The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Statistical procedures for developing earthquake damage fragility curves
Abstract
Summary: This paper describes statistical procedures for developing earthquake damage fragility functions. Although fragility curves abound in earthquake engineering and risk assessment literature, the focus has generally been on the methods for obtaining the damage data (i.e., the analysis of structures), and little emphasis is placed on the process for fitting fragility curves to this data. This paper provides a synthesis of the most commonly used methods for fitting fragility curves and highlights some of their significant limitations. More novel methods are described for parametric fragility curve development (generalized linear models and cumulative link models) and non‐parametric curves (generalized additive model and Gaussian kernel smoothing). An extensive discussion of the advantages and disadvantages of each method is provided, as well as examples using both empirical and analytical data. The paper further proposes methods for treating the uncertainty in intensity measure, an issue common with empirical data. Finally, the paper describes approaches for choosing among various fragility models, based on an evaluation of prediction error for a user‐defined loss function. Copyright © 2015 John Wiley & Sons, Ltd.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Vamvatsikos, D., & Cornell, C. A. (2002) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.141 · Cited by 4,220
Fuller, W. A. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316665 · Cited by 2,351
Boore, D. M., & Atkinson, G. M. (2008) · Earthquake Spectra · doi:10.1193/1.2830434 · Cited by 1,681
Efficient Analytical Fragility Function Fitting Using Dynamic Structural Analysis
Baker, J. W. (2015) · Earthquake Spectra · doi:10.1193/021113eqs025m · Cited by 1,565
Statistical Analysis of Fragility Curves
Shinozuka, M., Feng, M. Q., Lee, J., & Naganuma, T. (2000) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2000)126:12(1224 · Cited by 1,146
Federal Emergency Management Agency (FEMA)
(2015) · Encyclopedia of Trauma Care · doi:10.1007/978-3-642-29613-0_100622 · Cited by 998
Creating Fragility Functions for Performance‐Based Earthquake Engineering
Porter, K., Kennedy, R., & Bachman, R. (2007) · Earthquake Spectra · doi:10.1193/1.2720892 · Cited by 685
Gardoni, P., Kiureghian, A. D., & Mosalam, K. M. (2002) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2002)128:10(1024 · Cited by 671
Probabilistic seismic safety study of an existing nuclear power plant
Kennedy, R. P., Cornell, C. A., Campbell, R. D., Kaplan, S., & Perla, H. F. (1980) · Nuclear Engineering and Design · doi:10.1016/0029-5493(80)90203-4 · Cited by 385
Method for Probabilistic Evaluation of Seismic Structural Damage
Singhal, A., & Kiremidjian, A. S. (1996) · Journal of Structural Engineering · doi:10.1061/(asce)0733-9445(1996)122:12(1459 · Cited by 323
Self-citationProcessing Italian damage data to derive typological fragility curves
Rota, M., Penna, A., & Strobbia, C. L. (2008) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2007.10.010 · Cited by 277
ON CONFIDENCE INTERVALS FOR GENERALIZED ADDITIVE MODELS BASED ON PENALIZED REGRESSION SPLINES
Wood, S. N. (2006) · Australian & New Zealand Journal of Statistics · doi:10.1111/j.1467-842x.2006.00450.x · Cited by 205
Improved seismic fragility modeling from empirical data
Straub, D., & Der Kiureghian, A. (2008) · Structural Safety · doi:10.1016/j.strusafe.2007.05.004 · Cited by 202
Bayesian Updating of Fragilities with Application to RC Frames
Singhal, A., & Kiremidjian, A. S. (1998) · Journal of Structural Engineering · doi:10.1061/(asce)0733-9445(1998)124:8(922 · Cited by 154
Self-citationDevelopment of empirical and analytical fragility functions using kernel smoothing methods
Noh, H. Y., Lallemant, D., & Kiremidjian, A. S. (2014) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.2505 · Cited by 76
Self-citationNoh, H. Y., Lignos, D. G., Nair, K. K., & Kiremidjian, A. S. (2011) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.1151 · Cited by 64
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Cited by
On the application of machine learning techniques to derive seismic fragility curves
Kiani, J., Camp, C., & Pezeshk, S. (2019) · Computers & Structures · doi:10.1016/j.compstruc.2019.03.004 · Cited by 312
Metadata
- Authors
- Lallemant, D., Kiremidjian, A., & Burton, H.
- Venue
- Earthquake Engineering & Structural Dynamics
- Publisher
- Wiley
- Published
- 2015-01-26
- Volume
- 44
- Issue
- 9
- Pages
- 1373-1389
- ISSN
- 0098-8847 (ISSN-L)
- 1096-9845
- OpenAlex
- W2158701001
- Cited by
- 263
- References
- 19
- FWCI
- 16.51
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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