A numerical-informational approach for characterising the ductile behaviour of the T-stub component. Part 2: Parsimonious soft-computing-based metamodel

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.

[1]  Daniel R. Fairbairn,et al.  Neural-network applications in predicting moment-curvature parameters from experimental data , 1996 .

[2]  E. L. Hines,et al.  Application of artificial neural networks to the prediction of minor axis steel connections , 1997 .

[3]  Marley M. B. R. Vellasco,et al.  Neural networks assessment of beam-to-column joints , 2005 .

[4]  Emmanuel Attiogbe,et al.  Moment-Rotation Functions for Steel Connections , 1991 .

[5]  N Krishnamurthy.  A Fresh Look at Bolted End-Plate Behavior and Design , 1978 .

[6]  N. Kishi,et al.  Moment‐Rotation Relations of Semirigid Connections with Angles , 1990 .

[7]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  M. John Frye,et al.  Analysis of Flexibly Connected Steel Frames , 1975 .

[10]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[11]  Khalifa S. Al-Jabri,et al.  An advanced ANN model for predicting the rotational behaviour of semi-rigid composite joints in fire using the back-propagation paradigm , 2010 .

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  R. Bellman.  Dynamic programming. , 1957, Science.

[14]  Jean-Pierre Jaspart,et al.  Etude de la semi-rigidité des noeuds poutre-colonne et son influence sur la résistance et la stabilité des ossatures en acier , 1991 .

[15]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[16]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics (e1071), TU Wien , 2014 .

[17]  Wenjian Wang,et al.  Determination of the spread parameter in the Gaussian kernel for classification and regression , 2003, Neurocomputing.

[18]  W. Ramberg,et al.  Description of Stress-Strain Curves by Three Parameters , 1943 .

[19]  Vincenzo Piluso,et al.  Ultimate Behavior of Bolted T-Stubs I: Theoretical Model , 2001 .

[20]  T. Simpson,et al.  Analysis of support vector regression for approximation of complex engineering analyses , 2005, DAC 2003.

[21]  Amr S. Elnashai,et al.  Hysteretic mechanical-informational modeling of bolted steel frame connections , 2012 .

[22]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[23]  Amr S. Elnashai,et al.  Mechanical and informational modeling of steel beam-to-column connections , 2010 .

[24]  Ana M. Girão Coelho,et al.  Characterization of the ductility of bolted end plate beam-to-column steel connections , 2004 .

[25]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[26]  Vincenzo Piluso,et al.  Structural Steel Semirigid Connections: Theory, Design, and Software , 1999 .

[27]  Osvaldo M. Querin,et al.  Optimum design of semi-rigid connections using metamodels , 2012 .

[28]  Ralph M. Richard,et al.  Versatile Elastic-Plastic Stress-Strain Formula , 1975 .

[29]  Andrés Sanz-García,et al.  Parsimonious Support Vector Machines Modelling for Set Points in Industrial Processes Based on Genetic Algorithm Optimization , 2013, SOCO-CISIS-ICEUTE.

[30]  N. Kishi,et al.  Semirigid Steel Beam‐to‐Column Connections: Data Base and Modeling , 1989 .

[31]  Robert E. Melchers,et al.  Moment‐Rotation Curves for Bolted Connections , 1986 .

[32]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[33]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[34]  Reza Attarnejad,et al.  Assessment of load carrying capacity of castellated steel beams by neural networks , 2011 .

[35]  Turki Y. Abdalla,et al.  Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members , 2010 .

[36]  Georgios E. Stavroulakis,et al.  A Neural Network Approach to the Modelling, Calculation and Identification of Semi-Rigid Connections in Steel Structures , 1997 .