Home  About us  Contact
 

Response Problem (response + problem)

Distribution by Scientific Domains
Engineering60%
Mathematics and Statistics40%

Selected Abstracts

A generalized conditional intensity measure approach and holistic ground-motion selection

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 12 2010
Brendon A. Bradley
Abstract A generalized conditional intensity measure (GCIM) approach is proposed for use in the holistic selection of ground motions for any form of seismic response analysis. The essence of the method is the construction of the multivariate distribution of any set of ground-motion intensity measures conditioned on the occurrence of a specific ground-motion intensity measure (commonly obtained from probabilistic seismic hazard analysis). The approach therefore allows any number of ground-motion intensity measures identified as important in a particular seismic response problem to be considered. A holistic method of ground-motion selection is also proposed based on the statistical comparison, for each intensity measure, of the empirical distribution of the ground-motion suite with the ,target' GCIM distribution. A simple procedure to estimate the magnitude of potential bias in the results of seismic response analyses when the ground-motion suite does not conform to the GCIM distribution is also demonstrated. The combination of these three features of the approach make it entirely holistic in that: any level of complexity in ground-motion selection for any seismic response analysis can be exercised; users explicitly understand the simplifications made in the selected suite of ground motions; and an approximate estimate of any bias associated with such simplifications is obtained. Copyright © 2010 John Wiley & Sons, Ltd. [source]

Comparison between non-probabilistic interval analysis method and probabilistic approach in static response problem of structures with uncertain-but-bounded parameters

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 4 2004
Zhiping Qiu
Abstract The uncertainty present in many practical engineering analysis and design problems can be modelled using probabilistic or non-probabilistic interval analysis methods. One motivation for using non-probabilistic interval analysis models rather than probabilistic models for uncertain variables is the general dearth of information in characterizing the uncertainties. Non-probabilistic interval analysis methods are less information-intensive than probabilistic models, since no density information is required. Instead of conventional optimization studies, where the minimum possible response is sought, here an uncertainty model is developed as an anti-optimization problem of finding the least favourable response and the most favourable response under the constraints within the set-theoretical description. Non-probabilistic interval analysis methods have been used for dealing with uncertain phenomena in a wide range of engineering applications. This paper is concerned with the problem of comparison between the non-probabilistic interval analysis method and the probabilistic approach in the static response problem of structures with uncertain-but-bounded parameters from mathematical proofs and numerical calculations. The results show that under the condition of the interval vector of the uncertain parameters determined from the probabilistic and statistical information, the width of the static displacement obtained by the non-probabilistic interval analysis method is larger than that by the probabilistic approach for structures with uncertain-but-bounded structural parameters. This is just the result that we expect, since according to the definition of probabilistic theory and interval mathematics, the region determined by the non-probabilistic interval analysis method should contain one predicted by the probabilistic approach. Copyright © 2004 John Wiley & Sons, Ltd. [source]

FIR filter design problems of simultaneous approximation of magnitude and phase and magnitude and group delay

MATHEMATICAL METHODS IN THE APPLIED SCIENCES, Issue 8 2001
Rembert Reemtsen
Two of the four central design problems for FIR filters in the frequency domain are the problems of simultaneous approximation of prescribed magnitude and phase responses and prescribed magnitude and group delay responses, respectively. In the past, these problems have almost always been approached in indirect and approximative ways only. Especially (approximate) solutions of the simpler frequency response approximation problem have served as substitutes for solutions of the magnitude-phase problem. In this paper, at first a rigorous mathematical formulation of both problems is developed and then, for these problems, the existence of solutions and results on the convergence of the approximation errors are proved. (A method to solve both problems is simultaneously suggested in Görner et al. (Optimization and Engineering 2000; 1:123,154).) Also the improvement, obtained by use of a direct solution of the magnitude-phase response problem instead of a solution of the frequency response problem, is quantified by computable bounds. In the study, the approximation errors are measured by an arbitrary Lp -normresp. lp -norm with 1,p,,, and constraints on the filter coefficients are permitted. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Robust optimization for multiple responses using response surface methodology

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2010
Zhen He
Abstract Typically in the analysis of industrial data for product/process optimization, there are many response variables that are under investigation at the same time. Robustness is also an important concept in industrial optimization. Here, robustness means that the responses are not sensitive to the small changes of the input variables. However, most of the recent work in industrial optimization has not dealt with robustness, and most practitioners follow up optimization calculations without consideration for robustness. This paper presents a strategy for dealing with robustness and optimization simultaneously for multiple responses. In this paper, we propose a robustness desirability function distinguished from the optimization desirability function and also propose an overall desirability function approach, which makes balance between robustness and optimization for multiple response problems. Simplex search method is used to search for the most robust optimal point in the feasible operating region. Finally, the proposed strategy is illustrated with an example from the literature. Copyright © 2009 John Wiley & Sons, Ltd. [source]