scholarly journals Damage Identification in Bars with a Wave Propagation Approach and a Hybrid Optimization Method

2012 ◽  
Vol 19 (3) ◽  
pp. 301-321 ◽  
Author(s):  
R.A. Tenenbaum ◽  
K.M. Fernandes ◽  
L.T. Stutz ◽  
A.J. Silva Neto
Keyword(s):  
Wave Propagation ◽  
Damage Identification ◽  
Signal To Noise Ratio ◽  
Time History ◽  
Optimization Methods ◽  
Optimization Method ◽  
Stochastic Methods ◽  
Test Case ◽  
Hybrid Technique ◽  
Damage Scenarios

The formulation and solution of the inverse problem of damage identification based on wave propagation approach are presented. Different damage scenarios for a bar are considered. Time history responses, obtained from pulse-echo synthetic experiments, are used to identify damage position, severity and shape. In order to account for noise corrupted data, different levels of signal to noise ratio – varying from 30 to 0 dB – are introduced. In the identification process, different optimization methods are investigated: the deterministic Levenberg-Marquardt; the stochastic Particle Swarm Optimization; and a hybrid technique combining the aforementioned methods. It is shown that the damage identification procedure built on the wave propagation approach was successful, even for highly corrupted noisy data. Test case results are presented and a few comments on the advantages of deterministic and stochastic methods and their combination are also reported. Finally, an experimental validation of the sequential algebraic algorithm, used for modeling the direct problem, is presented.

scholarly journals Damage Identification in Bars with a Wave Propagation Approach: Performance Comparison of Five Hybrid Optimization Methods

2013 ◽  
Vol 20 (5) ◽  
pp. 863-878 ◽  
Author(s):  
R.A. Tenenbaum ◽  
L.T. Stutz ◽  
K.M. Fernandes
Keyword(s):  
Wave Propagation ◽  
Damage Identification ◽  
Optimization Methods ◽  
Performance Comparison ◽  
Hybrid Optimization ◽  
Particle Collision ◽  
Hybrid Technique ◽  
Hybrid Techniques ◽  
Hybrid Optimization Methods ◽  
Levenberg Marquardt

The formulation and solution of the inverse problem of damage identification based on an one-dimensional wave propagation approach are presented in this paper. Time history responses, obtained from pulse-echo synthetic experiments, are used to damage identification. The identification process is built on the minimization of the squared residue between the synthetic experimental echo, obtained by using a sequential algebraic algorithm, and the corresponding analytical one. Five different hybrid optimization methods are investigated. The hybridization is performed combining the deterministic Levenberg-Marquardt method and each one of the following stochastic techniques: The Particle Swarm Optimization; the Luus-Jaakola optimization method; the Simulated Annealing method; the Particle Collision method; and a Genetic Algorithm. A performance comparison of the five hybrid techniques is presented. Different damage scenarios are considered and, in order to account for noise corrupted data, signals with 10 dB of signal to noise ratio are also considered. It is shown that the damage identification procedure built on the Sequential Algebraic Algorithm yielded to very fast and successful solutions. In the performance comparison, it is also shown that the hybrid technique combining the Luus-Jaakola and the Levenberg-Marquardt optimization methods provides the faster damage recovery.

Enhanced Multi-Agent Normal Sampling Technique for Global Optimization

2008 ◽  
Author(s):  
Alireza Saremi ◽  
Amir H. Birjandi ◽  
G. Gary Wang ◽  
Tarek ElMekkawy ◽  
Eric Bibeau
Keyword(s):  
Global Optimization ◽  
Optimization Technique ◽  
Sampling Technique ◽  
Optimization Methods ◽  
Optimization Method ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Multi Agent ◽  
Mean Function ◽  
Specific Number

This paper describes an enhanced version of a new global optimization method, Multi-Agent Normal Sampling Technique (MANST) described in reference [1]. Each agent in MANST includes a number of points that sample around the mean point with a certain standard deviation. In each step the point with the minimum value in the agent is chosen as the center point for the next step normal sampling. Then the chosen points of all agents are compared to each other and agents receive a certain share of the resources for the next step according to their lowest mean function value at the current step. The performance of all agents is periodically evaluated and a specific number of agents who show no promising achievements are deleted; new agents are generated in the proximity of those promising agents. This process continues until the agents converge to the global optimum. MANST is a standalone global optimization technique and does not require equations or knowledge about the objective function. The unique feature of this method in comparison with other global optimization methods is its dynamic normal distribution search. This work presents our recent research in enhancing MANST to handle variable boundaries and constraints. Moreover, a lean group sampling approach is implemented to prevent sampling in the same region for different agents. The overall capability and efficiency of the MANST has been improved as a result in the newer version. The enhanced MANST is highly competitive with other stochastic methods such as Genetic Algorithm (GA). In most of the test cases, the performance of the MANST is significantly higher than the Matlab™ GA Toolbox.

scholarly journals Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 422-445
Author(s):  
Md Riasat Azim ◽  
Mustafa Gül
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Numerical Study ◽  
Damage Index ◽  
Strain Analysis ◽  
Time History ◽  
Principal Component ◽  
Element Model ◽  
Railway Bridges ◽  
Truss Bridge

Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis.

scholarly journals Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Satellite System ◽  
Radar System ◽  
Target Localization ◽  
Computational Load ◽  
Matrix Fusion ◽  
Global Navigation Satellite ◽  
Multistatic Radar ◽  
Combined Algorithm

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

Simulation Study of Damage Identification Method Based on Lamb Wave Scattering in Aluminium Plate

2012 ◽  
Vol 433-440 ◽  
pp. 2611-2618
Author(s):  
Zhen Hua Tian ◽  
Hong Yuan Li ◽  
Hong Xu
Keyword(s):  
Energy Distribution ◽  
Lamb Wave ◽  
Damage Identification ◽  
Optimization Method ◽  
Propagation Path ◽  
Transient Dynamic Analysis ◽  
Time Frequency ◽  
Damage Location ◽  
Identification Method ◽  
Short Time

The propagation of scattering Lamb wave in plate was simulated using transient dynamic analysis in ANSYS. In order to extract the characteristic information of received signal for damage identification, the short time Fourier transform based on time-frequency analysis was utilized, and then the energy distribution and envelop of received signal were obtained. Based on the displacement contour of simulation and energy distribution, the propagation of scattering wave in plate with a through hole was examined. Also, a mathematic relationship between damage location and scattering signal was developed, with the help of wave propagation path through actuator, damage and sensor. A nonlinear optimization method was applied on the mathematic relationship to obtain the damage location. The damage identification method using scattering Lamb wave was therefore established.

Comparison between Optimization Method and Experience-Based Method in Soil Conservation

2013 ◽  
Vol 726-731 ◽  
pp. 3811-3817
Author(s):  
Yuan Feng ◽  
Ji Xian Wang
Keyword(s):  
Slope Stability ◽  
Soil Conservation ◽  
Computer Aided Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Interaction Function ◽  
The Standard Model ◽  
Computer Aided ◽  
Slice Method ◽  
Aided Design

The analysis of the slope stability is important in soil conservation. To analyze the slope stability, optimization methods were coded and compared with the traditional experience-based methods. Furthermore, the results were visualized in the program, so that the user can easily check the results and can designate an area, in which the program seeks the center and radius of the most hazardous slide arc. Moreover, the graphic interaction function was implemented in the program. In addition, the Standard Model One, recommended by ACAD (The Association for Computer Aided Design), was calculated by the program, of which the results (safety factor Ks=0.95~0.96) were smaller than the official recommend value (Ks=1). It is because that the traditional slice method, which neglects the normal stress and shear stress between the slices, was applied for calculation of Ks.

A novel wave propagation method for measuring viscoelastic properties of pre-strained materials

2021 ◽  
pp. 1-19
Author(s):  
Pierre Lemerle
Keyword(s):  
Wave Propagation ◽  
Viscoelastic Properties ◽  
Time History ◽  
Main Concept ◽  
Frequency Dependent ◽  
Damping Characteristics ◽  
History Of ◽  
Propagation Methods ◽  
The Time Domain ◽  
Waveform Pattern

Abstract Viscoelastic materials are widely used for vibroacoustic solutions due to their ability to mitigate vibration and sound. Wave propagation methods are based on the measurement of the waveform pattern of a transitory pulse in one-dimensional structures. The time evolution of the pattern can be used to deduce the material elasticity and damping characteristics. The most popular propagation methods, namely Hopkinson bar methods, assume no dispersion, i.e. the complex elasticity modulus is not frequency-dependent. This is not significant for resilient materials such as elastomers. More recent approaches have been developed to measure frequency-dependent properties from a pulse propagating in a slender bar. We showed in previous works how to adapt these techniques for shorter samples of materials, representing a real advance, as extrusion is a cumbersome process for many materials. The main concept was to reconstruct the time history of the wave propagating in a composite structure composed of a long incident bar made of a known material and extended by a shorter sample bar. Then the viscoelastic properties of the sample material were determined in the frequency domain within an inverse method held in the time domain. In industry, most isolation solutions using mounts or bushings must support structural weights. This is why it is particularly interesting to know the viscoelastic properties of the material in stressed state. Here, we show how to overcome this challenging issue. The theoretical framework of the computational approach is detailed and the method is experimentally verified.

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

Nonsmooth Optimization Method for Image Restoration

1995 ◽  
Author(s):  
Kazufumi Ito ◽  
Karl Kunisch
Keyword(s):  
Image Restoration ◽  
Nonsmooth Optimization ◽  
Numerical Optimization ◽  
Augmented Lagrangian ◽  
Energy Criterion ◽  
Optimization Methods ◽  
Optimization Method ◽  
Augmented Lagrangian Methods ◽  
Active Set ◽  
First Order

Abstract In this paper we discuss applications of the numerical optimization methods for nonsmooth optimization, developed in [IK1] for the variational formulation of image restoration problems involving bounded variation type energy criterion. The Uzawa’s algorithm, first order augmented Lagrangian methods and Newton-like update using the active set strategy are described.

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