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Uncertainty in structural dynamic models. / Jose Manuel Rios Fonseca

Swansea University Author: Jose Manuel Rios Fonseca

Abstract

Modelling of uncertainty increases trust in analysis tools by providing predictions with confidence levels, produces more robust designs, and reduces design cycle time/cost by reducing the amount of experimental verification and validation that is required. However, uncertainty-based methods are mor...

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Published: 2005
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42563
first_indexed 2018-08-02T18:55:00Z
last_indexed 2018-08-03T10:10:29Z
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recordtype RisThesis
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spelling 2018-08-02T16:24:29.6809968 v2 42563 2018-08-02 Uncertainty in structural dynamic models. 7c92865532c1e4c2cb888dd736bf4d34 NULL Jose Manuel Rios Fonseca Jose Manuel Rios Fonseca true true 2018-08-02 Modelling of uncertainty increases trust in analysis tools by providing predictions with confidence levels, produces more robust designs, and reduces design cycle time/cost by reducing the amount of experimental verification and validation that is required. However, uncertainty-based methods are more complex and computationally expensive than their deterministic counterparts, the characterisation of uncertainties is a non-trivial task, and the industry feels comfortable with the traditional design methods. In this work the three most popular uncertainty propagation methods (Monte Carlo simulation, perturbation, and fuzzy) are extensively benchmarked in structural dynamics applications. The main focus of the benchmark is accuracy, simplicity, and scalability. Some general guidelines for choosing the adequate uncertainty propagation method for an application are given. Since direct measurement is often prohibitively costly or even impossible, a novel method to characterise uncertainty sources from indirect measurements is presented. This method can accurately estimate the probability distribution of uncertain parameters by maximising the likelihood of the measurements. The likelihood is estimated using efficient variations of the Monte Carlo simulation and perturbation methods, which shift the computational burden to the outside of the optimisation loop, achieving a substantial time-saving without compromising accuracy. The approach was verified experimentally in several applications with promising results. A novel probabilistic procedure for robust design is proposed. It is based on reweighting of the Monte Carlo samples to avoid the numerical inefficiencies of resampling for every candidate design. Although not globally convergent, the proposed method is able to quickly estimate with high accuracy the optimum design. The method is applied to a numerical example, and the obtained designs are verified with regular Monte Carlo. The main focus of this work was on structural dynamics, but care was taken to make the approach general enough to allow other kinds of structural and non- structural analyses. E-Thesis Civil engineering. 31 12 2005 2005-12-31 COLLEGE NANME Engineering COLLEGE CODE Swansea University Doctoral Ph.D 2018-08-02T16:24:29.6809968 2018-08-02T16:24:29.6809968 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Jose Manuel Rios Fonseca NULL 1 0042563-02082018162504.pdf 10805312.pdf 2018-08-02T16:25:04.3470000 Output 4406004 application/pdf E-Thesis true 2018-08-02T16:25:04.3470000 false
title Uncertainty in structural dynamic models.
spellingShingle Uncertainty in structural dynamic models.
Jose Manuel Rios Fonseca
title_short Uncertainty in structural dynamic models.
title_full Uncertainty in structural dynamic models.
title_fullStr Uncertainty in structural dynamic models.
title_full_unstemmed Uncertainty in structural dynamic models.
title_sort Uncertainty in structural dynamic models.
author_id_str_mv 7c92865532c1e4c2cb888dd736bf4d34
author_id_fullname_str_mv 7c92865532c1e4c2cb888dd736bf4d34_***_Jose Manuel Rios Fonseca
author Jose Manuel Rios Fonseca
author2 Jose Manuel Rios Fonseca
format E-Thesis
publishDate 2005
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description Modelling of uncertainty increases trust in analysis tools by providing predictions with confidence levels, produces more robust designs, and reduces design cycle time/cost by reducing the amount of experimental verification and validation that is required. However, uncertainty-based methods are more complex and computationally expensive than their deterministic counterparts, the characterisation of uncertainties is a non-trivial task, and the industry feels comfortable with the traditional design methods. In this work the three most popular uncertainty propagation methods (Monte Carlo simulation, perturbation, and fuzzy) are extensively benchmarked in structural dynamics applications. The main focus of the benchmark is accuracy, simplicity, and scalability. Some general guidelines for choosing the adequate uncertainty propagation method for an application are given. Since direct measurement is often prohibitively costly or even impossible, a novel method to characterise uncertainty sources from indirect measurements is presented. This method can accurately estimate the probability distribution of uncertain parameters by maximising the likelihood of the measurements. The likelihood is estimated using efficient variations of the Monte Carlo simulation and perturbation methods, which shift the computational burden to the outside of the optimisation loop, achieving a substantial time-saving without compromising accuracy. The approach was verified experimentally in several applications with promising results. A novel probabilistic procedure for robust design is proposed. It is based on reweighting of the Monte Carlo samples to avoid the numerical inefficiencies of resampling for every candidate design. Although not globally convergent, the proposed method is able to quickly estimate with high accuracy the optimum design. The method is applied to a numerical example, and the obtained designs are verified with regular Monte Carlo. The main focus of this work was on structural dynamics, but care was taken to make the approach general enough to allow other kinds of structural and non- structural analyses.
published_date 2005-12-31T04:20:50Z
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score 11.089572

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