No abstract

Background: Earth pressure balance shield machines are widely used in underground engineering. To prevent ground deformation even disastrous accidents, the earth pressure in soil chamber must be kept balance to that on excavation face during shield tunneling. Therefore, in this paper an advanced control strategy that a least squares support vector machine model-based predictive control scheme for earth pressure balance is developed. Methods: A prediction model is established to predict the earth pressure in chamber during the tunneling process by means of least squares support vector machine technology. On this basis, an optimization function is given which aims at minimizing the difference between the predicted earth pressure and the desired one. To obtain the optimal control actions, an improved ant colony system algorithm is used as rolling optimization for earth pressure balance control in real time. Results: Based on the field data the simulation experiments are performed. The results demonstrate that the method proposed is very effective to control earth pressure balance, and it has good stability. Conclusion: The screw conveyor speed and advance speed are the major factors affecting the earth pressure in chamber. The excavation face could be controlled balance better by adjusting the screw conveyor speed and advance speed.

In order to avoid the safety accidents caused by earth pressure imbalance during shield machine tunneling process, the earth pressure between excavation face and that in chamber must be maintained balance, but it is difficult for practical engineering. Therefore, a data-driven multi-variable optimization method based on dual heuristic programming (DHP) is proposed. First, a cost function with respect to the chamber’s earth pressure is given in light of Bellman’s principle. Then, based on back propagation neural networks (BPNN), the action network, model network and critic network are established that compose the DHP controller. The networks’ weights are updated through the gradient descent algorithm. By minimizing the cost function, the action network utilizes the critic network’s error to optimize the control variables, so that the optimal advance speed, cutter head torque, cutter head speed, total thrust and screw conveyor speed are obtained. Finally, the simulation experiments are carried out, and the results indicate that the method can effectively control the earth pressure balance in chamber and has strong anti-interference ability.

Earth pressure in sealed chamber is affected by multisystem and multifield coupling during shield tunneling process, so it is difficult to establish a mechanism earth pressure control model. Therefore, a data-driven modeling method of earth pressure in sealed chamber is proposed, which is based on parallel least squares support vector machine optimized by parallel cooperative particle swarm (parallel cooperative particle swarm optimization-partial least squares support vector machine). The vectors are parallel studied according to different hierarchies firstly, then the initial classifiers are updated by using cross-feedback method to retrain the vectors, and finally the vectors are merged to obtain the support vectors. The parameters of least squares support vector machine are optimized by the parallel cooperative particle swarm optimization, so as to predict quickly for large-scale data. Finally, the simulation experiment is carried out based on in-site measured data, and the results show that the method has high computing efficiency and prediction accuracy. The method has directive significance for engineering application.

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