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dc.contributor.authorTseranidis, Stavros
dc.contributor.authorBrown, Nathan Collin
dc.contributor.authorMueller, Caitlin T
dc.date.accessioned2018-12-04T16:07:42Z
dc.date.available2018-12-04T16:07:42Z
dc.date.issued2016-12
dc.identifier.issn0926-5805
dc.identifier.urihttp://hdl.handle.net/1721.1/119411
dc.description.abstractThis paper explores the use of data-driven approximation algorithms, often called surrogate modeling, in the early-stage design of structures. The use of surrogate models to rapidly evaluate design performance can lead to a more in-depth exploration of a design space and reduce computational time of optimization algorithms. While this approach has been widely developed and used in related disciplines such as aerospace engineering, there are few examples of its application in civil engineering. This paper focuses on the general use of surrogate modeling in the design of civil structures and examines six model types that span a wide range of characteristics. Original contributions include novel metrics and visualization techniques for understanding model error and a new robustness framework that accounts for variability in model comparison. These concepts are applied to a multi-objective case study of an airport terminal design that considers both structural material volume and operational energy consumption. Key Words: surrogate modelling, machine learning, approximation, structural designen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.autcon.2016.02.002en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceCaitlin Muelleren_US
dc.titleData-driven approximation algorithms for rapid performance evaluation and optimization of civil structuresen_US
dc.typeArticleen_US
dc.identifier.citationTseranidis, Stavros, Nathan C. Brown, and Caitlin T. Mueller. “Data-Driven Approximation Algorithms for Rapid Performance Evaluation and Optimization of Civil Structures.” Automation in Construction 72 (December 2016): 279–293.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Architectureen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computation for Design and Optimization Programen_US
dc.contributor.approverMueller, Caitlin T.en_US
dc.contributor.mitauthorTseranidis, Stavros
dc.contributor.mitauthorBrown, Nathan Collin
dc.contributor.mitauthorMueller, Caitlin T
dc.relation.journalAutomation in Constructionen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsTseranidis, Stavros; Brown, Nathan C.; Mueller, Caitlin T.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-9554-8292
dc.identifier.orcidhttps://orcid.org/0000-0003-1538-9787
dc.identifier.orcidhttps://orcid.org/0000-0001-7646-8505
mit.licensePUBLISHER_CCen_US


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