Estimate-at-completion (EAC) prediction using Archimedes optimization with adaptive fuzzy and neural networks

Construction companies estimate project costs at the beginning of the project; however, many factors impact the final project cost. Estimate at Completion (EAC) is a critical approach for estimating the final cost based on actual project performance. This paper aims to improve EAC predictions by int...

Celý popis

Podrobná bibliografie

Vydáno v:	Automation in construction Ročník 166; s. 105653
Hlavní autoři:	Abo Mhady, Ahmed, Budayan, Cenk, Gurgun, Asli Pelin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.10.2024
Témata:	Adaptive neuro-fuzzy inference systems Archimedes optimization algorithm (AOA) Civil engineering Cost estimation Machine learning Neural network Cost estimation Neural network Archimedes optimization algorithm (AOA) Civil engineering Adaptive neuro-fuzzy inference systems Machine learning
ISSN:	0926-5805
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!