Experimental Seismic Damage Quantification in a 3-Storey Laboratory Structure |
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| Journal | Key Engineering Materials (Volume 347) |
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| Volume | Damage Assessment of Structures VII |
| Edited by | L. Garibaldi, C. Surace, K. Holford and W.M. Ostachowicz |
| Pages | 297-302 |
| DOI | 10.4028/www.scientific.net/KEM.347.297 |
| Citation | Oliver R. de Lautour et al., 2007, Key Engineering Materials, 347, 297 |
| Online since | September, 2007 |
| Authors | Oliver R. de Lautour, Piotr Omenzetter |
| Keywords | Artificial Neural Network (ANN), Autoregressive Models, Damage Detection, Earthquake Engineering, Structural Health Monitoring (SHM) |
| Abstract | Time series based Structural Health Monitoring (SHM) methods are being increasingly explored. In this study, Autoregressive (AR) models were used to fit the acceleration time histories of a 3-storey laboratory structure under excitation by earthquake records in several damaged and undamaged states. The coefficients of the AR models were used as inputs into an Artificial Neural Network (ANN) and the ANN was trained to relate the AR coefficients to the damage at each storey. The results showed that proposed method was able to detect, locate and quantify the damage in the structure with a very high accuracy. |
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