Intelligent Computational Paradigms in Earthquake Engineering

By: Nikos D. Lagaros

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Hardcover | 31 December 2006

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464 Pages

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25.4 x 17.78 x 2.54

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The enormous advances in computational hardware and software resources over the last fifteen years resulted in the development of non-conventional data processing and simulation methods. Among these methods artificial intelligence (AI) has been mentioned as one of the most eminent approaches to the so-called intelligent methods of information processing that present a great potential for engineering applications. ""Intelligent Computational Paradigms in Earthquake Engineering"" contains contributions that cover a wide spectrum of very important real-world engineering problems, and explore the implementation of neural networks for the representation of structural responses in earthquake engineering. This book assesses the efficiency of seismic design procedures and describes the latest findings in intelligent optimal control systems and their applications in structural engineering. ""Intelligent Computational Paradigms in Earthquake Engineering"" presents the application of learning machines, artificial neural networks and support vector machines as highly-efficient pattern recognition tools for structural damage detection. It includes an AI-based evaluation of bridge structures using life-cycle cost principles that considers seismic risk, and emphasizes the use of AI methodologies in a geotechnical earthquake engineering application.

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Foreword	p. vi
Preface	p. viii
Structural Optimization Applications
Improved Seismic Design Procedures and Evolutionary Tools	p. 1
Applying Neural Networks for Performance-Based Design in Earthquake Engineering	p. 22
Evolutionary Seismic Design for Optimal Performance	p. 42
Optimal Reliability-Based Design Using Support Vector Machines and Artiticial Life Algorithms	p. 59
Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network	p. 80
Developments in Structural Optimization and Applications to Intelligent Structural Vibration Control	p. 101
Structural Assessment Applications
Neuro-Fuzzy Assesment of Building Damage and Safety After an Earthquake	p. 123
Learning Machines for Structural Damage Detection	p. 158
Structural Assessment of RC Constructions and Fuzzy Expert Systems	p. 188
Life-Cycle Cost Evaluation of Bridge Structures Considering Seismic Risk	p. 231
Soft Computing Techniques in Probabilistic Seismic Analysis of Structures	p. 248
Structural Identification Applications
Inverse Analysis of Weak and Strong Motion Downhole Array Data: A Hybrid Optimization Algorithm	p. 271
Genetic Algorithms in Structural Identification and Damage Detection	p. 316
Neural Network-Based Indentification of Structural Parameters in Multistory Buildings	p. 342
Application of Neurocomputing to Parametric Identification Using Dynamic Responses	p. 362
Neural Networks for the Simulation and Identification Analysis of Buildings Subjected to Paraseismic Excitations	p. 393
About the Authors	p. 433
Index	p. 442
Table of Contents provided by Ingram. All Rights Reserved.

Intelligent Computational Paradigms in Earthquake Engineering

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Hardcover

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