The fundamental components in predicting the TBM performance (advance rate) are the rate of penetration and utilization. Extensive studies have been conducted to accurately estimate these parameters. Inclusion of various tunneling activities and their interdependencies, the impact of the site set up and management, and the role of human factors on the operation make an estimation of utilization a complex task.

A few models have been developed to predict machine utilization, and these models are based on a statistical analysis of available TBM project databases. Apart from the existing models, the use of discrete event simulation (DES) for estimation has also been examined for TBM performance prediction. The sensitivity and accuracy of all these prediction models demonstrate a strong influence of geological conditions and site settings. The present study compares outcome predictions from six empirical models and DES simulations for eight projects. The variability of the predictions is assessed, and the prediction outcomes are tested for “validation” by comparison with results from the recorded observation at the tunneling sites.

The result showed that the difference between the predicted utilization using discrete-event simulations and the actual value available from site data was 7%. Comparing the utilization obtained from other empirical methods, the considerable deviation can be seen. Hence, it is concluded that the DES approach provides for a more accurate estimation of machine utilization while offering the flexibility to customize the model on the basis of both equipment selection and the availability of data to incorporate the complex interdependencies of different tunneling activities.

预测TBM性能（前进率）的基本要素是渗透率和利用率。已经进行了广泛的研究以准确估计这些参数。包括各种隧道活动及其相互依赖性，站点设置和管理的影响以及人为因素对运营的作用，使利用率估算成为一项复杂的任务。

已经开发了一些模型来预测机器利用率，并且这些模型基于对可用的TBM项目数据库的统计分析。除了现有模型外，还已经研究了使用离散事件模拟（DES）进行估计以进行TBM性能预测。所有这些预测模型的敏感性和准确性证明了地质条件和现场设置的强烈影响。本研究比较了八个项目的六个经验模型和DES模拟的结果预测。通过与隧道现场记录的观测结果进行比较，评估了预测的可变性，并测试了预测结果的“有效性”。

结果表明，使用离散事件模拟预测的利用率与可从站点数据获得的实际值之间的差异为7％。比较从其他经验方法获得的利用率，可以看到相当大的偏差。因此，可以得出结论，DES方法可以更准确地估计机器利用率，同时可以根据设备选择和数据可用性（包括不同隧道活动的复杂相互依赖关系）灵活地定制模型。