Mariani, S., Bruggi, M., Caimmi, F., Bendiscioli, P., De Fazio, M.: Sensor deployment over damage-containing plates: a topology optimization approach. Mariani, S., Corigliano, A., Caimmi, F., Bruggi, M., Bendiscioli, P., De Fazio, M.: MEMS-based surface mounted health monitoring system for composite laminates. doi: 10.1111/mice.12178Ĭho, S., Spencer, B.F.: Sensor attitude correction of wireless sensor network for acceleration-based monitoring of civil structures. Memarzadeh, M., Pozzi, M.: Integrated inspection scheduling and maintenance planning for infrastructure systems.
Pom qm upper boundary crack#
Yeum, C.M., Dyke, S.J.: Vision-based automated crack detection for bridge inspection. Glaser, S.D., Tolman, A.: Sense of sensing: from data to informed decisions for the built environment. Ko, J.M., Ni, Y.Q.: Technology developments in structural health monitoring of large-scale bridges. 5, 107–113 (2000)Īktan, A., Catbas, F., Grimmelsman, K., Tsikos, C.: Issues in infrastructure health monitoring for management. Stallings, J.M., Tedesco, J.W., El-Mihilmy, M., McCauley, M.: Field performance of FRP bridge repairs. Glaser, S.D., Li, H., Wang, M.L., Ou, J., Lynch, J.: Sensor technology innovation for the advancement of structural health monitoring: a strategic program of US-China research for the next decade. Capability and performance of the proposed approach are assessed in terms of tracked variation of the stiffness terms of the reduced-order model, identified damage location and speed-up of the whole health monitoring procedure.
Results are reported for a (pseudo-experimental) benchmark test on an eight-story shear building. Both filters exploit the information conveyed by measurements of the structural response to the external excitations.
Pom qm upper boundary update#
Such tracking is accomplished via two Kalman filters: a first (extended) one to deal with the time evolution of a joint state vector, gathering the reduced-order state and the stiffness terms degraded by damage a second one to deal with the update of the reduced-order model in case of damage evolution.
The reduced-order model of a structure is obtained during an (offline) initial training stage of monitoring afterward, effective estimations of a possible structural damage are provided online by tracking the evolution in time of stiffness parameters and projection bases handled in the model order reduction procedure. In this paper, an approach based on the synergistic use of proper orthogonal decomposition and Kalman filtering is proposed for the online health monitoring of damaged structures.