Development of seismic demand prediction models for bridges based on probability approach using symbolic regression method
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- @Article{REZAEI:2023:compstruc,
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author = "Hossein Rezaei and Panam Zarfam and
Emadaldin Mohammadi Golafshani and Gholamreza Ghodrati Amiri",
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title = "Development of seismic demand prediction models for
bridges based on probability approach using symbolic
regression method",
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journal = "Computer \& Structures",
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volume = "282",
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pages = "106991",
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year = "2023",
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ISSN = "0045-7949",
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DOI = "doi:10.1016/j.compstruc.2023.106991",
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URL = "https://www.sciencedirect.com/science/article/pii/S0045794923000214",
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keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Machine learning, Fragility curve,
Multispan bridges, Regional risk assessment",
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abstract = "The resilience assessment of existing structures and
designing new resilient ones need to have a precise
estimation of seismic responses of the structures
traditionally carried out by time-consuming
calculations of finite element method. This paper
presents interpretable, reliable, and fast prediction
models developed by two novel branches of the symbolic
regression approach (multigene genetic programming and
multi biogeography-based programming) in a probability
manner for curved bridges. These newly developed
techniques seek to find interpretable equations with
arbitrary forms that conform to a specific dataset. The
reliable prediction models were developed considering
the different uncertainties involved, including
mechanical, geometrical, structural, and seismic
uncertainties. Due to a large number of input variables
for the bridge, the evolutionary correlation
coefficient was employed to identify the most
influential parameters for seismic demands of bridge
components. Parameters with the highest correlation
were set as inputs for symbolic regression algorithms,
generating closed-form mathematical expressions to
predict seismic demands. The resulting explicit models
present a fast and accurate model that do not require
further simulation or the seismic demand model
assumptions to develop bridge fragility curves. In
comparison to cloud approach and multiple strips
analysis, the new method generates fragility curves
with less dispersion",
- }
Genetic Programming entries for
Hossein Rezaei
Panam Zarfam
Emadaldin Mohammadi Golafshani
Gholamreza Ghodrati Amiri
Citations