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...

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Veröffentlicht in:	Automation in construction Jg. 166; S. 105653
Hauptverfasser:	Abo Mhady, Ahmed, Budayan, Cenk, Gurgun, Asli Pelin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2024
Schlagworte:	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
Online-Zugang:	Volltext
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