STATISTICAL ANALYSIS OF FRAGILITY CURVES
By Masanobu Shinozuka, Honorary Member, Maria Qing Feng, Jongheon Lee, T. Naganuma. 2000
1,294 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/cd0307fd973f31abb2504c0b85174b1072e43df8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- David Lallemant
- Anne S. Kiremidjian
- Henry V. Burton
- H. Noh
- Yang Liu
- Lamine Mili
- Chu V. Mai
- José C. Matos
In Statistics and Probability
- Anne S. Kiremidjian
- David Lallemant
- Henry V. Burton
- H. Noh
- Lamine Mili
- Chu V. Mai
- Ruiyang Zhang
- Bruno Sudret
Citations in the corpus
- Fragility curves for free and restrained rocking masonry façades in one-sided motion (2018, background): “These procedures started to be applied in seismic field almost twenty years ago, with the introduction of the concept of fragility, or conditional probability of failure [23].”
- Seismic fragility curves for structures using non-parametric representations (2017, positive): “[22] proposed the use of maximum likelihood estimation to determine these parameters as follows: One denotes by w the event that the demand threshold δo is reached or exceeded and assumes that Y (w) is a random variable with a Bernoulli distribution.”
- Development of empirical and analytical fragility functions using kernel smoothing methods (2015, methodology): “Indeed, the lognormal CDF has often been used to model earthquake damage fragility [4, 5, 7, 8, 10, 11].”
- Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling (2019, methodology): “[68, 69] estimated μ and β using the maximum likelihood estimation (MLE), denoted by L (·).”
- Statistical procedures for developing earthquake damage fragility curves (2015, background): “It also has the mathematical convenience that when a lognormally distributed random variable is multiplied or divided by factors (factors of safety for instance), which are themselves uncertain and lognormally distributed, the resulting fragility curve is still lognormally distributed [24].”
- On the Definition of Cyber-Physical Resilience in Power Systems (2015, background): “In risk assessment studies, a common practice for determining infrastructure physical vulnerability is performing the fragility curve estimation [57].”