• Structural Engineering and Mechanics
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• v.85 no.2
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• pp.197-206
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• 2023

The deterioration caused by chloride penetration and carbonation plays a significant role in a concrete structure in a marine environment. The chloride corrosion in some marine concrete structures is invisible but can be dangerous in a sudden collapse. Therefore, as a novelty, this research investigates the ability of a non-destructive damage detection method named the Power Spectral Density (PSD) to diagnose damages caused only by chloride ions in concrete structures. Furthermore, the accuracy of this method in estimating the amount of annual damage caused by chloride in various parts and positions exposed to seawater was investigated. For this purpose, the RC Arosa bridge in Spain, which connects the island to the mainland via seawater, was numerically modeled and analyzed. As the first step, each element's bridge position was calculated, along with the chloride corrosion percentage in the reinforcements. The next step predicted the existence, location, and timing of damage to the entire concrete part of the bridge based on the amount of rebar corrosion each year. The PSD method was used to monitor the annual loss of reinforcement cross-section area, changes in dynamic characteristics such as stiffness and mass, and each year of the bridge structure's life using sensitivity equations and the linear least squares algorithm. This study showed that using different approaches to the PSD method based on rebar chloride corrosion and assuming 10% errors in software analysis can help predict the location and almost exact amount of damage zones over time.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.217-223
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• 2022

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k^th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.

• Earthquakes and Structures
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• v.22 no.1
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• pp.25-38
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• 2022

The RC private constructions represent a large part of the housing stock in the north part of Algeria. For various reasons, they are mostly built without any seismic considerations and their seismic vulnerability remains unknown for different levels of seismic intensity possible in the region. To support future seismic risk mitigation efforts in northern Algeria, this document assesses the seismic vulnerability of typical private RC constructions built after the Boumerdes earthquake (May 21, 2003) without considering existing seismic regulation, through the development of analytical fragility curves. The fragility curves are developed for four representative RC frames in terms of slight, moderate, extensive, and complete damage states suggested in HAZUS-MH 2.1, using nonlinear time history analyses. The numerical simulation of the nonlinear seismic response of the structures is performed using the SeismoStruct software. An original intensity measure (IM) is proposed and used in this study. It is the zone acceleration coefficient "A", through which the seismic hazard level is represented in the Algerian Seismic Regulations. The efficiency, practicality, and proficiency of the choice of IM are demonstrated. Incremental dynamic analyses are conducted under fifteen ground motion accelerograms compatible with the elastic target spectrum of the Algerian Seismic Regulations. In order to cover all the seismic zones of northern Algeria, the accelerograms are scaled from 0.1 to 2.5 in increments of 0.1. The results mainly indicate that private constructions built after the Boumerdes earthquake in the moderate and high seismic zones with four (04) or more storeys are highly vulnerable.

• Geomechanics and Engineering
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• v.28 no.5
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• pp.521-529
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• 2022

This research work deals with the development of air pluviation method for preparing uniform sand specimens for conducting large scale laboratory testing. Simulating real field conditions and to get reliable results, air pluviation method is highly desirable. This paper presents a special technique called air pluviation or sand raining technique for achieving uniform relative density. The apparatus is accompanied by a hopper, shutters with different orifice sizes and numbers and set of sieves. Before using this apparatus, calibration curves are drawn for relative density against different height of fall (H) and shutter sizes. From these calibration curves, corresponding to the desired relative density of 60%, the shutter size of 13mm and height of fall of 457.2 mm, are selected and maintained throughout the pluviation process. The density obtained from the mobile pluviator is then verified using the Dynamic Cone Penetrometer (DCP) test where the soil is poured in the box using defined shutter size and fall height. The results obtained from the DCP test are averaged as 60±0.5 which was desirable. The mobile pluviator used in this research is also capable of obtaining relative densities up to 90%. The instrument is validated using experimental and numerical approach. In numerical study, Plaxis 3D software is used in which the soil mass is defined by 10-Node tetrahedral elements and 6-Node plate is used to simulate plate behavior in the validation phase. The results obtained from numerical approach were compared with that of experimental one which showed very close correlation.

• Wind and Structures
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• v.34 no.2
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• pp.185-197
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• 2022

This study aimed to analyze the wind-induced mechanical energy (WME) of a proposed super high-rise and long-span transmission tower-line system (SHLTTS), which, in 2021, is the tallest tower-line system with the longest span. Anew index - the WME, accounting for the wind-induced vibration behavior of the whole system rather than the local part, was first proposed. The occurrence of the maximum WME for a transmission tower, with or without conductors, under synoptic winds, was analyzed, and the corresponding formulae were derived based on stochastic vibration theory. Some calculation data, such as the drag coefficient, dynamic parameters, windshielding areas, mass, calculation point coordinates, mode shape and influence function, derived from wind tunnel testing on reducedscale models and finite element software were used in calculating the maximum WME of the transmission tower under three cases. Then, the influence of conductors, wind speed, gradient wind height and wind yaw angle on WME components and the energy transfer relationship between substructures (transmission tower and conductor) were analyzed. The study showed that the presence of conductors increases the WME of transmission towers and changes the proportion of the mean component (MC), background component (BC) and resonant component (RC) for WME; The RC of WME is more susceptible to the wind speed change. Affected by the gradient wind height, the WME components decrease. With the RC decreasing the fastest and the MC decreasing the slowest; The WME reaches the its maximum value at the wind yaw angle of 30°. Due to the influence of three factors, namely: the long span of the conductors, the gradient wind height and the complex geometrical profile, it is important that the tower-line coupling effect, the potential for fatigue damage and the most unfavorable wind yaw angle should be given particular attention in the wind-resistant design of SHLTTSs

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1060-1070
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• 2022

With the development of information technology, the size of the system has become larger and diversified, and the existing role-based access control has faced limitations. Blockchain technology is being used in various fields by presenting new solutions to existing security vulnerabilities. This paper suggests efficient role-based access control in a blockchain where the required gas and processing time vary depending on the access frequency and capacity of the storage. The proposed method redefines the role of reusable units, introduces a hierarchical structure that can efficiently reflect dynamic states to enhance efficiency and scalability, and includes user-centered authentication functions to enable cryptocurrency linkage. The proposed model was theoretically verified using Markov chain, implemented in Ethereum private network, and compared experiments on representative functions were conducted to verify the time and gas efficiency required for user addition and transaction registration. Based on this in the future, structural expansion and experiments are required in consideration of exception situations.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.309-316
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• 2022

This study proposes a finite element analysis method that can analyze the vibration of a beam by considering the inertia effect of moving masses in a vertical direction. The proposed method is effective when a precise interaction analysis is not required. The inertial effects of the moving masses are included in the equation of motion, and the interaction forces between the masses and the beam are considered only as external loads. Time domain analyses were performed using Abaqus, a general-purpose finite element analysis software, and an implementation method using multi-point constraints wais presented to link the displacements of the beam element nodes and moving rigid masses. The proposed method was verified by comparing its solution with that obtained using an existing analytical method, and the analysis results for continuous beam vibrations under dynamic gait loadings were used to examine the mass effect of pedestrians.

• Steel and Composite Structures
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• v.46 no.4
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• pp.451-469
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• 2023

The use of precast hollow-core slabs (PCHCS) in civil construction has been increasing due to the speed of execution and reduction in the weight of flooring systems. However, in the literature there are no studies that present a finite element model (FEM) to predict the load-slip relationship behavior of pushout tests, considering headed stud shear connector and PCHCS placed at the upper flange of the downstand steel profile. Thus, the present paper aims to develop a FEM, which is based on tests to fill this gap. For this task, geometrical non-linear analyses are carried out in the ABAQUS software. The FEM is calibrated by sensitivity analyses, considering different types of analysis, the friction coefficient at the steel-concrete interface, as well as the constitutive model of the headed stud shear connector. Subsequently, a parametric study is performed to assess the influence of the number of connector lines, type of filling and height of the PCHCS. The results are compared with analytical models that predict the headed stud resistance. In total, 158 finite element models are processed. It was concluded that the dynamic implicit analysis (quasi-static) showed better convergence of the equilibrium trajectory when compared to the static analysis, such as arc-length method. The friction coefficient value of 0.5 was indicated to predict the load-slip relationship behavior of all models investigated. The headed stud shear connector rupture was verified for the constitutive model capable of representing the fracture in the stress-strain relationship. Regarding the number of connector lines, there was an average increase of 108% in the resistance of the structure for models with two lines of connectors compared to the use of only one. The type of filling of the hollow core slab that presented the best results was the partial filling. Finally, the greater the height of the PCHCS, the greater the resistance of the headed stud.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.