Dynamic Study and Analysis of Gravity Dam Considering Foundation Interaction Effect and Sloshing Effect

Abraham, Margaret; N, Mani; Mathew, Anu; Gopinath, Deepthi I.; Balan, Arun

doi:10.1007/978-981-13-1202-1_24

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the simulation results fully depend on the initial setting of parameters, without any calibration with the actual displacement. Many researches have improved the accuracy of finite element method, especially when complex situations are involved [8][9][10]. However, there is still a gap in integrating the numerical simulation with monitored data in real world and avoiding the inaccuracies due to structural simplifications.…”

Section: Introductionmentioning

confidence: 99%

Integrating the Finite Element Method with a Data‐Driven Approach for Dam Displacement Prediction

Shao

Meng

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%