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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010190201 | TA654.6 I574 2007 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Author Notes
Nikos D. Lagaros is an Assistant Professor of Civil Engineering at the Faculty of Civil Engineering, University of Thessaly and Research Associate of the National Technical University of Athens. His research activity is focused in the area of the optimum design of structures under static and seismic loading conditions using evolutionary and hybrid optimization methods. The optimum design problem of real world problems with multiple objective functions has also been a subject of research using specially tailored genetic algorithms and evolution strategies. He has more than 120 publications including 37 refereed international journal papers Yiannis Tsompanakis has received his M.Sc. and Ph.D. in Civil Engineering from the Department of Civil Engineering, National Technical University of Athens, Greece. He is currently an Assistant Professor of structural earthquake engineering at the Department of Applied Sciences, Technical University of Crete, Greece. He teaches undergraduate and postgraduate courses in structural mechanics and earthquake engineering and he is a supervisor of diploma, master and doctoral theses. He is a reviewer for archival journals and he has participated in the organization of several international congresses.
Table of Contents
| Foreword | p. vi |
| Preface | p. viii |
| Section I Structural Optimization Applications | |
| Chapter I Improved Seismic Design Procedures and Evolutionary Tools | p. 1 |
| Chapter II Applying Neural Networks for Performance-Based Design in Earthquake Engineering | p. 22 |
| Chapter III Evolutionary Seismic Design for Optimal Performance | p. 42 |
| Chapter IV Optimal Reliability-Based Design Using Support Vector Machines and Artiticial Life Algorithms | p. 59 |
| Chapter V Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network | p. 80 |
| Chapter VI Developments in Structural Optimization and Applications to Intelligent Structural Vibration Control | p. 101 |
| Section II Structural Assessment Applications | |
| Chapter VII Neuro-Fuzzy Assesment of Building Damage and Safety After an Earthquake | p. 123 |
| Chapter VIII Learning Machines for Structural Damage Detection | p. 158 |
| Chapter IX Structural Assessment of RC Constructions and Fuzzy Expert Systems | p. 188 |
| Chapter X Life-Cycle Cost Evaluation of Bridge Structures Considering Seismic Risk | p. 231 |
| Chapter XI Soft Computing Techniques in Probabilistic Seismic Analysis of Structures | p. 248 |
| Section III Structural Identification Applications | |
| Chapter XII Inverse Analysis of Weak and Strong Motion Downhole Array Data: A Hybrid Optimization Algorithm | p. 271 |
| Chapter XIII Genetic Algorithms in Structural Identification and Damage Detection | p. 316 |
| Chapter XIV Neural Network-Based Indentification of Structural Parameters in Multistory Buildings | p. 342 |
| Chapter XV Application of Neurocomputing to Parametric Identification Using Dynamic Responses | p. 362 |
| Chapter XVI Neural Networks for the Simulation and Identification Analysis of Buildings Subjected to Paraseismic Excitations | p. 393 |
| About the Authors | p. 433 |
| Index | p. 442 |
