Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties
In the field of structural damage detection through vibration measurements, most existing methods demand extensive data collection, including vibration readings at multiple levels, strain data, temperature measurements, and numerous vibration modes. These requirements result in high costs and comple...
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MDPI AG
2024-10-01
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| Series: | Mathematics |
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| author | Fredi Alegría Eladio Martínez Claudia Cortés-García Quirino Estrada Andrés Blanco-Ortega Mario Ponce-Silva |
| author_facet | Fredi Alegría Eladio Martínez Claudia Cortés-García Quirino Estrada Andrés Blanco-Ortega Mario Ponce-Silva |
| author_sort | Fredi Alegría |
| collection | DOAJ |
| description | In the field of structural damage detection through vibration measurements, most existing methods demand extensive data collection, including vibration readings at multiple levels, strain data, temperature measurements, and numerous vibration modes. These requirements result in high costs and complex instrumentation processes. Additionally, many approaches fail to account for model uncertainties, leading to significant discrepancies between the actual structure and its numerical reference model, thus compromising the accuracy of damage identification. This study introduces an innovative computational method aimed at minimizing data requirements, reducing instrumentation costs, and functioning with fewer vibration modes. By utilizing information from a single vibration sensor and at least three vibration modes, the method avoids the need for higher-mode excitation, which typically demands specialized equipment. The approach also incorporates model uncertainties related to geometry and mass distribution, improving the accuracy of damage detection. The computational method was validated on a steel frame structure under various damage conditions, categorized as single or multiple damage. The results indicate up to 100% accuracy in locating damage and up to 80% accuracy in estimating its severity. These findings demonstrate the method’s potential for detecting structural damage with limited data and at a significantly lower cost compared to conventional techniques. |
| format | Article |
| id | doaj-art-2876350aab5e4e33b50185474dae63a5 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-2876350aab5e4e33b50185474dae63a52024-11-08T14:37:39ZengMDPI AGMathematics2227-73902024-10-011221336210.3390/math12213362Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model UncertaintiesFredi Alegría0Eladio Martínez1Claudia Cortés-García2Quirino Estrada3Andrés Blanco-Ortega4Mario Ponce-Silva5Tecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, MexicoTecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, MexicoTecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, MexicoInstituto de ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Chihuahua, MexicoTecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, MexicoTecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, MexicoIn the field of structural damage detection through vibration measurements, most existing methods demand extensive data collection, including vibration readings at multiple levels, strain data, temperature measurements, and numerous vibration modes. These requirements result in high costs and complex instrumentation processes. Additionally, many approaches fail to account for model uncertainties, leading to significant discrepancies between the actual structure and its numerical reference model, thus compromising the accuracy of damage identification. This study introduces an innovative computational method aimed at minimizing data requirements, reducing instrumentation costs, and functioning with fewer vibration modes. By utilizing information from a single vibration sensor and at least three vibration modes, the method avoids the need for higher-mode excitation, which typically demands specialized equipment. The approach also incorporates model uncertainties related to geometry and mass distribution, improving the accuracy of damage detection. The computational method was validated on a steel frame structure under various damage conditions, categorized as single or multiple damage. The results indicate up to 100% accuracy in locating damage and up to 80% accuracy in estimating its severity. These findings demonstrate the method’s potential for detecting structural damage with limited data and at a significantly lower cost compared to conventional techniques.https://www.mdpi.com/2227-7390/12/21/3362structural damagedamage identificationgenetic algorithmsuncertaintiesincomplete dataMATLAB |
| spellingShingle | Fredi Alegría Eladio Martínez Claudia Cortés-García Quirino Estrada Andrés Blanco-Ortega Mario Ponce-Silva Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties Mathematics structural damage damage identification genetic algorithms uncertainties incomplete data MATLAB |
| title | Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties |
| title_full | Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties |
| title_fullStr | Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties |
| title_full_unstemmed | Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties |
| title_short | Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties |
| title_sort | efficient structural damage detection with minimal input data leveraging fewer sensors and addressing model uncertainties |
| topic | structural damage damage identification genetic algorithms uncertainties incomplete data MATLAB |
| url | https://www.mdpi.com/2227-7390/12/21/3362 |
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