Ali Amini

Quantification of structural vibration characteristics is an essential task prior to perform any dynamic health monitoring and system identification. Anatomy of vibration in concrete arch dams (especially tall dams with un-symmetry shape) is very complicated and requires special techniques to solve the eigenvalue problem. The situation becomes even more complicated if the material distribution is assumed to be heterogeneous within the dam body (as opposed to conventional isotropic homogeneous relationship). This paper proposes a hybrid Random Field (RF)–Polynomial Chaos Expansion (PCE) surrogate model for uncertainty quantification and sensitivity assessment of dams. For different vibration modes, the most sensitive spatial locations within dam body are identified using both Sobol’s indices and correlation rank methods. Results of the proposed hybrid model is further validated using the classical random forest regression method. The outcome of this study can improve the results of system identification and dynamic analysis by properly determining the vibration characteristics.

The Probabilistic Seismic Demand Analysis (PSDA) which is frequently implemented in the first generation performance-based earthquake engineering quantifies seismic behavior of a structure by computing mean annual frequency of exceeding a specific value of a desired demand parameter given all anticipated earthquakes. This framework, based on the total probability integration formula, provides a technical basis on which aleatory uncertainties, uncertainties originated due to inherent randomness of the phenomena, are explicitly addressed. However, variability in the mean value of different model parameters, referred to as epistemic uncertainties and mainly due the finite-sample size of observations, is neglected. In this study, as an alternative to total probability integration, a reliability-based formulation tailored to effortlessly reflect both aleatory and epistemic uncertainties is put-forward to perform unified PSDA. Next, as an application of the proposed methodology, a reliability-based seismic demand curve of a 4-story example building is developed. Results demonstrate that the Second-Order Reliability Method (SORM) and important sampling method (ISM) along with multi-step Monte Carlo simulation (MSMCS) methods are appropriate candidates for computing reliability-based PSDA with differentiable and nondifferentiable performance functions, respectively.