Stochastic Decomposition and Application to Probabilistic Dynamics

Li, Yousun; Kareem, Ahsan

doi:10.1061/(asce)0733-9399(1995)121:1(162)

Cited by 41 publications

(16 citation statements)

References 8 publications

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“…The digital filtering based schemes offer efficient methods of simulating random processes . Typical schemes are: auto-regressive (AR), moving average (MA), and their combination autoregressive and moving averages (ARMA) (Samaras et al 1985, Spanos and Mignolet 1992, Li and Kareem, 1989, 1990, 1991. The ARMA representation entails weighted recursive relations that connect the random quantity being simulated at successive time increments .…”

Section: Discussionmentioning

confidence: 99%

“…Lumley (1970) and Armitt (1968) introduced this technique to address turbulence and wind-related problems, respectively, and it was later used by many researchers in describing pressure fluctuations on buildings and structures and a host of wind-related problems (e.g.,Lee 1975; Kareem and Cermak 1984; Holmes 1992; Kareem 1999; Tamura et al 1999;Carassale et al 2001). In stochastic structural mechanics, the POD technique based on the covariance matrix has been utilized for the simulation of spatially varying correlated random variables (e.g.,Yamazaki and Shinozuka 1990), stochastic finite element analysis (e.g.,Ghanem and Spanos 1991), and stochastic dynamics (Li and Kareem 1989, Vasta and Schueller 2000.…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

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Invited Lectures

Tamura

Kareem

Mochida

et al. 2006

Wind Engineers, JAWE

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In order to numerically simulate unsteady flow phenomena, LES or DNS technique should be used. Especially LES is appropriate for application in the wind engineering problem, because the requirement of computational power and memories is reasonable. The present study discusses the applicability of LES to several issues such as practical use for windresistant design, wind velocity affected by the terrain or the ground surface condition, turbulence structures and atmospheric diffusion in the urban area. Comparison with full-scale measurement data is important for all cases to clarify the effectiveness of LES.

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Section: Discussionmentioning

confidence: 99%

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

Invited Lectures

Tamura

Kareem

Mochida

et al. 2006

Wind Engineers, JAWE

Self Cite

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“…In these cases, the issue of having an independent-component input has been tackled by introducing suitable pre-filters aimed to provide the external excitation, characterized by the desired correlation properties, given an independent-component process [17,18]. Among the possible choices, some authors have recognized some advantages in adopting filters based on the proper orthogonal decomposition (POD) [19].…”

Section: Introductionmentioning

confidence: 99%

POD-based filters for the representation of random loads on structures

Carassale

2005

Probabilistic Engineering Mechanics

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“…Typical schemes are: auto-regressive (AR), moving average (MA), and their combination autoregressive and moving averages (ARMA) (Samaras et al 1985, Spanos and Mignolet 1992, Li and Kareem, 1989, 1990, 1993. The ARMA representation entails weighted recursive relations that connect the random quantity being simulated at successive time increments.…”

Section: Parametric Time-series Methodsmentioning

confidence: 99%

“…Lumley (1970) and Armitt (1968) introduced this technique to address turbulence and wind-related problems, respectively, and it was later used by many researchers in describing pressure fluctuations on buildings and structures and a host of wind-related problems (e.g., Lee 1975; Kareem and Cermak 1984;Holmes 1992;Kareem 1999;Tamura et al 1999;Carassale et al 2001). In stochastic structural mechanics, the POD technique based on the covariance matrix has been utilized for the simulation of spatially varying correlated random variables (e.g., Yamazaki and Shinozuka 1990), stochastic finite element analysis (e.g., Ghanem and Spanos 1991), and stochastic dynamics (Li and Kareem 1989, 1995, Vasta and Schueller 2000.…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

Numerical simulation of wind effects: A probabilistic perspective

Kareem

2008

Journal of Wind Engineering and Industrial Aerodynamics

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132

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Numerical simulations of wind loads and their effects are critical in the design of structures to ensure their safety under winds. The simulations range from generation of time histories of wind velocity, pressure and force fluctuations to structural response and assessment of attendant functionality and safety under service and design loads, respectively. Typically these schemes employ Monte Carlo based approaches that encompass model based simulations or information derived from observed data. The scope of simulations spans uni-variate to multivariate processes; uni-dimensional to multi-dimensional fields; Gaussian to non-Gaussian; stationary to non-stationary; conditional and unconditional cases. In order to accomplish these tasks, methods based on the time, frequency, or time-frequency domains are employed. This paper summarizes a historical perspective, recent developments and future challenges. Also included in the discussion are computational tools employed for data analysis, response analysis and its management, and prediction. Examples are presented to illustrate some of the topics discussed.

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Stochastic Decomposition and Application to Probabilistic Dynamics

Cited by 41 publications

References 8 publications

Invited Lectures

Invited Lectures

POD-based filters for the representation of random loads on structures

Numerical simulation of wind effects: A probabilistic perspective

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