TY - GEN
T1 - Vibration-Based SHM Strategy for a Real Time Alert System with Damage Location and Quantification
AU - Fernández-Navamuel, Ana
AU - Zamora-Sánchez, Diego
AU - Varona-Poncela, Tomás
AU - Jiménez-Fernández, Carlos
AU - Díez-Hernández, Jesús
AU - García-Sánchez, David
AU - Pardo, David
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - We present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the eigenfrequencies and mode shapes in real time from acceleration measurements, allowing to provide a diagnosis based on a Support Vector Machine algorithm trained with a database of the modal properties in undamaged and damaged scenarios accounting for temperature variability. The result is an alert system for controlling the correct performance of the structure in real time with a simple but efficient approach. Once the alert is triggered, the undamaged mode shapes (which could be previously stored in a database of modal parameters classified by temperature) and the current (damaged) mode shapes, can provide guidance for further application of Finite Element Model Updating (FEMU) techniques. The method is trained and validated with simulations from a FE model that is calibrated employing a genetic algorithm with real data from a short-term vibration measurement campaign on a truss railway bridge in Alicante (Spain).
AB - We present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the eigenfrequencies and mode shapes in real time from acceleration measurements, allowing to provide a diagnosis based on a Support Vector Machine algorithm trained with a database of the modal properties in undamaged and damaged scenarios accounting for temperature variability. The result is an alert system for controlling the correct performance of the structure in real time with a simple but efficient approach. Once the alert is triggered, the undamaged mode shapes (which could be previously stored in a database of modal parameters classified by temperature) and the current (damaged) mode shapes, can provide guidance for further application of Finite Element Model Updating (FEMU) techniques. The method is trained and validated with simulations from a FE model that is calibrated employing a genetic algorithm with real data from a short-term vibration measurement campaign on a truss railway bridge in Alicante (Spain).
KW - Bridge maintenance
KW - Machine learning
KW - Structural dynamics
KW - Structural health monitoring
UR - http://www.scopus.com/inward/record.url?scp=85101223626&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-64594-6_25
DO - 10.1007/978-3-030-64594-6_25
M3 - Conference contribution
AN - SCOPUS:85101223626
SN - 9783030645939
T3 - European Workshop on Structural Health Monitoring - Special Collection of 2020 Papers - Volume 1
SP - 245
EP - 255
BT - European Workshop on Structural Health Monitoring - Special Collection of 2020 Papers - Volume 1
A2 - Rizzo, Piervincenzo
A2 - Milazzo, Alberto
PB - Springer Science and Business Media Deutschland GmbH
T2 - European Workshop on Structural Health Monitoring, EWSHM 2020
Y2 - 6 July 2020 through 9 July 2020
ER -