Evaluation of the seismic behavior of reinforced concrete structures with flat slab-column gravity frame and shear walls through nonlinear analysis methods

This paper presents an investigation of the seismic behavior of reinforced concrete(RC)structures in which shear walls are the main lateral load-resisting elements and the participation of flat slab floor systems is not considered in the seismic design procedure.In this regard,the behavior of six prototype structures(with different heights and plan layouts)is investigated through nonlinear static and time history analyses,implemented in the OpenSees platform.The results of the analyses are presented in terms of the behavior of the slab-column connections and their mode of failure at different loading stages.Moreover,the global response of the buildings is discussed in terms of some parameters,such as lateral overstrength due to the gravity flat slab-column frames.According to the nonlinear static analyses,in structures in which the slab-column connections were designed only for gravity loads,the slab-column connections exhibited a punching mode of failure even in the early stages of loading.However,the punching failure was eliminated in structures in which a minimum transverse reinforcement recommended in ACI 318(2019)was provided in the slabs at joint regions.Furthermore,despite neglecting the contribution of gravity flat slab-column frames in the lateral load resistance of the structures,a relatively significant overstrength was imposed on the structures by the gravity frames.

A Cross-Reference Method for Nonlinear Time Series Analysis in Semi-Blind Case

本文提出一种交互参考方法，用于半盲情况下非线性时间序列的分析，这时描述该序列的动力学方程形式已知而相应的参数未知．通常是分别完成的噪声抑制和参数估计这２个任务在这里被组合在一起迭代完成．由于２个处理模块间的积极相互作用，该方法可获得更好的性能．以前的一些分析方法可以看作是这一处理框架的特例．将其用于含噪声混沌时间序列的分析，实验结果表明了该方法对性能的显著改善．

Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.

Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy

We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component. We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments.

Study of Polluted Insulator Flashover Forecasting Based on Nonlinear Time Series Analysis

To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESDD is the key of flashover on polluted insulator.The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper.The forecasting model consists of two artificial neural networks that reflect relationship of environment,ESDD and flashover probability.The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover.A series of artificial pollution tests show that the results of the forecasting model is acceptable.

Nonlinear Analysis of Physiological Time Series

The heart rate variability could be explained by a low-dimensional governing mechanism. There has been increasing interest in verifying and understanding the coupling between the respiration and the heart rate. In this paper we use the nonlinear detection method to detect the nonlinear deterministic component in the physiological time series by a single variable series and two variables series respectively, and use the conditional information entropy to analyze the correlation between the heart rate, the respiration and the blood oxygen concentration. The conclusions are that there is the nonlinear deterministic component in the heart rate data and respiration data, and the heart rate and the respiration are two variables originating from the same underlying dynamics.

Nonlinear Time History Analysis for the Different Column Orientations under Seismic Wave Synthetic Approach

The significant impact of earthquakes on human lives and the built environment underscores the extensive human and economic losses caused by structural collapses. Over the years, researchers have focused on improving seismic design to mitigate earthquake-induced damages and enhance structural performance. In this study, a specific reinforced concrete (RC) frame structure at Kyungpook National University, designed for educational purposes, is analyzed as a representative case. Utilizing SAP 2000, the research conducts a nonlinear time history analysis to assess the structural performance under seismic conditions. The primary objective is to evaluate the influence of different column section designs, while maintaining identical column section areas, on structural behavior. The study employs two distinct seismic waves from Abeno (ABN) and Takatori (TKT) for the analysis, comparing the structural performance under varying seismic conditions. Key aspects examined include displacement, base shear force, base moment, joint radians, and layer displacement angle. This research is anticipated to serve as a valuable reference for seismic restraint reinforcement work on RC buildings, enriching the methods used for evaluating structures through nonlinear time history analysis based on the synthetic seismic wave approach.

Non-linear buffeting response analysis of long-span suspension bridges with central buckle

The rigid central buckle employed in the Runyang Suspension Bridge (RSB) was the first time it was used in a suspension bridge in China. By using a spectral representation method and FFT technique combined with measured data,a 3D fluctuating wind field considering the tower wind effect is simulated. A novel FE model for buffeting analysis is then presented,in which a specific user-defined Matrix27 element in ANSYS is employed to simulate the aeroelastic forces and its stiffness or damping matrices are parameterized by wind velocity and vibration frequency. A nonlinear time history analysis is carried out to study the influence of the rigid central buckle on the wind-induced buffeting response of a long-span suspension bridge. The results can be used as a reference for wind resistance design of long-span suspension bridges with a rigid central buckle in the future.

Lateral load pattern in pushover analysis

The seismic capacity curves of three types of buildings including frame,frame-shear wall and shear wall ob- tained by pushover analysis under different lateral load patterns are compared with those from nonlinear time history analy- sis.Based on the numerical results obtained a two-phase load pattern:an inverted triangle(first mode)load pattern until the base shear force reaches β times its maximum value,V_(max)followed by a(x/H)~α form,here β and α being some coeffi- cients depending on the type of the structures considered,is proposed in the paper,which can provide excellent approxima- tion of the seismic capacity curve for low-to-mid-rise shear type buildings.Furthermore,it is shown both the two-phase load pattern proposed and the invariant uniform pattern can be used for low-to-mid-rise shear-bending type and low-rise bending type of buildings.No suitable load patterns have been found for high-rise buildings.

Adaptive Neural Network Dynamic Surface Control for Perturbed Nonlinear Time-delay Systems

This paper proposes an adaptive neural network control method for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function neural networks are used to approximate unknown intermediate control signals. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control technique is used to overcome the problem of ＂explosion of complexity＂ in backstepping design procedure. In addition, the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system is proved. A main advantage of the proposed controller is that both problems of ＂curse of dimensionality＂ and ＂explosion of complexity＂ are avoided simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the approach.

AUTO-EXTRACTING TECHNIQUE OF DYNAMIC CHAOS FEATURES FOR NONLINEAR TIME SERIES

The main purpose of nonlinear time series analysis is based on the rebuilding theory of phase space, and to study how to transform the response signal to rebuilt phase space in order to extract dynamic feature information, and to provide effective approach for nonlinear signal analysis and fault diagnosis of nonlinear dynamic system. Now, it has already formed an important offset of nonlinear science. But, traditional method cannot extract chaos features automatically, and it needs man＇s participation in the whole process. A new method is put forward, which can implement auto-extracting of chaos features for nonlinear time series. Firstly, to confirm time delay r by autocorrelation method; Secondly, to compute embedded dimension m and correlation dimension D; Thirdly, to compute the maximum Lyapunov index λmax; Finally, to calculate the chaos degree Dch of Poincare map, and the non-circle degree Dnc and non-order degree Dno of quasi-phase orbit. Chaos features extracting has important meaning to fault diagnosis of nonlinear system based on nonlinear chaos features. Examples show validity of the proposed method.

Seismic Vulnerability Analysis of Single-Story Reinforced Concrete Industrial Buildings with Seismic Fortification

As there is a lack of earthquake damage data for factory buildings with seismic fortifications in China,seismic vulnerability analysis was performed by numerical simulation in this paper.The earthquake-structure analysis model was developed with considering the influence of uncertainties of the ground motion and structural model parameters.The small-size sampling was conducted based on the Latin hypercube sampling and orthogonal design methods.Using nonlinear analysis,the seismic vulnerability curves and damage probability matrix with various seismic fortification intensities(SFI)were obtained.The seismic capacity of the factory building was then evaluated.The results showed that,with different designs at different SFIs,the factory building could consistently achieve the three seismic fortification objectives.For the studied factory buildings with the SFI of 6,they satisfied the seismic fortification requirements of“no damage in moderate earthquakes,mendable in strong earthquakes”;for those buildings with SFIs of 7 and 8,the requirement of“no collapsing in super strong earthquakes”was generally met;while for those with SFIs of 9,the requirement of“mendable in moderate earthquakes”was almost satisfied.The results showed factory buildings designed with low SFIs are better at achieving the seismic fortification objectives than those designed with high SFIs.

Adaptive Fuzzy Observer Backstepping Control for a Class of Uncertain Nonlinear Systems with Unknown Time-delay

In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the state observer and applying the backstepping technique, an adaptive fuzzy observer control approach is developed. The main features of the proposed adaptive fuzzy control approach not only guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded, but also contain less adaptation parameters to be updated on-line. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems

An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.

RESEARCH ON NONLINEAR MODELS OF TIME SERIES

This paper presents some nonlinear models for time series. The structures and training methods for each model have been analyzed and studied. Experimental results for some common time series are given.

A Nonlinear Autoregressive Scheme for Time Series Prediction via Artificial Neural Networks

This article is devoted to a time series prediction scheme involving the nonlinear autoregressive algorithm and its applications. The scheme is implemented by means of an artificial neural network containing a hidden layer. As a training algorithm we use scaled conjugate gradient (SCG) method and the Bayesian regularization (BReg) method. The first method is applied to time series without noise, while the second one can also be applied for noisy datasets. We apply the suggested scheme for prediction of time series arising in oil and gas pricing using 50 and 100 past values. Results of numerical simulations are presented and discussed.

Influences of load pattern selection and higher modes on pushover analysis for R.C. structures

Load pattern selection is one of the critical issues in pushover analysis (POA) when the influence of higher modes is evident. In terms of interstory drift, comparisons between the nonlinear time history analysis (NL-THA) and the pushover analysis (POA) were conducted for three typical RC frame buildings under a variety of ground motion levels. Eight typical earthquake inputs, including four earthquake records and four artificial earthquake waves, were employed as the input of NL-THA; five typical lateral load patterns were considered in POA. By means of modal participation factor, the higher mode effect in POA was quantified considering floor numbers and the ground motion intensity. Suggestions about load pattern selection in POA were provided when higher mode influence was found evident.

IDENTIFICATION OF NONLINEAR DYNAMIC SYSTEMS:TIME-FREQUENCY FILTERING AND SKELETON CURVES

The nonlinear behavior varying with the instantaneous response was analyzed through the joint time-frequency analysis method for a class of S. D. O. F nonlinear system. A masking operator an definite regions is defined and two theorems are presented. Based on these, the nonlinear system is modeled with a special time-varying linear one, called the generalized skeleton linear system (GSLS). The frequency skeleton curve and the damping skeleton curve are defined to describe the main feature of the non-linearity as well. Moreover, an identification method is proposed through the skeleton curves and the time-frequency filtering technique.

Multi-parametric Sensitivity Analysis of Improved Transformer Thermal Models Considering Nonlinear Effect of Oil Time Constant

This paper aims to develop an approach to investigating the effect of a particular parameter on the output accuracy of transformer thermal models,i.e.sensitivity analysis,which can not only reveal the most sensitive parameter of a thermal model but also improve model output accuracies.For the first time,the nonlinear time constant(NTC)of transformer oil is proposed to reshape three practical top-oil temperature models based on an expression of nonlinear thermal conductance:the modified IEEE clause 7 model,Swift’s model,and Susa’s model.Then,the multi-parametric sensitivity analysis(MPSA)is undertaken to reveal the effect of each parameter on the model output accuracy.Through onsite data validation,the results show that the accuracy performance of the proposed NTC thermal models are improved significantly by considering the nonlinear effect of oil time constant.Moreover,the derived sensitivity performances can clearly reveal the most dominant parameter of the model,so as to simplify the model parameter identification process by reducing the number of insensitive parameters.Finally,the heat-run test data is used as a reference to validate parameters optimized through a genetic algorithm(GA),which demonstrates that the proposed NTC IEEE model has not only one sensitive parameter but also superior accuracy performance.