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Embracing Generative AI in Construction through a Quantitative Analysis and Weighted Score Ranking of Perceptions, Applications, and Complexities

dc.contributor.authorSadeh, Hooman
dc.contributor.authorZhang, Ruichuan
dc.contributor.authorGaedicke, Cristian
dc.contributor.authorShahbodaghlou, Farzad
dc.contributor.authorLee, Min Jae
dc.contributor.authorTodorov, Dimitar
dc.date.accessioned2026-08-20T10:42:35Z
dc.date.available2026-08-20T10:42:35Z
dc.date.copyright2026/03/13
dc.date.issued2026
dc.identifier.otherJCEMD4.COENG-17444.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311152
dc.description.abstractAbstract As generative artificial intelligence (AI) tools such as generative pretrained transformer–based language models gain prominence, the construction industry faces adoption challenges that differ from deterministic tools. Generative AI (GenAI) is ...
dc.publisherAmerican Society of Civil Engineers
dc.titleEmbracing Generative AI in Construction through a Quantitative Analysis and Weighted Score Ranking of Perceptions, Applications, and Complexities
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue5
dc.journal.titleJournal of Construction Engineering and Management
dc.identifier.doi10.1061/JCEMD4.COENG-17444
dc.journal.fristpage04026041-1
dc.journal.lastpage04026041-20
dc.page20
dc.treeJournal of Construction Engineering and Management:;2026:;Volume ( 152 ):;issue: 005
dc.contenttypeFulltext


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