Abstract
Inverse analysis is necessary for concrete dams in normal operation to overcome the discrepancy between the true mechanical parameters and test results. In view of the uncertain characteristics of concrete dams, a stochastic inverse model is proposed in this study to solve the undetermined mechanical parameters with sequential and spatial randomness using measured displacement data and Bayesian back analysis theory. An inversion method for the mechanical parameters of concrete dams is proposed. Fast Fourier transform algorithm is introduced to generate random fields for SFEM analysis. The case study shows that the proposed inversion method can reflect the random characteristics of concrete dams, the mechanical parameters obtained are reasonable, and the inverse model is feasible.
Highlights
Uncertainties exist in the dam body, dam foundation, and reservoir basin because of the complexity of dam foundation and the heterogeneity of concrete. e corresponding main physical and mechanical parameters usually vary with time and space in a random manner [8, 9]. erefore, a stochastic inversion method is more suitable for obtaining parameters that can reflect these uncertain characteristics
In the stochastic inversion of physical and mechanical parameters, the structural response and the external load are regarded as random variables. e deformation of a concrete dam consists of a hydraulic deformation component, a temperature deformation component, and time-effect deformation component and is defined as [12]
Inversion Method Based on a BP Neural Network e inversion of the mechanical parameters of the dam body and foundation can be summarized as an optimization problem
Summary
Where Almn and Blmn are Fourier coefficients. e correlation of a random variable [ X X(x, y, z)] in. Artificial neural networks (ANNs) are widely used artificial intelligent algorithms in many fields owing to their excellent capability of modeling nonlinear systems and superior data fitting performance [21]. E ANN can perform nonlinear mapping with different precision levels by choosing an appropriate number of nodes in a hidden layer. Where bj is the output of the hidden layer, f is the transfer function, ωij is the weight from the input layer to the hidden layer, ζj is the threshold value of the hidden layer, n is the input node number, and s is the node number of the hidden layer. Where ωjk is the weight from the hidden layer to the output layer, ζk′ is the threshold value of the output layer, and m is the output node number. E error function of the actual network system output e is as follows:.
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