Crack identification is an essential yet challenging task in structural health monitoring. This study presents a novel method for automatic crack detection based on optical frequency-domain reflectometry (OFDR) sensors, tackling the challenge of extracting crack-induced information from sensor measurements. Initially, a damage index is created by removing the substrate strain field obtained via the inverse finite-element method (iFEM) from the strain distribution recorded by OFDR sensors, allowing for the automatic identification of crack locations with an error margin of 7.68 mm. Next, using the established anomaly index, the support vector regression model, optimized by particle swarm optimization, predicts both the depth and width of the cracks simultaneously. The effectiveness of this crack detection method is confirmed through concentrated loading tests performed on four simply supported H-steel beams. Finally, the study examines and discusses how the size and diversity of training sets, as well as noise, affect prediction accuracy. This method enables the automated identification of crack depth and width, which is essential for structural safety evaluation and practical engineering applications.
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