Factorial Experiments of Soil Conditioning for Earth Pressure Balance Shield Tunnelling in Water-Rich Gravel Sand and Conditioning Effects’ Prediction Based on Particle Swarm Optimization–Relevance Vector Machine Algorithm

The high permeability of gravel sand increases the risk of water spewing from the screw conveyor during earth pressure balance (EPB) shield tunnelling. The effectiveness of soil conditioning is a key factor affecting EPB shield tunnelling and construction safety. In this paper, using polymer, a foaming agent, and bentonite slurry as conditioning additives, the permeability coefficient tests of conditioned gravel sand are carried out under different injection conditions based on the factorial experiment design. The interactions between different concentrations of conditioning additives are analyzed. A prediction model for soil conditioning during shield tunneling based on particle swarm optimization (PSO) and relevance vector machine (RVM) algorithms is proposed to accurately and efficiently obtain the soil conditioning parameters in the water-rich gravel sand layer. The experimental results indicate that the improvement effect of the foaming agent on the permeability of the conditioned gravel sand gradually diminishes with the growing concentration of bentonite slurry. Under conditions of high polymer concentration, further increasing the concentration of bentonite slurry and foaming agent has a weak impact on the permeability coefficient when the concentration of bentonite slurry exceeds 10%. The significance of main effects, first_order interactions, and second-order interaction on the permeability of conditioned gravel sand are as follows: polymer concentration (A) > foaming agent concentration (B) > bentonite slurry concentration (C) > first_order interactions (A × B, A × C, B × C) > second-order interaction (A × B × C). The first_order interaction mainly manifests as a synergistic effect, while the second-order interaction primarily exhibits an antagonistic effect. Case studies show that the maximum relative error between predicted and experimental values is less than 3%. A field application of shield tunneling demonstrates the good performance of real-time optimization of soil conditioning parameters based on the PSO–RVM algorithm. This research provides a new method for evaluating the effectiveness of soil conditioning in the water-rich gravel sand layer.

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