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An uncertainty-aware dynamic shape optimization framework: Gravity dam design
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DOI：10.1016/j.ress.2022.108402.png)
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Abstract
En 中文
Uncertainties such as material randomness, manufacturing anomalies, and external loading play an important role in the design of engineering structures. Therefore, reliability-based design optimization (RBDO) is frequently used as a tool to guarantee economic aspects without compromising safety. In this paper, an uncertainty-aware framework is proposed for seismic optimization of structures. This algorithm is founded on the stochastic dynamic stressor, and therefore, reduces the bias due to the aleatory nature of ground motion. Safety constraints are evaluated in quantiles instead of the exact solution of reliability analysis. Kriging approach approximates the actual model, while a global search algorithm solves the RBDO problem. The proposed algorithm is used for the optimal shape design of gravity dams with a series of local and global time-variant/invariant performance indices. Two sets of deterministic and probabilistic shape optimization algorithms were compared to demonstrate the impact of uncertainty quantification. Finally, the framework is extended to a class of generic dams with different heights, concrete strengths, and foundation-to-concrete flexibility. By probabilistic seismic performance evaluation, system safety is guaranteed using the proposed dynamic RBDO compared to the median response in applying a set of as-recorded ground motions. This paper provides a new paradigm for RBDO of structures with dynamic loading and a robust decision-making during dam design.
Keywords:
Shape optimization
Reliability-based design optimization
Probabilistic safety assessment
Uncertainty quantification
Stochastic excitation
System safety
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