scholarly journals A Study of Variation of Maximum Responses of Bi-Linear Hysteretic System Subjected to Artificial Ground Motions with Time-Frequency Characteristics of Actual Earthquake Motions

2012 ◽  
Vol 78 (788) ◽  
pp. 1055-1071
Author(s):  
Ichiro ICHIHASHI ◽  
Akira SONE ◽  
Arata MASUDA
Author(s):  
Akira Sone ◽  
Ichiro Ichihashi ◽  
Arata Masuda

A number of artificial earthquake ground motions compatible with time-frequency characteristics of recorded actual earthquake ground motions as well as the given target response spectrum are generated using wavelet transform. The coefficient of variation (C.O.V..) of maximum displacement on elasto-plastic SDOF systems excited by these artificial ground motions are numerically evaluated.


Author(s):  
Ichiro Ichihashi ◽  
Akira Sone ◽  
Arata Masuda ◽  
Daisuke Iba

In this paper, a number of artificial earthquake ground motions compatible with time-frequency characteristics of recorded actual earthquake ground motion as well as the given target response spectrum are generated using wavelet transform. The maximum non-dimensional displacement of elasto-plastic structures excited these artificial earthquake ground motions are calculated numerically. Displacement response, velocity response and cumulative input energy are shown in the case of the ground motion which cause larger displacement response. Under the given design response spectrum, a selection manner of generated artificial earthquake ground motion which causes lager maximum displacement response of elasto-plastic structure are suggested.


Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.


Author(s):  
Fabio Sabetta ◽  
Antonio Pugliese ◽  
Gabriele Fiorentino ◽  
Giovanni Lanzano ◽  
Lucia Luzi

AbstractThis work presents an up-to-date model for the simulation of non-stationary ground motions, including several novelties compared to the original study of Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996). The selection of the input motion in the framework of earthquake engineering has become progressively more important with the growing use of nonlinear dynamic analyses. Regardless of the increasing availability of large strong motion databases, ground motion records are not always available for a given earthquake scenario and site condition, requiring the adoption of simulated time series. Among the different techniques for the generation of ground motion records, we focused on the methods based on stochastic simulations, considering the time- frequency decomposition of the seismic ground motion. We updated the non-stationary stochastic model initially developed in Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996) and later modified by Pousse et al. (Bull Seism Soc Am 96:2103–2117, 2006) and Laurendeau et al. (Nonstationary stochastic simulation of strong ground-motion time histories: application to the Japanese database. 15 WCEE Lisbon, 2012). The model is based on the S-transform that implicitly considers both the amplitude and frequency modulation. The four model parameters required for the simulation are: Arias intensity, significant duration, central frequency, and frequency bandwidth. They were obtained from an empirical ground motion model calibrated using the accelerometric records included in the updated Italian strong-motion database ITACA. The simulated accelerograms show a good match with the ground motion model prediction of several amplitude and frequency measures, such as Arias intensity, peak acceleration, peak velocity, Fourier spectra, and response spectra.


1999 ◽  
Author(s):  
Ki-Woo Nam ◽  
Kun-Chan Lee ◽  
Jeong-Hwan Oh

Abstract Application of signal processing techniques to nondestructive evaluation (NDE) in general and acoustic emission (AE) studies in particular has become a standard tool in determining the frequency characteristics of the signals and relating these characteristics to the integrity of the structure under consideration. Recent studies have shown that the frequency characteristics of ultrasonic signals from evolving damage during cyclic (fatigue) and dynamic loads change with time; in other words, the signals are nonstationary, and that these changes can be related to the nature of the damage taking place during loading. A joint time-frequency analysis such as Short Time Fourier Transform (STFT) and Wigner-Ville distribution (WVD), can in principle be used to determine the time dependent frequency characteristics of nonstationary signals in presence of background noise. In this study these techniques are applied to analyze AE signals from fatigue crack propagation in 5083 aluminum alloys and ultrasonic signals in degraded austenitic 316 stainless steels, to study the evolution of damage in these materials. It is demonstrated that the nonstationary characteristics of both AE and ultrasonic signals could be analyzed effectively by these methods. STFT was found to be more effective in analyzing AE signals, and WVD was more effective for analyzing the attenuation and frequency characteristics of degraded materials through ultrasonics. It is indicated that the time-frequency analysis methods should also be useful in evaluating crack propagation and final fracture process resulting from various damages and defects in structural members.


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