Boosted bagging A hybrid ensemble deep learning framework for point cloud semantic segmentation of shield tunnel leakage

Accurate leakage detection in subway shield tunnels remains challenging due to the complex geometry and subtle moisture signatures in structural point clouds. Single-model architectures present limited adaptability to heterogeneous leakage and different tunnel scenarios, leading to substantial performance and compromised generalization capacity. To address these challenges, this research proposes Boosted Bagging, a hybrid ensemble deep learning framework for precise semantic segmentation of leakage in tunnel point clouds. In this method, we propose a boosted learner that integrates three specialized base classifiers to learn complex feature representations of diverse leakage patterns. The boosted learner further synergizes with the bagging strategy to enhance generalization capacity by parallel training on randomized data subsets and voting strategy. Integrating boosting and bagging strategies results in a more precise and robust tunnel leakage segmentation. Moreover, the Lovasz Hinge Loss is introduced to address severe sample imbalance between the leakage and background classes. The experimental results demonstrate the effectiveness of Boosted Bagging in terms of segmentation accuracy and robustness. Comparative experiments with state-of-the-art segmentation methods reveal a notable enhancement in segmentation accuracy. Moreover, its superior performance on test datasets highlights the strong generalization capability of the method.