Di Sarno, Luigi 
ORCID: 0000-0001-6244-3251 and Jingren, Wu
(2022)
A machine-learning method for deriving state-dependent fragility curves of existing steel moment frames with masonry infills.
Engineering Structures, 276.
p. 115345.
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Abstract
Seismic assessment of existing buildings is usually a building-specific task that relies on refined finite element models. Such a task may require considerable computational demand, especially when predicting the seismic fragility of existing buildings under the framework of performance-based earthquake engineering. However, the computational cost can be significantly reduced by replacing the finite element model with a well-trained machine learning-based model, for example, an artificial neural network model. This paper presents the application of feedforward neural networks to derive the state-dependent fragility curves of existing steel moment frames, taking into account the effects of masonry infills. The network models can be trained to predict explicitly whether a structure exceeds the target limit state based on representative intensity measures of ground motions, which is in nature a binary classification problem. The number of non-linear time-history analysis required to generate the training data for the network models tends to be significantly lower compared to the case of conventional incremental dynamic analysis, particularly when a great number of ground motions are adopted aiming at higher accuracy of the fragility curves.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Neural networks, Existing steel frames, Masonry infills, Fragility curves |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 05 Dec 2022 09:29 |
| Last Modified: | 25 Jan 2023 13:32 |
| DOI: | 10.1016/j.engstruct.2022.115345 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3166494 |
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