• Structural Engineering and Mechanics
• /
• v.47 no.3
• /
• pp.401-419
• /
• 2013

In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

• Computers and Concrete
• /
• v.4 no.6
• /
• pp.477-497
• /
• 2007

The ultrasonic pulse velocity method has been widely used to evaluate the quality of concrete and assess the structural integrity of concrete structures. But its use for predicting strength is still limited since there are many variables affecting the relationship between strength and pulse velocity of concrete. This study is focused on establishing a complicated correlation between known input data, such as pulse velocity and mixture proportions of concrete, and a certain output (compressive strength of concrete) using artificial neural networks (ANN). In addition, the results predicted by the developed multilayer perceptrons (MLP) networks are compared with those by conventional regression analysis. The result shows that the correlation between pulse velocity and compressive strength of concrete at various ages can be well established by using ANN and the accuracy of the estimates depends on the quality of the information used to train the network. Moreover, compared with the conventional approach, the proposed method gives a better prediction, both in terms of coefficients of determination and root-mean-square error.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.24 no.1
• /
• pp.25-32
• /
• 2016

This study deals with the light environments and weather conditions affecting to the night illuminance over the Korean peninsula. The experiment was executed to analyze the effects on the illuminance at separate sites(Gyeryong and Pilseung) considering the different light environments. The analysis was applied to illuminance measurement from the lightmeter, which was developed for the IYA(International Year of Astronomy) 2009, in order to observe the illuminance of areal networks. The weather observations, such as the cloud cover and visibility, were used to understand the quantitative influence of the illuminance to the selected sites. The results show that the illuminance measurements are significantly different from data of the operational illuminance prediction model which simply applies extinction effect for the illuminance. It shows that these differences are caused by the light environments and weather conditions for each site. Therefore, it can be confirmed that the night illuminance is the output of interaction with the characteristics of light for luminous sources.

• Bulletin of the Korean Space Science Society
• /
• 2008.10a
• /
• pp.28.4-29
• /
• 2008

The aim of the test is to provide an experimental basis to validate the prediction of the FEM for high frequency jitter analysis due to reaction wheel. The principle is to measure structural transfer functions between the input disturbances at RWA base plate and the accelerations near the end tips of payload, in a configuration close to the operational model. The spacecraft shall have to be suspended, in order to be representative of on-orbit boundary conditions. The results of the test shall be compared to the output of the FEM analysis, and if needed, local upgrades of the FEM and/or margin policy shall be defined in order to guarantee a good test/FEM consistency. Test results were compared with the transfer functions of the FEM, which is globally tuned based on the results of vibration test and consequently have lower damping coefficients values than 1% in the frequency range of 60~200Hz. The damping coefficients estimated from the figures of FRF test results are different from the theoretical FEM, but the magnitude trend of FRF of the test results is somewhat similar with the analytical, it is expected that the overall jitter effect of final estimation is nearly same with the preliminary analysis result in which the damping coefficients were assumed to be 1% for all modes in FEM.

• Computers and Concrete
• /
• v.5 no.6
• /
• pp.573-597
• /
• 2008

Among artificial intelligence-based computational techniques, adaptive neuro-fuzzy inference systems (ANFIS) are particularly suitable for modelling complex systems with known input-output data sets. Such systems can be efficient in modelling non-linear, complex and ambiguous behaviour of cement-based materials undergoing single, dual or multiple damage factors of different forms (chemical, physical and structural). Due to the well-known complexity of sulfate attack on cement-based materials, the current work investigates the use of ANFIS to model the behaviour of a wide range of self-consolidating concrete (SCC) mixture designs under various high-concentration sodium sulfate exposure regimes including full immersion, wetting-drying, partial immersion, freezing-thawing, and cyclic cold-hot conditions with or without sustained flexural loading. Three ANFIS models have been developed to predict the expansion, reduction in elastic dynamic modulus, and starting time of failure of the tested SCC specimens under the various high-concentration sodium sulfate exposure regimes. A fuzzy inference system was also developed to predict the level of aggression of environmental conditions associated with very severe sodium sulfate attack based on temperature, relative humidity and degree of wetting-drying. The results show that predictions of the ANFIS and fuzzy inference systems were rational and accurate, with errors not exceeding 5%. Sensitivity analyses showed that the trends of results given by the models had good agreement with actual experimental results and with thermal, mineralogical and micro-analytical studies.

• Geomechanics and Engineering
• /
• v.22 no.1
• /
• pp.81-95
• /
• 2020

Measurement of the unconfined compressive strength (UCS) of the rock is critical to assess the quality of the rock mass ahead of a tunnel face. In this study, extensive field studies have been conducted along 3,885 m of the new Nagasaki tunnel in Japan. To predict UCS, a hybrid model of artificial neural network (ANN) based on genetic algorithm (GA) optimization was developed. A total of 1350 datasets, including six parameters of the Measurement-While- Drilling data and the UCS were considered as input and output parameters respectively. The multiple linear regression (MLR) and the ANN were employed to develop contrast models. The results reveal that the developed GA-ANN hybrid model can predict UCS with higher performance than the ANN and MLR models. This study is of great significance for accurately and effectively evaluating the quality of rock masses in tunnel engineering.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.29 no.7
• /
• pp.28-37
• /
• 2015

Owing to the variability of large-scaled wind power system, the development of wind farm management technologies and related compensation methods have been receiving attention. To provide an accurate and reliable output power, certain wind farm adopts a specified management system including a wind prediction model and grid expectation solutions for considering grid condition. Those technologies are focused on improving the reliability and stability issues of wind farms, which can affect not only nearby system devices but also a voltage condition of utility grid. Therefore, to adapt the develop management system, an expectation process about voltage condition of Point of Common Coupling should be integrated in operating system for responding system requirements in real-time basis. This paper introduce a grid imposing method for a real-time based wind farm management system. The expected power can be transferred to the power flow section and the required quantity about reactive power can be calculated through the proposed system. For the verification process, the gauss-seidel method is introduced in the Matlab/Simulink for analysing power flow condition. The entire simulation process was designed to interwork with PSCAD for verifying real power system condition.

• Journal of Power Electronics
• /
• v.18 no.2
• /
• pp.445-455
• /
• 2018

A novel algorithm is proposed to regulate the neutral point potential in neutral point clamped three-level inverters. Oscillations of the neutral point potential and an unbalanced dc-link voltage cause distortions of the output voltage. Large capacitors, which make the application costly and bulky, are needed to eliminate oscillations. Thus, the algorithm proposed in this paper utilizes the finite-control-set model predictive control and the multistage medium vector to solve these issues. The proposed strategy consists of a two-step prediction and a cost function to evaluate the selected multistage medium vector. Unlike the virtual vector method, the multistage medium vector is a mixture of the virtual vector and the original vector. In addition, its amplitude is variable. The neutral point current generated by it can be used to adjust the neutral point potential. When compared with the virtual vector method, the multistage medium vector contributes to decreasing the regulation time when the modulation index is high. The vectors are rearranged to cope with the variable switching frequency of the model predictive control. Simulation and experimental results verify the validity of the proposed strategy.

• Journal of Korean Society of Steel Construction
• /
• v.13 no.2
• /
• pp.115-122
• /
• 2001

Even in today's computer-oriented world with all its sophisticated analysis tools, engineering judgement is required to assess the adequacy of computer output. Approximate analysis method can be a feasible tool to check solutions from computer softwares roughly. It can be a simple tool for structural engineer to check force distribution in frame. Also, it can serve as a basis in selecting preliminary member sizes. The objective of this study is length factor and inflection points. The validity of this method is examined by comparing the results of this method with those of existing methods, showing improvement in the prediction of structural behavior.

• Transactions of Materials Processing
• /
• v.29 no.2
• /
• pp.89-96
• /
• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.