Knowledge-based system, equipped with cluster analysis for eco-material selection: An automobile structure case study

Ahmad Mayyas, Mohammed A. Omar, Abdel Raouf Mayyas, Ala Qattawi, Qin Shen

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

The aim of this study is to develop a material selection framework structured around a knowledge-based system (KBS). Specifically, a hybrid data mining technique is employed to extract knowledge from large datasets using cluster analysis techniques; the mined knowledge then serves as the inference logic within the KBS designed for material selection purposes. Cluster analysis results are used as a basis for the tree-based structure of the KBS where if-then rules are developed based on the general cluster properties; that is, inference logic is structured in a way such that it can predict general sustainability characteristics of the material as well as its exact mechanical, cost and physical properties. To develop the structure of the KBS, the selection structure employs sustainable material indices. Additionally, the proposed material selection model of the KBS is purposefully composed of material sustainability, functionality and cost indices. The constructed knowledge is then demonstrated for selecting automobile structural panels.

Original languageEnglish (US)
Pages (from-to)200-213
Number of pages14
JournalInternational Journal of Sustainable Engineering
Volume7
Issue number3
DOIs
StatePublished - Jul 2014
Externally publishedYes

Keywords

  • automobile
  • data mining
  • design for sustainability
  • knowledge-based systems
  • material selection

ASJC Scopus subject areas

  • Engineering(all)

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