A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Bridge monitoring: Application of the extreme function theory for damage detection on the I-40 case study
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.
Bridge monitoring: Application of the extreme function theory for damage detection on the I-40 case study
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.
Bridge monitoring: Application of the extreme function theory for damage detection on the I-40 case study
Martucci, D. (author) / Civera, M. (author) / Surace, C. (author)
Engineering Structures ; 279
2022-01-01
Article (Journal)
Electronic Resource
English
Hybrid Sensor-Camera Monitoring for Damage Detection: Case Study of a Real Bridge
| Online Contents | 2016
Hybrid Sensor-Camera Monitoring for Damage Detection: Case Study of a Real Bridge
| British Library Online Contents | 2016