Tunneling Paper: Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques

Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques


Jian Zhou
Behnam Yazdani Bejarbaneh
Danial Jahed Armaghani
M. M. Tahir

First Online: 10 December 2019


The efficiency of tunnel boring machine (TBM) is regarded as a key factor in successfully undertaking any mechanical tunneling project. In fact, an accurate forecasting of TBM performance, especially in a specified rock mass condition, can minimize capital costs and scheduling for tunnel excavation. This study puts an effort to propose two accurate and practical predictive models of TBM performance via artificial neural network (ANN) and genetic programming (GP) approaches. To set a certain prediction target for the proposed models, the advance rate (AR) of TBM is considered as its performance metric. For modeling purpose, a large experimental database containing 1286 data sets was set up as the result of conducting site investigation operations for a tunneling project in Malaysia, called the Pahang–Selangor Raw Water Transfer Tunnel and performing a number of laboratory tests on the collected rock samples. To design the desired intelligent models of AR based on the training and test patterns, a mix of rock and machine characteristics with the most influence on AR has been used as input parameters, i.e., rock quality designation (RQD), uniaxial compressive strength (UCS), rock mass rating (RMR), Brazilian tensile strength (BTS), thrust force (TF), and revolution per minute (RPM). In addition, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R-square), and variance account for (VAF) are utilized to evaluate and compare the prediction precision of the developed models. Based on the simulation results and the computed values of indices, it is observed that the proposed GP model with the training and test RMSE values 0.0427 and 0.0388, respectively, performs noticeably better than the proposed ANN model giving RMSE values 0.0509 and 0.0472 for the training and test sets, respectively. Additionally, a parametric analysis has been conducted on the proposed GP model to further verify its generalization capability. The obtained results demonstrate that this GP-based model could provide a new applicable equation for accuratly predicting TBM performance.


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