1410 Structural Design and Maintenance Optimization under Uncertainty

Younes Aoues, Normandie Univ, INSA Rouen Normandie
Christian Gogu, Université Paul Sabatier, Toulouse
Stefano Marelli, ETH Zürich, Institute of Structural Engineering
Bruno Sudret, ETH Zürich, Institute of Structural Engineering
Currently, uncertainty quantification takes a considered part in the research activities in mechanical modeling and in several fields of applied science. In fact, mechanical model predictions are based on the knowledge of the mechanical parameters and physical properties of the materials, the applied loads, and the initial and boundary conditions. Nevertheless, the knowledge of these properties remains imperfect because it is affected by uncertainties. Uncertainty quantification aims to study the influence of the uncertain parameters of the prediction models on the structural performance.

A very common framework for modeling uncertain parameters is within probabilistic approaches leading to a probabilistic characterization of the structural response. Alternatives include non-probabilistic approaches, or interval methods.

Structural design optimization is frequently applied for effective design cost reduction of engineering systems. Maintenance optimization aims at finding the best inspection/repair action policies to maximize the investment and minimize the expected total cost. Both of these decision making tools seek to increasingly consider uncertainties in the structural performance.

The goal of this mini-symposium is to provide an opportunity for researchers to present recent work and exchange ideas on new methods for maintenance optimization and structural design optimization under uncertainty, as well as the use of metamodels to reduce the computational cost induced by the mechanical models. We welcome contributions on the following topics:

Reliability based design
Maintenance optimization
Prognosis and structural health monitoring
Uncertainty quantification, probabilistic modelling and analysis
Risk based design and maintenance optimization
Risk-informed decision making
Robust and performance-based optimization under uncertainty
Non-probabilistic approaches based design and maintenance optimization
Surrogate models for uncertainty quantification and robust design optimization.