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The current paper is aimed to investigate the effects of waviness, random orientation, and agglomeration factor of nanoreinforcements on wave propagation in fluid-conveying multi-walled carbon nanotubes (MWCNTs)-reinforced nanocomposite cylindrical shell based on first-order shear deformable theory (FSDT). The effective mechanical properties of the nanocomposite cylindrical shell are estimated employing a combination of a novel form of Halpin-Tsai homogenization model and rule of mixture. Utilized fluid flow obeys Newtonian fluid law and it is axially symmetric and laminar flow and it is considered to be fully developed. The effect of flow velocity is explored by implementing Navier-Stokes equation. The kinetic relations of nanocomposite shell are calculated via FSDT. Moreover, the governing equations are derived using the Hamiltonian approach. Afterward, a method which solves problems analytically is applied to solve the obtained governing equations. Effects of a wide range of variants such as volume fraction of MWCNTs, radius to thickness ratio, flow velocity, waviness factor, random orientation factor, and agglomeration factor on the phase velocity and wave frequency of a fluid conveying MWCNTs-reinforced nanocomposite cylindrical shell were comparatively illustrated and the results were discussed in detail.
Mohammad Alkhedher; Pouyan Talebizadehsardari; Arameh Eyvazian; Afrasyab Khan; Naeim Farouk. Wave Dispersion Analysis of Fluid Conveying Nanocomposite Shell Reinforced by MWCNTs Considering the Effect of Waviness and Agglomeration Efficiency. Polymers 2021, 13, 153 .
AMA StyleMohammad Alkhedher, Pouyan Talebizadehsardari, Arameh Eyvazian, Afrasyab Khan, Naeim Farouk. Wave Dispersion Analysis of Fluid Conveying Nanocomposite Shell Reinforced by MWCNTs Considering the Effect of Waviness and Agglomeration Efficiency. Polymers. 2021; 13 (1):153.
Chicago/Turabian StyleMohammad Alkhedher; Pouyan Talebizadehsardari; Arameh Eyvazian; Afrasyab Khan; Naeim Farouk. 2021. "Wave Dispersion Analysis of Fluid Conveying Nanocomposite Shell Reinforced by MWCNTs Considering the Effect of Waviness and Agglomeration Efficiency." Polymers 13, no. 1: 153.
Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.
Mohammed Ghazal; Tasnim Basmaji; Maha Yaghi; Mohammad Alkhedher; Mohamed Mahmoud; Ayman El-Baz. Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things. Sensors 2020, 20, 6348 .
AMA StyleMohammed Ghazal, Tasnim Basmaji, Maha Yaghi, Mohammad Alkhedher, Mohamed Mahmoud, Ayman El-Baz. Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things. Sensors. 2020; 20 (21):6348.
Chicago/Turabian StyleMohammed Ghazal; Tasnim Basmaji; Maha Yaghi; Mohammad Alkhedher; Mohamed Mahmoud; Ayman El-Baz. 2020. "Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things." Sensors 20, no. 21: 6348.
The increasing demand for construction materials and the need to improve the performance of asphalt mixes have led to the depletion of available resources of aggregates. Lots of aggregate alternatives had been studied to partially or fully replace aggregate in asphalt concrete mixes. Indeed, date seeds are found easily in Kuwait. Their properties; such as light weight, very low crushing strength, and low impact value; represent a potential domain to enhance asphalt mix performance. The main objective of this paper is investigating the possibility of using activated date seeds additives in Hot Mix Asphalt (HMA) as a partial replacement of Fine Aggregates (sand). A total of 3 asphalt concrete mixes with different percentages by weight were prepared. Physical and mechanical properties of date seeds and Fine Aggregates (sand) were determined and compared in the asphalt concrete mix design of ratios 7, 10, and 15%. Compressive Strength and Marshall Stability tests were performed to investigate the effect of activated date seeds additives on HMA. Eventually, the findings indicated that the ratio of 10% gave the highest Marshall Stability. On the other hand, the ratio of 7% found to give the highest retained strength index. a deeper statistical analysis is performed to evaluate the robustness and significance of studied factors on response variables represented in Marshal Stability and Flow. The first part of the analysis included recognizing data outliers in data sets and eliminating them to compare basic statistical values with raw data. The minor differences in these values implies minor variability in the measurement and indicates minimal experimental error. T-test analysis was performed to study the influence of date seed percentages on different performed measurements. ANOVA tests were also represented to demonstrate the statistical significance factors on considered response variables. The results supported the significant influence of the included measurements on Marshal stability and flow values.
Sharaf AlKheder; Mohammad Alkhedher; Khaled A. Alshraiedeh. The effect of using activated dates seed on Hot Mix Asphalt performance. Construction and Building Materials 2020, 269, 121239 .
AMA StyleSharaf AlKheder, Mohammad Alkhedher, Khaled A. Alshraiedeh. The effect of using activated dates seed on Hot Mix Asphalt performance. Construction and Building Materials. 2020; 269 ():121239.
Chicago/Turabian StyleSharaf AlKheder; Mohammad Alkhedher; Khaled A. Alshraiedeh. 2020. "The effect of using activated dates seed on Hot Mix Asphalt performance." Construction and Building Materials 269, no. : 121239.
Undoubtedly, one of the greatest issues nowadays is congestion. To face such problem, forecasting of traffic is required. Bayesian combined neural network (BCNN) is applied to four different locations in Kuwait (Cairo Street, Riyadh Street, Maghreb Road and Istiqlal Road) to predict the short-term traffic volume at the middle section due to traffic flow from adjacent intersections. All data were collected for a period of 1 week over 15-min observation intervals using loop detectors. In addition to time-series responses and regression plots, mean square error (MSE) has been used to validate the network performance after data normalization. In comparison with MSE and R values, both values were slightly less precise during weekdays compared to weekends. After standardizing, the average MSE during weekdays was 0.003468 and regression (R) was 0.98113 for the four streets. For weekends model, the average MSE was 0.003563 and regression (R) was 0.97374 for the four streets. Istiqlal Street weekday model was the best model that fits the information among all the four models; as it has the smallest MSE value equivalent to 0.0010087 and the highest R value of 0.9959. BCNN model has achieved outstanding prediction performance with great potential to be generalized for various locations at different times of the day. These results can allow transportation planners to forecast traffic congestions and take prior measures to avoid them. Further modeling can assist in studying factors causing intersection congestions.
Sharaf AlKheder; Wasan Alkhamees; Reyouf Almutairi; Mohammad Alkhedher. Bayesian combined neural network for traffic volume short-term forecasting at adjacent intersections. Neural Computing and Applications 2020, 33, 1785 -1836.
AMA StyleSharaf AlKheder, Wasan Alkhamees, Reyouf Almutairi, Mohammad Alkhedher. Bayesian combined neural network for traffic volume short-term forecasting at adjacent intersections. Neural Computing and Applications. 2020; 33 (6):1785-1836.
Chicago/Turabian StyleSharaf AlKheder; Wasan Alkhamees; Reyouf Almutairi; Mohammad Alkhedher. 2020. "Bayesian combined neural network for traffic volume short-term forecasting at adjacent intersections." Neural Computing and Applications 33, no. 6: 1785-1836.