Prediction and risk assessment of stress corrosion failures of prestressed anchors in underground mines

Abstract

Stress corrosion cracking (SCC) in deep underground anchor systems presents a growing threat to mining infrastructure integrity, driven by extreme environmental corrosion coupled with mechanical stress. To anticipate the progression of stress corrosion in deep anchor bolts, this study establishes a machine learning framework integrating 12 critical corrosion-influencing features with optimised support vector machine (SVM) modelling. The corrosion data collected regarding bolt failures in underground conditions furnished valuable references for the relevant research. The rigorous data cleaning methodologies are employed to refine the raw data sourced from anchoring materials. The statistical and machine learning models are utilised to execute data imputation and normalisation, facilitating the establishment of an SVM model tailored specifically for anchor material corrosion prediction. To enhance the predictive capabilities of the SVM algorithm, the model was optimised through the integration of principal component analysis and gradient boosting tree algorithms. The high accuracy of the model in predicting bolt corrosion risk was verified, and the weight of influences of environmental factors on corrosion failure are analysed. Key mechanistic insights reveal that stress and drip water flow rate dominate corrosion progression through synergistic electrochemical-stress interactions. The validated framework provides mine operators with a decision-support tool for proactive maintenance planning and corrosion-resistant anchoring system design in extreme environments.