The Extreme Function Theory (EFT) offers a convenient tool for mode shape-based damage detection. When coupled with Gaussian Process Regression (GPR), this statistical framework can provide an automatic and efficient means for Structural Health Monitoring (SHM), especially to reduce the number of false positive errors (i.e. false alarms). Here, the technique is tested experimentally for bridge monitoring purposes on the well-known case study of the I-40 bridge. The EFT-based approach proved able to recognise deviations from the normality model (the undamaged conditions) on this experimental dataset, validating its applicability for large and massive civil structures and infrastructures.

Bridge monitoring: Application of the extreme function theory for damage detection on the I-40 case study / Martucci, D; Civera, M; Surace, C. - In: ENGINEERING STRUCTURES. - ISSN 0141-0296. - 279:(2023), p. 115573. [10.1016/j.engstruct.2022.115573]

Bridge monitoring: Application of the extreme function theory for damage detection on the I-40 case study

Martucci, D;Civera, M;Surace, C
2023

Abstract

The Extreme Function Theory (EFT) offers a convenient tool for mode shape-based damage detection. When coupled with Gaussian Process Regression (GPR), this statistical framework can provide an automatic and efficient means for Structural Health Monitoring (SHM), especially to reduce the number of false positive errors (i.e. false alarms). Here, the technique is tested experimentally for bridge monitoring purposes on the well-known case study of the I-40 bridge. The EFT-based approach proved able to recognise deviations from the normality model (the undamaged conditions) on this experimental dataset, validating its applicability for large and massive civil structures and infrastructures.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2977342