Decision making Techniques for Smart Engineering Project Management Based on Random Forest Algorithm

In the current global engineering project management field, due to the expansion of project scale and the increasing complexity of the environment, traditional project management methods are no longer able to meet the requirements of high efficiency and high precision management. This study provides an in-depth discussion around the application of Random Forest (RF) algorithm in intelligent engineering project management, aiming at constructing a decision support system based on RF in order to improve the quality of decision-making and management efficiency of engineering projects. In terms of the specific methodology, the RF model is firstly trained by collecting historical engineering project data, including multi-dimensional data such as project cost, time, quality, and risk, and then utilizing these data to predict the potential risk and success of the project. In addition, this study also verifies the effectiveness and accuracy of the model's application in actual engineering project management through case studies. Initially, when the number of trees is increased from 10 to about 100, the accuracy of the model is significantly improved. This indicates that increasing the number of trees can effectively improve the predictive ability of the RF algorithm. The introduction of the RF algorithm can help project managers to make more scientific and accurate decisions in a complex decision-making environment, so as to effectively control the project risks, ensure the quality and schedule of the project, and promote the field of engineering project management to the development of intelligence and refinement.