Paper: Extended and Generalized Fragility Functions – Andriotis & Papakonstantinou
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| Primary software used | Python |
| Software version | 1.0 |
| Course | Computational Intelligence for Integrated Design |
| Primary subject | AI & ML |
| Secondary subject | Machine Learning |
| Level | Expert |
| Last updated | N/A |
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Paper: Extended and Generalized Fragility Functions – Andriotis & Papakonstantinou 0/0
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Authors: C. P. Andriotis and K. G. Papakonstantinou
This paper describes fragility functions and aims to create fragility functions that are exceptionally comprehensive. These fragility functions take into consideration two essential aspects: (1) using multivariate intensity measurements with different damage states, and (2) accounting for the time dependencies of longitudinal damage state.
The first part of the paper introduces fragility functions and how they give a complete picture fragility. The second part introduces the authors’ work of creating “generalized fragility functions” which can handle a much larger number of states. These functions are intended to handle scenarios where many transitions between system states must be captured. Dependent Markov and hidden Markov models are used to correctly capture these transitions.
The paper offers numerical results as well as extensive insights into their implementation, statistical features, and practical advice to help in the efficient use of these fragility functions.
APA: Andriotis, C. P., Papakonstantinou, K. G. (2018). Extended and Generalized Fragility Functions. Journal of Engineering Mechanics, Volume 144, Issue 9. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001478
Software & Plug-Ins Used
- MATLAB for generalized fragility functions
Paper Information
- Title: Extended and Generalized Fragility Functions
- Author(s): C. P. Andriotis and K. G. Papakonstantinou
- Year: 2018
- Link: https://www.researchgate.net/publication/326331208_Extended_and_Generalized_Fragility_Functions
- Type: Journal Paper
- ML Tags: Gaussian Mixtures, dependent Markov Decision Processes, Hidden Markov Decision Processes
- Topic Tags: Earthquake Engineering, Seismic Design