The deep continual learning framework for prediction of blast-induced overbreak in tunnel construction

Abstract

Blast-induced overbreak, characterized by the excessive removal of rock mass beyond the planned tunnel profile, poses significant safety risks, increases costs, and causes project delays during tunneling. Traditional machine/deep learning models have been developed to predict overbreak. However, these models are often inadequate because they are static and lack the flexibility to adapt to new, real-world data continuously. This study addresses this limitation by introducing a novel data-driven approach based on deep continual learning. The primary objective is to develop an adaptable predictive model with the ability of continual learning, which is particularly advantageous in dynamic environments like tunnel blasting. To achieve this, a self-attention multi-layer perceptron (MLP) model for overbreak prediction, integrated with two continual learning strategies (elastic weight consolidation (EWC) and memory replay (MR)), is developed. This step enables the overbreak prediction model to possess the ability to continuously learn real-world scenarios and adapt to the dynamic environment of tunnel blasting. The findings show that the continuous MLP model, empowered by EWC and MR, demonstrates superior adaptability and accuracy in predicting overbreak. Compared with the standard MLP model, which achieves a predictive accuracy of 0.831, the continuous MLP model achieves a predictive accuracy of 0.845 on unseen data. The integration of EWC and MR strategies proves to be a pivotal factor in developing deep learning models for the dynamic task of predicting overbreak. The continual learning strategies ensure that the models remain adaptable and accurate over time, which is essential for practical applications in dynamic environments of tunnel blasting operations.