Probabilistic models for wind loads and reliability analysis

Abstract

Probabilistic models and surrogate solution are developed to characterize wind loads acting on the structures and assess the corresponding structural responses of linear systems, respectively. These developments can be regarded as essential building blocks in the prediction of structural reliability subject to extreme wind. In this dissertation, we first review the commonly-used probabilistic models in literature and benchmark these models on a test example to illustrate their properties and examine their advantages and disadvantages. It is shown that the approximations based on these existing models on the extreme estimates exhibit large discrepancies. A new probabilistic model is then proposed to overcome this limitation. The model utilized Markov process whose finite dimensional distribution is characterized in terms of copulas. Finally, an efficient and accurate surrogate model is presented as an alternative to the traditional Monte Carlo method to evaluate the structural responses. The responses are approximated by translation processes whose second-moment properties and marginal distribution are obtained from linear random vibration theory and moment equations. The statements in this dissertation are supported by theoretical arguments and numerical examples.