© 2004

Structural Reliability

Statistical Learning Perspectives


Part of the Lecture Notes in Applied and Computational Mechanics book series (LNACM, volume 17)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Jorge E. Hurtado
    Pages 45-79
  3. Jorge E. Hurtado
    Pages 81-105
  4. Jorge E. Hurtado
    Pages 107-143
  5. Jorge E. Hurtado
    Pages 191-218
  6. Back Matter
    Pages 241-257

About this book


This monograph presents an original approach to Structural Reliability from the perspective of Statistical Learning Theory. It proposes new methods for solving the reliability problem utilizing the recent developments in Computational Learning Theory, such as Neural Networks and Support Vector machines. It also demonstrates important issues on the management of samples in Monte Carlo simulation for structural reliability analysis purposes and examines the treatment of the structural reliability problem as a pattern recognition or classification task. This carefully written monograph is aiming at researchers and students in civil and mechanical engineering, especially in reliability engineering, structural analysis, or statistical learning.


Chaos Regression Stab Transformation Vibration Wavelet algorithms classification complexity linear optimization mechanical engineering simulation stability structural analysis structure

Authors and affiliations

  1. 1.Civil EngineeringUniversidad Nacional de ColombiaManizalesColombia

Bibliographic information

Industry Sectors
Oil, Gas & Geosciences


From the reviews:

"The methods presented and exemplified in the book are what in the statistical world would be called nonlinear and nonparametric regression or pattern recognition techniques … . The book is written from an algorithmic perspective … . the book is a valuable overview of problems and techniques used in structural safety analysis." (Georg Lindgren, Mathematical Reviews, Issue 2006 h)