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

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@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",
  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