{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2266"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2266","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Fine-tuning a domain-specific language model for truss structural analysis","abstract":"This research investigates the feasibility of fine-tuning a domain-specific vison-language and large-language for truss structural analysis. General-purpose AI models often struggle with engineering-specific problems due to insufficient domain knowledge. To address this, we propose a hybrid workflow for truss analysis via the stiffness method as a case study. The project leverages a curated dataset of 27 truss templates and expanded through geometric augmentation, load randomization, and support variations. Llama 3.2 Vision Instruct was fine-tuned using the parameter-efficient fine-tuning to produce truss description from images, and T5-large was fine-tuned to convert these text description into JSON format for analysis using the stiffness method. Model performance was evaluated against the ground truth for node coordinates, elements, loads, and support conditions. This research demonstrates the potential of fine-tuned domain-specific language models to automate engineering analysis and design workflows, offering engineers and students a practical tool for rapid and accurate structural analysis.","abstract_html":"This research investigates the feasibility of fine-tuning a domain-specific vison-language and large-language for truss structural analysis. General-purpose AI models often struggle with engineering-specific problems due to insufficient domain knowledge. To address this, we propose a hybrid workflow for truss analysis via the stiffness method as a case study. The project leverages a curated dataset of 27 truss templates and expanded through geometric augmentation, load randomization, and support variations. Llama 3.2 Vision Instruct was fine-tuned using the parameter-efficient fine-tuning to produce truss description from images, and T5-large was fine-tuned to convert these text description into JSON format for analysis using the stiffness method. Model performance was evaluated against the ground truth for node coordinates, elements, loads, and support conditions. This research demonstrates the potential of fine-tuned domain-specific language models to automate engineering analysis and design workflows, offering engineers and students a practical tool for rapid and accurate structural analysis.","abstract_has_math":false,"creators":["Dey, Rajon"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wu, Weidong","Owino, Joseph; Liang, Yu; Fomunung, Ignatius; Onyango, Mbakisya A.","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2027,"date_issued":"2027-05-31T07:00:00Z","date_published":"2027-05-31T07:00:00Z","updated_at":"2026-07-24T05:47:28Z","subjects":["Machine learning","Natural language processing (Computer science)","Structural analysis (Engineering)"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/1070","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wu, Weidong","Owino, Joseph; Liang, Yu; Fomunung, Ignatius; Onyango, Mbakisya A.","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Dey, Rajon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-05-01T07:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2027-05-31T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning","Natural language processing (Computer science)","Structural analysis (Engineering)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/1070"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Civil and Chemical Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["This research investigates the feasibility of fine-tuning a domain-specific vison-language and large-language for truss structural analysis. General-purpose AI models often struggle with engineering-specific problems due to insufficient domain knowledge. To address this, we propose a hybrid workflow for truss analysis via the stiffness method as a case study. The project leverages a curated dataset of 27 truss templates and expanded through geometric augmentation, load randomization, and support variations. Llama 3.2 Vision Instruct was fine-tuned using the parameter-efficient fine-tuning to produce truss description from images, and T5-large was fine-tuned to convert these text description into JSON format for analysis using the stiffness method. Model performance was evaluated against the ground truth for node coordinates, elements, loads, and support conditions. This research demonstrates the potential of fine-tuned domain-specific language models to automate engineering analysis and design workflows, offering engineers and students a practical tool for rapid and accurate structural analysis."]},{"key":"dc:title","label":"Title","values":["Fine-tuning a domain-specific language model for truss structural analysis"]}]}],"canonical_facts":{"dc:contributor":["Wu, Weidong","Owino, Joseph; Liang, Yu; Fomunung, Ignatius; Onyango, Mbakisya A.","College of Engineering and Computer Science"],"dc:creator":["Dey, Rajon"],"dc:date":["2026-05-01T07:00:00Z"],"dc:date.available":["2027-05-31T07:00:00Z"],"dc:description":["Dept. of Civil and Chemical Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["This research investigates the feasibility of fine-tuning a domain-specific vison-language and large-language for truss structural analysis. General-purpose AI models often struggle with engineering-specific problems due to insufficient domain knowledge. To address this, we propose a hybrid workflow for truss analysis via the stiffness method as a case study. The project leverages a curated dataset of 27 truss templates and expanded through geometric augmentation, load randomization, and support variations. Llama 3.2 Vision Instruct was fine-tuned using the parameter-efficient fine-tuning to produce truss description from images, and T5-large was fine-tuned to convert these text description into JSON format for analysis using the stiffness method. Model performance was evaluated against the ground truth for node coordinates, elements, loads, and support conditions. This research demonstrates the potential of fine-tuned domain-specific language models to automate engineering analysis and design workflows, offering engineers and students a practical tool for rapid and accurate structural analysis."],"dc:identifier":["https://scholar.utc.edu/theses/1070"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Machine learning","Natural language processing (Computer science)","Structural analysis (Engineering)"],"dc:title":["Fine-tuning a domain-specific language model for truss structural analysis"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:28Z"}