in this work was opening's configuration (quadrilateral or circular) with the same upper and lower chords depth. The results indicate improvement in the beams’ flexural behavior when circular openings were used compared with that of quadrilateral openings, represented by an increase in ultimate load capacity and a decrease in deflection at the service limit. Also, there was an

In the present study, an attempt has been made to experimentally investigate the flexural performance of ten simply supported reinforced concrete gable roof beams, including solid control specimen (i.e., without openings) and nine beams with web openings of different dimensions and configurations. The nine beams with openings have identical reinforcement details. All beams were monotonically loaded to failure under mid-span loading. The main variables were the number of the created openings, the total area of the created openings, and the inclination angle of the posts between openings. Of interest is the load-carrying capacity, cracking resistance and propagation, deformability, failure mode, and strain development that represent the behav

Many researchers have tackled the shear behavior of Reinforced Concrete (RC) beams by using different kinds of strengthening in the shear regions and steel fibers. In the current paper, the effect of multiple parameters, such as using one percentage of Steel Fibers (SF) with and without stirrups, without stirrups and steel fibers, on the shear behavior of RC beams, has been studied and compared by using Finite Element analysis (FE). Three-dimensional (3D) models of (RC) beams are developed and analyzed using ABAQUS commercial software. The models were validated by comparing their results with the experimental test. The total number of beams that were modeled for validation purposes was four. Extensive pa

In this study, simply supported reinforced concrete (RC) beams were analyzed using the Extended Finite Element Method (XFEM). This is a powerful method that is used for the treatment of discontinuities resulting from the fracture process and crack propagation in concrete. The mesoscale is used in modeling concrete as a two-phasic material of coarse aggregate and cement mortar. Air voids in the cement paste will also be modeled. The coarse aggregate used in the casting of these beams is a rounded aggregate consisting of different maximum sizes. The maximum size is 25 mm in the first model, and in the second model, the maximum size is 20 mm. The compressive strength used in these beams is equal to 26 MPa.

The subje

This study aimed at evaluating the torsional capacity of reinforced concrete (RC) beams externally wrapped with fiber reinforced polymer (FRP) materials. An analytical model was described and used as a new computational procedure based on the softened truss model (STM) to predict the torsional behavior of RC beams strengthened with FRP. The proposed analytical model was validated with the existing experimental data for rectangular sections strengthened with FRP materials and considering torque-twist relationship and crack pattern at failure. The confined concrete behavior, in the case of FRP wrapping, was considered in the constitutive laws of concrete in the model. Then, an efficient algorithm was developed in MATLAB environment t

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.