Analysis for Time-Dependent Behavior of Soft Rock Through a Reinforced Learning Fusion Constitutive Model

Abstract

The long-term stability of tunnel structures is significantly influenced by the time-dependent behavior of the surrounding rock. Existing constitutive models often deviated from surrounding deformation due to the anisotropic nature of rock mass. In response, this study introduces a novel reinforced learning fusion constitutive model to accurately capture the time-dependent behaviors of soft rock. The framework and methodologies are first outlined, followed by the instantiation of the constitutive model of Burgers and creep parameters using laboratory testing data. To enhance accuracy, an XGBoost model is trained to reinforce the results of the constitutive model. The reliability of the proposed model is then validated against the original constitutive model and other representative machine learning models. Experimental findings demonstrate the superior characterization ability and stability of the presented reinforced model, where the calculation error reduces by 7.2E−06 at least, and [Formula: see text] score is improved by at least 1% to others. Consequently, the proposed model is reliable, offering a promising approach to capturing the actual time-dependent behaviors of tunnel surroundings in practical field applications.