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Estimation of the utilization rate for pressurized face shielded TBMs using discrete event simulation
Tahernia, Tala
Tahernia, Tala
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2023
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Abstract
During tunnel construction using a Tunnel Boring Machine (TBM), various tasks are performed to excavate and support the ground. The ratio of TBM excavation duration to the total working time is called the machine utilization rate and represents the efficiency of operations, operational, and management factors. Calculation of the TBM utilization rate involves dividing the duration of the boring time by the total duration of all construction activities, which includes all the delays and downtimes in the operation that are difficult to quantify in many operations. The type and duration of activities and their interactions are influenced by project-specific factors and TBM types. The high variability and uncertainty in activity type and durations complicate the prediction of utilization rate and make theoretical and deterministic approaches less accurate for this purpose. Often experts rely on past experiences and empirical equations derived from historical data to estimate machine utilization. The results are usually unreliable and biased because they cannot
incorporate the impacts of new developments and different site setups.
This thesis explores the application of Discrete Event Simulation (DES) techniques in predicting the TBM utilization rate, as a follow-up to previous studies in this area. Previously, the CSM2020 model was established using ARENA and MATLAB for hard rock TBMs. The existing modules in the CSM2020 DES model were adjusted and improved with a focus on simplicity to enhance usability and adaptability and to minimize the complexity of setting up and running the model by users. Additionally, the activity modules and workflow of the pressurized face TBMs are added to the simulation model to extend the model’s area of application. The new model employs Python and relevant libraries. The shift to Python and integration of the SimPy framework improve the model’s flexibility and accessibility. The developed simulation tool is a versatile, customizable, and predictive tool that can be applied to various types of TBM and transportation systems, ground conditions, and project specifications to forecast TBM performance, recognize the bottleneck and do what-if scenario analysis during the construction. Due to insufficient data, an evaluation of the model’s accuracy in adapting to varying ground conditions was not conducted. While the prediction of components of operations and pertinent activity times is very helpful, the results of the prediction using DES models rely on the available database of the recorded activity times. Hence, the accuracy of the model and its prediction can be improved when used during construction by incorporating field measurements during the tunnel construction phase.
In this study, construction activity durations, geological data, and design parameters were collected from recently completed pressurized face TBM projects. After collecting, cleaning, and restructuring the data, in comparative statistical studies, the effects of tunnel diameter and curvatures on operational parameters and machine utilization were explored. Based on the results, it is clear that the duration of ring building is affected by the diameter of the tunnel. However, the skills of the ring builder, the equipment used, and the ring building systems also play a significant role. The curvatures along the alignment have a minimal operational impact but increase surveying duration and segment failures. Similarly, the preliminary analysis of the recorded activities showed an increase in machine diameter can increase the duration of activities and hence reduce machine utilization, especially when the segment installation is the dominant activity.
As part of this study, an analysis was conducted to investigate activity times and corresponding downtime allocation. The study can be practical in identifying the critical causes of delays in projects by indicating the impact of parallel activities on delays and, consequently, the machine utilization rate. The analysis reveals that recording downtimes related to parallel activities are rather biased, suggesting a need for closer scrutiny of shift reports. The investigation also highlights the difficulty in retroactively calculating activity time based on recorded delay times. After completing the data analysis phase and preparing the data for use in the DES model, a user data entry interface is created to feed the input data into the DES model. This interface ensures the consistency and appropriateness of input parameters according to the model setup while facilitating the modification of data and execution of what-if scenario analysis. The data entry platform is integrated into the DES model, and simulations were conducted using the DES model for validation. The simulation model is verified by comparing the model’s predictions to the observed and recorded downtime and utilization rates. The model accurately predicted machine performance revealing functionality in operational settings for pressurized face TBMs. The model’s effectiveness spans across various phases of tunneling operations. However, its accuracy and reliability can be significantly enhanced by incorporating more precise data, specifically field measurements obtained during construction. With this integration, the model achieves improved forecasts of tunnel cost, completion time, and identification bottlenecks in the operation. This valuable information plays a crucial role in guiding the planning, optimization, and management throughout the entire tunneling process.
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