Dimension-reduction Representation of Stochastic Ground Motion and Engineering Applications - Couverture rigide

Liu, Zhangjun; Liu, Zixin; Ruan, Xinxin

Couverture rigide

ISBN 10 : 9819201675 ISBN 13 : 9789819201679

Editeur : Springer Nature, 2026

Synopsis

This Open Access book delves into the random orthogonal function-based dimension-reduction simulation method for stochastic ground motion processes (fields), and verifies the effectiveness and engineering applicability through numerical case studies. By incorporating the constraint form of random orthogonal function for standard orthogonal random variables’ set in the original spectral (decomposition) representation of non-stationary stochastic processes (fields), the stochastic ground motion can be accurately represented using only a few elementary random variables. This approach effectively overcomes the challenges encountered by the conventional Monte Carlo methods in the nonlinear analysis of stochastic dynamic systems. In terms of research subjects, the dimension-reduction method facilitates the simulations of various stochastic seismic actions, including univariate (1D-1V) and multivariate (1D-nV) processes, as well as continuous spatio-temporal random fields (mD-1V). Theoretically, the dimension-reduction method enables a unified expression of stochastic processes (fields) across frequency and time domain analysis. The dimension-reduction representation of stochastic ground motion is a full probability model, and it can be naturally integrated with probability density evolution theory, enabling refined random seismic response analysis and reliability evaluation of complex engineering structures. This book is intended for graduate students, researchers, and practicing engineers interested in stochastic ground motion simulation and seismic resistance of engineering structures.

A propos du livre

À propos de l'auteur

Prof. Zhangjun Liu received his Ph.D. degree in civil engineering from Tongji University, Shanghai, China, in 2007. From Mar. 2009 to Mar. 2013, he was an Associate Professor in China Three Gorges University. From Apr. 2013 to Dec. 2018, he served as a Professor and Doctoral Supervisor in China Three Gorges University. Since 2019, he has served as a Distinguished Professor, Doctoral Supervisor, and Discipline Leader in Wuhan Institute of Technology.

Dr. Zixin Liu received his B.E. and M.E. degrees in Civil Engineering from Shenyang University of Technology, Shenyang, China, in 2011 and 2014, respectively, and the Ph.D. degree in Civil Engineering from China Three Gorges University, Yichang, China, in 2020. From Aug. 2020 to Dec. 2022, Dr. Liu was a Lecturer in University of Emergency Management and has been an Associate Professor since 2023.

Dr. Xinxin Ruan received his B.E. and M.E. degrees in Civil Engineering from China Three Gorges University, Yichang, China, in 2016 and 2019, respectively, and the Ph.D. degree in Material Science and Engineering from Wuhan Institute of Technology, Wuhan, China, in 2023. Since Oct. 2023, Dr. Ruan has been in College of Architecture and Civil Engineering, Xinyang Normal University, as a Lecturer.

À propos de la quatrième de couverture

This Open Access book delves into the random orthogonal function-based dimension-reduction simulation method for stochastic ground motion processes (fields), and verifies the effectiveness and engineering applicability through numerical case studies. By incorporating the constraint form of random orthogonal function for standard orthogonal random variables’ set in the original spectral (decomposition) representation of non-stationary stochastic processes (fields), the stochastic ground motion can be accurately represented using only a few elementary random variables. This approach effectively overcomes the challenges encountered by the conventional Monte Carlo methods in the nonlinear analysis of stochastic dynamic systems. In terms of research subjects, the dimension-reduction method facilitates the simulations of various stochastic seismic actions, including univariate (1D-1V) and multivariate (1D-nV) processes, as well as continuous spatio-temporal random fields (mD-1V). Theoretically, the dimension-reduction method enables a unified expression of stochastic processes (fields) across frequency and time domain analysis. The dimension-reduction representation of stochastic ground motion is a full probability model, and it can be naturally integrated with probability density evolution theory, enabling refined random seismic response analysis and reliability evaluation of complex engineering structures. This book is intended for graduate students, researchers, and practicing engineers interested in stochastic ground motion simulation and seismic resistance of engineering structures.

Détails de l'édition

• Éditeur: Springer Nature
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 9819201675
• ISBN 13: 9789819201679
• Reliure: Copertina rigida
• Nombre de pages: 309
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Allemagne