Damage detection of fiber reinforced polymer plate repaired steel structure using percussion and machine learning
| dc.contributor.advisor | Song, Gangbing | |
| dc.contributor.committeeMember | Chen, Zheng | |
| dc.contributor.committeeMember | Chen, Xuemin | |
| dc.creator | Xu, Yong | |
| dc.creator.orcid | 0000-0003-3730-9661 | |
| dc.date.accessioned | 2023-01-11T17:10:52Z | |
| dc.date.created | May 2022 | |
| dc.date.issued | 2022-05-12 | |
| dc.date.updated | 2023-01-11T17:10:53Z | |
| dc.description.abstract | The recent collapse of the Fern Hollow Bridge in Pittsburgh, Pennsylvania, brings attention to the structurally deficient infrastructure. The fiber reinforced polymer (FRP) has been proven to be a cost-effective, efficient, and reliable method for structure rehabilitation or reinforcement. Damage detection is an important measure to ensure the integrity and performance of such repairs. A novel method of using percussion and machine learning to detect the damage of FRP plate repaired steel structure was developed and discussed in this work. A steel beam with bonded carbon fiber reinforced polymer (CFRP) and known bonding defects was used as a test specimen. Then, different locations with different bonding conditions on the beam were tapped to generate the percussion sound, which was recorded by an iPhone. The mel-frequency cepstral coefficient (MFCC) algorithm was employed to extract features from percussion sound. The support vector machine (SVM) and recurrent neural network (RNN) method were implemented to learn the training samples and achieved high accuracy when predicting the healthy status of new test samples. The SVM used a new way of feature transformation, which is based on mean and standard deviation of MFCC. The high accuracy of 98.5% demonstrated the new feature transformation method is effective for SVM in percussion application. Then, the unsupervised clustering algorithms, k-means and Gaussian mixture model (GMM), were implemented on the sample data. The accuracy of k-means algorithm varies in a wide range from 51.5% to 70.4%, while the GMM clustering uses transformed MFCC and manually selected features, achieves an accuracy of 93.8%, The results of this work have demonstrated that the novel method of percussion and machine learning is reliable for damage detection of the FRP repaired steel structure. Compared with the conventional method, the proposed method does not require installation of sensors or implementing data acquisition systems or test instruments. The damage detection using percussion and machine learning brings great potential contribution to restoration of the deteriorated structure. | |
| dc.description.department | Mechanical Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10657/13301 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | CFRP | |
| dc.subject | Steel | |
| dc.subject | Percussion | |
| dc.subject | Machine learning | |
| dc.subject | MFCC | |
| dc.subject | SVM | |
| dc.subject | RNN | |
| dc.subject | GMM | |
| dc.title | Damage detection of fiber reinforced polymer plate repaired steel structure using percussion and machine learning | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2024-05-01 | |
| local.embargo.terms | 2024-05-01 | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Mechanical Engineering, Department of | |
| thesis.degree.discipline | Mechanical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science in Mechanical Engineering |
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