Optimum design of semi-rigid connections using metamodels

Díaz, Concepción; Victoria, Mariano; Querin, Osvaldo M.; Martí, Pascual

doi:10.1016/j.jcsr.2012.06.013

Cited by 30 publications

(21 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This type of behavior, very common in GAs, because the GAs have a probabilistic nature. Table 3 shows the configuration of the connections obtained in this study, the configuration of the optimized connection presented by Cabrero and Bayo (2005) and the configuration of the connections A and C obtained by Díaz et al (2012).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal design of beam-column connections of plane steel frames using the component method

Hortêncio

Falcón

2018

Lat. Am. j. solids struct.

View full text Add to dashboard Cite

This paper presents a methodology for optimization of beam-column connections of plane steel frames. The objective is to obtain beam-column connections mechanically more efficient and with minimum cost by determination of the optimal dimensions for the components of the connection; satisfying mechanical constraints associated with the bending moment and the rotational stiffness of the connection, without compromising its safety and integrity. Minimum and maximum limits of geometric parameters are considered, according to current regulations. Algorithms were developed to calculate the bending moment and the rotational stiffness of the connection using the "Method of Components" of Eurocode 3. Initially, it was developed a digital database with structural profiles, steel plates and commercial bolts obtained from catalogs of manufacturers, with automatic access of the data by the computational modules of structural analysis and optimization. In the optimization model, it is adopted the connection with extended end plate without stiffeners, the design variables are the dimensions and the thickness of the end plate, the diameter and the location of the bolts. In the optimization process, we use genetic algorithms with continuous and discrete variables, with the discrete variables being associated to the database. In this way, this paper presents a computational tool fully developed in MATLAB® environment for analysis and optimal design of beam-column connections for plane steel frames. Applications that show quite satisfactory results when compared with results available in the literature are presented.

show abstract

Section: Resultsmentioning

confidence: 99%

“…(3). This formulation was proposed by Pavlovčič et al (2004) and it was also used by Díaz et al (2012).…”

Section: Formulation Of the Problemmentioning

confidence: 99%

Optimal design of beam-column connections of plane steel frames using the component method

Hortêncio

Falcón

2018

Lat. Am. j. solids struct.

View full text Add to dashboard Cite

show abstract

“…The geometrical dimensions of the profiles for the joint were obtained from the Arcelor group catalogue (ArcelorMittal, 2016) and for the bolts from EN 14399-3:2005EN 14399-3: (2005. In order to prevent local effects under the reactions, the thickness of the stiffeners (tsM, tsn) were set to be equal to that of the beam flange thicknesses (tfbM, tfbn) (Díaz, 2010;Díaz et al, 2011). Table 1.…”

Section: Definition Of the Internal Joint Configurationmentioning

confidence: 99%

FE Model of Three-Dimensional Steel Beam-to-Column Bolted Extended End-Plate Joint

et al. 2018

Self Cite

View full text Add to dashboard Cite

The rotational behaviour of three-dimensional steel end-plate connections can be studied using the finite element method for the following four reasons: 1) such models are inexpensive and robust; 2) they allow the understanding of local effects; 3) they can be used to generate extensive parametric studies; 4) current version of the component method lacks the appropriate components to predict the behaviour of minor-axis and three-dimensional joints. This work presents a full ANSYS finite element parametric model of a three-dimensional steel beam-to-column bolted extended end-plate joint in both axes for use to obtain their behaviour. The model allows to study four joint configurations (internal, external, corner, and plane) and includes: contact and sliding between different elements; bolt-pretension, and geometric and material non-linearity. This model was calibrated and validated with experimental results found in the literature. The results from the finite element analysis were verified by comparing the obtained moment-rotation curve of the joint. Three parametric studies are presented to show the versatility of the FE model. The results were compared with those obtained with the model proposed by Eurocode 3. The developed ANSYS FE model can be downloaded for free as a single ZIP compressed file from the Technical University of Cartagena (UPCT): http://www.upct.es/goe/publicaciones/FEM_3D_EEP.zip.

show abstract

“…We also wish to use a sample of points whose projections onto each variable axis are uniform, the logic being that it is wasteful to sample a variable more than once at the same value. The Latin hypercube [5] sampling technique is more favorable [24][25][26][27][28][29] especially using the Morris and Mitchell's optimal Latin hypercube approach [1,7].…”

Section: Screening and Sampling Plansmentioning

confidence: 99%

Optimization of Multi-Fidelity Data Using Co-Kriging for High Dimensional Problems

Elsayed

Lacor

2014

The International Conference on Applied Mechanics and Mechanica

View full text Add to dashboard Cite

This paper deals with an efficient and multi-fidelity design strategy for high dimensional industrial problems. The most significant factors have been determined based on the Muschelknautz method of modeling (MM) using the screening approach. For cyclone separator, only four (from seven) geometrical parameters are significant. An optimized sampling plan based on random Latin hypercube (LHS) has been used to fit Co-Kriging based on CFD data and an analytical model for estimation of pressure drop. Co-Kriging exhibits better accuracy than ordinary Kriging and blind Kriging if only the high fidelity data is used. For global optimization, the Co-Kriging surrogate in conjunction with genetic algorithms (GA) is used. CFD simulations based on the Reynolds stress turbulence model are also used in this study. A new set of geometrical ratios (design) has been obtained (optimized) to achieve minimum pressure drop. A comparison of numerical simulation of the new design and the Stairmand design confirms the superior performance of the new design compared to the Stairmand design.Recently, the meta-models have been used as tool to calibrate the less accurate (simplified) codes. Such multi-fidelity or multi-level approaches can also deal with experimental data and CFD simulations data or for CFD simulation on different grid levels, or fully developed flow results and developing flow results. Co-Kriging is a typical example of surrogate models which can handle multi-fidelity data. To filter the noise of the experimental data or the computational noise stems from the used schemes [1], meta-models can help the engineer as well. A typical example is the Regression Kriging. The surrogate models have been used as a data-mining tool several decades ago, when the polynomial regression was the cheap tool for data-mining. Constructing The Surrogate ModelThe application of met-model instead of the experimental or computational approach is not an easy task. The following difficulties (rocks) should be overcome (passed). The following summary is inspired by the book of Forrester et al. [1].Many parameters affecting this selection; number of independent variables, the expected input-output relationship (linear, second order, etc.), the possible interaction between independent variables. These factors need some prior knowledge of the problem and the surrogate modeling.Generally speaking, not all the independent variables are significant, some are negligible. To efficiently decide that, a screening study is needed, one common approach is via first order polynomial regression after creating a design of experiment, then from the analysis of variance, one could decide the most significant parameters. A better and more accurate (but expensive) is by using Morris algorithm [2], where a surrogate model is fitted with limited number of points and then the distribution of the elementary effects is plotted (mean against standard deviation) [1]. Moreover, the dimensionless number is also helpful in reducing the number of design parameters.

show abstract

Optimum design of semi-rigid connections using metamodels

Cited by 30 publications

References 48 publications

Optimal design of beam-column connections of plane steel frames using the component method

Optimal design of beam-column connections of plane steel frames using the component method

FE Model of Three-Dimensional Steel Beam-to-Column Bolted Extended End-Plate Joint

Optimization of Multi-Fidelity Data Using Co-Kriging for High Dimensional Problems

Contact Info

Product

Resources

About