Construction Research Congress 2020
Concrete Pouring Production Rate Estimation: A Bayesian Network Approach
Publication: Construction Research Congress 2020: Project Management and Controls, Materials, and Contracts
ABSTRACT
Production rate estimation is important at the planning stage to provide adequate project schedule and cost prediction. Most production rate estimation tools in the construction field are empirical and require adjustments to fit any specific construction company. Moreover, the existing tools do not account for the stochastic nature of construction crews and their interdependencies that impact their production rates. The purpose of this paper is to present a generic statistical-based model that utilizes site specific data for accurate production rates estimation. The presented model utilizes Bayesian statistics that is commonly recognized for reasoning in stochastic environment. The authors developed the model for concrete pouring activities. The required data was gathered through site observations to determine the probability distribution function for parameters affecting the production rates of construction crews. The proposed model was tested against actual concrete pouring activities in a construction site and provided adequate estimates for the activities’ production rates. The modeling approach is transformable and can be applied to various other activities in the construction field based on the heterogenic and stochastic properties of the construction crews and site conditions.
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ACKNOWLEDGEMENT
The authors would like to acknowledge the generous support and help by China State Construction Engineering Corporation, Ltd., in developing the presented model.
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Information & Authors
Information
Published In
Construction Research Congress 2020: Project Management and Controls, Materials, and Contracts
Pages: 602 - 611
Editors: David Grau, Ph.D., Arizona State University, Pingbo Tang, Ph.D., Arizona State University, and Mounir El Asmar, Ph.D., Arizona State University
ISBN (Online): 978-0-7844-8288-9
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Nov 9, 2020
Published in print: Nov 9, 2020
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