{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/20699"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/20699","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Leveraging Gaussian Process Sampling for Sensitivity Analysis and Optimization in Engineering Design","abstract":"High-fidelity simulations and physical experiments are fundamental in engineering analysis and design. However, their high computational cost often prohibits their application in global sensitivity analysis (GSA), optimization, and automated structural health monitoring (SHM). Gaussian processes (GPs) are proposed as a promising solution to this challenge. GPs inherently facilitate efficient sampling strategies, enabling informed decision-making under uncertainty by extracting information from a subset of potential functions for the model of interest. Despite their widespread use in machine learning and scientific computing, and the potential they hold for realizing intelligent infrastructural systems via Digital Twin, GP sampling strategies have received little attention in engineering applications. This thesis thus presents the mathematical foundations of GPs and provides a detailed implementation of two sampling methods—Fourier decomposition-based and pathwise conditioning—for generating approximate stochastic functions from GPs. It then discusses the application of these sampled stochastic functions in engineering tasks such as GSA, single-objective optimization, and multi-objective optimization. Towards realizing intelligent engineering systems, this thesis finally proposes a DT framework that leverages GP for efficient model updating and optimal decision-making.","abstract_html":"High-fidelity simulations and physical experiments are fundamental in engineering analysis and design. However, their high computational cost often prohibits their application in global sensitivity analysis (GSA), optimization, and automated structural health monitoring (SHM). Gaussian processes (GPs) are proposed as a promising solution to this challenge. GPs inherently facilitate efficient sampling strategies, enabling informed decision-making under uncertainty by extracting information from a subset of potential functions for the model of interest. Despite their widespread use in machine learning and scientific computing, and the potential they hold for realizing intelligent infrastructural systems via Digital Twin, GP sampling strategies have received little attention in engineering applications. This thesis thus presents the mathematical foundations of GPs and provides a detailed implementation of two sampling methods—Fourier decomposition-based and pathwise conditioning—for generating approximate stochastic functions from GPs. It then discusses the application of these sampled stochastic functions in engineering tasks such as GSA, single-objective optimization, and multi-objective optimization. Towards realizing intelligent engineering systems, this thesis finally proposes a DT framework that leverages GP for efficient model updating and optimal decision-making.","abstract_has_math":false,"creators":["Ajenifuja, Nafeezat Adetoro 1999-"],"institution":"University of Houston","degree_name":"Master of Science in Civil Engineering","degree_level":null,"degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Zhang, Ruda"],"committee_chairs":[],"committee_members":["Beck, Abigail","Cao, Jian","Nakshatrala, Kalyana Babu"],"year":2025,"date_issued":"2025-08","date_published":"2025-08","updated_at":"2026-07-24T02:33:01Z","subjects":["Civil engineering"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/20699","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zhang, Ruda"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Beck, Abigail","Cao, Jian","Nakshatrala, Kalyana Babu"]},{"key":"dc:creator","label":"Author","values":["Ajenifuja, Nafeezat Adetoro 1999-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-10-06T19:41:03Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Civil Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Civil engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/20699"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["High-fidelity simulations and physical experiments are fundamental in engineering analysis and design. 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It then discusses the application of these sampled stochastic functions in engineering tasks such as GSA, single-objective optimization, and multi-objective optimization. Towards realizing intelligent engineering systems, this thesis finally proposes a DT framework that leverages GP for efficient model updating and optimal decision-making."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Leveraging Gaussian Process Sampling for Sensitivity Analysis and Optimization in Engineering Design"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zhang, Ruda"],"dc:contributor.committeemember":["Beck, Abigail","Cao, Jian","Nakshatrala, Kalyana Babu"],"dc:creator":["Ajenifuja, Nafeezat Adetoro 1999-"],"dc:date.accessioned":["2025-10-06T19:41:03Z"],"dc:date.issued":["2025-08"],"dc:description.abstract":["High-fidelity simulations and physical experiments are fundamental in engineering analysis and design. However, their high computational cost often prohibits their application in global sensitivity analysis (GSA), optimization, and automated structural health monitoring (SHM). Gaussian processes (GPs) are proposed as a promising solution to this challenge. GPs inherently facilitate efficient sampling strategies, enabling informed decision-making under uncertainty by extracting information from a subset of potential functions for the model of interest. Despite their widespread use in machine learning and scientific computing, and the potential they hold for realizing intelligent infrastructural systems via Digital Twin, GP sampling strategies have received little attention in engineering applications. This thesis thus presents the mathematical foundations of GPs and provides a detailed implementation of two sampling methods—Fourier decomposition-based and pathwise conditioning—for generating approximate stochastic functions from GPs. It then discusses the application of these sampled stochastic functions in engineering tasks such as GSA, single-objective optimization, and multi-objective optimization. Towards realizing intelligent engineering systems, this thesis finally proposes a DT framework that leverages GP for efficient model updating and optimal decision-making."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/20699"],"dc:language.iso":["English"],"dc:subject":["Civil engineering"],"dc:title":["Leveraging Gaussian Process Sampling for Sensitivity Analysis and Optimization in Engineering Design"],"dc:type":["Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_name":["Master of Science in Civil Engineering"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:33:01Z"}