Estimation of backbone model parameters for simulation of exposed column base plates
An approach is presented for the estimation of the parameters required to simulate the nonlinear monotonic (i.e., backbone) rotational response of Exposed-Column-Base-Plate (ECBP) connections subjected to moment and axial compression. A trilinear backbone curve is selected to represent the rotationa...
- Autores:
-
Villar-Salinas, Sergio
Kanvinde, Amit
Lopez-Almansa, Francisco
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12740
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12740
https://doi.org/10.1016/j.jcsr.2024.109034
- Palabra clave:
- Exposed-column-baseplates
Moment-rotation curves
Axial compression ratio
Regression models
Performance-based assessment
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
Summary: | An approach is presented for the estimation of the parameters required to simulate the nonlinear monotonic (i.e., backbone) rotational response of Exposed-Column-Base-Plate (ECBP) connections subjected to moment and axial compression. A trilinear backbone curve is selected to represent the rotational response, defined by three deformation and two strength parameters; these properly represent the stiffness, strength, and ductility of the connections. This approach is accompanied by a tool to facilitate convenient estimation of the parameters. The approach is based on a combination of behavioral insights and physics-based models (for some parameters) as well as regression for other parameters, which are estimated from a dataset of eighty-four experiments on ECBP connections conducted over the last forty years in the United States, Europe, and Asia. Predictive equations are provided to estimate the various parameters defining the nonlinear response, and their efficacy is examined by comparing them with the test data; in addition, well-established techniques are implemented to avoid collinearity and the overfitting of regression models. The results show that the models presented in this work provide robust and accurate predictions for in-sample and out-of-sample data. Limitations are outlined. |
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