Prediction of Catenary Action Capacity of RC Beam-Column Substructures under a Missing Column Scenario Using Evolutionary Algorithm

Catenary action plays crucial role in resisting the applied vertical load at large deformations stage in reinforced concrete (RC) structures. This paper aims to predict the catenary action capacity of RC beam-column substructures by utilizing the distinctive properties of gene expression programming...

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Vydané v:KSCE Journal of Civil Engineering Ročník 25; číslo 3; s. 891 - 905
Hlavní autori: Azim, Iftikhar, Yang, Jian, Iqbal, Muhammad Farjad, Mahmood, Zafar, Javed, Muhammad Faisal, Wang, Feiliang, Liu, Qing-feng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Seoul Korean Society of Civil Engineers 01.03.2021
Springer Nature B.V
대한토목학회
Predmet:
Beam-columns
Catenaries
Catenary
Chromosomes
Civil Engineering
Constraints
Engineering
Evolutionary algorithms
Gene expression
Genetic algorithms
Geotechnical Engineering & Applied Earth Sciences
Industrial Pollution Prevention
Load
Mathematical models
Mutation
Naval engineering
Parameters
Parametric analysis
Regression analysis
Regression models
Reinforced concrete
Sensitivity analysis
Statistical analysis
Stiffness
Structural Engineering
Vertical loads
Yield stress
토목공학
Progressive collapse
Regression analysis
RC beam-column substructure
Catenary action
Gene expression programming
ISSN:1226-7988, 1976-3808
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Shrnutí:Catenary action plays crucial role in resisting the applied vertical load at large deformations stage in reinforced concrete (RC) structures. This paper aims to predict the catenary action capacity of RC beam-column substructures by utilizing the distinctive properties of gene expression programming (GEP). The input parameters selected for the modelling are: double-beam span-to-depth ratio, relative axial restraints stiffness, relative rotational restraints stiffness, bottom and top longitudinal reinforcement ratios, and yield strength of longitudinal rebars. A comprehensive and reliable database was collated from internationally published research articles to develop and verify the model. The GEP-based model was assessed by comparing its performance with regression based model. Various statistical indicators and external validation criteria suggested in literature proved that the model is accurate and possess high prediction and generalization capacity. Sensitivity analysis was carried out to show the contributions of the input parameters, while parametric analysis was performed to show that the proposed model is not merely a combination of the input parameters but can accurately represent the given physical system. The proposed formulation from GEP is found to be simple, robust, and easy to utilize for pre-design purposes.
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ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-021-0431-0