Professor Wei Gao has been working in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW), Australia since 2008. He obtained his PhD in Mechanical Manufacturing and Automation from Xidian University, China in 2003. He worked at UNSW as a Vice-Chancellor’s postdoctoral research fellow from 2004-2007 and at University of Technology Sydney as a Chancellor’s postdoctoral research fellow from 2007-2008. Professor Gao works in challenging research areas bringing together computational mechanics, stochastic mechanics, mechanical and structural engineering. He has made profound contributions to stochastic/uncertain finite element analyses, robustly safety assessment and reliability-based optimization of structures with the intrinsic uncertainties in structural parameters and loads. He has published over 240 papers including over 130 international journal articles. His research outcomes have strong impact on the areas and have been cited frequently by researchers worldwide. The developed computational uncertainty mechanics and safety assessment framework has been further extended to solve many problems in other research areas. He is currently serving on the editorial board of Composites Part B Engineering, Applied Mathematical Modelling and Committee on Probability and Statistics in the Physical Sciences of the Bernoulli Society.

Artificial Intelligence (AI) provides wide applications in current society, including predicting, classifying, and solving both social and scientific problems. Civil Engineering (CE), as one of the most mature engineering disciplines, is covering various aspects from the design, construction to the maintenance of the built environment. Generally, CE provides ample practical scope for abilities of AI; in turn, AI improves the quality of human life and originate novel approaches to solving engineering problems. For instance, the structural response analysis and reliability assessment incorporated with AI, also named as data-driven structural analysis, directly applies the data to derive the relationship between the variables and responses, and further establishes surrogate model to reflect this mathematical relationship implicitly involving the complex constitutive relationship. Data-driven analysis method partially avoids the challenge from physics and shares the benefits of self-adaption, self-optimization, and self-correction. In this study, practically significant investigations are conducted to demonstrate the sparks ignited by the new era of civil engineering which is the integration of CE and AI.