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Satellite-Inspired Time-Frequency Deep Learning for Predicting and Assessing Financial Health in General Contractors

dc.contributor.authorCheng, Min-Yuan
dc.contributor.authorKhitam, Akhmad F. K.
dc.contributor.authorVu, Quoc-Tuan
dc.contributor.authorWidjaja, Daniel Darma
dc.date.accessioned2026-08-20T10:44:56Z
dc.date.available2026-08-20T10:44:56Z
dc.date.copyright2026/05/31
dc.date.issued2026
dc.identifier.otherJCEMD4.COENG-18108.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311207
dc.description.abstractAbstractThe ability to accurately predict and evaluate financial health is crucial for project owners and stakeholders to ensure stability and minimize risk. Traditional models such as the Altman Z-score, while widely used, adapt poorly to rapidly ...
dc.publisherAmerican Society of Civil Engineers
dc.titleSatellite-Inspired Time-Frequency Deep Learning for Predicting and Assessing Financial Health in General Contractors
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue8
dc.journal.titleJournal of Construction Engineering and Management
dc.identifier.doi10.1061/JCEMD4.COENG-18108
dc.journal.fristpage04026120-1
dc.journal.lastpage04026120-16
dc.page16
dc.treeJournal of Construction Engineering and Management:;2026:;Volume ( 152 ):;issue: 008
dc.contenttypeFulltext


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