The primary aim of this project is to develop an innovative lifecycle semantic-based decision making approach through asset intelligence so as to maximise the operational effectiveness maintenance, repair and rehabilitation planning of infrastructure assets, such as concrete pavement. The research intends to address an important gap by providing logical formalisms and real-time capability to life-cycle asset information through computational intelligence. The expected outcome will be an intelligent asset management platform that provides structured and semantically enriched lifecycle asset information for optimised solutions to help reduce the cost, time and effort in asset information storage and retrieval, and decision-making.
Partners:
Australian Research Council
Curtin University
Western Sydney University
Griffith University
Queensland University of Technology
University of Washington
Main Roads Western Australia,
AURECON.
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