{"id":{"repo_id":"tdl","oai_identifier":"oai:tdl-ir.tdl.org:10877/8164"},"canonical_url":"https://search.dev.ndltd.org/etd/tdl/oai:tdl-ir.tdl.org:10877/8164","repository":{"repo_id":"tdl","name":"Texas Digital Library","base_url":"https://tdl-ir.tdl.org/server/oai/request"},"display":{"title":"Construction Demand Forecasting Based on Conventional and Supervised Machine Learning Methods","abstract":"No abstract prepared.","abstract_html":"No abstract prepared.","abstract_has_math":false,"creators":["Ghanbari, Ali"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Ameri, Farhad","Mendez Mediavilla, Francis A.","Torres, Anthony"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-05","date_published":"2019-05","updated_at":"2026-07-27T21:19:08Z","subjects":["construction","demand","indicators","machine learning","total market size","total fleet size"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["Ghanbari, A. (2019). Construction demand forecasting based on conventional and supervised machine learning methods [Master's thesis, Texas State University]."],"render_values":[{"text":"Ghanbari, A. (2019). Construction demand forecasting based on conventional and supervised machine learning methods [Master's thesis, Texas State University].","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10877/8164","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ameri, Farhad","Mendez Mediavilla, Francis A.","Torres, Anthony"]},{"key":"dc:creator","label":"Author","values":["Ghanbari, Ali"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-05-07T20:36:07Z","2026-02-27T15:05:38Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-05-07T20:36:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["construction","demand","indicators","machine learning","total market size","total fleet size"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["Ghanbari, A. (2019). Construction demand forecasting based on conventional and supervised machine learning methods [Master's thesis, Texas State University].","https://hdl.handle.net/10877/8164"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10877/8164"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["No abstract prepared."]},{"key":"dc:title","label":"Title","values":["Construction Demand Forecasting Based on Conventional and Supervised Machine Learning Methods"]}]}],"canonical_facts":{"dc:contributor":["Ameri, Farhad","Mendez Mediavilla, Francis A.","Torres, Anthony"],"dc:creator":["Ghanbari, Ali"],"dc:date.accessioned":["2019-05-07T20:36:07Z","2026-02-27T15:05:38Z"],"dc:date.available":["2019-05-07T20:36:07Z"],"dc:date.issued":["2019-05"],"dc:description.abstract":["No abstract prepared."],"dc:identifier":["Ghanbari, A. (2019). Construction demand forecasting based on conventional and supervised machine learning methods [Master's thesis, Texas State University].","https://hdl.handle.net/10877/8164"],"dc:identifier.uri":["https://hdl.handle.net/10877/8164"],"dc:language":["en"],"dc:subject":["construction","demand","indicators","machine learning","total market size","total fleet size"],"dc:title":["Construction Demand Forecasting Based on Conventional and Supervised Machine Learning Methods"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T21:19:08Z"}