Back-analysis for initial ground stress field at a diamond mine using machine learning approaches

Exact knowledge for ground stress field guarantees the construction of various underground engineering projects as well as prediction of some geological hazards such as the rock burst. Limited by costs, field measurement for initial ground stresses can be only conducted on several measure points, which necessitates back-analysis for initial stresses from limited field measurement data. This paper employed a multioutput decision tree regressor (DTR) to model the relationship between initial ground stress field and its impact factor. A full-scale finite element model was built and computed to gain 400 training samples for DTR using a submodeling strategy. The results showed that correlation coefficientrbetween field measurement values and back-analysis values reached 0.92, which proved the success of DTR. A neural network was employed to store the global initial ground stress field. More than 600,000 node data extracted from the full-scale finite element model were used to train this neural network. After training, the stresses on any location can be investigated by inputting corresponding coordinates into this neural network.

Automated summary

By using decision tree regressor and neural network models, the relationship between ground stress field and its impact factor can be modeled with limited field measurement data. This allows for the back-analysis of initial stress and the prediction of geological hazards, supporting the construction of underground engineering projects.