Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Gandomi:2014:ASC, author = "Amir H. Gandomi and Danial {Mohammadzadeh S.} and Juan Luis Perez-Ordonez and Amir H. Alavi", title = "Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups", journal = "Applied Soft Computing", year = "2014", volume = "19", pages = "112--120", month = jun, keywords = "genetic algorithms, genetic programming, Linear genetic programming, Shear strength, Reinforced concrete beam, Design equation", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494614000751", DOI = "doi:10.1016/j.asoc.2014.02.007", size = "9 pages", abstract = "Highlights We have introduced LGP algorithm for shear capacity modelling of RC beams without stirrups. An extensive experimental database including 1938 test results gathered from literature. A simplified LGP based formula is obtained for different kinds of concrete. Our results are better than the nine different code models. A new design equation is proposed for the prediction of shear strength of reinforced concrete (RC) beams without stirrups using an innovative linear genetic programming methodology. The shear strength was formulated in terms of several effective parameters such as shear span to depth ratio, concrete cylinder strength at date of testing, amount of longitudinal reinforcement, lever arm, and maximum specified size of coarse aggregate. A comprehensive database containing 1938 experimental test results for the RC beams was gathered from the literature to develop the model. The performance and validity of the model were further tested using several criteria. An efficient strategy was considered to guarantee the generalisation of the proposed design equation. For more verification, sensitivity and parametric analysis were conducted. The results indicate that the derived model is an effective tool for the estimation of the shear capacity of members without stirrups (R = 0.921). The prediction performance of the proposed model was found to be better than that of several existing buildings codes.", }

Genetic Programming entries for A H Gandomi D Mohammadzadeh Shadmehri Juan Luis Perez A H Alavi