• Structural Engineering and Mechanics
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• v.86 no.2
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• pp.221-235
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• 2023

To calculate the vibrations of a tout cable subjected to axial support excitations, a nonlinear relationship of cable force and the support displacement under static situations are employed to depict the quasi-static vibration of the cable. The dynamic components of quasi-static vibration are inputted as "direct loads" to cause the parametric vibrations on the cable. Both the governing equations of motion and deformation compatibility for parametric vibrations are then derived, which indicates the high coupling of cable parametric force and deformation. Numerical solutions, based on the finite difference method, are put forward for the parametric vibrations, which is validated by the finite element method under periodic axial support excitations. For the quasi-static response, the shorter cables are more sensitive to support excitations than longer ones at small cable force. The quasi-static cable force makes the greatest contribution to the total cable force, but the parametric cable force is responsible for the occurrence of cable loosening at large excitation amplitudes. Moreover, this study also revealed that the traditional approach, assuming a linear relationship between quasi-static cable force and axial support displacement, would result in some great error of the cable parametric responses.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• Structural Engineering and Mechanics
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• v.25 no.1
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• pp.75-89
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• 2007

Post-earthquake reconnaissance and experimental research indicate that squat reinforced concrete (RC) columns in existing buildings or bridge piers are vulnerable to non-ductile shear failure. Recently, several experimental studies were conducted to investigate upgrading the shear resistance capacity of such columns in order to modify their failure mode to ductile one. Among these upgrading methods is the use of fibre-reinforced polymer (FRP) jackets. One of the preferred analytical tools to simulate the response of frame structures to earthquake loading is the lumped plasticity macromodels due to their computational efficiency and reasonable accuracy. In these models, the columns' nonlinear response is lumped at its ends. The most important input data for such type of models is the element's lateral force-displacement backbone curve. The objective of this study is to verify an analytical method to predict the lateral force-displacement ductility relationship of axially and laterally loaded rectangular RC squat columns retrofitted with FRP composites. The predicted relationship showed good accuracy when compared with tests available in the literature.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.493-505
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• 2006

Call center staffing problems have often relied upon queueing models, which are traditionally used to compute average call waiting time. However, the relationship between the in-bound call volume and call abandon rate is not directly explained even with the complex queueing formula while that relationship is a major interest to the hospital due to profitability. In this paper we provide a novel approach for the call center staffing problem by incorporating the relationship between the in-bound call volume and call abandon rate with a nonlinear integer programming, rather than using the traditional queueing model. We perform numerical analyses with actual data obtained from a reservation call center in a hospital.

• Journal of the Korean Chemical Society
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• v.58 no.6
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• pp.543-552
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• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• Journal of the Korea Concrete Institute
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• v.12 no.6
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• pp.67-74
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• 2000

The usage of nondestructive testing on early-aged concrete leads to enhacned safty and allows effective scheduling of construction, thus making it possible to maximize the time and cost efficiencies. In this study, a reliable nondestructive strength evaluation method for early-aged concrete using the longitudinal wave velocity is proposed. Compression tests were performed to examine factors influencing the velocity-strength relationship of concrete, such as water-cement (w/c) ratio, fine aggregate ratio, curing temperature, and curing condition. The test results show that a change in the w/c ratio and curing temperature has minor effect on the velocity-strength relationship/ However, curing condition significantly influences the velocity-strength relationship of early-aged concrete. Moreover, the longitudinal wave velocity increases with decreasing fine aggregate ratio. It is concluded from this study that the strength evaluation of early-age concrete can be achieved by a nonlinear equation which considers the effects of curing condition and fine aggregate ratio.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.190-197
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• 2000

A moment-curvature relationship to simulate the behavior of reinforced concrete beam under cyclic loading is introduced. Unlike previous moment-curvature models and the layered section approach, the proposed model takes into consideration the bond-slip effect by using monotonic moment-curvature relationship constructed on the basis of the bond-slip relation and corresponding equilibrium equation at each nodal point. In addition, the use of curved unloading and reloading branches inferred from the stress-strain relation of steel gives more exact numerical result. The advantages of the proposed model, comparing to layered section approach, may be on the reduction in calculation time and memory space in case of its application to large structures. The modification of the moment-curvature relation to reflect the fixed-end rotation and pinching effect is also introduced. Finally, correlation studies between analytical results and experimental studies are conducted to establish the validity of the proposed model.

• Structural Engineering and Mechanics
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• v.12 no.4
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• pp.429-448
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• 2001

An analytical model incorporating bending and shear behavior is presented to predict the lateral loading characteristic for rectangular hollow columns. The moment-curvature relationship for the rectangular hollow sections of a column is firstly determined. Then the nonlinear lateral load-displacement relationship for the hollow column can be obtained accordingly. In this model, thirteen constitutive laws for confined concrete and five approaches to estimate the shear capacity are used. A series of tests on 12 model hollow columns aimed at the seismic shear behavior are reported, and the test data are compared to the analytical results. It is found that the analytical model reflects the experimental results rather closely.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.3-11
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• 2002

A moment-curvature relationship to simulate the behavior of reinforced concrete (RC) columns under cyclic loading is introduced. Unlike previous moment-curvature models and the layered section approach, the unposed model takes into account the bond-slip effect by using a monotonic moment-curvature relationship constructed on the basis of the bond-slip relation and corresponding equilibrium equation at each nodal point. In addition, the use of curved unloading and reloading branches inferred from the stress-strain relation of steel gives more exact numerical result. The pinching enact caused by axial force is considered with an assumption that the absorbing energy corresponding to any deformation level maintains constant regardless of the magnitude of applied axial force. The advantages of the proposed model, comparing tn layered section approach, may be on the reduction in calculation time and memory space in case of its application to large structures.. Finally, correlation studies between analytical results and experimental studies are conducted to establish the validity of the proposed mood.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1648-1649
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• 2007

This paper proposes a very novel method which makes it possible that state feedback controller can be designed for unknown dynamic system with measurable states. The RLS algorithm is used for the identification of input-output relationship. A virtual state space representation is derived from the relationship and the SVM(Support Vector Machines) makes the relationship between actual states and virtual states. A state feedback controller can be designed based on the virtual system and the SVM makes the controller be with actual states. The results of this paper can give many opportunities that the state feedback control can be applied for unknown dynamic systems