Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming

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@Article{GonzalezTaboada:2016:CBM,

• author = "Iris Gonzalez-Taboada and Belen Gonzalez-Fonteboa and Fernando Martinez-Abella and Juan Luis Perez-Ordonez",
• title = "Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming",
• journal = "Construction and Building Materials",
• year = "2016",
• volume = "106",
• pages = "480--499",
• month = "1 " # mar,
• keywords = "genetic algorithms, genetic programming, Structural recycled concrete, Database, Mixing procedure, Mechanical properties, Multivariable regression",
• ISSN = "0950-0618",
• URL = "http://www.sciencedirect.com/science/article/pii/S0950061815308072",
• DOI = "doi:10.1016/j.conbuildmat.2015.12.136",
• abstract = "This study focuses on the prediction of some of the most important properties of structural recycled concrete (compressive strength, modulus of elasticity and splitting tensile strength) taking into account, not only the recycled percentage and the quality of the recycled aggregates used, but also the production method. For said purpose, a database has been developed with 1831 mixes obtained from 81 papers. Firstly, in this manner, these properties have been compared with those of conventional concrete. Then, the need to adapt the prediction code expressions (adjusted for conventional concretes) was analysed to take into account the use of recycled concrete, developing, if finally necessary, the correction coefficients which allow engineers to predict the recycled properties with the same approximation degree as in conventional concretes. These correction coefficients have been adjusted using multivariable regression, and have been analysed using different statistical indexes. Lastly, specific expressions used to predict these properties in structural recycled concretes have been optimized. Two different tools have been used to develop these expressions: multivariable regression and genetic programming. The proposed expressions have been analysed using statistical parameters which have been compared with those obtained using the expressions proposed by other authors. In this regard, and finally, the best prediction expressions for the modulus of elasticity and the splitting tensile strength of structural recycled concretes have been proposed.",

}

Genetic Programming entries for Iris Gonzalez-Taboada Belen Gonzalez-Fonteboa Fernando Martinez Abella Juan Luis Perez

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