• Steel and Composite Structures
• /
• v.2 no.5
• /
• pp.355-378
• /
• 2002

Use of composite steel construction with precast hollow core slabs is now popular in the UK, but the present knowledge in shear capacity of the headed shear studs for this type of composite construction is very limited. Currently, all the information is based on the results obtained from experimental push-off tests. A finite element model to simulate the behaviour of headed stud shear connection in composite beam with precast hollow core slabs is described. The model is based on finite element method and takes into account the linear and non-linear behaviour of all the materials. The model has been validated against the test results, for which the accuracy of the model used is demonstrated. Parametric studies showing the effect of the change in transverse gap size, transverse reinforcement diameter and in-situ concrete strength on the shear connection capacity are presented.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1367-1372
• /
• 2000

Recently, many new constructuion and large scale modification of railway are performed for cost down of goods delivery charge and effective transportation in various aspect. Although railway traffic is environmentally frendly in many part but weak in noise and vibration problem. For the reduction and efficient management of railway noise, first of all prediction of railway noise is necessarily requisted. In domestic and abroad many studies for prediction of railway nearby noise are done. In this study simple modelling technique is investigated for railway noise prediction when railway passes above elevated concrete bridge as well as ground. Predicted results are compared with measured results and it is known that suggested modelling technique can be used for more precise prediction of railway nearby noise.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.117-121
• /
• 1990

The control process involving pure time delays presents a continuing challenge to the control system engineer. The nonlinear nature of the delay which can be introduced into the system make the use of conventional control algorithms a poor prospect. The Smith Predictor was developed to alleviate this problem. Unfortunately the quality of control achieved with the Smith Predictor is known to be sensitive to modelling errors. Only recently have researchers attempted to quantify the Smith Predictor controller's robustness to modelling errors. In several studies stability boundaries were plotted as functions of errors in parameters. But the research results address the question of performance of Smith Predictor controllers, In this paper, the Rule based Expert Systems for performance improvement of the Smith Predictor controller are developed.

• Structural Engineering and Mechanics
• /
• v.26 no.1
• /
• pp.31-42
• /
• 2007

The unsymmetric finite element formulation has been proposed recently to improve predictions from distorted finite elements. Studies have also shown that this special formulation using parametric functions for the test functions and metric functions for the trial functions works surprisingly well because the former satisfy the continuity conditions while the latter ensure that the stress representation during finite element computation can retrieve in a best-fit manner, the actual variation of stress in the metric space. However, a question that remained was whether the unsymmetric formulation was variationally correct. Here we determine that it is not, using the simplest possible element to amplify the principles.

• Wind and Structures
• /
• v.5 no.1
• /
• pp.1-14
• /
• 2002

Artificial Neural Networks (ANN) have the capability to develop functional relationships between input-output patterns obtained from any source. Thus ANN can be conveniently used to develop a generalised relationship from limited and sometimes inconsistent data, and can therefore also be applied to tackle the data obtained from wind tunnel tests on building models with large number of variables. In this paper ANN model has been developed for predicting wind induced pressures in various zones of a Gable Building from limited test data. The procedure is also extended to a case wherein interference effects on a gable roof building by a similar building are studied. It is found that the Artificial Neural Network modelling is seen to predict successfully, the pressure coefficients for any roof slope that has not been covered by the experimental study. It is seen that ANN modelling can lead to a reduction of the wind tunnel testing effort for interference studies to almost half.

• Geophysics and Geophysical Exploration
• /
• v.7 no.1
• /
• pp.33-40
• /
• 2004

Numerical and laboratory studies have been conducted to test the ability of Electrical Resistance Tomography-a technique used to map the electrical resistivity of the subsurface-to delineate contaminant plumes. Two-dimensional numerical models were created to investigate survey design and resolution. Optimal survey design consisted of both downhole and surface electrode sites. Resolution models revealed that while the bulk fluid flow could be outlined, small-scale fingering effects could not be delineated. Laboratory experiments were conducted in a narrow glass tank to validate theoretical models. A visual comparison of fluid flow with ERT images also showed that, while the bulk fluid flow could be seen in most instances, fine-scale effects were indeterminate.

• Structural Engineering and Mechanics
• /
• v.44 no.4
• /
• pp.465-485
• /
• 2012

Different mathematical models are proposed and their analytical solutions derived for the analysis of linear elastic Reissner's multilayer beams. The models take into account different combinations of contact plane conditions, different material properties of individual layers, different transverse shear deformations of each layer, and different boundary conditions of the layers. The analytical studies are carried out to evaluate the influence of different contact conditions on the static and kinematic quantities. A considerable difference of the results between the models is obtained.

• The Korean Journal of Ecology
• /
• v.28 no.6
• /
• pp.405-415
• /
• 2005

This paper considers the advantage of Machine Learning (ML) implemented to freshwater ecosystem research. Currently, many studies have been carried out to find the patterns of environmental impact on dynamics of communities in aquatic ecosystems. Ecological models popularly adapted by many researchers have been a means of information processing in dealing with dynamics in various ecosystems. The up-to-date trend in ecological modelling partially turns to the application of ML to explain specific ecological events in complex ecosystems and to overcome the necessity of complicated data manipulation. This paper briefly introduces ML techniques applied to freshwater ecosystems in Korea. The manuscript provides promising information for the ecologists who utilize ML for elucidating complex ecological patterns and undertaking modelling of spatial and temporal dynamics of communities.

• Wind and Structures
• /
• v.28 no.6
• /
• pp.369-380
• /
• 2019

Statistical distributions are very useful in describing wind speed characteristics and in predicting wind power potential of a specified region. Although the Weibull distribution is the most popular one in wind energy literature, it does not seem to be able to perfectly fit all the investigated wind speed data in nature. Thus, many studies are still being conducted to find flexible distribution for modelling wind speed data. In this study, we propose a new Odd-Burr Rayleigh distribution for wind speed characterization. The Odd-Burr Rayleigh distribution with two shape parameters is flexible enough to model different shapes of wind speed data and thus it can be an alternative wind speed distribution for the assessment of wind energy potential. Therefore, suitability of the Odd-Burr Rayleigh distribution is investigated on real wind speed data taken from different regions in the South Africa. Numerical results of the conducted analysis confirm that the new Odd-Burr Rayleigh distribution is suitable for modelling most of the considered real wind speed cases and it also can be used for predicting wind power.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.185-192
• /
• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.