Safety evaluation method for operational shield tunnels based on semi-supervised learning and a stacking algorithm

The safety assessment of structural defects in operational shield tunnels is crucial for ensuring their serviceability and safe operation. This study developed a novel comprehensive evaluation method for tunnel safety assessment based on semi-supervised learning and a stacking ensemble algorithm. First, in the membership degree calculation of the multidimensional normal cloud model, the importance coefficients of the tunnel safety evaluation indicators were used instead of their weights to improve the cloud model. This allowed generating unlabeled samples with patterns consistent with those of the collected samples with structural defects. Three classifiers, namely, random forest, extra trees, and gradient boosting, were employed for semi-supervised learning. This process converted unlabeled samples into pseudo-labeled samples, expanded the structural defects database, and ultimately optimized the classifier performance. Subsequently, a stacking algorithm was used to integrate the three optimized classifiers. This resulted in the creation of three stacking models, each containing multiple metamodels. Finally, the optimal metamodels were selected based on accuracy, precision, recall, and F1 score for a voting scheme to determine the tunnel safety level. The developed evaluation method was applied to a realworld engineering project. The evaluation results demonstrated consistency with those obtained from actual field conditions, thus validating the reliability and rationality of the proposed method.