The invention discloses a real-time prediction method for
muck improvement parameters of a shield under-crossing existing tunnel, and the method comprises the steps: carrying out the refined preprocessing of non-under-crossing section
shield tunneling data, enabling an obtained
data set to be used for the training of a
machine learning integrated prediction model, and obtaining a
muck improver dosage integrated prediction model; meanwhile,
slump experiments are carried out for different types of stratums, the optimal
water content and foam injection ratio parameter range is determined, and a reliable decision basis is provided for a shield driver. The quality of a
data set and the prediction precision of a
machine learning model are remarkably improved through a finer data preprocessing means; the
machine learning integrated prediction model fuses learning capabilities of different
machine learning models, so that the prediction precision of the model is further improved; and
machine learning and experimental means are combined to realize accurate and rapid prediction of the use amount of the modifier, so that safe and smooth tunneling of the shield under the existing tunnel is ensured, and the method has certain timeliness and economical efficiency.