Fuzzy Analytic Hierarchy Process-Based Investment Risk Evaluation for Infrastructure Construction Projects
Author(s): |
Xiaowen Zhao
Wen Xue Kaidong Liu Feng Guo Miaomiao Chen |
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Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 20 February 2025, n. 5, v. 15 |
Page(s): | 756 |
DOI: | 10.3390/buildings15050756 |
Abstract: |
Government investment is one of the main sources of financing for large-scale infrastructure development. These government-invested infrastructure construction projects are often characterized by large investments, long construction periods, and relatively high investment risks. In this study, the process of infrastructure projects was divided into three stages, namely pre-decision, construction, and post-evaluation. Research methods of literature review and expert interviews were adopted to determine the factors that influenced the investment risk of infrastructure construction projects. A corresponding evaluation index system was established. By using the Fuzzy Analytic Hierarchy Process (FAHP) and survey questionnaire research methods, the weights for each risk factor were derived, and an investment risk evaluation model for infrastructure projects was constructed. The reliability and effectiveness of the model in project investment risk evaluation are verified by combining a practical case study and the relevant sensitivity analysis of indices. The reliable assessment model for the investment risk of government-invested infrastructure construction projects was established, and the optimizing management of the project investment was proposed to entirely improve the construction quality. |
Copyright: | © 2025 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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data sheet - Reference-ID
10820571 - Published on:
11/03/2025 - Last updated on:
11/03/2025