S R Ayers

The Australian approach to composites in civil engineering

Over the past ten years, Australian researchers have pursued the development of new structural systems utilising composites for the rehabilitation and replacement of deteriorating civil infrastructure. Several of these systems have been successfully demonstrated in real world projects. Gerard Van Erp, Craig Cattell and Stephen Ayers of the University of Southern Queensland, Australia, present an overview of Australian research into infrastructure applications of composites and associated industry development programmes.

Characterization of new structural core materials based on vinyl ester and hollow ceramic microspheres

Abstract A new class of structural core material has been developed at the University of Southern Queensland for applications of composite materials in civil and structural engineering. These materials combine polymer resins with hollow ceramic microspheres to produce core materials with high structural capacity at low cost. A number of prototype structural elements using these materials have displayed significant potential for application in civil engineering structures. An ongoing research programme has been initiated to improve fundamental understanding of these materials and to provide the knowledge required for broad utilization. This current study has investigated the behaviour of core material formulations based on vinyl ester resins and hollow ceramic cenospheres. Investigations have focused on identifying key relationships between the constituent materials and resulting mechanical properties of the core material. A variety of matrix and filler characteristics have been examined. This work has shown that, at the type of filler levels considered feasible for structural engineering applications (vf > 30 per cent), the behaviour of the material is largely determined by the filler particles, with only minimal influence from the matrix material. Further investigations are continuing to quantify these effects and to develop predictive models for key relationships.