Generating prediction rules for liquefaction through data mining

Baykasoglu A., Cevik A., ÖZBAKIR L., KULLUK S.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.36, sa.10, ss.12491-12499, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 10
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.eswa.2009.04.033
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.12491-12499
  • Anahtar Kelimeler: Liquefaction, Neural networks, Ant colony optimization, Data mining, ARTIFICIAL NEURAL-NETWORKS, SOIL LIQUEFACTION, EXTRACTING RULES, ENERGY, EARTHQUAKES, RESISTANCE, ALGORITHM, MODEL
  • Dokuz Eylül Üniversitesi Adresli: Hayır

Özet

Prediction of liquefaction is an important subject in geotechnical engineering. Prediction of liquefaction is also a complex problem as it depends on many different physical factors, and the relations between these factors are highly non-linear and complex. Several approaches have been proposed in the literature for modeling and prediction of liquefaction. Most of these approaches are based on classical statistical approaches and neural networks. In this paper a new approach which is based on classification data mining is proposed first time in the literature for liquefaction prediction. The proposed approach is based on extracting accurate classification rules from neural networks via ant colony optimization. The extracted classification rules are in the form of IF-THEN rules which can be easily understood by human. The proposed algorithm is also compared with several other data mining algorithms. It is shown that the proposed algorithm is very effective and accurate in prediction of liquefaction. (C) 2009 Elsevier Ltd. All rights reserved.