Surrogate models for high performance control systems in wind-excited tall buildings Micheli, Laura Hong, Jonathan Laflamme, Simon Laflamme, Simon Alipour, Alice
dc.contributor.department Civil, Construction and Environmental Engineering
dc.contributor.department Electrical and Computer Engineering
dc.contributor.department Center for Nondestructive Evaluation (CNDE) 2020-02-10T19:45:09.000 2020-06-30T01:13:16Z 2020-06-30T01:13:16Z Wed Jan 01 00:00:00 UTC 2020 2022-01-31 2020-05-01
dc.description.abstract <p>High-performance control systems (HPCSs), including active, semi-active, and hybrid systems, have been demonstrated as promising methods to mitigate a variety of excitations. However, their deployment in the field is still very limited, attributable to reliability concerns in the closed loop configuration. A solution to promote their applicability is the development of an uncertainty-based design procedure, but such solution comes at a high computational cost due to the large number of possible scenarios to consider on both the closed-loop configuration and external load sides. To alleviate the computational demand of such analysis, this paper investigates the use of data-driven surrogate assisted techniques for uncertainty quantification of HPCSs deployed in wind-excited tall buildings. Both a Kriging surrogate and an adaptive wavelet network (AWN) are investigated and compared to map the unknown relationship between structural inputs and responses. The Kriging model exploits an offline batch learning process while the AWN uses an online sequential process. The surrogate models are applied to a 39-story building equipped with semi-active friction devices exposed to wind load and are compared in terms of accuracy and computational time. Two applications of the surrogate models for uncertainty analysis of the case study building are presented. One is for uncertainty quantification and the other for identification of the most influential uncertain variables. Results show that Kriging provides a more accurate representation to map uncertainties to the system response and to quantify the uncertain performance of HPCSs, but that the AWN provides a significantly faster computational alternative. In particular, for a case containing 17 uncertain variables, Kriging found a representation in 3h20, while the AWN converged in 37 min. Under 41 uncertain variables, these metrics increased to 16h20 and 3h22 for Kriging and the AWN, respectively. These representations were leveraged to identify and remove the uncertainties from three key variables yielding high variance in structural response. Results showed that variables identified under Kriging yielded a 34.9% decrease in variance under 17 uncertain inputs and a 22.9% decrease in variance under 41 uncertain inputs, while AWN yielded a 29.0% and 19.8% decrease, respectively.</p>
dc.description.comments <p>This is a manuscript of an article published as Micheli, Laura, Jonathan Hong, Simon Laflamme, and Alice Alipour. "Surrogate models for high performance control systems in wind-excited tall buildings." <em>Applied Soft Computing</em> 90 (2020): 106133. DOI: <a href="" target="_blank">10.1016/j.asoc.2020.106133</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1265
dc.identifier.contextkey 16484490
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_pubs/264
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 23:03:03 UTC 2022
dc.source.uri 10.1016/j.asoc.2020.106133
dc.subject.disciplines Civil and Environmental Engineering
dc.subject.disciplines Controls and Control Theory
dc.title Surrogate models for high performance control systems in wind-excited tall buildings
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 84547f08-8710-4934-b91e-ba5f46ab9abe
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relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
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