To read the full version of this content please select one of the options below:

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

Miao Fan (College of Railway Engineering, Zheng Zhou Railway Vocational and Technical College, Zhengzhou, China)
Ashutosh Sharma (Institute of Computer Technology and Information Security, Southern Federal University, Rostov na Donu, Russia)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 6 January 2021

Issue publication date: 23 April 2021

242

Abstract

Purpose

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/approach

In the competitive growth and industries 4.0, the prediction in the cost plays a key role.

Findings

At 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/value

The 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.

Keywords

Citation

Fan, M. and Sharma, A. (2021), "Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0", International Journal of Intelligent Computing and Cybernetics, Vol. 14 No. 2, pp. 145-157. https://doi.org/10.1108/IJICC-10-2020-0142

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

Related articles