Lightweight evaluation to uniaxial compressive strength of rock by nondestructive characteristic impedance and explainable data-driven techniques

Uniaxial compressive strength (UCS) serves as a fundamental mechanical property for evaluating the failure resistance of rock. Traditional destructive tests, while accurate, are time-consuming, costly, and less suitable for soft or fractured rocks. To address these issues, this study proposes a novel non-destructive approach for lightweight and accurate UCS prediction by integrating characteristic impedance with deep learning. Five hybrid Deep Extreme Learning Machine (DELM) models were developed by adjusting hyperparameters via meta-heuristic algorithms, and their performance was evaluated on two databases containing characteristic impedance and P-wave velocity, respectively. Model interpretability was enhanced through Shapley Additive Explanations (SHAP) analysis. The results demonstrate that all optimised DELM models significantly outperformed than the unoptimised model. When using characteristic impedance database, the CTCM-DELM model could achieve the highest accuracy (R2 = 0.967 for training, R2 = 0.968 for testing). Note that models utilising characteristic impedance consistently exceeded those using P-wave velocity, indicating characteristic impedance as a more effective input parameter. SHAP analysis further identified characteristic impedance as the most influential variable in UCS prediction, followed by point load index and Schmidt hammer rebound value. This study confirmed that leveraging characteristic impedance and explainable data-driven model in UCS prediction could give high-fidelity UCS prediction value.