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 The limited amount of research in this area is outdated and does not reflect the advances in machine manufacturing techniques.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.

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Pennsylvania State University
University Park
United States
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