2005
DOI: 10.1590/s1678-58782005000300015
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Neural networks assessment of beam-to-column joints

Abstract: This paper proposes the use of artificial neural networks to predict the flexural resistance and initial stiffness of beam-to-column steel joints using the back propagation supervised learning algorithm. Three types of steel beam-to-column joints were investigated: welded, endplate and bolted with top, seat and double web angles, respectively. The neural networks results proved to be consistent with experimental and design code reference values

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Cited by 31 publications
(13 citation statements)
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“…De Lima et al [13] used the concept of neural networks to determine the initial stiffness of beam-to-column joints. However, the method was limited in scope and the authors did not back their results with test data to validate the method developed.…”
Section: Introductionmentioning
confidence: 99%
“…De Lima et al [13] used the concept of neural networks to determine the initial stiffness of beam-to-column joints. However, the method was limited in scope and the authors did not back their results with test data to validate the method developed.…”
Section: Introductionmentioning
confidence: 99%
“…12(a-f). A comparison of the ratio between the predicted initial rotation stiffness values by the LGP single and team solutions and likewise those obtained from European design code, ANNs [16], GP/SA [27,28], Kishi et al [3] and experimental values are illustrated in Figs. [13][14][15].…”
Section: Discussion Of Resultsmentioning
confidence: 99%
“…The database contains 33 flexural resistance and initial rotational stiffness of welded joint test results [16], given in Table 2. The inputs for LGP, shown in Fig.…”
Section: Welded Jointsmentioning
confidence: 99%
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