Evaluating the Resilience of Graph Neural Network Architectures to Adversarial and Noisy Data in High-Stakes Construction Project Management

dc.contributor.author	Toğan, Vedat
dc.contributor.author	Mostofi, Fatemeh
dc.contributor.author	Tokdemir, Onur Behzat
dc.date.accessioned	2026-08-20T10:41:33Z
dc.date.available	2026-08-20T10:41:33Z
dc.date.copyright	2026/02/06
dc.date.issued	2026
dc.identifier.other	JCEMD4.COENG-17244.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311131
dc.description.abstract	AbstractHigh-stakes mega-construction projects present a challenging environment for decision-support models, as they are exposed to risks from both deliberate attacks and unintentional errors. These vulnerabilities can degrade model performance, leading ...
dc.publisher	American Society of Civil Engineers
dc.title	Evaluating the Resilience of Graph Neural Network Architectures to Adversarial and Noisy Data in High-Stakes Construction Project Management
dc.type	Journal Article
dc.journal.volume	152
dc.journal.issue	4
dc.journal.title	Journal of Construction Engineering and Management
dc.identifier.doi	10.1061/JCEMD4.COENG-17244
dc.journal.fristpage	04026029-1
dc.journal.lastpage	04026029-16
dc.page	16
dc.tree	Journal of Construction Engineering and Management:;2026:;Volume ( 152 ):;issue: 004
dc.contenttype	Fulltext