Earth pressure prediction in soil chamber of shield machine based on data-driven

Shield tunneling machine is a kind of special machine for underground tunneling. To ensure the safety and efficiency of shield tunneling, the earth pressure in soil chamber must be controlled in balance. Earth pressure in chamber is affected by multi-system,multi-field coupling and abrupt geological conditions and so on during the tunne-ling process,so it easily leads to earth pressure imbalance in soil chamber which can cause safety accidents such as surface subsidence. It is difficult to establish an effective mechanism model to predict the earth pressure for balance control,therefore,a data-driven modeling method of earth pressure in soil chamber is proposed,which is based on par-allel support vector machine optimized by parallel cooperative particle swarm (PCPSO-PSVM). The data samples are divided into four subsets and they are studied in parallel according to three different hierarchies,then the initial sam-ples are updated for retraining by using feedback to obtain the support vector. The parameters of SVM are optimized by using the PCPSO. The particle swarms are grouped in parallel to be optimized and they search solution independently in the separate processes. Each swam assembles to the main process to compute the global optimal parameters and,and the earth pressure predictive model of soil chamber is finally obtained. The simulation experiments are carried out based on the measured data of shield machine construction site. The prediction models are established for the four pressure monitoring points in the chamber. The simulation results indicate that the method has high computing efficien-cy and prediction accuracy which can meet the requirement of real time online calculation and prediction. Therefore, the earth pressure change of entire excavation face can be predicted through the four monitoring points,so as to make early warnings and decisions for earth pressure imbalance in the chamber. The method achieves a quick modeling and prediction based on data,and it can provide timely and accurate judgement evidence for the stability of excavation face which can guide engineering practice.