• Computers and Concrete
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• v.14 no.1
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• pp.19-40
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• 2014

Reinforced concrete deep beams are structural beams with low shear span-to-depth ratio, and hence in which the strain distribution is significantly nonlinear and the conventional beam theory is not applicable. A strut-and-tie model is considered one of the most rational and simplest methods available for shear strength prediction and design of deep beams. The strut-and-tie model approach describes the shear failure of a deep beam using diagonal strut and truss mechanism: The diagonal strut mechanism represents compression stress fields that develop in the concrete web between diagonal cracks of the concrete while the truss mechanism accounts for the contributions of the horizontal and vertical web reinforcements. Based on a database of 406 experimental observations, this paper proposes a new strut-and-tie-model for accurate prediction of shear strength of reinforced concrete deep beams, and further improves the model by correcting the bias and quantifying the scatter using a Bayesian parameter estimation method. Seven existing deterministic models from design codes and the literature are compared with the proposed method. Finally, a limit-state design formula and the corresponding reduction factor are developed for the proposed strut-andtie model.

• Computers and Concrete
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• v.20 no.1
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• pp.111-118
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• 2017

The present study established prediction models based on multiple nonlinear regressions (MNLRs) and backpropagation neural networks (BPNs) for the expansion of cement mortar caused by oxidization slag that was used as a replacement of the aggregate. The data used for the models were obtained from actual laboratory tests on specimens that were produced with water/cement ratios of 0.485 or 1.5, within which 0%, 10%, 20%, 30%, 40%, or 50% of the cement had been replaced by oxidization slag from electric-arc furnaces; the samples underwent high-temperature curing at either $80^{\circ}C$ or $100^{\circ}C$ for 1-4 days. The varied mixing ratios, curing conditions, and water/cement ratios were all used as input parameters for the expansion prediction models, which were subsequently evaluated based on their performance levels. Models of both the MNLR and BPN groups exhibited $R^2$ values greater than 0.8, indicating the effectiveness of both models. However, the BPN models were found to be the most accurate models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.12
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• pp.3892-3898
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• 1996

In this study, the prediction of the fatigue life as well as the extimation of the characteristics of fatigue cumulative damage on GFRP under random loading were performed. The constant amplitude tests and the ramdom loading test were carried on notched GFRP specimens with a circular hole. Random waves were generated with a micro-computer and had wide band spectra. Since it is useful that the prediction of fatigue life ot the given load sequences is based on S-N curves under constant amplitude loading, the estimation of equivalent stress is done on every random waves. The equivalent stress wasat first estimated by Miner's rule and then by the proposed model which was based on Hashin-Rotem's comulative damage theory regarding nonlinear fatigue cumulative damage behavior. The fatigue lives were predicted from each equivalent stress evaluated. And each predicted fatigue llife was compared with experimental results. The number of cycles of random loads were counted by mean-cross counting method. The reuslts showed that the fatigue life predicted by proposed model was correlated well with the experimental results in comparison with Miner's model.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.6
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• pp.661-669
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• 2018

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.970-977
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• 2014

In this paper, the artificial neural network is used to predict the junction temperature of the IGBT power module, by measuring the temperature sensitive electrical parameters (TSEP) of the module. An experiment circuit is built to measure saturation voltage drop and collector current under different temperature. In order to solve the nonlinear problem of TSEP approach as a junction temperature evaluation method, a Back Propagation (BP) neural network prediction model is established by using the Matlab. With the advantages of non-contact, high sensitivity, and without package open, the proposed method is also potentially promising for on-line junction temperature measurement. The Matlab simulation results show that BP neural network gives a more accuracy results, compared with the method of polynomial fitting.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.10a
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• pp.43-51
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• 2008

Currently an increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). For rational management of tunnels from planning to construction and maintenance stages, prediction, control and monitoring of displacements of and around the tunnel have to be performed with high accuracy. Computational method tools, such as finite element method, have been and are indispensable tool for tunnel engineers for many years. It is, however, a commonly acknowledged fact that determination of input parameters, especially material properties exhibiting nonlinear stress-strain relationship, is not an easy task even for an experienced engineer. Use and application of the acquired tunnel information is important for prediction accuracy and improvement of tunnel behavior on construction. Artificial Neural Network (ANN) model is a form of artificial intelligence that attempts to mimic behavior of human brain and nervous system. The main objective of this paper is to perform the deformation analysis in NATM tunnel by means of numerical simulation and artificial neural network (ANN) with field database. Developed ANN model can achieve a high level of prediction accuracy.

• Earthquakes and Structures
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• v.24 no.6
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• pp.429-437
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• 2023

This paper studied the prediction of structural damage indices to buildings after earthquake occurrence using Multiple Linear Regression (MLR) and Fuzzy Linear Regression (FLR) methods. Particularly, the structural damage degree, represented by the Maximum Inter Story Drift Ratio (MISDR), is an essential factor that ensures the safety of the building. Thus, the seismic response of a steel building was evaluated, utilizing 65 seismic accelerograms as input signals. Among the several response quantities, the focus is on the MISDR, which expresses the postseismic damage status. Using MLR and FLR methods and comparing the outputs with the corresponding evaluated by nonlinear dynamic analyses, it was concluded that the FLR method had the most accurate prediction results in contrast to the MLR method. A blind prediction applying a set of another 10 artificial accelerograms also examined the model's effectiveness. The results revealed that the use of the FLR method had the smallest average percentage error level for every set of applied accelerograms, and thus it is a suitable modeling tool in earthquake engineering.

• Korean Journal of Mathematics
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• v.19 no.2
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• pp.191-203
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• 2011

The interface between fluids of different densities is unstable under acceleration by a shock wave. A previous small amplitude linear theory for the compressible Euler equation failed to provide a quantitatively correct prediction for the growth rate of the unstable interface. In this paper, to include dominant nonlinear effects in a large amplitude regime, we present high-order perturbation equations of the Euler equation, and boundary conditions for the contact interface and shock waves.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.163-167
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• 2007

For numerical analysis of a tidal flow, a two-dimensional hydrodynamic model is developed by solving the nonlinear shallow-water equations. The governing equations are discretized explicitly with a finite difference leap-frog scheme and a first-order upwind scheme on adaptive hierarchical quadtree grids. The developed model is verified by applying to prediction of tidal behaviors. The calculated tidal levels are compared to available field measurements. A very reasonable agreement is observed.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.6
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• pp.216-222
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• 1996

This paper presents a computational technique to predict roof crush resistance in early design stage of passenger car development. This technique use a simple F.E. model with nonlinear spring elements which represent plastic hinge behavior at weak areas. By assuming actual sections as equivalent simple sections, maximum bending moments which weak areas in major members can stand are theoretically calculated. Results from prediction of roof crush resistance are correlated well with test results.