Abstract
Both numerical simulations and data‐driven methods have been applied in dam’s displacement modeling. For monitored displacement data‐driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and temporal correlations among all monitoring points into account, we took the first step toward integrating the finite element method into a data‐driven model. As the data‐driven method, we selected the random coefficient model, which can make each explanatory variable coefficient of all monitoring points following one or several normal distributions. In this way, explanatory variables are constrained. Another contribution of the proposed model is that the actual elastic modulus at each monitoring point can be back‐calculated. Moreover, with a Lagrange polynomial interpolation, we can obtain the distribution field of elastic modulus, rather than gaining one value for the whole dam in previous studies. The proposed model was validated by a case study of the concrete arch dam in Jinping‐I hydropower station. It has a better prediction precision than the random coefficient model without the finite element method.
Highlights
For dams that have been running for years, their actual physical and mechanical properties often have considerable differences from the initial or design values, due to the effect of external environment
For the dam’s displacement prediction, two approaches have been commonly used: one is numerical simulation using finite element method [2, 3]; another is data-driven method based on monitored displacement data [4, 5]. e principle of numerical simulations is to solve the constitutive equations; emphasis is given on physical mechanism governing the displacement [6, 7]
In order to quantify the spatial distribution of displacement of all monitoring points, we first established a three-dimensional finite element model based on the actual geological characteristics of the dam. e threedimensional model includes three sections: dam body, dam foundation, and surrounded mountains; and the dam body was discretized into 38537 elements and 31941 nodes. e boundary of surrounding mountains was set to 2–3 times higher than the dam body in all the directions (x, y, and z)
Summary
For dams that have been running for years, their actual physical and mechanical properties often have considerable differences from the initial or design values, due to the effect of external environment. The ratio of applied external stress to the elastic deformation of material, is usually difficult to measure; it is often obtained by back-analysis based on monitored displacement data. For the dam’s displacement prediction, two approaches have been commonly used: one is numerical simulation using finite element method [2, 3]; another is data-driven method based on monitored displacement data [4, 5]. The numerical simulation often has considerable inaccuracies induced by unavoidable simplifications and idealizations of the dam’s structural and material properties. There is still a gap in integrating the numerical simulation with monitored data in real world and avoiding the inaccuracies due to structural simplifications
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