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Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0

Design and implementation of construction cost prediction model based on SVM and LSSVM in... In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.Design/methodology/approachIn the competitive growth and industries 4.0, the prediction in the cost plays a key role.FindingsAt the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.Originality/valueThe prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1756-378X
DOI
10.1108/ijicc-10-2020-0142
Publisher site
See Article on Publisher Site

Abstract

In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.Design/methodology/approachIn the competitive growth and industries 4.0, the prediction in the cost plays a key role.FindingsAt the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.Originality/valueThe prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.

Journal

International Journal of Intelligent Computing and Cybernetics – Emerald Publishing

Published: Apr 23, 2021

Keywords: Cost prediction; Principal component analysis; SVM; LSSVM; Industries 4.0

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