Use of soft computing techniques for tunneling optimization of tunnel boring machines

Thanks to advances in tunnel boring machine (TBM) and monitoring, significant progress has been achieved in the application of soft computing techniques for the optimization of TBM tunneling and the reduction of disturbance related to tunneling in urban areas. Because experimental, analytical, and numerical methods have limitations in solving problems related to TBM tunneling, engineers can use soft computing techniques in analyzing the relationship between the target tunneling responses and influential design inputs parameters, including the geometrical, geological, and TBM operational factors. These techniques are useful in achieving robust and low-cost solutions. However, engineers face difficulties in making an optimal choice of the soft computing technique to solve the complex problems related to TBM tunneling. To help with this choice, this study presents state of the art about the use of soft computing techniques in TBM tunneling through practical applications. The study proposes recommendations for the optimal use of these techniques, in particular (i) the importance of preliminary analyses for the selection and reduction of input parameters, (ii) the necessity to complete insufficient data using laboratory tests and numerical modeling, (iii) the selection of reduced number of hidden layers and nodes to avoid overfitting, (iv) the use of recurrent neural networks to deal with time-series data, and (v) the association of soft computing methods with hybrid optimization techniques to reduce the risk of convergence to local minima.

Automated summary

The application of soft computing techniques in TBM tunneling has achieved significant progress in optimizing solutions and reducing costs. Engineers face challenges in selecting the appropriate technique, but recommendations like preliminary analysis, data completion, selection of hidden layers and nodes, use of recurrent neural networks, and hybrid optimization techniques can help overcome these challenges and improve efficiency.