Predicting tunnel convergence using Multivariate Adaptive Regression Spline and Artificial Neural Network

Abstract Determining the tunnel convergence is an indispensable task in tunneling, especially when adopting the New Austrian Tunneling Method. The interpretation of the monitoring allows adjusting the construction methods in order to achieve more effective tunneling conditions and to avoid problems like rock collapse, trapping and jamming of boring machine, delay of the project or even geological disasters. In this research, a model capable of predicting the diameter convergence of a high-speed railway tunnel in weak rock was established based on two approaches: Multivariate Adaptive Regression Spline (MARS) and Artificial Neural Network (ANN). A tunnel construction project located in Hunan province (China) was used as case study. The input parameters included the class index of the surrounding rock mass, angle of internal friction, cohesion, Young’s modulus, rock density, tunnel overburden, distance between the monitoring station and the tunnel heading face and the elapsed monitoring time. The performance of the models was evaluated by comparing the predicted convergence to the measured data using several performance indices. Overall, the results showed high accuracy of the model predictability of tunnel convergence with MARS showing a light lesser accuracy. However, MARS was more flexible and computationally efficient. It is concluded that MARS can constitute a reliable alternative to ANN in modeling nonlinear geo-engineering problem such as the tunnel convergence.

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