## 5 Training Model and Prediction for Subsidence  
### Challenges  
The training of machine data and subsidence measurements are carried out in two phases. The first phase trains the machine data and generates input for the second phase. The subsequent phase trains the input from Phase One, which is the weight of the hidden layer of Phase One neural network model, and eventually predicts subsidence.  
The machine data from various TBM sensors consisted of more than 700 items. The followings were chosen to serve as the input for Phase One training. Penetration is a key parameter for training as the sequence is organized in such a way that the rest of the parameters support the penetration prediction. The input sequence was a series of chosen records: penetration, chainage, thrust cylinder, torque of cutting wheel, current pressure of real lance one, pressure of excavation chamber one, rotation speed of screw conveyor drive, calculated excavated material depending on advance one, the actual quantity of excavated material belt scale one, and stroke-thrust cylinder.  
Machine data were recorded every 10 s over a span of several years while TBM was driving. The amount of machine data from a TBM can be overwhelming, as a human cannot review and interpret it all to derive tangible outcomes, such as the potential risk of subsidence around a job site.  
The diversity of methods of operating the machine and the resulting range in construction quality make the problem more complicated. For example, it is relatively easy to infer that subsidence will be correlated with the number of rings installed. However, it is difficult to estimate how closely subsidence will correlate with the skill and experience of a TBM operator. While face pressure should be well managed and controlled, because the balance of the volume of excavation and spoil must be cross-checked repeatedly, it is not so easy in practice to simultaneously supervise all critical elements.  
Machine learning in Phase One is an attempt by artificial intelligence to overlook the TBM's entire advance, and capture the features that may be related to adversarial events. Penetration, revolutions per minute, and chainage may indicate the location and speed of the excavation, chamber pressure, and thrust force may indicate the earth pressure balance, and the actual and calculated excavation volume may indicate the material balance. If the face pressure is well controlled, then subsidence will be minor, and vice versa. We anticipated the output of Phase One would capture the relevant features in this regard.  
Machine data were produced at 10 s intervals while subsidence was measured at intervals of every few days to weeks, according to the distance from the excavation face to the settlement marker. For outputs of a single settlement recording, the maximum amount of machine data as input was 95,040 fields, even if only 11 of 700 fields were recorded. To train the machine data and subsidence in a single phase would have required a period of trial-and-error training to produce a useful LSTM model, which is impractical in terms of both training time and computing cost. We split the process into two phases, for training the machine data and identifying critical features associated with subsidence. By training for subsidence with features identified in the previous phase, LSTM models for phases can be obtained successfully with fewer resources.  
Ten records of machine data with the above 11 fields were flattened into a column and then fed into the LSTM model, which generated one output prediction, which was then used as the input for the next forward propagation. The loss, which is the mean square root of the difference between the ground-truth penetration and the predicted penetration, was defined, and an Adam optimizer with a learning rate of 0.001 steps was used to minimize errors. We assumed that the machine data encapsulated the overall performance of the TBM and construction quality so that it had a substantial impact on the occurrence of subsidence.