A damage detection algorithm and procedure is presented in this dissertation that utilizes static response data and Optimality Criterion optimization. Static displacement measurements are used as constraints in the damage detection algorithm that identifies potential areas of damage in structural systems. The research aims to improve upon the master's level research performed by the author. First, the robustness of the algorithm is improved by use of a least squares approximation for determining the Lagrange multipliers that are necessary for optimization. Second, an active parameter selection subroutine is used to improve the accuracy of damage detection in the presence of experimental error. Third, an optimal load case algorithm is presented to eliminate procedural ambiguity and help engineers determine the best load case locations for damage detection. Last, modeling was improved with the creation of a new finite element that better models reduced moment resistance in connections. The new element is derived from elementary principles and is very well suited for optimization. The research attempts to utilize experimental test data whenever possible. When test data is not available, efforts are made to simulate real test conditions for damage detection. To illustrate the robustness of the algorithm, damage is detected in three different structural types. Reduced flexural stiffness is detected in a steel moment frame, reduced cross sectional area is detected in a three dimensional truss, and lastly a combination of reduced flexural stiffness and reduced moment capacity of connections is detected in a three dimensional structural grid.
","abstract_html":"<p>A damage detection algorithm and procedure is presented in this dissertation that utilizes static response data and Optimality Criterion optimization. Static displacement measurements are used as constraints in the damage detection algorithm that identifies potential areas of damage in structural systems. The research aims to improve upon the master's level research performed by the author. First, the robustness of the algorithm is improved by use of a least squares approximation for determining the Lagrange multipliers that are necessary for optimization. Second, an active parameter selection subroutine is used to improve the accuracy of damage detection in the presence of experimental error. Third, an optimal load case algorithm is presented to eliminate procedural ambiguity and help engineers determine the best load case locations for damage detection. Last, modeling was improved with the creation of a new finite element that better models reduced moment resistance in connections. The new element is derived from elementary principles and is very well suited for optimization. The research attempts to utilize experimental test data whenever possible. When test data is not available, efforts are made to simulate real test conditions for damage detection. To illustrate the robustness of the algorithm, damage is detected in three different structural types. Reduced flexural stiffness is detected in a steel moment frame, reduced cross sectional area is detected in a three dimensional truss, and lastly a combination of reduced flexural stiffness and reduced moment capacity of connections is detected in a three dimensional structural grid.</p>","abstract_has_math":false,"creators":["Terlaje, Augusto"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering and Materials Science","degree_department":null,"school":null,"contributors":["Kevin Truman"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-05-10T07:00:00Z","date_published":"2010-05-10T07:00:00Z","updated_at":"2026-07-24T06:13:14Z","subjects":["Engineering","Civil","damage detection","health monitoring","lagrange multiplier method","optimality criterion","static response","structural optimization"],"languages":["English (en)"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7936/K70863CF"],"render_values":[{"text":"https://doi.org/10.7936/K70863CF","href":"https://doi.org/10.7936/K70863CF","code":true}]}]},"links":{"outbound_url":"https://openscholarship.wustl.edu/etd/345","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kevin Truman"]},{"key":"dc:creator","label":"Author","values":["Terlaje, Augusto"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2010-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering and Materials Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering","Civil","damage detection","health monitoring","lagrange multiplier method","optimality criterion","static response","structural optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/etd/345"]},{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7936/K70863CF"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A damage detection algorithm and procedure is presented in this dissertation that utilizes static response data and Optimality Criterion optimization. Static displacement measurements are used as constraints in the damage detection algorithm that identifies potential areas of damage in structural systems. The research aims to improve upon the master's level research performed by the author. First, the robustness of the algorithm is improved by use of a least squares approximation for determining the Lagrange multipliers that are necessary for optimization. Second, an active parameter selection subroutine is used to improve the accuracy of damage detection in the presence of experimental error. Third, an optimal load case algorithm is presented to eliminate procedural ambiguity and help engineers determine the best load case locations for damage detection. Last, modeling was improved with the creation of a new finite element that better models reduced moment resistance in connections. The new element is derived from elementary principles and is very well suited for optimization. The research attempts to utilize experimental test data whenever possible. When test data is not available, efforts are made to simulate real test conditions for damage detection. To illustrate the robustness of the algorithm, damage is detected in three different structural types. Reduced flexural stiffness is detected in a steel moment frame, reduced cross sectional area is detected in a three dimensional truss, and lastly a combination of reduced flexural stiffness and reduced moment capacity of connections is detected in a three dimensional structural grid.
"]},{"key":"dc:title","label":"Title","values":["An Algorithm and Methodology for Static Response Based Damage Detection in Structural Systems"]}]}],"canonical_facts":{"dc:contributor":["Kevin Truman"],"dc:creator":["Terlaje, Augusto"],"dc:date.available":["2010-01-01T08:00:00Z"],"dc:description.abstract":["A damage detection algorithm and procedure is presented in this dissertation that utilizes static response data and Optimality Criterion optimization. Static displacement measurements are used as constraints in the damage detection algorithm that identifies potential areas of damage in structural systems. The research aims to improve upon the master's level research performed by the author. First, the robustness of the algorithm is improved by use of a least squares approximation for determining the Lagrange multipliers that are necessary for optimization. Second, an active parameter selection subroutine is used to improve the accuracy of damage detection in the presence of experimental error. Third, an optimal load case algorithm is presented to eliminate procedural ambiguity and help engineers determine the best load case locations for damage detection. Last, modeling was improved with the creation of a new finite element that better models reduced moment resistance in connections. The new element is derived from elementary principles and is very well suited for optimization. The research attempts to utilize experimental test data whenever possible. When test data is not available, efforts are made to simulate real test conditions for damage detection. To illustrate the robustness of the algorithm, damage is detected in three different structural types. Reduced flexural stiffness is detected in a steel moment frame, reduced cross sectional area is detected in a three dimensional truss, and lastly a combination of reduced flexural stiffness and reduced moment capacity of connections is detected in a three dimensional structural grid.
"],"dc:identifier":["https://openscholarship.wustl.edu/etd/345"],"dc:identifier.doi":["https://doi.org/10.7936/K70863CF"],"dc:language":["English (en)"],"dc:subject":["Engineering","Civil","damage detection","health monitoring","lagrange multiplier method","optimality criterion","static response","structural optimization"],"dc:title":["An Algorithm and Methodology for Static Response Based Damage Detection in Structural Systems"],"thesis:degree_discipline":["Mechanical Engineering and Materials Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T06:13:14Z"}