Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components
Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment t...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/10/7/174 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849733459286687744 |
|---|---|
| author | Romaine Byfield Ahmed Shabaka Milton Molina Vargas Ibrahim Tansel |
| author_facet | Romaine Byfield Ahmed Shabaka Milton Molina Vargas Ibrahim Tansel |
| author_sort | Romaine Byfield |
| collection | DOAJ |
| description | Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment to conduct inspections. In contrast, SHM employs permanently installed, cost-effective sensors to enable continuous monitoring, though often with reduced detail. This study presents an integrated hybrid SHM-NDT methodology enhanced by deep learning to enable the real-time monitoring and classification of mechanical stresses in structural components. As a case study, a 6-foot-long parallel flange I-beam, representing bridge truss elements, was subjected to variable bending loads to simulate operational conditions. The hybrid system utilized an ultrasonic transducer (NDT) for excitation and piezoelectric sensors (SHM) for signal acquisition. Signal data were analyzed using 1D and 2D convolutional neural networks (CNNs), long short-term memory (LSTM) models, and random forest classifiers to detect and classify load magnitudes. The AI-enhanced approach achieved 100% accuracy in 47 out of 48 tests and 94% in the remaining tests. These results demonstrate that the hybrid SHM-NDT framework, combined with machine learning, offers a powerful and adaptable solution for continuous monitoring and precise damage assessment of structural systems, significantly advancing maintenance practices and safety assurance. |
| format | Article |
| id | doaj-art-d0b3012bc25547e5a9b771df1484ed26 |
| institution | DOAJ |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-d0b3012bc25547e5a9b771df1484ed262025-08-20T03:08:01ZengMDPI AGInfrastructures2412-38112025-07-0110717410.3390/infrastructures10070174Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal ComponentsRomaine Byfield0Ahmed Shabaka1Milton Molina Vargas2Ibrahim Tansel3Department of Mechanical and Materials Engineering, Florida International University, Miami, FL 33174, USADepartment of Mechanical and Materials Engineering, Florida International University, Miami, FL 33174, USADepartment of Mechanical and Materials Engineering, Florida International University, Miami, FL 33174, USADepartment of Mechanical and Materials Engineering, Florida International University, Miami, FL 33174, USAStructural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment to conduct inspections. In contrast, SHM employs permanently installed, cost-effective sensors to enable continuous monitoring, though often with reduced detail. This study presents an integrated hybrid SHM-NDT methodology enhanced by deep learning to enable the real-time monitoring and classification of mechanical stresses in structural components. As a case study, a 6-foot-long parallel flange I-beam, representing bridge truss elements, was subjected to variable bending loads to simulate operational conditions. The hybrid system utilized an ultrasonic transducer (NDT) for excitation and piezoelectric sensors (SHM) for signal acquisition. Signal data were analyzed using 1D and 2D convolutional neural networks (CNNs), long short-term memory (LSTM) models, and random forest classifiers to detect and classify load magnitudes. The AI-enhanced approach achieved 100% accuracy in 47 out of 48 tests and 94% in the remaining tests. These results demonstrate that the hybrid SHM-NDT framework, combined with machine learning, offers a powerful and adaptable solution for continuous monitoring and precise damage assessment of structural systems, significantly advancing maintenance practices and safety assurance.https://www.mdpi.com/2412-3811/10/7/174structural health monitoringnondestructive testingdeep learningsignal processingacousticstransducers |
| spellingShingle | Romaine Byfield Ahmed Shabaka Milton Molina Vargas Ibrahim Tansel Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components Infrastructures structural health monitoring nondestructive testing deep learning signal processing acoustics transducers |
| title | Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components |
| title_full | Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components |
| title_fullStr | Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components |
| title_full_unstemmed | Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components |
| title_short | Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components |
| title_sort | intelligent hybrid shm ndt approach for structural assessment of metal components |
| topic | structural health monitoring nondestructive testing deep learning signal processing acoustics transducers |
| url | https://www.mdpi.com/2412-3811/10/7/174 |
| work_keys_str_mv | AT romainebyfield intelligenthybridshmndtapproachforstructuralassessmentofmetalcomponents AT ahmedshabaka intelligenthybridshmndtapproachforstructuralassessmentofmetalcomponents AT miltonmolinavargas intelligenthybridshmndtapproachforstructuralassessmentofmetalcomponents AT ibrahimtansel intelligenthybridshmndtapproachforstructuralassessmentofmetalcomponents |