Intelligent Approach Based on Random Forest for Safety Risk Prediction of Deep Foundation Pit in Subway Stations

Abstract

The number of safety accidents caused by excavation of deep foundation pits in subway stations has been increasing rapidly in recent years. Thus, precisely predicting the safety risks for subway deep foundation pits bears importance. Existing methods, such as machine learning models, have been established for predicting such risks. However, these methods are unable to provide accurate results for deep foundation pits in subway stations due to small and unbalanced data samples. In this research, an intelligent model based on random forest (RF) was established for risk prediction of deep foundation pits in subway stations. To achieve such a goal, different types of monitoring data and risk level monitoring were introduced to the RF for training the model and estimating unknown relationships between monitoring values and safety risks of pits. An actual deep foundation pit in a subway station of the Wuhan Metro was used to demonstrate the applicability of the developed RF risk prediction model. The results showed that the superiority of the proposed RF risk prediction model can be used as a basis to implement a decision-making tool for predicting safety risks of subway foundation pits. The importance evaluation function of the model provides the ability to aid onsite engineers in determining the causes of safety risks, thus facilitating the implementation of emergency measures in advance.