CAREER: Optimal Engineering Decision-making Under Uncertainties for Enhanced Structural Life-cycle

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The objective of this Faculty Early Career Development Program (CAREER) award is to advance innovation in optimal engineering decision-making under uncertainty and risks, with a focus on life-cycle analysis and structural applications, in order to address ongoing and emerging scientific and societal challenges relevant to the aging infrastructure environment. An integrated structural life-cycle analysis, design, maintenance, retrofit and recovery framework is thus suggested in this project with the aim to reduce infrastructure life-cycle costs, optimize societal investments to infrastructure, lead to safer structures and assist in improving national security and economic competitiveness. Apart from structural engineering applications that is the main focus of the project, the findings can be also used for a plethora of applications involving intelligent, automated, autonomous decision support frameworks. This project will also educate the next diverse generation of engineers and scientists, who, in addition to other skills, need increased computational competence. The planned activities impact a wide audience including school students and teachers, undergraduate and graduate students, researchers, faculty and practicing engineers.In this project, engineering decision-making is suggested to be approached from a stochastic optimal control and reinforcement learning perspective, embracing and fully integrating predictive physics-based stochastic models and uncertain life-cycle observations. Owing to the vital role of decision-making in engineering problems, this viewpoint leads to condition-based estimation rules for structural systems and an enhanced new meaning of performance-based analysis. The traditional approach to structural design, analysis, retrofit, recovery and maintenance is also reconsidered, from a conventional static optimization problem to an integrated lifelong controlling process of ever changing structures. The central stochastic control component is based on fully and Partially Observable Markov Decision Processes, asynchronous dynamic programming and deep reinforcement learning techniques. Several theoretical pursuits and advances are needed to enable such a decision-making approach to aging structures and infrastructure systems in the presence of risks and uncertainties. In this project, answers to main challenges will be investigated, such as the curses of dimensionality and history, together with solutions for multiple objectives and decision makers, incorporation of nonlinear filtering, structural reliability updating, and generalized fragility functions, among others.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.