Learning-assisted intelligent risk assessment of highway project investment Online publication date: Wed, 03-May-2023
by Hongwei Liu; Zihao Zhang
International Journal of Computing Science and Mathematics (IJCSM), Vol. 17, No. 2, 2023
Abstract: Highway project has the characteristics of large investment scale and high investment risk. Aiming at the problem of investment risk management, this paper takes 15 highway investment projects in recent ten years as the research object, and establishes an investment risk index system including 12 first-class indexes and 30 second-class indexes. The hierarchical weight model of highway engineering investment risk assessment is proposed. The intelligent evaluation of highway engineering investment risk by extreme learning machine and broad learning system algorithm is discussed. The comparative experimental results show that the improved intelligent evaluation model can evaluate and predict the investment risk of highway engineering projects more effectively. The R-square value of the improved intelligent evaluation model is increased by 0.35, and the accuracy is greatly improved. It can provide decision support for highway engineering project investment risk management.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook