SOIL TRANSITION MODELS | BTSYM
FIELD VALIDATION USING EPBM OPERATIONAL DATA In terms of methods for validating results from a probabilistic assessment, for the ART project, the soil transition location uncertainty was examined using the rate of chamber pressure dissipation using the operation data collected from the earth pressure balanced machine (EPBM). The analysis was informed by the Bezuijen and Dias
(2017) model that discusses reduction in chamber pressure during standstill, influenced by the hydraulic conductivity of the soil immediately in front of the cutterhead. Rate of chamber pressure dissipation was examined
for each ring to infer the permeability of soil type at the face and compared to the permeability baseline values in a GBR. From the ART project’s GBR, the baseline permeability of G1/G2 soils was kc ε [1E-9 to 1E-6] m/s, and the permeability of G3/G4 soils was ks ε [1E-6 to 1E-3] m/s.
For each ring, the analytical fit to chamber pressure dissipation was used to evaluate the permeability and identify the presence of dominant soil type at the tunnel face. Figure 6 shows the maximum EPBM chamber
pressure dissipation rate between ring #250 and ring# 274 is between 0 and 0.05 kPa/min, except at ring# 273. Low rate of chamber pressure dissipation indicates low permeable G1/G2 soil at the EPBM face. Given no change in the overburden and geostatic groundwater conditions, the observed increase in chamber pressure dissipation rate is very likely a result of formation soil permeability increase at the face. The observations reveal that the transition from
cohesive to cohesionless soil, within the tunnel envelope, does not occur at or near ring #260, as indicated would be the case in the GBR. Results confirm that the transition from G1/G2 to G3/G4 soil occurs (P95
for a 50% or greater proportion of G3/G4 soil at the
EPBM face) at ring #275. Chamber pressure dissipation between rings #500
and #550 was relatively high, between 0.05 and 0.3 kPa/ min, indicating dominant presence of highly permeable G3/G4 soil at the EPBM face. The dip in chamber pressure dissipation beyond ring #550 indicates the presence of G1/G2 soil in relatively higher proportions at the face. The probabilistic approach shows that the transition from G3/G4 to G1/G2 soil (P95 for 50% or greater
proportion of cohesive soil at the face) occurs at about ring #550, compared to ring #520 indicated in the GBR.
VALIDATION WITH MODELS BUILD ON AI EPBMs are outfitted with extensive sensors to measure both human operations and machine reactions. Measurements are taken frequently (every 5-10 seconds) and are readily available to all stakeholders. Since the behaviour of the EPBM is influenced by the ground condition, it is possible to develop a data-driven model to relate the EPBM construction data with the as- encountered ground condition. Such modeling can yield a more detailed and accurate description of the ground to enhance the operator’s geological awareness. Characterizing the as-encountered ground using EPBM data involves finding features as model inputs to compute the expected ground condition as the model output. The choice of the input predictors, ideally, capture
the differences of various ground in aspects such as strength, density, elasticity, etc. Using our tunneling domain knowledge, the following measurements are included: the total thrust force (F) and advance rate (AR), cutterhead rotation speed (ω) and torque (T), chamber pressure at springline (pc), screw conveyor rotation speed (ωs) and torque (Ts), specific energy of excavation (SEE) and field penetration index (FPI), as well as the excavated muck mass (EMM) calculated from the belt conveyor scale. Except for the EMM, all these predictors are evolving
during excavation and are measured by the EPBM every five seconds. To make them a predictor vector of equal length, their average values are taken during stable EPBM advancement. This treatment leads to a predictor vector xi
SEE, FPI, EMM]. Yu and Mooney (2021) utilised EPBM operation and
reaction data while tunneling to characterize the as- encountered ground type. A semi-supervised learning (SSL) model – a type of machine learning – is applied. The SSL model uses a small portion of labeled data and a large portion on unlabeled data to interpret the occurrence probability of soils at each ring. To characterise the as-encountered ground conditions
within the ART project, soil fractions within the tunnel envelope were extracted from 43 boreholes by associating the boreholes to the nearest ring. Within the SSL model, each ring was represented by the EPBM operation parameters and the estimated soil fractions within the tunnel envelope for the SSL model training. The data-driven models thus utilized the ring-level EPBM data rather than only the sparse boreholes for prediction. Therefore, the results from the SSL model can be considered reliable to validate the occurrence of transitions at a finer resolution.
for each ring i, namely xi = [F, AR, ω, T, pc, ωs, T,
Table 1: Comparing geostatistics, SSL modeling, and GBR locations Soil Transition Soil Transition 1
Ring# (P95) from the geostatistical modeling
Soil Transition 2
275 550
Ring# from SSL model (Yu and Mooney 2021)
268 547
Ring# from GBR 260
520
Fall 2023
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