This work presents the development of a model-based online damage identification system for a 13(th) century rammed earth (RE) tower in the Alhambra, the Muhammad Tower. The system is fed with continuous data from an ambient vibration-based monitoring system and a meteorological station. Ambient vibrations are continuously processed through Operational Modal Analysis (OMA), and environmental effects are minimised via statistical pattern recognition. The normalized modal signatures are used to update the stiffness properties of certain parts of the tower through inverse model calibration. To do so, a high-fidelity three-dimensional finite element model (FEM) of the tower is developed. Since its computational burden precludes conducting online calibration, the FEM is bypassed by a light Kriging surrogate model (SM). In this light, the developed SM-assisted system identification constitutes a long-term Structural Health Monitoring (SHM) system outputting quasi-real-time series of modal properties and local stiffness parameters, so providing full damage assessment (detection, localization and quantification). The presented results refer to a time period of three months since January until March 2022. Numerical results and discussion are reported concerning the characterization and removal of environmental effects, and synthetic damage scenarios through non-linear simulations are used to validate the developed damage identification system.
Meta-Model Assisted Continuous Vibration-Based Damage Identification of a Historical Rammed Earth Tower in the Alhambra Complex
Garcia Macias E
;Ubertini F.
2022
Abstract
This work presents the development of a model-based online damage identification system for a 13(th) century rammed earth (RE) tower in the Alhambra, the Muhammad Tower. The system is fed with continuous data from an ambient vibration-based monitoring system and a meteorological station. Ambient vibrations are continuously processed through Operational Modal Analysis (OMA), and environmental effects are minimised via statistical pattern recognition. The normalized modal signatures are used to update the stiffness properties of certain parts of the tower through inverse model calibration. To do so, a high-fidelity three-dimensional finite element model (FEM) of the tower is developed. Since its computational burden precludes conducting online calibration, the FEM is bypassed by a light Kriging surrogate model (SM). In this light, the developed SM-assisted system identification constitutes a long-term Structural Health Monitoring (SHM) system outputting quasi-real-time series of modal properties and local stiffness parameters, so providing full damage assessment (detection, localization and quantification). The presented results refer to a time period of three months since January until March 2022. Numerical results and discussion are reported concerning the characterization and removal of environmental effects, and synthetic damage scenarios through non-linear simulations are used to validate the developed damage identification system.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.