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 real-world engineering project. The evaluation results demonstrated consistency with those obtained from actual field conditions, thus validating the reliability and rationality of the proposed method.

中文翻译：

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基于半监督学习和叠加算法的运营盾构隧道安全评价方法


运营盾构隧道结构缺陷的安全评估对于确保盾构隧道的正常使用和安全运行至关重要。本研究开发了一种基于半监督学习和堆叠集成算法的隧道安全评估综合评价方法。首先，在多维正态云模型的隶属度计算中，采用隧道安全评价指标的重要性系数代替其权重，对云模型进行改进。这允许生成未标记的样本，其模式与收集的具有结构缺陷的样本的模式一致。半监督学习采用了三种分类器，即随机森林、额外树和梯度提升。该过程将未标记样本转换为伪标记样本，扩展了结构缺陷数据库，最终优化了分类器性能。随后，使用堆叠算法来集成三个优化的分类器。这导致创建了三个堆叠模型，每个模型包含多个元模型。最后，根据准确度、精确度、召回率和 F1 分数选择最佳元模型进行投票方案以确定隧道安全级别。所开发的评估方法已应用于实际工程项目。评价结果与现场实际情况一致，验证了所提方法的可靠性和合理性。