Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties

Fredi Alegría, Eladio Martínez, Claudia Cortés-García, Quirino Estrada, Andrés Blanco-Ortega and Mario Ponce-Silva ()
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Fredi Alegría: Tecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, Mexico
Eladio Martínez: Tecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, Mexico
Claudia Cortés-García: Tecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, Mexico
Quirino Estrada: Instituto de ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Chihuahua, Mexico
Andrés Blanco-Ortega: Tecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, Mexico
Mario Ponce-Silva: Tecnológico Nacional de México-CENIDET, Cuernavaca 62490, Morelos, Mexico

Mathematics, 2024, vol. 12, issue 21, 1-23

Abstract: 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.

Keywords: structural damage; damage identification; genetic algorithms; uncertainties; incomplete data; MATLAB (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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