Non-prismatic reinforced concrete (RC) beams are widely used for various practical purposes, including enhancing architectural aesthetics and increasing the overall thickness in the support area above the column, which gives high assurance to services that this will not result in the distortion of construction features and can reduce heights. The hollow sections (recess) can also be used for the maintenance of large structural sections and the safe passage of utility lines of water, gas, telecommunications, electricity, etc. They are generally used in large and complex civil engineering works like bridges. This study conducted a numerical study using the commercial finite element software ANSYS version 15 for analysing RC beams, hollow longitudinally sectioned and retrofitted with carbon fibre reinforced polymers (CFRPs), which were subjected to concentrated vertical loads. The numerical analysis results on the simulated beam models were in excellent agreements with the previous experimental test results. This convergence was confirmed by a statistical analysis, which considered the correlation coefficients, individual arithmetic means and standard deviations for all the calculated deflections of the simulated beam models. A proposed numerical simulation model with the hypotheses can be considered suitable for modelling the behaviours of simple supported non-prismatic RC beams under vertical concentrated loads. The numerical results showed that altering the cross-section from solid to hollow could reduce the load carrying capacities of the beams by up to 53% and increase the corresponding deflections by up to 40%, respectively. Using steel pipes for making recesses could enhance the loading capacity by up to 56%, increase the ductility, and reduce the corresponding deflections by up to 30%, respectively. Finally, it was found that bonding the CFRP sheets in the lower middle tensile areas of the hollow beams could improve the resistance and reduce the deformations by up to 27%. The failure patterns for all the numerical models were shear failure. The cylinder compressive strength could be used as a mechanical parameter for modelling and assessing the structural behaviours of the beam models, as its increase could improve the load carrying capacities and reduce the deflections by 30–50%.
We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
Little is known about hesitancy to receive the COVID‐19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID‐19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination.
This was a cross‐sectional study based on electronic survey data that was collected in Iraq during December first‐19th, 2020. The electronic surve
Chronic kidney disease is one of the leading public health problems that affect millions of women and men worldwide.
This study aims to examine the effect of deep breathing to reduce discomfort amongst patient undergoing haemodialysis (HD).
This randomised controlled experimental study was conducted consisted of 108 patients (54 in each group) who undergoing HD in hospitalised adults’ patients between November 2024 an
Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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