Year: 2025
Type: article
Abstract: With the development of modern transport, highway construction is chang-ing day by day, the total mileage of China's highways has jumped to the first in the world, highway has become an important part of transport infrastructure con-struc-tion. Accompanied by the increase in demand for high-quality highway construc-tion, highway construction projects to improve the level of risk man-agement is imperative. In the fast-changing transport technology today, the number, scale, quality, frequency and environmental conditions of highway re-construction and expansion projects are evolving rapidly, which puts forward new requirements for the risk control of highway reconstruction and expansion projects. To deeply explore the risk management of highway construction projects under BOT mode, this paper has sorted out the relevant concepts and theoretical foundations, and applied the Delphi method and other methods to identify and optimize the risk factors of highway projects under BOT mode. On this basis, the risk index system of highway projects under BOT mode is established after com-bining risk classification, and the risk evaluation system is established to facilitate the risk evaluation of the project. Finally, using the case study method, taking the ZL motorway project as an example, the fuzzy comprehensive evalua-tion method of AHP risk evaluation model can effectively carry out the com-prehensive as-sessment and weight calculation of these factors, and carry out the systematic as-sessment and analysis of its risk. Based on the analysis results, relevant sugges-tions and countermeasures are proposed to reduce the risk of motorway construc-tion projects. This study provides certain reference and guidance for the risk management of highway construction projects under BOT mode, and provides useful expe-ri-ence and inspiration for other similar projects.
DOI: 10.63313/aerpc.9023
Author: Tianyu Qing
Institution: Southwest Petroleum University
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4410965268