An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

Abstract

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.

Study of Structural Stability of a Concrete Gravity Dam Using a Reliability Approach

“…This ability can be measured using the probabilistic theory that it will perform the function for which it was designed under given conditions and for a given duration. Structural reliability is formulated in terms of a vector of structural system random variables, X = {X1, X2, ..., Xn}, where {X1, X2, ..., Xn} are the basic random variables which can describe loads, structural system dimensions, materials and these characteristics and properties of the cross-section [18,19]. A limit state function, g (X) = 0 describes the operation of the system in terms of the basic random variables X, where S is the strength of the material making up the structure and L is the stresses (loads) exerted on the structure [20].…”

Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit

“…Recently, different ML and surrogate modeling algorithms have been applied in various hydraulic engineering problems such as dams, sedimentation, spillway, etc. Bhattacharya et al, 2007;Hariri-Ardebili et al, 2021;Roushangar et al, 2014;Torres-Rua et al, 2012). It is recognized that an empirical relationship for discharge coefficient based on experimental or hydraulic models faces some limitations regarding hydraulic and geometric parameters (Ebtehaj et al, 2018).…”

Long-term viscoelastic deformation monitoring of a concrete dam: A multi-output surrogate model approach for parameter identification

“…Surrogate models are a popular tool to approximate the functional relationship of expensive simulation models. Common techniques include artificial neural networks (ANNs) [19], support vector regression (SVR) [20], Gaussian process (GP) [21], polynomial chaos expansion (PCE) [22], etc. In order to address the aforementioned challenge, the surrogate models have been utilized for the purpose of reducing the computational burden in inverse analysis.…”