• Smart Structures and Systems
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• v.26 no.1
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• pp.63-75
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• 2020

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.252-256
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• 2011

We analyze the channel capacity with considering the effect of the loading conditions of indoor PLC networks on channel state information. We consider various numbers of load for two kinds of the networks with regular length branches and a deployed network of indoor PLC. For calculating the channel capacity degradation, two noise scenarios and impedances are considered. From the simulation results, we suggest the robust regression lines for modeling the channel capacity degradation. In the cases of 0 $\Omega$ and $Z_0$ loads, natural log and linear function curve show the best goodness of fit, respectively. For the deployed indoor PLC network with 0 $\Omega$ loads, compared with the networks with regular length branches, the goodness of fit decreases by the amount of 0.12 and 0.15 for low noise and high noise scenarios, respectively. Using the regression lines, we can estimate the channel capacity degradation without measurement.

• Computers and Concrete
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• v.22 no.6
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• pp.539-550
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• 2018

Concrete is highly non-linear material which is originating from the transition zone in the form of micro-cracks, governs material response under various loadings. In this paper, the constitutive models published by many researchers have been used to generate novel stiffness parameters and constitutive curves for concrete. Following such linear material formulations, where the energy is conservative during the curvature, and a nonlinear contribution to the concrete has been made and investigated. In which, nonlinear concrete elastic modulus modeling has been developed that is capable-of representing concrete elasticity for grades ranging from 10 to 140 MPa. Thus, covering the grades range of concrete up to the ultra-high strength concrete, and replacing many concrete models that are valid for narrow ranges of concrete strength grades. This has been followed by the introduction of the nonlinear Hooke's law for the concrete material through the replacement of the Young constant modulus with the nonlinear modulus. In addition, the concept of concrete elasticity index (${\varphi}$) has been proposed and this factor has been introduced to account for the degradation of concrete stiffness in compression under increased loading as well as the multi-stages micro-cracking behavior of concrete under uniaxial compression. Finally, a sub-routine artificial neural network model has been developed to capture the concrete behavior that has been introduced to facilitate the prediction of concrete properties under increased loading.

• Steel and Composite Structures
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• v.43 no.6
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• pp.689-706
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• 2022

Thin-walled square concrete-filled double steel tubular (CFDST) columns composed of the inner circular tube filled with concrete can be used to carry the large axial loads or strengthen existing CFST columns in composite constructions. This paper reports an experimental program carried out on short square CFDST columns loaded concentrically. The influences of important column parameters on the post-buckling performance of such columns are investigated. Test results exhibit that the inner circular tube significantly improves the ultimate loads and the ductility of such columns compared to conventional concrete-filled steel tubular (CFST) and double-skin CFST (DCFST) columns with an inner void. A mathematical model developed is used to simulate the ultimate strengths and load-strain curves of such columns loaded axially. Furthermore, the ultimate strengths of such columns are predicted using existing codified design models for conventional CFST columns as well as the formulas proposed by previous researchers and compared against a large database comprising 500 CFDST columns. Lastly, an accurate artificial neural network model is developed for the practical applications of such columns under axial loading.

• Geomechanics and Engineering
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• v.28 no.5
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• pp.477-491
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• 2022

In the present study, the effects of thermal loading on the buckling and resonance frequency of graphene platelets (GPL) reinforced nano-composites are examined. Functionally graded (FG) material properties are considered in thickness direction for the thermal responses of the composite. The equivalent material properties are obtained using Halphin-Tsai nano-mechanical model for composite layers. Moreover, the effects of nano-scale sizes are taken into account, employing functionally modified couple stress (FMCS) parameter. In this regard, for the first time, it is demonstrated that at certain values of GPL weight fraction, thermal buckling occurs. In obtaining results of vibrational behavior, both analytical solution and deep neural network (DNN) methods are used. The DNN method needs low computational costs to predict the resonance behavior. A comprehensive parametric study is conducted to indicate the effects of several geometrical, material, and loading conditions on the vibrational and buckling behavior of cylindrical shell structures made of GPL-nanocomposites. It is shown that the effect of temperature change on the occurrence of buckling is vital while it has a negligible impact on the resonance frequency of the structure. Moreover, the size-dependency of the results is demonstrated, and it cannot be neglected in nano-scales.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.12
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• pp.1053-1058
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• 2016

The eLoran(enhanced Long Range Navigation) transmitting antenna is analyzed for co-location with an AM transmitting antenna in an MF transmitting site. To compensate for the loading effect, the umbrella-type loading is applied for eLoran antenna. The validity of the co-location between the MF antenna and the eLoran antenna is verified through the simulation results of the radiation pattern and the return loss. Also, coupling including antenna matching circuit is analyzed to verify the effect of the transmitting circuit. The coupling between the LF and eLoran antenna is -53.3 dB at 100 kHz and -64.8 dB at 1,053 kHz, respectively.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.113-130
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• 2002

In this paper, we developed DNL(Dynamic Network Loading) model based on Moving cell theory to analyze the dynamic characteristics of traffic flow in congested network. In this paper vehicles entered into link at same interval would construct one cell, and the cells moved according to Cell following rule. In the past researches relating to DNL model a continuous single link is separated into two sections such as running section and queuing section to describe physical queue so that various dynamic states generated in real link are only simplified by running and queuing state. However, the approach has some difficulties in simulating various dynamic flow characteristics. To overcome these problems, we present Moving cell theory which is developed by combining Car following theory and Lagrangian method mainly using for the analysis of air pollutants dispersion. In Moving cell theory platoons are represented by cells and each cell is processed by Cell following theory. This type of simulation model is firstly presented by Cremer et al(1999). However they did not develop merging and diverging model because their model was applied to basic freeway section. Moreover they set the number of vehicles which can be included in one cell in one interval so this formulation cant apply to signalized intersection in urban network. To solve these difficulties we develop new approach using Moving cell theory and simulate traffic flow dynamics continuously by movement and state transition of the cells. The developed model are played on simple network including merging and diverging section and it shows improved abilities to describe flow dynamics comparing past DNL models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.167-176
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• 2010

Managing the growing number of data generated through various processes requires the aid of Database Management System (DBMS) to efficiently handle the huge amount of data. These data can be inserted into database m real time or in batch, that come from multiple sources, including those that are coming from inside and outside of a network. The insertion of large amount of data is commonly done through specific bulk loading or insertion function supplied by each individual DBMS. In this paper, we analyze and evaluate on handling data bulk loading for heterogeneous systems that is organised as multi-tiered architecture and compare the result of DBMS bulk loader against program insertion from a software development perspective. We propose a hybrid solution using staging database that can be easily deployed for enhancing bulk loading performance compared to insertion by application.

• Water for future
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• v.27 no.4
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• pp.161-171
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• 1994

The water distribution system problem consists of finding a minimum cost system design subject to hydraulic and operation constraints. The design of new branching network in a paddy irrigation system is presented here. The program based on the linear programming formulation is aimed at finding the optimal economical combination of two main factors: the capital cost of pipe network and the energy cost. Two loading conditions and booster pumps for design of pipe network are considered to obtain the least cost design.

• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.97-113
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• 2014

In the present work appropriate concrete material models have been proposed to predict drying shrinkage and specific creep of High-performance concrete (HPC) using Artificial Neural Network (ANN). The ANN models are trained, tested and validated using 106 different experimental measured set of data collected from different literatures. The developed models consist of 12 input parameters which include quantities of ingredients namely ordinary Portland cement, fly ash, silica fume, ground granulated blast-furnace slag, water, and other aggregate to cement ratio, volume to surface area ratio, compressive strength at age of loading, relative humidity, age of drying commencement and age of concrete. The Feed-forward backpropagation networks with Levenberg-Marquardt training function are chosen for proposed ANN models and same implemented on MATLAB platform. The results shows that the proposed ANN models are more rational as well as computationally more efficient to predict time-dependent properties of drying shrinkage and specific creep of HPC with high level accuracy.