On Response Prediction of Degrading Structures

2005 ◽  
Author(s):  
C. H. Ng ◽  
N. Ajavakom ◽  
F. Ma
Keyword(s):  
Structure Prediction ◽  
Hysteresis Loops ◽  
Response Prediction ◽  
Parametric Identification ◽  
Practical Significance ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
Sea Waves ◽  
Future Performance ◽  
Brute Force Approach

All structures degrade when acted upon by cyclic forces associated with earthquakes, high winds, and sea waves. Identification and prediction of degradation is thus a problem of considerable practical significance in the field of engineering mechanics. Under cyclic excitations, system degradation manifests itself in the evolution of the associated hysteresis loops. In this paper, a robust identification algorithm is devised to generate hysteretic models of a deteriorating structure from its experimental load-displacement traces. This algorithm is based upon the generalized Bouc-Wen model and the latest theory of differential evolution, streamlined through global sensitivity analysis. It can account for strength degradation, stiffness degradation, and pinching characteristics in the evolution of hysteretic traces, whereby earlier studies in parametric identification of hysteresis are extended. In addition, it is shown experimentally that a hysteretic model obtained by identification can be used to predict the future performance of a degrading structure. Prediction of degradation through identification is a brute-force approach that offers a close representation of reality. There is not any method based upon the fundamental postulates of mechanics that can predict the response of a degrading structure well beyond its linear range.

System Identification and Response Prediction of Deteriorating Structures

2006 ◽  
Author(s):  
C. H. Ng ◽  
N. Ajavakom ◽  
F. Ma
Keyword(s):  
System Identification ◽  
Real Life ◽  
Response Prediction ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
Sea Waves ◽  
Major Barrier ◽  
Differential Model ◽  
Successful Design ◽  
Load Displacement

The lack of a fundamental theory of hysteresis is a major barrier to successful design of structures against deterioration associated with earthquakes, high winds, and sea waves. Development of a practical model of degrading structures that would match experimental observations is an important task. This paper has a two-fold objective. First, a superior system identification algorithm is devised to estimate the unspecified parameters in a differential model of hysteresis from experimental load-displacement traces. This algorithm is based upon the latest theory of genetic evolution and it will be streamlined through global sensitivity analysis. Second, the utility of identification of hysteresis is demonstrated through response prediction. Suppose a hysteretic model is generated with a given load-displacement trace. It will be shown experimentally that the model will predict the response of the same system driven by other cyclic loads. The requirements for precise prediction will be addressed. Through identification of hysteresis, it becomes possible to assess, for the first time in analysis, the performance of a real-life structure that has previously been damaged. In the open literature, there is not any other method that can perform the same task.

Identification and Performance Prediction of Nonlinear Degrading Structures

2007 ◽  
Author(s):  
Nopdanai Ajavakom ◽  
Ching H. Ng ◽  
Fai Ma
Keyword(s):  
Real Life ◽  
Response Prediction ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
Sea Waves ◽  
Major Barrier ◽  
Differential Model ◽  
Successful Design ◽  
Load Displacement ◽  
And Performance

The lack of a fundamental theory of hysteresis is a major barrier to successful design of structures against deterioration associated with earthquakes, high winds, and sea waves. Development of a practical model of degrading structures that would match experimental observations is an important task. This paper has a two-fold objective. First, a superior system identification algorithm is devised to estimate the unspecified parameters in a differential model of hysteresis from experimental load-displacement traces. This algorithm is based upon the latest theory of genetic evolution and it will be streamlined through global sensitivity analysis. Second, the utility of identification of hysteresis is demonstrated through nonlinear response prediction, which is important in structural design. Suppose a hysteretic model is generated with a given load-displacement trace. It will be shown experimentally that the model will predict the response of the same system driven by other cyclic loads. The requirements for accurate prediction will be addressed. Through identification of hysteresis, it becomes possible to assess the performance of a real-life structure that has previously been damaged. In the open literature, there is not any other method that can perform the same task.

scholarly journals Experimental Analysis of Pore Structure and Fractal Characteristics of Soft and Hard Coals With Same Coalification

2021 ◽  
Author(s):  
Barkat Ullah ◽  
Yuanping Cheng ◽  
Liang Wang ◽  
Weihua Yang ◽  
Izhar Mithal Jiskani ◽  
...  
Keyword(s):  
Pore Structure ◽  
Adsorption Capacity ◽  
Gas Adsorption ◽  
Hysteresis Loops ◽  
Control Measures ◽  
Practical Significance ◽  
Coal Bed ◽  
Hard Coal ◽  
Pore Surface ◽  
Soft Coal

Abstract Accurate and quantitative investigation of the physical structure and fractal geometry of coal has important theoretical and practical significance for coal bed methane and the prevention of dynamic disasters such as coal and gas outbursts. This study investigates the pore structure and fractural characteristics of soft and hard coals using nitrogen and carbon dioxide (N2/CO2) adsorption. Coal samples from Pingdingshan Mine in Henan province of China were collected and pulverized to the required size (0.2-0.25mm). N2/CO2 adsorption tests were performed to evaluate the pore size distribution (PSD), specific surface area (SSA), and pore volume (PV). The pore structure was characterized based on fractural theory. The results unveiled that the strength of coal has a significant influence on pore structure and fracture dimensions. The obvious N2-adsorption isotherms of the coals were verified as Type IV (A) and Type II. The shape of the hysteresis loops indicates the presence of slit-shaped pores. There are significant differences in SSA and PV between both coals. The soft coal showed larger SSA and PV than hard coal that shows consistency with adsorption capacity. The fractal dimensions of soft coal are respectively larger than that of hard coal. The greater the value of D1 (complexity of pore surface) of soft coal is, the larger the pore surface roughness and gas adsorption capacity is. The results enable us to conclude that the characterization of pores and fractures of soft and hard coals is different, tending to different adsorption/desorption characteristics and outburst sensitivity. In this regard, results provide a reference for formulating corresponding coal and gas outburst prevention and control measures.

scholarly journals Parametric identification algorithm for asynchronous electric motor inverter on the switching intervals of power transistors

2019 ◽  
Vol 124 ◽  
pp. 02017
Author(s):  
A. Andreev ◽  
M. Andreev ◽  
D. Kolesnihenko ◽  
R.R. Dyganova ◽  
G.T. Merzadinova ◽  
...  
Keyword(s):  
Real Time ◽  
Electric Drive ◽  
Electric Motor ◽  
Parametric Identification ◽  
Identification Algorithm ◽  
Power Transistors ◽  
Asynchronous Electric Drive ◽  
Subsequent Calculation ◽  
Asynchronous Electric Motor ◽  
A Current

The authors propose an algorithm for identifying the parameters of a controlled asynchronous electric drive in real time, which provides calculation of stator and rotor resistances which change as a function of temperature. The algorithm is based on the analysis of a current tube of electric motor phase with the subsequent calculation of resistances.

scholarly journals ProteinUnet2 for Fast Protein Secondary Structure Prediction: A Step Towards Proper Evaluation

2021 ◽  
Author(s):  
Katarzyna Stapor ◽  
Krzysztof Kotowski ◽  
Tomasz Smolarczyk ◽  
Irena Roterman
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
State Of The Art ◽  
Protein Secondary Structure ◽  
Evaluation Study ◽  
Practical Significance ◽  
Statistical Methodology ◽  
Extensive Evaluation ◽  
Benchmark Datasets

Abstract Background: The importance of protein secondary structure (SS) prediction is widely known, its solution enables learning about the role of a protein in organisms. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman-Pearson approach is not appropriate. Also, the state-of-the-art predictors have usually relatively long prediction times.Results: We present a new deep network ProteinUnet2 for SS prediction which is based on U-Net convolutional architecture. We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher-Pitman permutation tests accompanied by practical significance measured by Cohen’s effect size. Through an extensive evaluation study, we report the performance of ProteinUnet2 in comparison with two state-of-the-art methods SAINT and SPOT-1D on benchmark datasets TEST2016, TEST2018, and CASP12. Conclusions: Our results suggest that ProteinUnet2 has much shorter prediction times while maintaining (or outperforming) the mentioned predictors. We strongly believe that our proposed statistical methodology will be adopted and used (and even expanded) by the research community.

Simplified analysis of a low-rise building seismically isolated with stable unbonded fiber reinforced elastomeric isolators

2009 ◽  
Vol 36 (7) ◽  
pp. 1182-1194 ◽  
Author(s):  
Hamid Toopchi-Nezhad ◽  
Michael J. Tait ◽  
Robert G. Drysdale
Keyword(s):  
Seismic Response ◽  
Lateral Displacement ◽  
Hysteresis Loops ◽  
Time History ◽  
Response Prediction ◽  
Analytical Models ◽  
Fiber Reinforced ◽  
Cyclic Shear ◽  
Compression Load ◽  
Seismically Isolated

The seismic response of an ordinary low-rise base isolated (BI) structure, employing stable unbonded-fiber reinforced elastomeric isolator (SU-FREI) bearings, is predicted by using two different simplified analytical models. Subsequently, the accuracy of the two models is evaluated by using measured test results from a shake table study. Two models simulate the nonlinear experimental lateral load–displacement hysteresis loops of these bearings. The experimental hysteresis loops were obtained from cyclic shear tests on prototype bearings under a constant compression load. Because of the nonlinear lateral response behavior of the SU-FREIs, these models are employed in an iterative time-history analysis approach, enabling the model variables and the calculated peak lateral displacement of the bearings to converge to their unique values. Analysis results show that the presented simplified models may be used effectively in seismic response prediction of ordinary low-rise buildings that are seismically isolated by SU-FREI bearings.

Parameter identification of a differentiable Bouc-Wen model using constrained extended Kalman filter

2020 ◽  
pp. 147592172092943
Author(s):  
Dan Li ◽  
Yang Wang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Structural Safety ◽  
Model Parameters ◽  
Suitable Model ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
System Equations ◽  
Parameter Constraints

Hysteresis is of critical importance to structural safety under severe dynamic loading conditions. One of the widely used hysteretic models for civil structures is the Bouc-Wen model, the effectiveness of which depends on suitable model parameters. The locally non-differentiable governing equation of the conventional Bouc-Wen model poses difficulty on existing identification algorithms, especially the extended Kalman filter, which relies on linearized system equations to propagate state estimates and covariance. In addition, the standard extended Kalman filter usually does not incorporate parameter constraints, and therefore may result in unreasonable estimates. In this article, a modified and differentiable Bouc-Wen model, together with a constrained extended Kalman filter (CEKF), is proposed to identify the hysteretic model parameters in a reliable way. The partial derivatives of the differentiable Bouc-Wen model with respect to hysteretic parameters can be easily calculated for implementing the identification algorithm. Constrained extended Kalman filter restricts the Kalman gain to ensure that the estimates of parameters satisfy constraints from physical laws. Parameter identification using simulated and experimental data collected from a four-story structure demonstrates that constrained extended Kalman filter can achieve more reliable identification results than the standard extended Kalman filter.

scholarly journals Parametric Identification of Chaotic Base-Excited Double Pendulum Experiment

2004 ◽  
Author(s):  
Yang Liang ◽  
B. F. Feeny
Keyword(s):  
Harmonic Balance ◽  
Parametric Identification ◽  
Identification Algorithm ◽  
Double Pendulum ◽  
Unstable Periodic Orbits ◽  
Identification Process ◽  
Response Data ◽  
Regression Techniques ◽  
Linearized System ◽  
Pendulum Experiment

An improved parametric identification of chaotic systems was investigated for a double pendulum. From recorded experimental response data, the unstable periodic orbits were extracted and later used in a harmonic balance identification process. By applying digital filtering, digital differentiation and linear regression techniques for optimization, the results were improved. Verification of the related simulation system and linearized system also corroborated the success of the identification algorithm.

Parametric Identification of Ship Maneuvering Models by Using Support Vector Machines

2009 ◽  
Vol 53 (01) ◽  
pp. 19-30 ◽  
Author(s):  
W. L. Luo ◽  
Z. J. Zou
Keyword(s):  
Support Vector Machines ◽  
Predictive Ability ◽  
Parametric Identification ◽  
Inner Product ◽  
Support Vector ◽  
Identification Algorithm ◽  
Hydrodynamic Coefficients ◽  
Ship Maneuvering ◽  
Vector Machines ◽  
Maneuvering Motion

System identification combined with free-running model tests or full-scale trials is one of the effective methods to determine the hydrodynamic coefficients in the mathematical models of ship maneuvering motion. By analyzing the available data, including rudder angle, surge speed, sway speed, yaw rate, and so forth, a method based on support vector machines (SVM) to estimate the hydrodynamic coefficients is proposed for conventional surface ships. The coefficients are contained in the expansion of the inner product of a linear kernel function. Predictions of maneuvering motion are conducted by using the parameters identified. The results of identification and simulation demonstrate the validity of the identification algorithm proposed. The simultaneous drift and multicollinearity are diminished by introducing an additional ramp signal to the training samples. Comparison between the simulated and predicted motion variables from different maneuvers shows good predictive ability of the trained SVM.

scholarly journals Carreau's Rheological Model and A.N. Tikhonov’s Regularization Method: Parametric Identification Based on a CFD model

2021 ◽  
pp. 615-627
Author(s):  
Anatoly Khvostov ◽  
Gazibeg Magomedov ◽  
Victor Ryazhskikh ◽  
Aleksey Kovalev ◽  
Aleksey Zhuravlev ◽  
...  
Keyword(s):  
Regularization Method ◽  
Rheological Model ◽  
Three Dimensional ◽  
Parametric Identification ◽  
Rheological Parameters ◽  
Identification Algorithm ◽  
Cfd Model ◽  
Shear Rates ◽  
Tikhonov’S Regularization ◽  
Limiting Values

Introduction. Carreau's rheological model can describe the three-dimensional flows of non-Newtonian media. However, it requires modeling parameters for the viscosity of the medium at the limiting values of shear rates, which cannot be achieved by instrumental methods. The present article introduces a novel method that can identify the parameters of Carreau’s model using a regularization algorithm. Study objects and methods. The study featured fondant mass produced according to the traditional formulation for Creamy Fondant unglazed candies. Standard methods were used to describe the properties of the raw materials and semi-finished products, as well as methods of mathematical processing, modeling, and optimization. Results and its discussion. The research produced an algorithm based on A.N. Tikhonov’s regularization method of the parametric identification of Carreau's rheological model. The calculation residual was minimized by the viscometric measurements and the CFD model, which provided the calculation of the hydrodynamic flow regime at the limiting values of shear rates. The CFD model of a steady non-isothermal flow of a nonlinear viscous medium through a cylindrical capillary was based on the equations of conservation of mass, energy, and momentum. The rheological parameters of Carreau’s model were illustrated by the case of fondant mass. The error for the viscosity prediction did not exceed 14.07%. Conclusion. The parametric identification algorithm made it possible to evaluate the rheological parameters of structured liquid media with Carreau's rheological law in cases that lack experimental information on the behavior of the medium at limiting shear rates. The algorithm eliminated the computational problems typical of Ostwald and de Ville’s rheological model, which usually arise when solving practical problems of three-dimensional flows of non-Newtonian media with limiting viscosity values.

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