Abstract
Seismic damage to reinforced concrete (RC) beam-column joints in reinforced concrete (RC) structures significantly impacts their stability and safety after an earthquake. Cracks caused by earthquakes weaken these joints, reducing their ductility, strength, and stiffness. Therefore, a systematic method for assessing damage in RC beam-column joints is crucial. This study introduces a new method that uses image analysis, along with ranking algorithms and machine learning, to assess seismic damage in RC exterior beam-column joints. The study identified three key indicators of severe damage in joints: drift ratio, strength, and stiffness. The method uses image analysis to extract features from preprocessed, binary images of cracks. These features capture both the texture and geometry of the cracks. The chosen features, including crack area, aspect ratio, and distribution of line widths, along with structural parameters like geometry and bond index, were selected using F-test and MRMR algorithms. Seismic damage assessments were conducted on 115 images from 37 specimens using four machine learning models (Regression Trees, Support Vector Machine, Gaussian Process Regression, and Gradient Boosting). The study achieved a high R-squared value of 0.80, indicating strong accuracy, for assessing seismic damage based on drift ratio using the image-based method. This demonstrates that image analysis techniques can effectively link surface crack patterns to quantifiable image features, leading to a more precise assessment of seismic damage.
Published Version
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