Investment Probabilistic Interval Estimation for Construction Project Using the Hybrid Model of SVR and GWO
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 5
Abstract
Investment estimation is a key component of early decision-making for a construction project, which is crucial to the project cost control. Currently, most investment estimation researches render the point value results, which could lead to considerable uncertainty in the estimation results and increase the risk of decision-making. Therefore, it is essential to explore a type of systematic, accurate, and effective estimation method. This study proposed an innovative estimation method of probability interval prediction based on the distribution of prediction errors. First, the dimension reduction of the construction indexes was conducted by using exploratory factor analysis (EFA). Then, a model was developed based on the fusion of the support vector regression (SVR) and grey wolf optimization (GWO) algorithm. Finally, cost intervals with different confidence levels were obtained on the basis of kernel density estimation (KDE). The case results indicated that when the confidence was 95%, the comprehensive evaluation index coverage width-based criterion (CWC) and the interval coverage rate PICC of the cost estimation were 2.17 and 93.33%, respectively. Hence, the proposed interval prediction model was fairly reliable, which could provide practical guidance for the investment decisions in the early stage of construction projects and give the decision makers more abundant forecasting information.
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Data Availability Statement
Some or all of the data, models, or codes generated or used during the study are available from the corresponding author by request.
Acknowledgments
This research has been supported by the National Natural Science Foundation of China Grants No.71701033, the Dalian high level Talents Innovation Support Plan No. 2017 RQ005, and the Research Project of Department of Education of Liaoning Province (LN2020Q04).
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Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 5 • May 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 16, 2020
Accepted: Nov 17, 2020
Published online: Feb 26, 2021
Published in print: May 1, 2021
Discussion open until: Jul 26, 2021
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