Prediction of Surface Settlement in the Cross-passage by FIG-SVM Approach

Introduction: An optimized support vector machine prediction model (SVM) with fuzzy information granulation (FIG) is established for surface prediction in this paper. Based on the measured data, cubic spline interpolation was processed and FIG approach was applied to granulate the surface settlement parameters into fuzzy particles Low, R and Up, and the particles are used to represent the range of the measured data variation. Method: For each fuzzy particle, particle swarm optimization (PSO) was used to select the best penalty and kernel function parameters to minimize the K-fold cross-validation (K-CV) error. With the optimized parameters, the prediction model was trained for the nonlinear prediction of fuzzy particles. Finally, the surface settlement trend in terms of Low, R, Up for the next construction stage can be predicted effectively based on the previous test data. Taking the surface settlement monitoring point D5-5 as an example, the error of the predicted range of surface settlement at D5-5 is 5.82%, 5.42%, and 8.0%, respectively Result: The error of other predicted points is also less than 10%, indicating the effectiveness of the prediction model. Compared with the numerical simulation results, the accuracy of the prediction model was further verified. Conclusion: Combined with the simulation method, the "simulation - prediction" patent scheme for monitoring the surface settlement of the Cross Passage is proposed in this paper. The research results indicate that the model proposed in this paper can easily and effectively predict the range and trend of changes in surface settlement, and is suitable for practical engineering applications.