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Predicting EPBM advance rate performance using support vector regression modeling
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DOI：10.1016/j.tust.2020.103520.png)
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Earth pressure balance shield tunnel boring machines (EPBM) are widely used in tunneling practice yet the mechanics that define ground-EPBM interaction, specifically the advance rate, are not well understood. In the study presented here, machine learning techniques including feature selection, support vector regression (SVR) and partial dependence plots (PDP), were successfully applied to EPBM data to develop and explain EPBM advance rate modeling through five widely varying soil types. The geotechnical conditions were implicitly incorporated into the analysis by developing soil formation-specific SVR models. The SVR models were highly successful in capturing AR behavior, exhibiting R-2 values of 0.88-0.95 when independently evaluated with test data. Automatic feature selection revealed the same EPBM parameters of notable influence on AR across all ESUs, including net thrust, cutterhead torque, foam flow rate and screw conveyor torque. The SVR models, however, revealed considerably different relationships between these key parameters and AR, indicating that the soil plays a significant role in AR behavior. PDP analysis captured the sensitivity of AR to each key parameter as a function of parameter magnitude. The PDP results show that AR is positively correlated (increasing AR with increasing parameter value) and/or negatively correlated (decreasing AR with increasing parameter value) to varying degrees as a function of parameter value, all of which is strongly soil dependent.
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