Inspection method of the corrosion rate for underwater grouting sleeves by integrating ultrasonic data augmentation and interpretable ensemble learning

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To addresses the challenge of underwater grouting sleeve corrosion affecting bridge service life, we propose an innovative inspection approach by integrating a modified ultrasonic testing instrument, ultrasonic data augmentation method, and an improved ensemble learning prediction model. Firstly, the ultrasonic data was collected from grouting sleeve specimens by the modified instrument. Secondly, a data generation model, incorporating Bray Curtis distance and rejection sampling algorithm, was developed to generate samples and filter abnormal data. Then, Circle Chaotic Map improved Grey Wolf Optimizer was proposed to optimize parameters from the Random Forest (RF) algorithm for establishing the prediction model. Finally, SHapley Additive exPlanations (SHAP) method was used to interpret the model globally and locally. Experimental results validate the effectiveness of the data augmentation method, ensuring high-quality ultrasonic data for accurate corrosion rate predictions. The proposed inspection method provide technical support for assessing remaining bridge bearing capacity and service life, showcasing high accuracy, interpretability, and noise tolerance.