Predicting the effectiveness of rolling dynamic compaction using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Ranasinghe:2017:pICEgi,

• author = " Ranasinghe Arachchilage Tharanga Madhushani Ranasinghe and Mark B. Jaksa and Fereydoon {Pooya Nejad} and Yien Lik Kuo",
• title = "Predicting the effectiveness of rolling dynamic compaction using genetic programming",
• journal = "Proceedings of the Institution of Civil Engineers: Ground Improvement",
• year = "2017",
• volume = "170",
• number = "4",
• month = nov,
• keywords = "genetic algorithms, genetic programming, field testing, monitoring geotechnical engineering strength, testing of materials",
• ISSN = "1755-0750",
• URL = "https://www.icevirtuallibrary.com/doi/10.1680/jgrim.17.00009",
• DOI = "doi:10.1680/jgrim.17.00009",
• publisher = "ICE Publishing",
• bibsource = "OAI-PMH server at digital.library.adelaide.edu.au",
• language = "en",
• oai = "oai:digital.library.adelaide.edu.au:2440/109365",
• URL = "http://hdl.handle.net/2440/109365",
• size = "15 pages",
• abstract = "Rolling dynamic compaction (RDC) is a soil compaction method that involves impacting the ground with a non-circular roller. This technique is currently in widespread use internationally and has proven to be suitable for many compaction applications, with improved capabilities over traditional compaction equipment. However, there is still a lack of knowledge about a priori estimation of the effectiveness of RDC on different soil profiles. To this end, the aim of this paper is to develop a reliable predictive tool based on a machine-learning approach: linear genetic programming (LGP). The models are developed from a database of cone penetration test (CPT)-based case histories. It is shown that the developed LGP-based correlations yield accurate predictions for unseen data and, in addition, that the results of a parametric study demonstrate its generalisation capabilities. Furthermore, the selected optimal LGP-based model is found to yield superior performance when compared with an artificial neural network model recently developed by the authors. It is concluded that the LGP-based model developed in this study is capable of providing reliable predictions of the effectiveness of RDC under various ground conditions.",
• notes = "School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australia

  Also known as \cite{oai:digital.library.adelaide.edu.au:2440/109365}",

}

Genetic Programming entries for Ranasinghe Arachchilage Tharanga Madhushani Ranasinghe Mark B Jaksa Fereydoon Pooya Nejad Yien Lik Kuo

Citations