Improving analytical models of circular concrete columns with genetic programming polynomials

Tsai, Hsing‐Chih; Pan, Chan-Ping

doi:10.1007/s10710-012-9176-3

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…It is true that more data may exist in the literature but they may be not readily available or published or in some cases, the published data are not complete considering the same variables. Besides, the collected database has also been used by various researchers, for example, Oreta and Kawashima, Tsai, and Tsai et al, to develop models for estimations of compressive strength of confined concrete RC columns,

f_{cc}^{'}

, and corresponding confined strain, ε cc . Thus, the same database is also used here for the development of GEP‐based models.…”

Section: Experimental Databasementioning

confidence: 99%

“…Recently, Oreta and Kawashima have used artificial neural networks (ANNs), and Tsai developed many models for prediction of

f_{cc}^{'}

and ε cc factors through genetic programming (GP), weight genetic programming, and soft‐computing polynomials . Additionally, Tsai and Pan proposed genetic programming polynomials for improving models for estimation of an

f_{cc}^{'}

and ε cc of circular RC columns . The proposed formulas are generated based on considering different combinations of independent variables as inputs; albeit, those models fail to incorporate all significant variables, that is,

f_{c}^{'}

, d , f yh , ρ s , s , ρ cc , in a unique formula.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed formulas are generated based on considering different combinations of independent variables as inputs; albeit, those models fail to incorporate all significant variables, that is,

f_{c}^{'}

, d , f yh , ρ s , s , ρ cc , in a unique formula. Considering the great number of models proposed by Tsai and Tsai and Pan, they are not represented in this paper, and respected readers are invited to see References for more details. Besides, they have not been evaluated form engineering viewpoints to be used as practical models.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the outcome would be regarded as a great combination of experimental studies with numerical methods. Furthermore, such approaches have been successfully applied to many civil engineering prediction problems …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Numerical formulation of confined compressive strength and strain of circular reinforced concrete columns using gene expression programming approach

Sadrossadat

Ghorbani

Hamooni

et al. 2017

Structural Concrete

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In reinforced concrete (RC), concrete's relatively low tensile strength and ductility are counteracted by reinforcement of materials having higher tensile strength or ductility, such as steel reinforcing bars. A remarkable concern in design of RC columns confined with transverse reinforcement is to achieve an acceptable estimation of confined compressive strength and strain factors when they are subjected to compressive and lateral loading. To cope with this issue, various models have been proposed by researchers due to the costly procedure of experimental studies and lack of high-capacity testing equipment. Despite this fact, there still exists the necessity to develop more robust estimation models. This paper explores the capability of gene expression programming for the prediction of confined compressive strength and strain of RC columns with circular cross section. A reliable database is used to develop two new models which can be used via hand calculations for design purposes. In order to verify and validate the proposed models, several analyses are conducted and the results are compared with those provided by other researchers. Consequently, the results explicitly represent that the proposed models accurately estimate the confined compressive strength and corresponding strain of circular concrete columns and reach a notably better prediction performance than the traditional models.

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f_{cc}^{'}

, and corresponding confined strain, ε cc . Thus, the same database is also used here for the development of GEP‐based models.…”

Section: Experimental Databasementioning

confidence: 99%

“…Recently, Oreta and Kawashima have used artificial neural networks (ANNs), and Tsai developed many models for prediction of

f_{cc}^{'}

f_{cc}^{'}

f_{c}^{'}

, d , f yh , ρ s , s , ρ cc , in a unique formula.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed formulas are generated based on considering different combinations of independent variables as inputs; albeit, those models fail to incorporate all significant variables, that is,