Deep learning-based surrogate modeling via physics-informed artificial image (PiAI) for strongly coupled multidisciplinary engineering systems

AdaBoost-Based Back Analysis for Determining Rock Mass Mechanical Parameters of Claystones in Goupitan Tunnel, China

The back analysis is an effective tool to determine the representative values of rock mass mechanical properties in rock engineering. The surrogate model is widely used in back analyses since analytical or numerical models are usually unavailable for practical engineering problems. This study proposes a novel back analysis framework by adopting the AdaBoost algorithm for deriving the surrogate model. Moreover, the simplicial homology global optimization (SHGO) algorithm, which is robust and applicable for a black-box global problem, is also integrated into the framework. To evaluate the performance, an experimental tunnel in Goupitan Hydropower Station, China, is introduced, and the representative rheological properties of the surrounding rock are obtained by applying the proposed framework. Then the computed displacements based on the acquired properties via both surrogate and numerical models are compared with field measurements. By taking triple-day data, the discrepancy between the calculated and field-measured displacements is less than 0.5 mm This validates the reliability of the obtained properties and the feasibility of the proposed framework. As an AdaBoost-based method, the proposed framework is sensitive to noise and outliers in the data, the elimination of which is recommended before application.