TECHNICAL | BTS MEETING/NORTH BRISTOL RELIEF SEWER
Below, figure 3: Deep Shaft 3D Model
Right, figure 4: TBM Launch Pit
● The TBM launch pit was serviced by two overhead gantry cranes (10t for segments and ancillary equipment/ supplies and 12t for processing the spoil wagons).
● The labour arrangements on the TBM were through a direct delivery model where the key operatives on the TBM were directly employed by Murphy, which was then supplemented using well-known labour suppliers for the backup positions.
● The TBM operated on a 5x2 shift pattern allowing for 10hr of production time and 2hrs planned maintenance at the end of each shift, Monday-Friday. Planned production-stops for maintenance were carried out over the weekends.
● Along the tunnel alignment were multiple third party crossings, including the M5 carriageway, rail lines, a flood attenuation reservoir and a high-pressure fuel main. All of these were successfully crossed without the installed monitoring showing any breach of agreed trigger levels.
Figure 8 shows an overview of the tunnel drive site during the construction stage.
CHALLENGES ENCOUNTERED DURING TUNNELLING In the tunnelling phase of the project the TBM encountered some significant challenges, these can be broken down into three areas: 1. The extent of conglomerate, sandstone and siltstone bands was larger than anticipated, resulting in higher cutting tool wear and slower advance rates at times.
2. Significantly higher than anticipated groundwater flows were encountered between Ch2500 to Ch4000 (Ring1500 – Ring3000), which coincided with mixed ground conditions.
3. The COVID-19 Pandemic and associated resource supply issues.
20 | November 2023
To overcome these challenges the project team had to develop a range of technical and innovative solutions.
Challenge 1 To overcome the first challenge ,Murphy commissioned Maxwell Geosystems to prepare a 3D ground model based on the available GI. Due to the extent of the conglomerate, sandstone and siltstone being larger than expected, it was important for the project to gain a better understanding of the upcoming ground conditions to allow for more accurate forecasting of production rates and cutterhead tool wear. The key aspect of the Maxwell model is that it was
a geostatistical model that assigned a probability to each different type of ground condition. This probability was shown through the opacity of the colours on the 2D sections (every 20m) and long sections of the tunnel. The higher the uncertainty in the model the more the opacity would decrease for that colour (fading to white). This is shown in Figures 9-10 where a comparison between two different sections can be made. The top image shows the model with only the tender boreholes, as can be seen there are large sections of it within the tunnel alignment that are showing an increased level of uncertainty. When the additional GI is fed into the model these areas are shown as a much darker shade of brown and yellow as the model is more confident of the geology that will be encountered. The team used this information to provide a more
accurate forecast for tunnel production rates. This allowed better planning of key tunnelling operations, such as third party crossings and planned maintenance. In addition, Murphy commissioned for the data from
the Maxwell model to be used in a study on Machine Learning (ML) that was undertaken, post PhD, by Dr Jake Rankin. The ML study aimed to provide a better forecast of the tunnelling production rates. For more information on the study, please refer to the BTS Harding Prize
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