Back analysis of rock mass parameters in mechanized twin tunnels based on coupled auto machine learning and multi-objective optimization algorithm

Accurate determination of rock mass parameters is essential for ensuring the accuracy of numerical simulations. Displacement back-analysis is the most widely used method; however, the reliability of the current approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameter back-analysis method, that considers the construction process and displacement losses is proposed and implemented through the coupling of numerical simulation, auto-machine learning (AutoML), and multi-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanized twin tunnels is developed, generating a dataset through extensive numerical simulations. Next, the AutoML method is utilized to establish a surrogate model linking rock parameters and displacements. The tunnel construction process is divided into multiple stages, transforming the rock mass parameter back-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithms are introduced to obtain the rock mass parameters. The newly proposed rock mass parameter back-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectiveness are demonstrated. Compared with traditional single-stage back-analysis methods, the proposed model decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving the accuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increases with the number of construction stages considered, the back analysis time is acceptable. This study provides a new method for displacement back analysis that is efficient and accurate, thereby paving the way for precise parameter determination in numerical simulations. • A mechanized twin tunnels parametric modeling method based on EPBM-hydraulic-rock-structure coupling is proposed, and a corresponding construction response simulation platform is developed. • A multi-stage rock mass parameter back analysis method based on AutoML-MOOA considering the construction process is proposed, which has good accuracy and effectiveness. • The accuracy of multi-stage back analysis method based on AutoML-MOOA is much higher than that of traditional single-stage back analysis. • The more excavation stages used, the higher the accuracy of the parameter back analysis.