Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage
The growing importance of state assessments in civil engineering has led to intensive research into the development of damage identification methods based on vibrations. Natural frequencies and modal shapes have garnered great interest because modal parameters are invariant of structure. Moreover, t...
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MDPI AG
2025-01-01
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| author | Giorgio de Alteriis Giulio Mariniello Tommaso Pastore Alessia Teresa Silvestri Giuseppe Augugliaro Ida Papallo Canio Mennuti Antonio Bilotta Rosario Schiano Lo Moriello Domenico Asprone |
| author_facet | Giorgio de Alteriis Giulio Mariniello Tommaso Pastore Alessia Teresa Silvestri Giuseppe Augugliaro Ida Papallo Canio Mennuti Antonio Bilotta Rosario Schiano Lo Moriello Domenico Asprone |
| author_sort | Giorgio de Alteriis |
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| description | The growing importance of state assessments in civil engineering has led to intensive research into the development of damage identification methods based on vibrations. Natural frequencies and modal shapes have garnered great interest because modal parameters are invariant of structure. Moreover, thanks to the global nature of modal parameters, their variations are not limited to the location of the damage. This is an important advantage that offers the opportunity to identify damage with sensors whose position does not have to coincide with the damaged area. The integration of MEMS sensors into structural health monitoring (SHM) systems offers a promising approach to long-term structural maintenance, especially in large-scale infrastructure. This paper presents an anomaly detection technique that analyzes raw sequential data within a statistical framework to detect damage that causes prestress loss of the tendon by exploiting a distributed monitoring system composed of six high-performance MEMS sensors. The proposed system is preliminarily evaluated to identify the frequency of the first mode, and then the proposed methodology is validated on acceleration data collected on a 240 cm beam in three different damage configurations, achieving a high detection accuracy and showing that its output can also evaluate the damage localization. |
| format | Article |
| id | doaj-art-d556a583676942219aca8e1789d1c0e3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d556a583676942219aca8e1789d1c0e32025-01-10T13:21:29ZengMDPI AGSensors1424-82202025-01-0125128910.3390/s25010289Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural DamageGiorgio de Alteriis0Giulio Mariniello1Tommaso Pastore2Alessia Teresa Silvestri3Giuseppe Augugliaro4Ida Papallo5Canio Mennuti6Antonio Bilotta7Rosario Schiano Lo Moriello8Domenico Asprone9Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyDepartment of Structures for Engineering and Architecture, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Structures for Engineering and Architecture, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyNational Institute for Accident Insurance at Work (INAIL), Monte Porzio Catone, 00040 Rome, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyNational Institute for Accident Insurance at Work (INAIL), Monte Porzio Catone, 00040 Rome, ItalyDepartment of Structures for Engineering and Architecture, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyDepartment of Structures for Engineering and Architecture, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyThe growing importance of state assessments in civil engineering has led to intensive research into the development of damage identification methods based on vibrations. Natural frequencies and modal shapes have garnered great interest because modal parameters are invariant of structure. Moreover, thanks to the global nature of modal parameters, their variations are not limited to the location of the damage. This is an important advantage that offers the opportunity to identify damage with sensors whose position does not have to coincide with the damaged area. The integration of MEMS sensors into structural health monitoring (SHM) systems offers a promising approach to long-term structural maintenance, especially in large-scale infrastructure. This paper presents an anomaly detection technique that analyzes raw sequential data within a statistical framework to detect damage that causes prestress loss of the tendon by exploiting a distributed monitoring system composed of six high-performance MEMS sensors. The proposed system is preliminarily evaluated to identify the frequency of the first mode, and then the proposed methodology is validated on acceleration data collected on a 240 cm beam in three different damage configurations, achieving a high detection accuracy and showing that its output can also evaluate the damage localization.https://www.mdpi.com/1424-8220/25/1/289distributed monitoring systemstructural health monitoringMEMSfrequency domain decompositionanomaly detection |
| spellingShingle | Giorgio de Alteriis Giulio Mariniello Tommaso Pastore Alessia Teresa Silvestri Giuseppe Augugliaro Ida Papallo Canio Mennuti Antonio Bilotta Rosario Schiano Lo Moriello Domenico Asprone Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage Sensors distributed monitoring system structural health monitoring MEMS frequency domain decomposition anomaly detection |
| title | Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage |
| title_full | Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage |
| title_fullStr | Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage |
| title_full_unstemmed | Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage |
| title_short | Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage |
| title_sort | tendon anomaly identification in prestressed concrete beams based on an advanced monitoring mems and data driven detection of structural damage |
| topic | distributed monitoring system structural health monitoring MEMS frequency domain decomposition anomaly detection |
| url | https://www.mdpi.com/1424-8220/25/1/289 |
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