Preface

At the dawn of the twenty-first century, the considerable influence of inherent uncertainties on structural behavior has led the engineering community to recognize the importance of a stochastic approach to structural problems. Issues related to uncertainty quantification and its influence on the reliability of the computational models, are continuously gaining in significance. In particular, the problems of dynamic response analysis and reliability assessment of structures with uncertain system and excitation parameters have been the subject of extensive research over the last two decades as a result of the increasing availability of powerful computing resources and technology. This book focuses on advanced computational methods and software tools which can highly assist in tackling complex problems in stochastic dynamic/seismic analysis and design of structures. The selected chapters are authored by some of the most active scholars in their respective areas and represent some of the most recent developments in this field. This edited book is primarily intended for researchers and post-graduate students who are familiar with the fundamentals and wish to study or to advance the state of the art on a particular topic in the field of computational stochastic structural dynamics. Nevertheless, practicing engineers could benefit as well from it as most code provisions tend to incorporate probabilistic concepts in the analysis and design of structures. The book consists of 16 chapters which are extended versions of papers presented at the recent COMPDYN 2009 and SEECCM 2009 Conferences. The chapters can be grouped into several thematic topics including dynamic response variability and reliability of stochastic systems, risk assessment, stochastic simulation of earthquake ground motions, efficient solvers for the analysis of stochastic systems, dynamic stability and stochastic modeling of heterogeneous materials. In Chapter 1, G.I. Schuëller gives an overview of the well established deterministic model reduction techniques (Guyan reduction, component mode synthesis) and approaches for efficient uncertainty propagation in structural dynamics. The recent advances in the combination of these two fields are reviewed and methodologies for the consideration of uncertainty when using model reduction schemes are presented. The capabilities of a Karhunen-Loève expansion of the substructure matrices and of a random combination of substructures are investigated. It is shown that the proposed approaches can combine high accuracy with a substantial reduction of the computational cost compared to Monte Carlo simulation (MCS) of the full model.