Description of field of research

Using recycled aggregate (RA) in concrete production can reduce the environmental impact and decrease the amount of demolition waste being sent to landfills. It is necessary to develop a strong tool to assess its impact on mechanical and durability properties of concrete. This project aims to utilise state-of-the-art deep learning techniques to predict the properties of recycled aggregate concrete (RAC). A predictive model will be developed, together with sensitivity analysis of model inputs. This project is partly supported by Transport for New South Wales (TfNSW). The outcome of this research will provide theoretical guidance for practical applications of the design of RAC by industry.

Research Area

Civil Engineering | Construction materials | Machine learning application

  1. Experimental tests on mechanical and durability properties should be conducted in the laboratory for performance evaluation of proposed predictive models of RAC mechanical and durability properties.
  2. High-performance computing facility is needed to train deep learning models for predicting mechanical and durability properties of RAC.
  1. A graphical user interface (GUI) will be developed for the practical application of the proposed predictive tool.
  2. Two articles will be published in JCR-Q1/Q2 journals.
Research Associate
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Associate Professor
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