Bridge monitoring: Application of the extreme function theory for damage detection on the I-40 case study. (15th March 2023) - Martucci, D.
Highlights: A data-driven procedure is presented for the Structural Health Monitoring of highway bridges. The method is based on Gaussian Progress Regression (GPR) and Extreme Function Theory (EFT). GPR is used to estimate the continuous mode shapes from the known values at the sensor locations. The Extreme Function Theory is then applied for mode shape-based damage detection. The EFT-based procedure outperforms its EVT-based counterpart in terms of less false positives. 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.