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The inspection of broken wires in prestressed concrete cylinder pipes is crucial for ensuring the safety and reliability of the pipeline. Traditional point detection techniques always require labor-intensive periodic inspections and cannot deployed along the entire pipeline, significantly limiting the development of the industry. Hence, there is an urgent need for more advanced and intelligent sensors that can achieve 100% coverage and provide sufficient accuracy assurance. In this work, we develop a distributed acoustic sensing -based automated monitoring system to accurately classify the rupture of prestressed wires. First, a computer vision approach is employed to primarily screen out potential vibrational signals from DAS array images. Then, a pre-trained support vector machine model is used to classify the vibrations as either wire breakages or non-wire breakages. This model's performance surpassed other classification strategies, achieving 99.62% accuracy, 99.41% precision, 98.82% recall, and 99.12% F1-score in a side-to-side comparison. Our innovative workflow provides a comprehensive solution for detecting broken wires and offers guidance for the application of artificial intelligence-based DAS to complex vibration systems with limited training data. [ABSTRACT FROM AUTHOR]