Vibration-based RF-SVM for PC structural defect detection and assessment

Abstract

Machine learning techniques have great potential in structural health monitoring (SHM) and damage assessment of precast concrete (PC) structures. However, traditional damage assessment methods, such as finite element analysis, structural reliability analysis, and model testing, have limitations in terms of accuracy, efficiency, and applicability to complex real-world scenarios. This article proposes a hybrid machine learning approach combining random forest (RF) and support vector machine (SVM) for damage detection and classification in PC structures. The proposed RFSVM method utilizes continuous wavelet transform (CWT) to extract damage-sensitive features from vibration response data and employs RF for initial feature selection and damage classification. The RF output is then concatenated with the original features to form an enhanced feature vector, which is fed into the SVM model for precise damage type identification. The RF-SVM model is trained and tested using vibration response data generated from a simplified PC frame structure finite element model built in OpenSEES software. The experimental results demonstrate that the proposed method achieves an overall accuracy of 82.5% in detecting and classifying typical damage types in PC structures, with high precision, recall. However, the model's performance in identifying joint connection damage is relatively lower due to the subtle changes in vibration responses caused by this damage type. Compared to traditional methods that require extensive damage scenario data, the RF-SVM method simplifies the data preparation process and reduces computational complexity by utilizing only baseline data for training. This hybrid approach not only advances the field of SHM for PC structures but also offers a robust tool for infrastructure management, potentially increasing safety and reducing maintenance costs.