Basic research on usefulness of convolutional autoencoders in detecting defects in concrete using hammering sound
Basic research on usefulness of convolutional autoencoders in detecting defects in concrete using hammering sound
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DOI：
10.1177/14759217221122296
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Y. Sonoda;Chi Lu;Yifan Yin
中科院分区：
文献类型：
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作者：
Y. Sonoda;Chi Lu;Yifan Yin
由于锤击声测试价格低廉且易于执行，因此它们通常被用作检查老化混凝土结构中存在缺陷区域（空洞或剥落）的检查方法。然而，使用锤击声对混凝土健康状况的评价取决于检查员的主观经验。因此，需要开发一种准确、高效、高可靠、客观的诊断方法。在本研究中，我们使用卷积自编码器（CAE）开发了一种诊断方法，可以帮助检查员在检测混凝土缺陷区域时获得敲击声的定量诊断结果。特别是，我们使用所提出的CAE模型验证了随着时间的推移而恶化的实际桥梁的锤击声数据的异常检测精度。
Because hammering sound tests are inexpensive and can be performed easily, they are commonly used as an inspection method for examining the presence of defect areas (voids or peelings) in aged concrete structures. However, the evaluation of the health of concrete using hammering sounds depends on the subjective experience of the inspector. Therefore, there is a demand to develop a highly reliable and objective diagnostic method that is accurate and efficient. In this study, we used a convolutional autoencoder (CAE) to develop a diagnostic method that could assist the inspectors with quantitative diagnostic results of tapping sound when detecting defect areas in concrete. In particular, we verified the anomaly detection accuracy of hammering sound data of actual bridges that have deteriorated over time using the proposed CAE model.