Title: Learning-assisted intelligent risk assessment of highway project investment

Authors: Hongwei Liu; Zihao Zhang

Addresses: School of Earth Resources, China University of Geosciences, Wuhan, Hubei, 430074, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, 430074, China

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.

Keywords: risk assessment; highway; risk index system; extreme learning machine; broad learning system.

DOI: 10.1504/IJCSM.2023.130691

International Journal of Computing Science and Mathematics, 2023 Vol.17 No.2, pp.195 - 206

Received: 20 Oct 2021
Accepted: 30 Dec 2021
Published online: 03 May 2023 *

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