• Advances in concrete construction
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• v.11 no.3
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• pp.193-206
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• 2021

Extensive research has been conducted on the basic mechanical property and structural applications of engineered cementitious composites (ECC). Despite the high tensile ductility and high toughness of ECC, transverse steel reinforcement is still necessary to confine ECC for high performance. However, limited research has examined performance of ECC confined with practical amount of transverse reinforcement. This paper presents the results of axial compression tests on 14 square ECC columns and 4 conventional concrete columns (used as control specimens) with transverse reinforcement. The test variables were spacing, configuration (square ties or square and diamond shape ties), and yield strength of stirrups. The test showed that ECC columns confined with steel stirrup had good compressive ductility, and the stirrup spacing had the greatest effect on the compressive performance. The self-confinement effect of ECC results in a more uniform but slower expansion of the whole column compared with CC ones. The test results are then compared against the predictions from a number of existing models for conventional confined concrete. It is indicated that these models fail to predict the axial strains at peak axial stress and the trend of the stress-strain curve of steel stirrups-confined ECC with sufficient accuracy. Several new equations are then proposed for the compressive properties of steel-confined ECC based on test results and potential approaches for future studies are proposed.

• Ocean Systems Engineering
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• v.11 no.1
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• pp.59-81
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• 2021

The nonlinear formulation using the principle of virtual work-energy for free vibration of a large-sag extensible catenary riser in two dimensions is presented in this paper. A support at one end is hinged and the other is a free-sliding roller in the horizontal direction. The catenary riser has a large-sag configuration in the static equilibrium state and is assumed to displace with large amplitude to the motion state. The total virtual work of the catenary riser system involves the virtual strain energy due to bending, the virtual strain energy due to axial deformation, the virtual work done by the effective weight, and the inertia forces. The nonlinear equations of motion for two-dimensional free vibration in the Cartesian coordinate system is developed based on the difference between the Euler's equations in the static state and the displaced state. The linear and nonlinear stiffness matrices of the catenary riser are obtained and the eigenvalue problem is solved using the Galerkin finite element procedure. The natural frequencies and mode shapes are obtained. The results are validated with regard to the reference research addressing the accuracy and efficiency of the proposed nonlinear formulation. The numerical results for free vibration and the effect of the nonlinear behavior for catenary riser are presented.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.2
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• pp.55-65
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• 2021

In this study, the design criteria is presented for Finocyl grain, which is easy to generate neutral thrust when designing solid rocket motors. For this purpose, an automated program using drafting method was developed for burn-back analysis and its accuracy was validated. Using this developed program, burn-back analysis was performed with various configuration parameters of Finocyl grain, and the tendency and sensitivity analysis on burning characteristics were performed. Based on this analysis, the design criteria were presented to generate the neutral burning surface area trace for a Finocyl grain.

• Journal of IKEEE
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• v.25 no.4
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• pp.650-663
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• 2021

Subsurface topology estimation is an important factor in the geophysical survey. Electrical impedance tomography is one of the popular methods used for subsurface imaging. The EIT inverse problem is highly nonlinear and ill-posed; therefore, reconstructed conductivity distribution suffers from low spatial resolution. The subsurface region can be approximated as piece-wise separate regions with constant conductivity in each region; therefore, the conductivity estimation problem is transformed to estimate the shape and location of the layer boundary interface. Each layer interface boundary is treated as an open boundary that is described using front points. The subsurface domain contains multi-layers with very complex configurations, and, in such situations, conventional methods such as the modified Newton Raphson method fail to provide the desired solution. Therefore, in this work, we have implemented a 7-layer artificial neural network (ANN) as an inverse problem algorithm to estimate the front points that describe the multi-layer interface boundaries. An ANN model consisting of input, output, and five fully connected hidden layers are trained for interlayer boundary reconstruction using training data that consists of pairs of voltage measurements of the subsurface domain with three-layer configuration and the corresponding front points of interface boundaries. The results from the proposed ANN model are compared with the gravitational search algorithm (GSA) for interlayer boundary estimation, and the results show that ANN is successful in estimating the layer boundaries with good accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2801-2815
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• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• Steel and Composite Structures
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• v.48 no.4
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• pp.475-483
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• 2023

In this paper, a new energy-efficient semi-active hybrid bulk damper is developed that is cost-effective for use in structural applications. In this work, the possibility of active and semi-active component configurations combined with suitable control algorithms, especially vibration control methods, is explored. The equations of motion for a container bridge equipped with an MDOF Mass Tuned Damper (M-TMD) system are established, and the combination of excitation, adhesion, and control effects are performed by a proprietary package and commercial custom submodel software. Systematic methods for the synthesis of structural components and active systems have been used in many applications because of the main interest in designing efficient devices and high-performance structural systems. A rational strategy can be established by properly controlling the master injection frequency parameter. Simulation results show that the multiscale model approach is achieved and meets accuracy with high computational efficiency. The M-TMD system can significantly improve the overall response of constrained structures by modestly reducing the critical stress amplitude of the frame. This design can be believed to build affordable, safe, environmentally friendly, resilient, sustainable infrastructure and transportation.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3088-3101
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• 2023

A pressurized vessel-pipe-safety valve (PVPSV) combination is a commonly used configuration in nuclear power plants, and a good numerical model is essential for the system design, sizing and performance optimization. However, owing to the large-scale and cross-scale features, it is still a challenge to build a system level numerical model with both high accuracy and efficiency. To overcome this, a novel system level modeling method which can synthesize the advantages of various models is proposed in this paper. For system modeling, the analytical approach, the method of characteristics (MOC) and the surrogate model approach are respectively adopted to predict the dynamics of the pressure vessel, the connecting pipe and the safety valve, and different models are connected through data interfaces. With this system model, dynamic simulations were carried out and both the stable and the unstable system responses were obtained. For the model verification purpose, the simulation results were compared with those obtained from experiments and full CFD simulations. A good agreement and a better efficiency were obtained, verifying the ability of the model and the feasibility of the modeling method proposed in this paper.

• Smart Structures and Systems
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• v.29 no.5
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• pp.677-691
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• 2022

Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (L_H) and cooling load (L_C) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the L_H and L_C is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the L_H and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the L_C) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.

• Steel and Composite Structures
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• v.47 no.5
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• pp.633-644
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• 2023

Taking a look at the previously published papers, it is revealed that there is a porosity index limitation (around 0.35) for the mechanical behavior analysis of the functionally graded porous (FGP) structures. Over mentioned magnitude of the porosity index, the elastic modulus falls below zero for some parts of the structure thickness. Therefore, the current paper is presented to analyze the vibrational behavior of the FGP Timoshenko beams (FGPTBs) using a novel refined formulation regardless of the porosity index magnitude. The silica aerogel foundation and various hydrothermal loadings are assumed as the source of external forces. To obtain the FGPTB's properties, the power law is hired, and employing Hamilton's principle in conjunction with Navier's solution method, the governing equations are extracted and solved. In the end, the impact of the various variables as different beam materials, elastic foundation parameters, and porosity index is captured and displayed. It is revealed that changing hygrothermal loading from non-linear toward uniform configuration results in non-dimensional frequency and stiffness pushing up. Also, Al - Al2O3 as the material composition of the beam and the porosity presence with the O pattern, provide more rigidity in comparison with using other materials and other types of porosity dispersion. The presented computational model in this paper hopes to help add more accuracy to the structures' analysis in high-tech industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1657-1673
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• 2023

With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.