A numerical-informational approach for characterising the ductile behaviour of the T-stub component. Part 2: Parsimonious soft-computing-based metamodel. (1st January 2015)

• Record Type: Journal Article
• Authors: Fernandez-Ceniceros, J.; Sanz-Garcia, A.; Antoñanzas-Torres, F.; Martinez-de-Pison, F.J.
• Is Part Of: Engineering structures. Volume 82(2015:Jan. 01)
• Page Start: 249
• Page End: 260
• Publication Date: 2015-01-01
• Subjects: Steel -- T-stub -- Bolted connection -- Metamodel -- Support vector regression -- Genetic algorithms -- Soft computing -- Optimisation; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2014.06.047
• Languages: English
• ISSNs: 0141-0296

Abstract

Graphical abstract: Highlights: A hybrid approach based on the FE method and metamodels is presented. Metamodels based on soft-computing are used to predict the T-stub response. A feature selection based on GA allows the most influential inputs to be selected. Strain properties of constitutive materials are automatically discarded. Predictions based on metamodels compare well with FE results. Abstract: The accuracy of the component-based method relies heavily on the characteristic response of their constitutive elements. To properly assess the deformation capacity of the whole connection, modelling the complete force–displacement curves of the components, from the initial stiffness to fracture, is necessary. This paper presents a numerical-informational method for calculating the ductile response of the T-stub component. In order to reduce the intensive computation of the finite element (FE) method, the results of numerical simulations are used to train a set of metamodels based on soft-computing (SC) techniques. These metamodels are capable of predicting, with a high degree of accuracy, the key parameters that define the force–displacement curve of the T-stub. In addition, a feature selection (FS) scheme based on genetic algorithms (GAs) is included in the training process to select the most influential input variables. This scheme leads to overall and parsimonious metamodels that improve the method's generalisation capacity. The mean absolute error (MAE) in the prediction of each key parameter reports values below 5% for both validation and test results. This demonstrates the strong performance of the SC-based metamodels when comparing them with the FE simulations. Finally, this hybrid method constitutes a suitable tool to be implemented in non-linear steel connections software.