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Simplified Dynamic Assessment for Reinforced-Concrete Structures Subject to Column Removal Scenarios

A simple yet effective method for dynamic assessment of building structures subject to a sudden column removal is presented. An equivalent single-degree-of-freedom (SDOF) dynamic model to predict the realistic dynamic response of the structure after column removal is proposed. This model considers nonzero initial conditions that are likely to occur under blast loading and structural damping, which can significantly affect the dynamic performance under large deformations. Based on the proposed method, dynamic response of a structure subject to a specific dynamic loading released from column removal is analytically solved. Four sets of physical tests of structures subject to column removal, including quasi-static tests and the corresponding multiple free-fall dynamic tests, are employed to verify the proposed assessment method. In addition, the classical pseudostatic assessment is conducted for the test series and compared with the present method. Results obtained from the verification study demonstrate the accuracy and effectiveness of the proposed simplified dynamic assessment. In addition, the proposed equivalent SDOF method is simple and can be easily implemented with a spreadsheet by practicing engineers. This advantage allows the proposed method to be employed as a routine design procedure for predicting the dynamic performance of structures subject to column removal

Identifying Buckling Resistance of Reinforced Concrete Columns during Inelastic Deformation

A simple solution method to identify buckling resistance of reinforced concrete (RC) columns during inelastic deformation is presented. Unlike conventional buckling solution methods, this proposed method predicts inelastic buckling loads of RC columns by directly solving the equilibrium differential equation under buckling. The method considers specific deflection configuration, end restraint conditions and inelastic material properties of the deformed column. In order to evaluate the reliability and accuracy of the proposed method, the results obtained from the purposed method are compared with the test results of eccentrically loaded RC columns. In addition, by using the proposed solution procedure, a parametric study is conducted to investigate the effects of critical RC column design parameters on column buckling behavior and resistance, including slenderness ratio, concrete strength, as well as longitudinal reinforcement and stirrup ratios. The results of the parametric study show that the proposed method is rational and can be adopted to effectively identify buckling resistance of RC columns subjected to inelastic damage, especially when load redistributions have occurred in the structure during progressive collapse

Stationary and adaptive color-shift reduction methods based on the bilevel driving technique for phosphor-converted white LEDs

The bilevel driving technique has realized a 2-D control of the luminosity and emitted color of white LEDs with duty cycle and forward current levels. Unfortunately, various combinations of these dimming control parameters can lead to significant changes in junction temperature, which further modify the luminosity and emitted color of LEDs. In this paper, the theoretical aspects of these complex interactions and the impact of bilevel drive on the color-shift properties of white LEDs are discussed in detail by using a mathematical color-shift model. Two color-shift reduction methods are proposed based on the insights obtained from this model. This study shows that a heat sinks thermal resistance that minimizes the overall color shift over dimming can be uniquely determined from the knowledge of some measurable LED parameters, and gives rise to a global minimum color shift. If such a thermal resistance cannot be realized due to practical limitations, the second method that utilizes an adaptive change of forward current levels over dimming can be adopted. Based on their nature, these methods are classified as stationary and adaptive methods, respectively. Their validity is supported by experimental measurements on a commercial white LED. © 2011 IEEE.published_or_final_versio

On the color stability of phosphor-converted white LEDs under DC, PWM, and bilevel drive

Most commercial white LEDs are made from nitride-based blue LEDs coated with yttrium aluminium garnet phosphor, which produce spectra that shift in opposite directions under the influences of drive current and junction temperature changes. This property gives rise to different emitted spectra, hence chromaticity properties, when the LED is driven/dimmed by different current waveforms. By using a commercial white LED sample, LUXEON K2, the effects of drive current and junction temperature on the changes of chromaticity coordinates are studied experimentally. The impact of dc, pulse width modulation (PWM), and bilevel current waveform is discussed through a graphical analysis, followed by experimental verification. It is proven that dc offers the best color stability over dimming due to the counteracting influences of drive current and junction temperature variations, whereas an LED constantly suffers from noneliminable chromaticity changes when driven by the PWM. Theoretical explanations are given to justify these cases, and it is found that, for the case of dc drive, an ideal heat sinks thermal resistance can be selected based on a simple equation to minimize the overall chromaticity change over dimming. This paper provides an in-depth discussion on the relations between the chromaticity properties of phosphor-converted (pc) white LEDs and the driving/dimming methods used. © 2011 IEEE.published_or_final_versio

Forecasting of wind energy generation using Self-Organizing Maps and Extreme Learning Machines

This paper aims to forecast wind energy generation. With accurate forecasting of energy generation, it will aid the energy sector in managing of stability and grid planning for supplied energy. The main focus of this project is Artificial Neural Network (ANN) while the training algorithms used in this project is a combination of Self-Organizing Maps (SOM) and Extreme Learning Machines (ELM). Furthermore, the training algorithm is applied into MATLAB and simulated several times in order to obtain the optimal parameters setting so as to accurately forecast wind energy generation