Civil and Environmental Engineering Department Seminar: Uncertainties, Risk, and Learning in Future Infrastructure Systems
Seminar | March 11 | 10-11 a.m. | 542 Davis Hall
Engineers increasingly rely on computational models to predict adverse events that influence engineering systems and to make critical decisions that impact the safety and well-being of our society. Examples include but are not limited to the safety of structure and infrastructure systems subjected to natural and man-made hazards and the performance and resilience of such systems. Given such high expectations, risk and safety predictions must be based on robust analyses and proper quantifications of epistemic and aleatory uncertainties. Furthermore, in the age of digitalization, ubiquitous sensing, data acquisition, and statistical learning are changing the nature of risk and safety predictions into into dynamic assessments. The advantage of Bayesian learning in answering these calls is unparalleled. In the first part of this talk, I will discuss the challenges associated with rare-event estimation in the presence of Bayesian learning, and introduce algorithmic solutions based on Hamiltonian Monte Carlo, structural reliability methods, and active learning meta-modeling. In the second part of the talk, I will present an application of Bayesian learning in the context of fluid-induced seismicity associated with subsurface exploitation for energy production. In particular, I formulate a general framework based on a hierarchical nonhomogeneous Poisson process. I will then show the power of Bayesian inference in developing a predictive model for the number and magnitudes of fluid-induced seismic events. I will conclude the talk with my vision on how new research trends emerging from computational and data sciences can address the challenges of risk-based design and safety assessment of future infrastructure systems.
Leori Gill, CA, email@example.com, 510-642-1762