• Earthquakes and Structures
• /
• v.15 no.3
• /
• pp.335-350
• /
• 2018

This paper presents numerical modelling, modal testing, finite element model updating, linear and nonlinear earthquake behavior of a reinforced concrete building model. A 1/2 geometrically scale, two-storey, reinforced concrete frame model with raft base were constructed, tested and analyzed. Modal testing on the model using ambient vibrations is performed to illustrate the dynamic characteristics experimentally. Finite element model of the structure is developed by ANSYS software and dynamic characteristics such as natural frequencies, mode shapes and damping ratios are calculated numerically. The enhanced frequency domain decomposition method and the stochastic subspace identification method are used for identifying dynamic characteristics experimentally and such values are used to update the finite element models. Different parameters of the model are calibrated using manual tuning process to minimize the differences between the numerically calculated and experimentally measured dynamic characteristics. The maximum difference between the measured and numerically calculated frequencies is reduced from 28.47% to 4.75% with the model updating. To determine the effects of the finite element model updating on the earthquake behavior, linear and nonlinear earthquake analyses are performed using 1992 Erzincan earthquake record, before and after model updating. After model updating, the maximum differences in the displacements and stresses were obtained as 29% and 25% for the linear earthquake analysis and 28% and 47% for the nonlinear earthquake analysis compared with that obtained from initial earthquake results before model updating. These differences state that finite element model updating provides a significant influence on linear and especially nonlinear earthquake behavior of buildings.

• Steel and Composite Structures
• /
• v.16 no.1
• /
• pp.21-44
• /
• 2014

The aim of this study is to determine the dynamic characteristics of long reinforced concrete highway bridges with post-tension tendons using analytical and experimental methods. It is known that the deck length and height of bridges are affected the dynamic characteristics considerably. For this purpose, Berta Bridge constructed in deep valley, in Artvin, Turkey, is selected as an application. The Bridge has two piers with height of 109.245 m and 85.193 m, and the total length of deck is 340.0 m. Analytical and experimental studies are carried out on Berta Bridge which was built in accordance with the balanced cantilever method. Finite Element Method (FEM) and Operational Modal Analysis (OMA) which considers ambient vibration data were used in analytical and experimental studies, respectively. Finite element model of the bridge is created by using SAP2000 program to obtain analytical dynamic characteristics such as the natural frequencies and mode shapes. The ambient vibration tests are performed using Operational Modal Analysis under wind and human loads. Enhanced Frequency Domain Decomposition (EFDD) and Stochastic Subspace Identification (SSI) methods are used to obtain experimental dynamic characteristics like natural frequencies, mode shapes and damping ratios. At the end of the study, analytical and experimental dynamic characteristic are compared with each other and the finite element model of the bridge was updated considering the material properties and boundary conditions. It is emphasized that Operational Modal Analysis method based on the ambient vibrations can be used safely to determine the dynamic characteristics, to update the finite element models, and to monitor the structural health of long reinforced concrete highway bridges constructed with the balanced cantilever method.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.49 no.7
• /
• pp.23-31
• /
• 2012

Resource allocation, such as joint rate control and scheduling, is an important issue in cognitive radio networks. However, it is difficult to jointly consider the rate control and scheduling problem due to the stochastic behavior of channel availability in cognitive radio networks. In this paper, we propose an asymmetric joint rate control and scheduling technique under reliability constraints in cognitive radio networks. The joint rate control and scheduling problem is formulated as a convex optimization problem and substantially decomposed into several sub-problems using a dual decomposition method. An algorithm for secondary users to locally update their rate that maximizes the utility of the overall system is also proposed. The results of simulations revealed that the proposed algorithm converges to a globally optimal solution.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4A
• /
• pp.605-609
• /
• 2006

In this study, two kinds of smart accelerometers are investigated for the application of smart sensors to the structural health monitoring of infrastructures. Smart optical Fiber Bragg Grating (FBG) type and Micro-Electo-Mechanical System (MEMS) type accelerometers are selected for this study and the high sensitive ICP type accelerometer is used for the reference sensor. Small size shaking table tests were performed with 3-story shear building model using random input ground motions. The output only modal identification was carried out using stochastic subspace identification and the performances of sensors are compared in modal domain indirectly. The modal sensitivity method was applied to update the story stiffness of numerical model and the updated results were verified using the additional experiments for the same structure with additional mass.

• Journal of Korea Water Resources Association
• /
• v.42 no.3
• /
• pp.213-225
• /
• 2009

A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.