| Autor: |
J. Ye, S. Takatsu, Y. Nozoe, K. Tsuno, Y. Nagata, Masaya Iwata, Yuichi Kubota, Takashi Okuma, Masahiro Murakawa, Yuji Kasai |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations ISBN: 9780429279119 |
| DOI: |
10.1201/9780429279119-152 |
| Popis: |
Many inspections of concrete structures are conducted using non-destructive testing, such as measuring equipment in addition to visual and tapping inspection. In general, the hammering test, widely used in practice, is based mostly on the experience and judgment of the inspector. The “Artificial Intelligence (AI)-aided hammering test system” that we have proposed and developed automatically identifies the anomalous parts of a structure and the extent of the anomalies via machine learning relating the differences in hammering echoes. In addition to presenting the detection results to the inspector in real time, the test automatically integrates the inspection hammer’s location information and an anomaly degree map is generated. This function can reduce some steps in inspection work, such as drawing. A large number of hammering echo samples were collected and the quality performance of AI-aided hammering test system was confirmed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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