首页> 外文期刊>Tunnelling and underground space technology >Predicting TBM utilization factor using discrete event simulation models
【24h】

Predicting TBM utilization factor using discrete event simulation models

机译:使用离散事件模拟模型预测TBM利用率

获取原文
获取原文并翻译 | 示例
           

摘要

Improving the accuracy of models for tunnel boring machine (TBM) performance prediction and estimation of utilization factor is the focus of many ongoing studies in mechanized tunneling. Utilization factor is controlled by tunnel geology, rock mass condition, and size of the tunnel as well as tunneling activities such as maintenance, utility installation, transportation, surveying, unexpected breakdowns and miscellaneous downtimes. In this study, modeling tunneling activities and downtimes using discrete event simulation approach is used to predict TBM utilization factor and advance rate. Two mechanized tunneling projects were considered for the development of the model and verification of the results. A database was developed using Karaj Water tunnel project to generate the time distributions for various tunneling activities required as input for simulation model. Arena (c) software was used for the simulation of TBM operation in this project. The result of modeling was used to simulate Nowsood tunnel project for verification of the concept and discrete event simulation approach. The results showed a good correlation between the predicted TBM performance parameters and observed machine utilization. This exercise showed that the model is capable of predicting utilization factor based on available data for the ground conditions, operational settings, and tunneling activities.
机译:提高隧道掘进机(TBM)性能预测模型的准确性和利用系数的估计是机械化隧道中许多正在进行的研究的重点。利用因子受隧道地质,岩体状况,隧道大小以及诸如维护,公用设施安装,运输,测量,意外故障和其他停机时间之类的隧道活动控制。在这项研究中,使用离散事件模拟方法对隧道活动和停机时间进行建模可用于预测TBM利用率和推进率。考虑了两个机械化隧道工程,以开发模型并验证结果。使用Karaj Water隧道项目开发了一个数据库,以生成各种隧道活动的时间分布,作为模拟模型的输入。 Arena(c)软件被用于该项目中的TBM操作模拟。建模结果用于模拟Nowsood隧道项目,以验证概念和离散事件模拟方法。结果表明,预测的TBM性能参数与观察到的机器利用率之间具有良好的相关性。该练习表明,该模型能够根据地面条件,运营环境和隧道活动的可用数据来预测利用率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号