Research Article
Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties
Table 5
Identified damages based on five model classes with two measurement schemes.
| | Model class | Damage parameter | Using data from 4 measure points | Using data from 10 measure points | | Identified value (actual value) | COV | | Identified value (actual value) | COV | |
| | M1 | | 0.9168 (0.85) | 8.226% | −2139.6 | 0.8115 (0.85) | 5.040% | −4560.6 | | 0.9540 (0.85) | 8.327% | 0.8378 (0.85) | 7.626% | | 0.9128 (0.85) | 9.896% | 0.7924 (0.85) | 7.669% | | 0.8972 (1.0) | 9.042% | 0.8373 (1.0) | 10.032% | | M2 | | 0.7688 (0.85) | 8.4173% | −1903.8 | 0.8266 (0.85) | 5.707% | −1723.3 | | 0.9961 (0.85) | 10.508% | 0.8433 (0.85) | 6.189% | | 0.8469 (0.85) | 5.8919% | 0.9071 (0.85) | 7.752% | | 0.9504 (1.0) | 6.3131% | 0.9555 (1.0) | 5.824% | | M3 | | 0.8880 (0.85) | 11.189% | −1760.5 | 0.8370 (0.85) | 7.181% | −1361.5 | | 0.9202 (0.85) | 16.211% | 0.8527 (0.85) | 6.662% | | 0.8612 (0.85) | 11.318% | 0.8602 (0.85) | 8.306% | | 0.9075 (1.0) | 9.654% | 0.9507 (1.0) | 5.887% | | M4 | | 0.8959 (0.85) | 6.7123% | −1752.5 | 0.8954 (0.85) | 6.662% | −1543.6 | | 0.8313 (0.85) | 10.538% | 0.8729 (0.85) | 9.782% | | 0.9340 (0.85) | 8.1471% | 0.8848 (0.85) | 5.845% | | 0.8833 (1.0) | 6.2183% | 0.9320 (1.0) | 8.620% | | M5 | | 0.8221 (0.85) | 9.107% | −1760.1 | 0.8744 (0.85) | 8.540% | −1375.7 | | 0.8452 (0.85) | 8.291% | 0.8779 (0.85) | 7.072% | | 0.9061 (0.85) | 5.788% | 0.8597 (0.85) | 7.188% | | 0.9537 (1.0) | 6.637% | 0.9333 (1.0) | 8.371% |
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Note: *log evidence here refers to the logarithm of the model evidence as defined in (5).
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