Papers

@article{Pérez201282,

title = {Optimization of existing equations using a new Genetic Programming algorithm: Application to the shear strength of reinforced concrete beams},

author = {Juan Luis Pérez-Ordóñez and Antoni Cladera and Juan R. Rabuñal-Dopico and Fernando Martínez-Abella},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861865268&doi=10.1016%2fj.advengsoft.2012.02.008&partnerID=40&md5=36b8a0d0ab0f2abd048ab58c10ab79fe},

doi = {10.1016/j.advengsoft.2012.02.008},

issn = {09659978},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

journal = {Advances in Engineering Software},

volume = {50},

number = {1},

pages = {82-96},

publisher = {Elsevier Ltd},

abstract = {A method based on Genetic Programming (GP) to improve previously known empirical equations is presented. From a set of experimental data, the GP may improve the adjustment of such formulas through the symbolic regression technique. Through a set of restrictions, and the indication of the terms of the expression to be improved, GP creates new individuals. The methodology allows us to study the need of including new variables in the expression. The proposed method is applied to the shear strength of concrete beams. The results show a marked improvement using this methodology in relation to the classic GP and international code procedures. © 2012 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.},

note = {cited By 39},

keywords = {Artificial intelligence; Concrete beams and girders; Concretes; Genetic programming; Regression analysis; Shear strength; Structural design, Empirical equations; Experimental data; International codes; Programming algorithms; Reinforced concrete beams; Strength of concrete; Symbolic regression, Genetic algorithms},

pubstate = {published},

tppubtype = {article}

}