Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves
This study investigated the applicability of using ultrasonic wave signals in detecting early fire damage in concrete. This study analyzed the reliability of using the linear (wave velocity) and nonlinear (coherence) parameters from ultrasonic pulse measurements and the applicability of machine lear...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/22/7914 |
| _version_ | 1797464746812768256 |
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| author | Ma. Doreen Esplana Candelaria Nhoja Marie Miranda Chua Seong-Hoon Kee |
| author_facet | Ma. Doreen Esplana Candelaria Nhoja Marie Miranda Chua Seong-Hoon Kee |
| author_sort | Ma. Doreen Esplana Candelaria |
| collection | DOAJ |
| description | This study investigated the applicability of using ultrasonic wave signals in detecting early fire damage in concrete. This study analyzed the reliability of using the linear (wave velocity) and nonlinear (coherence) parameters from ultrasonic pulse measurements and the applicability of machine learning in assessing the thermal damage of concrete cylinders. While machine learning has been used in some damage detections for concrete, its feasibility has not been fully investigated in classifying thermal damage. Data was collected from laboratory experiments using concrete specimens with three different water-to-binder ratios (0.54, 0.46, and 0.35). The specimens were subjected to different target temperatures (100 °C, 200 °C, 300 °C, 400 °C, and 600 °C) and another set of cylinders was subjected to room temperature (20 °C) to represent the normal temperature condition. It was observed that P-wave velocities increased by 0.1% to 10.44% when the concretes were heated to 100 °C, and then decreased continuously until 600 °C by 48.46% to 65.80%. Conversely, coherence showed a significant decrease after exposure to 100 °C but had fluctuating values in the range of 0.110 to 0.223 thereafter. In terms of classifying the thermal damage of concrete, machine learning yielded an accuracy of 76.0% while the use of P-wave velocity and coherence yielded accuracies of 30.26% and 32.31%, respectively. |
| first_indexed | 2024-03-09T18:12:37Z |
| format | Article |
| id | doaj.art-3631b8785a5c41f7bef1fa3709c0cc4a |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T18:12:37Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-3631b8785a5c41f7bef1fa3709c0cc4a2023-11-24T09:01:03ZengMDPI AGMaterials1996-19442022-11-011522791410.3390/ma15227914Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse WavesMa. Doreen Esplana Candelaria0Nhoja Marie Miranda Chua1Seong-Hoon Kee2Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, KoreaDepartment of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, KoreaDepartment of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, KoreaThis study investigated the applicability of using ultrasonic wave signals in detecting early fire damage in concrete. This study analyzed the reliability of using the linear (wave velocity) and nonlinear (coherence) parameters from ultrasonic pulse measurements and the applicability of machine learning in assessing the thermal damage of concrete cylinders. While machine learning has been used in some damage detections for concrete, its feasibility has not been fully investigated in classifying thermal damage. Data was collected from laboratory experiments using concrete specimens with three different water-to-binder ratios (0.54, 0.46, and 0.35). The specimens were subjected to different target temperatures (100 °C, 200 °C, 300 °C, 400 °C, and 600 °C) and another set of cylinders was subjected to room temperature (20 °C) to represent the normal temperature condition. It was observed that P-wave velocities increased by 0.1% to 10.44% when the concretes were heated to 100 °C, and then decreased continuously until 600 °C by 48.46% to 65.80%. Conversely, coherence showed a significant decrease after exposure to 100 °C but had fluctuating values in the range of 0.110 to 0.223 thereafter. In terms of classifying the thermal damage of concrete, machine learning yielded an accuracy of 76.0% while the use of P-wave velocity and coherence yielded accuracies of 30.26% and 32.31%, respectively.https://www.mdpi.com/1996-1944/15/22/7914thermal damageconcreteultrasonic pulse wavesmachine learning |
| spellingShingle | Ma. Doreen Esplana Candelaria Nhoja Marie Miranda Chua Seong-Hoon Kee Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves Materials thermal damage concrete ultrasonic pulse waves machine learning |
| title | Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves |
| title_full | Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves |
| title_fullStr | Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves |
| title_full_unstemmed | Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves |
| title_short | Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves |
| title_sort | evaluation of heat induced damage in concrete using machine learning of ultrasonic pulse waves |
| topic | thermal damage concrete ultrasonic pulse waves machine learning |
| url | https://www.mdpi.com/1996-1944/15/22/7914 |
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