To proactively assess the losses caused by the deterioration of metro tunnels during the operational period, a new method, the cloud model‐based random forests (CRFs), is proposed to discuss the inconsistencies induced by mapping the monitoring data into the health rate of the metro tunnels. On top of the CRF, a self‐training framework is introduced to improve the predictive accuracy and the stability of the CRF by adding more unlabeled data. The main contribution of this paper is proposing a novel CRF model along with a semisupervised approach to overcome the inapplicability of the random forests algorithm in an inconsistent small database, and to reduce the substantial time and cost for expert annotation. The results indicate that (1) the proposed CRF achieves higher accuracy and stability than random forests in predictions; (2) the CRF outperforms the other state‐of‐the‐art methods even in a small database; (3) the self‐training improved CRF keeps highly precise when the ratio of labeled to unlabeled data is no less than 1:11.4. In this study, the suggested ratio of labeled and unlabeled data is no lower than 1:5.7 to reduce the risk of wrongly forecasting a seriously damaged tunnel section as a slightly damaged tunnel section.

中文翻译：

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通过基于云模型的随机森林和自训练方法进行隧道状况评估

为了主动评估运营期间地铁隧道的恶化所造成的损失，提出了一种新的方法，即基于云模型的随机森林（CRF），以讨论将监测数据映射到健康率中导致的不一致性。地铁隧道。在CRF之上，引入了自训练框架，以通过添加更多未标记的数据来提高CRF的预测准确性和稳定性。本文的主要贡献是提出了一种新颖的CRF模型以及一种半监督方法，以克服随机森林算法在不一致的小型数据库中的不适用性，并减少了专家注释的大量时间和成本。结果表明：（1）所提出的CRF在预测方面比随机森林具有更高的准确性和稳定性；（2）即使在小型数据库中，CRF也要优于其他最新方法；（3）当标记的数据与未标记的数据之比不小于1：11.4时，自训练的CRF保持高精度。在这项研究中，建议的标记和未标记数据比率不低于1：5.7，以减少将严重损坏的隧道部分错误地预测为轻微损坏的隧道部分的风险。