Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao

He , Biao (2024) Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao. PhD thesis, Universiti Malaya.

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Abstract

Tunnel construction, a critical component of modern infrastructure development, faces the persistent challenge of blast-induced overbreak. Overbreak, the excessive removal of rock mass beyond the planned tunnel profile, poses significant safety risks, increases costs, and causes project delays. Traditional methods have been developed to predict overbreak. These predictions use either empirical-, statistical-, or numerical-based models. However, traditional methods for predicting overbreak are often inadequate because they simplify the dynamic and complex nature of rock blasting. The development of an advanced overbreak prediction model is, therefore, becoming essential. This thesis addresses the limitations of existing overbreak prediction methods by introducing a novel data-driven approach based on deep continual learning. The primary objectives are to develop a more accurate and adaptable predictive model and to integrate this model into the operational workflow of tunnel blasting. The developed model is expected to possess the ability of continual learning, which is particularly advantageous in dynamic environments like tunnel blasting. To achieve this, this thesis adopts a three-pronged methodological approach. Firstly, the Conditional Tabular Generative Adversarial Networks (CTGAN) model is used to augment the real-world overbreak dataset. This aims to ensure a comprehensive representation of various overbreak scenarios. Secondly, a self-attention multi-layer perceptron (MLP) model, integrated with two continual learning strategies (Elastic Weight Consolidation (EWC) and Memory Replay (MR)), is developed and trained on this augmented overbreak dataset. 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. Third, the overbreak prediction model is further integrated with metaheuristic algorithms, aiming to identify the optimal blasting parameters that can minimize overbreak. By adjusting the blasting parameters accordingly, the model can offer a dynamic and responsive approach to overbreak management. The findings of this thesis are significant. First, the CTGAN model effectively enriches the original overbreak dataset by capturing the complex nature of real-world overbreak scenarios. It can be utilized to create a comprehensive overbreak dataset that possesses high representativeness and diversity. Second, the self-attention MLP model, empowered by EWC and MR, demonstrates superior adaptability and accuracy in predicting overbreak. Its ability of continual learning is highly applicable to actual tunnel blasting cases. Third, the integration of metaheuristic algorithms further ascertains the optimal blasting parameters for overbreak minimization under specific rock sections. The achievements of this thesis indicate a substantial step forward in the application of deep continual learning in tunnel blasting. This thesis offers a promising solution to the longstanding challenge of blast-induced overbreak.

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