7. Postprocessing

The postprocessor class derives fragility and vulnerability models from nonlinear time-history analysis (NTHA) outputs. Supported workflows include Modified Cloud Analysis (MCA), Multiple Stripe Analysis (MSA), and Incremental Dynamic Analysis (IDA). Fragility fitting options are lognormal (with bootstrap or classical Bayesian MCMC estimation), probit, logit, and ordinal models. Vulnerability functions and average annual loss (AAL) are also computed.

• 7.1. Initialisation
  • postprocessor.__init__()
• 7.2. Lognormal Fragility Functions
  • postprocessor.calculate_lognormal_fragility()
• 7.3. Fragility Function Rotation
  • postprocessor.calculate_rotated_fragility()
• 7.4. GLM Fragility Functions
  • postprocessor.calculate_glm_fragility()
• 7.5. Ordinal Fragility Functions
  • postprocessor.calculate_ordinal_fragility()
• 7.6. Modified Cloud Analysis Postprocessing
  • postprocessor.process_mca_results()
• 7.7. Incremental Dynamic Analysis Postprocessing
  • postprocessor.process_ida_results()
• 7.8. Multiple Stripe Analysis Postprocessing
  • postprocessor.process_msa_results()
• 7.9. Vulnerability Functions
  • postprocessor.calculate_vulnerability_function()
• 7.10. Average Annual Damage Probabilities
  • postprocessor.calculate_average_annual_damage_probability()
• 7.11. Average Annual Loss
  • postprocessor.calculate_average_annual_loss()

7.12. References

1. Porter, K. (2017). “When Addressing Epistemic Uncertainty in a Lognormal Fragility Function, How Should One Adjust the Median?”, Proceedings of the 16th World Conference on Earthquake Engineering (16WCEE), Santiago, Chile.

2. Charvet, I., Ioannou, I., Rossetto, T., Suppasri, A., and Imamura, F. (2014). “Empirical fragility assessment of buildings affected by the 2011 Great East Japan tsunami using improved statistical models”, Natural Hazards, 73, 951-973.

3. Lallemant, D., Kiremidjian, A., and Burton, H. (2015). “Statistical procedures for developing earthquake damage fragility curves”, Earthquake Engineering and Structural Dynamics, 44, 1373-1389. doi: 10.1002/eqe.2522.

4. Baker, J.W. (2015). “Efficient Analytical Fragility Function Fitting Using Dynamic Structural Analysis”, Earthquake Spectra. 31(1):579-599. doi:10.1193/021113EQS025M

5. Nguyen, M., and Lallemant, D. (2022). “Order Matters: The Benefits of Ordinal Fragility Curves for Damage and Loss Estimation”. Risk Analysis, 42: 1136-1148. https://doi.org/10.1111/risa.13815

6. Silva, V. (2019). “Uncertainty and correlation in seismic vulnerability functions of building classes.” Earthquake Spectra. DOI: 10.1193/013018eqs031m.

7. Jalayer, F., De Risi, R., and Manfredi, G. (2015). “Bayesian Cloud Analysis: efficient structural fragility assessment using linear regression.” Bulletin of Earthquake Engineering, 13(4), 1183–1203.

8. Cornell, C.A., Jalayer, F., Hamburger, R.O., and Foutch, D.A. (2002). “Probabilistic basis for 2000 SAC Federal Emergency Management Agency steel moment frame guidelines.” Journal of Structural Engineering, 128(4), 526–533.

9. Vamvatsikos, D. and Cornell, C.A. (2002). “Incremental dynamic analysis.” Earthquake Engineering and Structural Dynamics, 31(3), 491–514.

10. McGuire, R.K. (2004). Seismic Hazard and Risk Analysis. Earthquake Engineering Research Institute, MNO-10, Oakland, CA.

11. Cornell, C.A. and Krawinkler, H. (2000). “Progress and challenges in seismic performance assessment.” PEER Center News, 3(2), 1–3.

12. Jalayer, F., Ebrahimian, H., Miano, A., Manfredi, G., and Sezen, H. (2017). “Analytical fragility assessment using unscaled ground motion records.” Earthquake Engineering and Structural Dynamics, 46: 2639–2663. https://doi.org/10.1002/eqe.2922