Prediction of the geological indicators in TBM tunnel based on optimized proportion of surrounding rock grades

Abstract

With tunnel boring machine being used in underground engineering, accurate geological indicators have been the important basis for tunnel boring machine (TBM) construction. Back propagation neural network (BPNN) has been used to predict the geological indicators of tunnels in previous studies. Nevertheless, these studies ignored the imbalance proportion of surrounding rock grades, leading to the indiscriminate use of data, thus affecting the predictive effect of BPNN. In order to prove the importance of the proportion of surrounding rock grade in geological prediction, we mainly attempt to utilize particle swarm optimization (PSO) to optimize the proportion of sample data, and integrate with BPNN to establish a PSO-BPNN theoretical model to predict geological indicators. At the same time, combined with the actual engineering data, 5 tunneling indicators were selected as input and 4 geological indicators were selected as output by a variety of dimensionality reduction methods. The geological indicators are density, uniaxial compressive strength, internal friction angle (φ) and Poisson's ratio (ε). On this basis, the PSO-BPNN prediction model was established in detail. By comparing the prediction of traditional BPNN, PSO-BPNN and other optimization-integrated models, the result shows that optimized proportion of surrounding rock grades reduces the prediction error and improves the interpretability of the prediction model. Meanwhile, we combined the theory of surrounding rock partition to illustrate the rationality of surrounding rock proportion in PSO result, that is, the proportion of complex surrounding rock should be increased appropriately to improve the prediction result. Ultimately, based on the optimization-integrated models with engineering data and the surrounding rock classification theory, the importance of proportion of surrounding rock grades for tunnel geological prediction is confirmed.

Keywords

• TBM
• BPNN
• Particle swarm optimization
• Prediction of geological indicators
• Sample imbalance