It is vital to address properly the effects of inevitable uncertainties, due to constructional defects, imprecise knowledge of design parameters and aging process, in the analysis, reliability assessment and design of engineering structures. Although considerable attempts have been made at solving structural engineering problems involving uncertainty, the progress has been slow worldwide, which is mainly due to these two reasons: establishing through experimental investigations an adequate statistical database of structural behaivour, cannot be created exclusively for a practical structure due to the unaffordable financial costs and its extremely time-consuming process.
This project will develop an advanced machine learning assisted stochastic analysis and safety assessment framework to harness the full potential of virtual reality modelling technologies and computational structural mechanics to enhance largely the efficiency of implementing stochastic structural analyses and safety assessments. The performance and applicability of the developed machine learning assisted framework will be tested through a variety of engineering problems.
This project will be undertaken within the context of the Australia Research Council (ARC) funded projects with the highly active research group. The scholar will interact with other research fellows and students and be working under the supervision of staff with many years of research and industrial experience.
The successful applicant is expected to:
(i) acquire in-depth knowledge of machine learning techniques and structural analysis,
(ii) obtain sound understanding of uncertainty qualification and mathematical programming,
(iii) be equipped with good programming skills