Structural Damage Identification Using Improved RBF Neural Networks in Frequency Domain
TL;DR: The novel improved RBF network is shown to be a good damage identification strategy for multiple member structures compared to conventional RBF and existing hybrid methods in terms of accuracy and computational effort.
Abstract: This paper presents a novel two stage improved Radial basis function (RBF) neural network for the damage identification of multimember structures in the frequency domain. The improvement of the proposed RBF network is carried out in two stages, viz. (i) first stage damage prediction by conventional RBF network trained with effective input-output patterns and (ii) in the second stage, minimization of the prediction error below the predefined error tolerance (3%) by training the network with patterns from reduced search space located after the first stage prediction. The network effective input patterns are fractional frequency change ratios (FFCs) and damage signature indices (DSIs), and the corresponding output patterns are stiffness values or damage severity of the structure at different damage levels. A Latin hypercube search (LHS) technique is used for finding the effective input-output patterns from the search space to improve the training efficiency. The numerical simulation of structural damage iden...
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TL;DR: In this article, a modified Cuckoo search (MCS) algorithm was used to identify structural damage in a simply supported beam and a 31-bar truss, and the proposed method can judge the damage location and degree of structures more accurately than its counterpart even under measurement noise.
Abstract: The Cuckoo search (CS) algorithm is a simple and efficient global optimization algorithm and it has been applied to figure out large range of real-world optimization problem. In this paper, a new formula is introduced to the discovering probability process to improve the convergence rate and the Tournament Selection Strategy is adopted to enhance global search ability of the certain algorithm. Then an approach for structural damage identification based on modified Cuckoo search (MCS) is presented. Meanwhile, we take frequency residual error and the modal assurance criterion (MAC) as indexes of damage detection in view of the crack damage, and the MCS algorithm is utilized to identifying the structural damage. A simply supported beam and a 31-bar truss are studied as numerical example to illustrate the correctness and efficiency of the propose method. Besides, a laboratory work is also conducted to further verification. Studies show that, the proposed method can judge the damage location and degree of structures more accurately than its counterpart even under measurement noise, which demonstrates the MCS algorithm has a higher damage diagnosis precision.
9 citations
TL;DR: In this article, two different Artificial Neural Networks (ANNs) were applied to determine the relationship between the damping ratio and system parameters based on extensive experiments carried out on an aluminium alloy beam.
8 citations
TL;DR: Civil structures suffer deterioration either for years of service, deficiency due to environmental factors or damages caused by factors such as earthquakes, winds, impact loads, and cyclical loads.
Abstract: Civil structures suffer deterioration either for years of service, deficiency due to environmental factors or damages caused by factors such as earthquakes, winds, impact loads, and cyclical loads....
8 citations
TL;DR: In this paper, a distributed damage identification approach based on dynamic response sensitivity of the moving vehicle is presented, where numerical studies are carried out on a simply supported beam and a co...
Abstract: This article presents a distributed damage identification approach based on dynamic response sensitivity of the moving vehicle. Numerical studies are carried out on a simply supported beam and a co...
6 citations
Cites background from "Structural Damage Identification Us..."
...Methods based on the frequency domain (Carden and Fanning, 2004; Machavaram and Shankar, 2012) are proposed in the early days, such as natural frequencies (Cawley and Adams, 1979; Majumdar et al....
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...Methods based on the frequency domain (Carden and Fanning, 2004; Machavaram and Shankar, 2012) are proposed in the early days, such as natural frequencies (Cawley and Adams, 1979; Majumdar et al., 2013), mode shapes (Pandey and Biswas, 1994), flexibility curvature (Lu et al., 2002; Wang et al.,…...
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TL;DR: From the results of test verification, it is found that the ANN with the statistical property as damage index can accurately detect the damage location and can identify the damage extent with high precision, which denotes that the proposed method is effective and reliable for damage identification in civil engineering.
Abstract: In this paper, test verification for a method of damage identification using artificial neural network (ANN) based on statistical properties of structural dynamic displacements was carried out. Six fixed beam models are fabricated, and damages on them are simulated by cutting a crack at their bottoms. Some damage samples were used to train the neural network and other samples were used to check the well-trained neural network. During the damage identification using back-propagation neural network (BPNN), the changes of variances (covariance) of structural displacements were adopted as input and location and extent of damage were as the output of BPNN. From the results of test verification in both single-damage case and multi-damage case, it is found that the ANN with the statistical property as damage index can accurately detect the damage location and can identify the damage extent with high precision, which denotes that the proposed method is effective and reliable for damage identification in civil engineering.
5 citations
References
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Book•
29 Dec 1995TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Abstract: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks. A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.
6,463 citations
Book•
01 Jan 1982
TL;DR: Within-subject and mixed designs of Factorial Design have been studied in this article, where the Principal Two-Factor Within-Factor Effects and Simple Effects have been used to estimate the effect size and power of interaction components.
Abstract: I. INTRODUCTION. 1. Experimental Design. II. SINGLE FACTOR EXPERIMENTS. 2. Sources of Variability and Sums of Squares. 3. Variance Estimates and F Ratio. 4. Analytical Comparisons Among Means. 5. Analysis of Trend. 6. Simultaneous Comparisons. 7. The Linear Model and Its Assumptions. 8. Effect Size and Power. 9. Using Statistical Software. III. FACTORIAL EXPERIMENTS WITH TWO FACTORS. 10. Introduction to the Factorial Design. 11. The Principal Two-Factor Effects. 12. Main Effects and Simple Effects. 13. The Analysis of Interaction Components. IV. NONORTHOGONALITY AND THE GENERAL LINEAR MODEL. 14. General Linear Model. 15. The Analysis of Covariance. V. WITHIN-SUBJECT DESIGNS. 16. The Single-Factor Within-Subject Design. 17. Further Within-Subject Topics. 18. The Two-Factor Within-Subject Design. 19. The Mixed Design: Overall Analysis. 20. The Mixed Design: Analytical Analyses. VI. HIGHER FACTORIAL DESIGNS AND OTHER EXTENSIONS. 21. The Overall Three-Factor Design. 22. The Three-Way Analytical Analysis. 23. Within-Subject and Mixed Designs. 24. Random Factors and Generalization. 25. Nested Factors. 26. Higher-Order Designs. Appendix A: Statistical Tables.
6,216 citations
TL;DR: This work proposes a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988).
Abstract: We propose a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988). We consider training such networks in a completely supervised manner, but abandon this approach in favor of a more computationally efficient hybrid learning method which combines self-organized and supervised learning. Our networks learn faster than backpropagation for two reasons: the local representations ensure that only a few units respond to any given input, thus reducing computational overhead, and the hybrid learning rules are linear rather than nonlinear, thus leading to faster convergence. Unlike many existing methods for data analysis, our network architecture and learning rules are truly adaptive and are thus appropriate for real-time use.
4,406 citations
"Structural Damage Identification Us..." refers background or methods in this paper
...They are an active research topic and have been applied to the solution of many problems (Moody and Darken 1989)....
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...Conventional RBF neural networks are trained in three stages (Moody and Darken 1989; Reddy and Ganguli 2003; Zang et al. 2007)....
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...…study is exact interpolation type where the number of centroids (J) and the size of training patterns (m) are equal. c) The two training parameters (Moody and Darken 1989): the centroids represented by cj and the width or spread represented by σj (Section 2) are selected from the LHS clusters and…...
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...c) The two training parameters (Moody and Darken 1989): the centroids represented by cj and the width or spread represented by σj (Section 2) are selected from the LHS clusters and random selection by trial and error basis respectively to train the network....
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01 May 1996
TL;DR: A review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response is presented in this article, where the authors categorize the methods according to required measured data and analysis technique.
Abstract: This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report first categorizes the methods according to required measured data and analysis technique. The analysis categories include changes in modal frequencies, changes in measured mode shapes (and their derivatives), and changes in measured flexibility coefficients. Methods that use property (stiffness, mass, damping) matrix updating, detection of nonlinear response, and damage detection via neural networks are also summarized. The applications of the various methods to different types of engineering problems are categorized by type of structure and are summarized. The types of structures include beams, trusses, plates, shells, bridges, offshore platforms, other large civil structures, aerospace structures, and composite structures. The report describes the development of the damage-identification methods and applications and summarizes the current state-of-the-art of the technology. The critical issues for future research in the area of damage identification are also discussed.
2,916 citations
"Structural Damage Identification Us..." refers methods in this paper
...Doebling et al. (1996) presented a review on detection, location, and characterization of the structural damage via techniques that examine changes in measured structural vibration response....
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Book•
29 Oct 2007TL;DR: The text presents both classic and modern statistical designs for discrete-event simulation and provides relatively simple solutions for selecting problems to simulate, how to analyze the resulting data from simulation, and computationally challenging simulation problems.
Abstract: Design and Analysis of Simulation Experiments (DASE)focuses on statistical methods for discrete-event simulation (such as queuing and inventory simulations). In addition, the book discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta) models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS). The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS. Design and Analysis of Simulation Experimentsis an authoritative textbook and reference work for researchers, graduate students, and technical practitioners in simulation. Basic knowledge of simulation and mathematical statistics are expected; however, the book does summarize these basics, for the readers' convenience. In addition, the book provides relatively simple solutions for (a) selecting problems to simulate, (b) how to analyze the resulting data from simulation, and (c) computationally challenging simulation problems.
591 citations
"Structural Damage Identification Us..." refers background or methods in this paper
...The size of an LHS design matrix thus grows much more slowly with k than more conventional experimental designs (e.g., factorial, fractional factorial or Central Composite Designs (Kleijnen 2008))....
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..., factorial, fractional factorial or Central Composite Designs (Kleijnen 2008))....
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