The using effect of fuzzy analytic hierarchy process in project engineering risk management

This work aims to explore the effectiveness of the fuzzy analytic hierarchy process (FAHP) in project engineering risk management and comprehensively investigate the application of genetic algorithm (GA) and neuro-fuzzy system in this field. Experimental research methods are employed, and three different types of projects, namely construction engineering, information technology projects, and manufacturing projects, are selected for risk evaluation. In the research process, an evaluation index system is established by identifying and analyzing the risk factors of each project, and a FAHP model is constructed. To more accurately assess the mutual influences and weights of the factors, fuzzy mathematics, and fuzzy logic methods are applied to fuzzify the parameters during the risk factor stratification and model construction stages. Besides, the GA and neuro-fuzzy system are applied to the model to further construct a decision support system. The research results indicate that the proposed model has an error rate of less than 10%, demonstrating high reliability and accuracy. Furthermore, the use of FAHP can improve the accuracy of risk management control. Compared to the traditional simple hierarchy analysis method, the proposed method improves accuracy by 9.6% and precision by 8.5%. This work provides a new and effective approach for project engineering risk evaluation, which can assist project managers in more accurately evaluating and managing risks, thereby enhancing the efficiency and quality of project management. This work has practical value in improving the efficiency and quality of project management. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.