Application of Neurocomputing to Parametric Identification Using Dynamic Responses

Application of Neurocomputing to Parametric Identification Using Dynamic Responses

Leonard Ziemianski (Rzeszów University of Technology, Poland), Bartosz Miller (Rzeszów University of Technology, Poland) and Grzegorz Piatkowski (Rzeszów University of Technology, Poland)
Copyright: © 2007 |Pages: 31
DOI: 10.4018/978-1-59904-099-8.ch015
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Abstract

The chapter focuses on the applications of neurocomputing to the analysis of identification problems in structural dynamics, the main attention is paid to back-propagation neural networks. The analysed problems relate to (a) application of dynamic response to parameter identification of structural elements with defects modelled as a local change of stiffness or material loss; (b) updating of FEM models of beams, including the identification of material parameters and parameters describing possible defect; (c) identification of circular void or supplementary mass in vibrating plates; (d) identification of a damage in frame structures using both eigenfrequencies and elements of eigenvectors as input data. In the examples involving the experimental measurements the application of a random noise to increase the not sufficient number of data is proposed. The presented results have proved the proposed method capable of carrying out the appointed task and indicated good prospects of neurocomputing application to dynamics of structures.

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Table of Contents
Foreword
Farzad Naeim
Preface
Nikos Lagaros, Yiannis Tsompanakis
Acknowledgments
Nikos Lagaros, Yiannis Tsompanakis
Chapter 1
Michalis Fragiadakis, Nikos D. Lagaros, Yiannis Tsompanakis, Manolis Papadrakakis
Four alternative analytical procedures are recommended by the design codes for the structural analysis of buildings under earthquake loading. The... Sample PDF
Improved Seismic Design Procedures and Evolutionary Tools
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Chapter 2
Ricardo O. Foschi
This chapter discusses the application of neural networks for the representation of structural responses in earthquake engineering, and their... Sample PDF
Applying Neural Networks for Performance-Based Design in Earthquake Engineering
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Chapter 3
Arzhang Alimoradi, Shahram Pezeshk, Christopher Foley
The chapter provides an overview of optimal structural design procedures for seismic performance. Structural analysis and design for earthquake... Sample PDF
Evolutionary Seismic Design for Optimal Performance
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Chapter 4
Jorge Hurtado
Reliability-based optimization is considered by many authors as the most rigorous approach to structural design, because the search for the optimal... Sample PDF
Optimal Reliability-Based Design Using Support Vector Machines and Artificial Life Algorithms
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Chapter 5
Eysa Salajegheh, Ali Heidari
Optimum design of structures for earthquake induced loading is achieved by a modified genetic algorithm (MGA). Some features of the simulated... Sample PDF
Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network
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Chapter 6
Faruque Ali, Ananth Ramaswamy
The chapter introduces developments in intelligent optimal control systems and their applications in structural engineering. It provides a good... Sample PDF
Developments in Structural Optimization and Applications to Intelligent Structural Vibration Control
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Chapter 7
Martha Carreño, Omar Cardona, Alex Barbat
This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-fuzzy system, developed with the objective... Sample PDF
Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake
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Chapter 8
Miguel Hernandez-Garcia, Mauricio Sanchez-Silva
The complexity of civil infrastructure systems and the need to keep essential systems operating after unexpected events such as earthquakes demands... Sample PDF
Learning Machines for Structural Damage Detection
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Chapter 9
Mauro Mezzina, Giuseppina Uva, Rita Greco, Giuseppe Acciani, Giuseppe Cascella, Girolamo Fornarelli
The chapter deals with the structural assessment of existing constructions, with a particular attention to seismic risk mitigation. Two aspects are... Sample PDF
Structural Assessment of RC Constructions and Fuzzy Expert Systems
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Chapter 10
Hitoshi Furuta, Kazuhiro Koyama
This chapter introduces a life-cycle cost (LCC) analysis of bridge structures considering seismic risk. Recently, LCC has been paid attention as a... Sample PDF
Life-Cycle Cost Evaluation of Bridge Structures Considering Seismic Risk
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Chapter 11
Nikos Lagaros, Yiannis Tsompanakis, Michalis Fragiadakis, Manolis Papadrakakis
Earthquake-resistant design of structures using probabilistic analysis is an emerging field in structural engineering. The objective of this chapter... Sample PDF
Soft Computing Techniques in Probabilistic Seismic Analysis of Structures
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Chapter 12
Dominic Assimaki
A seismic waveform inversion algorithm is proposed for the estimation of elastic soil properties using low amplitude, downhole array recordings.... Sample PDF
Inverse Analysis of Weak and Strong Motion Downhole Array Data: A Hybrid Optimization Algorithm
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Chapter 13
Chan Koh
Genetic algorithms (GA) have proved to be a robust, efficient search technique for many problems. In this chapter, the latest developments by the... Sample PDF
Genetic Algorithms in Structural Identification and Damage Detection
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Chapter 14
Snehashish Chakraverty
A detailed study of the capabilities and powerfulness of soft computing techniques such as artificial neural network with respect to the... Sample PDF
Neural Network-Based Identification of Structural Parameters in Multistory Buildings
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Chapter 15
Leonard Ziemianski, Bartosz Miller, Grzegorz Piatkowski
The chapter focuses on the applications of neurocomputing to the analysis of identification problems in structural dynamics, the main attention is... Sample PDF
Application of Neurocomputing to Parametric Identification Using Dynamic Responses
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Chapter 16
Krystyna Kuzniar, Zenon Waszczyszyn
The chapter deals with an application of neural networks to the analysis of vibrations of medium-height prefabricated buildings with load-bearing... Sample PDF
Neural Networks for the Simulation and Identification Analysis of Buildings Subjected to Paraseismic Excitations
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