GOALI: Study of Advance Rate of Hard Rock Tunnel Boring Machines (TBMs) and the Impacts of Ground Conditions and Machine Specifications

目标：研究硬岩隧道掘进机 (TBM) 的掘进速度以及地面条件和机器规格的影响

• 批准号: 1131404
• 负责人: Jamal Rostami
• 金额: $ 20万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1131404&HistoricalAwards=false
• 关键词: GOALI; Study; Advance; Rate; Hard

The objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) project is to develop a model for estimation of Tunnel Boring Machine (TBM) utilization and advance rate based on the machine specifications and ground conditions. Accurate estimation of the penetration, utilization, and daily advance rate of hard rock TBM has been a challenge due to the complexity of the machine rock interaction and the influence of operational and management issues on machine production. Having a reasonably accurate estimate is crucial in justification of the project, as well as planning and cost estimation. The errors in the estimates have caused many technical and legal problems and have engaged much of resources in construction claims. A quick review of the literature shows that much of research has focused on the estimation of rate of penetration (ROP) of certain machine types in a given geology. Yet, the estimation of machine utilization and analysis of downtime has not been treated in a systematic way, although downtime is the largest proportion of time spent in any tunneling operation involving TBMs. The limited amount of research in this area is outdated and does not reflect the advances in machine manufacturing techniques. The controlling parameters for TBM utilization and advance rate include the geological setting, machine type and specifications, operational parameters, the machine backup system and auxiliary equipment, and finally site management. This study will look at the case histories of recent TBM application to evaluate the impact of various geological parameters on machine performance. An existing TBM field performance database will be updated with additional data for statistical analysis and seeking new relationships between controlling parameters. The study will also develop activity based models of the tunneling operation and will establish correlation between time required to perform each activity and ground conditions to allow for more accurate estimate of machine utilization and advance rate. Also, the feasibility of using artificial intelligence methods for estimation of the machine performance will be evaluated based on the available data to complement the proposed models. This project will be performed with the participation and contributions by the Robbins Company, the largest manufacturer of tunnel boring machines in the US (and one of the largest in the world), and Frontier Kemper, a leading tunneling contractor in North America. Both companies will assist the research project by providing field data and expertise to expand the database of TBM field performance and realistic activity time models. The proposed work will improve the accuracy of the existing performance prediction models and offer means to achieve more efficient operation. With a more reliable estimation of TBM advance rate, more accurate cost estimation for hard rock tunneling can be achieved and many of unnecessary construction claims can be avoided. This study can also lead to an objective evaluation of machine backup system and impact of various components on machine utilization, which can lead to a systematic evaluation of ground conditions for selection of proper machine and backup system to avoid long delays in tunneling operation. Overall, the result of this study leads to more efficient and cost effective tunneling with reduced delays, improved safety, and prospects for better risk management for tunnel construction using TBM. The main beneficiary of the study will be the general public through the cost savings on the construction of critically needed civil infrastructure upgrades such as water, sewer, rail, subway, and road tunnels.

该学术与工业联络资助机会 (GOALI) 项目的目标是开发一个模型，用于根据机器规格和地面条件估算隧道掘进机 (TBM) 利用率和推进率。 由于机器岩石相互作用的复杂性以及操作和管理问题对机器生产的影响，准确估计硬岩 TBM 的穿透力、利用率和日推进速度一直是一个挑战。 合理准确的估算对于项目合理性以及规划和成本估算至关重要。 估算中的错误导致了许多技术和法律问题，并在施工索赔中占用了大量资源。 对文献的快速回顾表明，许多研究都集中在给定地质条件下某些机器类型的渗透率 (ROP) 估计上。 然而，尽管停机时间在涉及 TBM 的任何隧道施工中所花费的时间中所占的比例最大，但对机器利用率的估计和停机时间的分析尚未得到系统的处理。 这方面的研究数量有限，已经过时，不能反映机器制造技术的进步。 TBM利用率和推进速度的控制参数包括地质背景、机器类型和规格、操作参数、机器备份系统和辅助设备，最后是现场管理。 本研究将着眼于近期 TBM 应用的案例，以评估各种地质参数对机器性能的影响。 现有的 TBM 现场性能数据库将使用额外的数据进行更新，以进行统计分析并寻找控制参数之间的新关系。 该研究还将开发基于活动的隧道施工模型，并将建立执行每项活动所需的时间与地面条件之间的相关性，以便更准确地估计机器利用率和推进速度。 此外，将根据现有数据评估使用人工智能方法估计机器性能的可行性，以补充所提出的模型。 该项目将在美国最大的隧道掘进机制造商（也是世界上最大的隧道掘进机制造商之一）Robbins Company 和北美领先的隧道承包商 Frontier Kemper 的参与和贡献下进行。 两家公司将通过提供现场数据和专业知识来协助该研究项目，以扩展 TBM 现场性能和实际活动时间模型的数据库。 所提出的工作将提高现有性能预测模型的准确性，并提供实现更高效运行的方法。 通过更可靠地估算 TBM 推进速度，可以实现更准确的硬岩隧道成本估算，并避免许多不必要的施工索赔。 这项研究还可以对机器备份系统以及各种组件对机器利用率的影响进行客观评估，从而对地面条件进行系统评估，以选择合适的机器和备份系统，以避免隧道施工中的长时间延误。 总体而言，这项研究的结果使隧道施工更加高效、更具成本效益，减少了延误，提高了安全性，并为使用 TBM 进行隧道施工提供了更好的风险管理前景。 该研究的主要受益者将是公众，通过建设急需的民用基础设施升级（如供水、下水道、铁路、地铁和公路隧道）节省成本。