Genetic programming for experimental big data mining: A case study on concrete creep formulation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Gandomi:2016:AiC,

• author = "Amir H. Gandomi and Siavash Sajedi and Behnam Kiani and Qindan Huang",
• title = "Genetic programming for experimental big data mining: A case study on concrete creep formulation",
• journal = "Automation in Construction",
• year = "2016",
• volume = "70",
• pages = "89--97",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Multi-gene genetic programming, Big data, Multi-objective optimization, Non-dominated sorting, Concrete creep",
• ISSN = "0926-5805",
• URL = "http://www.sciencedirect.com/science/article/pii/S0926580516301315",
• DOI = "doi:10.1016/j.autcon.2016.06.010",
• size = "9 pages",
• abstract = "This paper proposes a new algorithm called multi-objective genetic programming (MOGP) for complex civil engineering systems. The proposed technique effectively combines the model structure selection ability of a standard genetic programming with the parameter estimation power of classical regression, and it simultaneously optimizes both the complexity and goodness-of-fit in a system through a non-dominated sorting algorithm. The performance of MOGP is illustrated by modelling a complex civil engineering problem: the time-dependent total creep of concrete. A Big Data is used for the model development so that the proposed concrete creep model (referred to as a genetic programming based creep model or G-C model in this study) is valid for both normal and high strength concrete with a wide range of structural properties. The G-C model is then compared with currently accepted creep prediction models. The G-C model obtained by MOGP is simple, straightforward to use, and provides more accurate predictions than other prediction models.",

}

Genetic Programming entries for A H Gandomi Siavash Sajedi Behnam Kiani Qindan Huang

Citations