Abstract:The traditional statistical models for concrete dam deformation do not consider the effective contribution to residual sequence and thus results in the low accuracy of deformation prediction of the monitoring model and in the insufficient prediction of dam deformation behavior.This study proposes a combined model for concrete dam deformation, considering residual correction by frequency division. The horizontal displacement of concrete dams is driven by water pressure, temperature, and aging factors. In this s… Show more
“…In addition, the intrinsic nonlinear characteristics of dam deformations present further difculties in accurately estimating and predicting these trends [4,5]. Consequently, the development of a mathematical model that correlates the deformation of dams with its infuencing factors is essential for predicting trends and ensuring the structural integrity of these vital infrastructures [6,7].…”
This paper introduces a novel and comprehensive model for the analysis of dam deformation trends, integrating the variational mode decomposition (VMD) method, fractal theory, and the whale optimization algorithm (WOA) to refine the deep extreme learning machine (DELM) model. This integration allows for a meticulous denoising process through VMD, effectively isolating pertinent signal characteristics from noise and measurement interference. Following this, fractal theory is utilized to conduct an in-depth qualitative analysis of the denoised data, capturing intricate patterns within the deformation trends. The model further evolves with the application of WOA to optimize the DELM model, thereby facilitating an integrated approach that merges qualitative insights with quantitative analysis. The efficacy of this advanced model is demonstrated through a case study, highlighting its capability to deliver accurate and reliable predictions that are in harmony with practical engineering scenarios. This research not only offers a robust framework for analyzing dam deformation trends but also sets a new standard in the field, providing a new solution for assessing structural integrity in hydrological engineering.
“…In addition, the intrinsic nonlinear characteristics of dam deformations present further difculties in accurately estimating and predicting these trends [4,5]. Consequently, the development of a mathematical model that correlates the deformation of dams with its infuencing factors is essential for predicting trends and ensuring the structural integrity of these vital infrastructures [6,7].…”
This paper introduces a novel and comprehensive model for the analysis of dam deformation trends, integrating the variational mode decomposition (VMD) method, fractal theory, and the whale optimization algorithm (WOA) to refine the deep extreme learning machine (DELM) model. This integration allows for a meticulous denoising process through VMD, effectively isolating pertinent signal characteristics from noise and measurement interference. Following this, fractal theory is utilized to conduct an in-depth qualitative analysis of the denoised data, capturing intricate patterns within the deformation trends. The model further evolves with the application of WOA to optimize the DELM model, thereby facilitating an integrated approach that merges qualitative insights with quantitative analysis. The efficacy of this advanced model is demonstrated through a case study, highlighting its capability to deliver accurate and reliable predictions that are in harmony with practical engineering scenarios. This research not only offers a robust framework for analyzing dam deformation trends but also sets a new standard in the field, providing a new solution for assessing structural integrity in hydrological engineering.
“…Among them, optimization combination models, digital filtering, and principal component regression are widely used in the field of dam safety monitoring [12][13][14]. At present, in practical engineering, deformation monitoring models can be divided into three main categories, including statistical models, deterministic models, and machine learning models [15,16].…”
The construction of a reasonable and reliable deformation prediction model is of great practical significance for dam safety assessment and risk decision-making. Traditional dam deformation prediction models are susceptible to interference from redundant features, weak generalization ability, and a lack of model interpretation. Based on this, a deformation prediction model that considers the lag effect of environmental quantities is proposed. The model first constructs a new deformation lag influence factor based on the plain HST model through the lag quantization algorithm. Secondly, the attention and memory capacity of the model is improved by introducing a multi-head attention mechanism to the features of the long-time domain deformation influence factor, and finally, the extracted dynamic features are transferred to the ConvLSTM model for learning, training, and prediction. The results of the simulation tests based on the measured deformation data of an active dam show that the introduction of the deformation lag factor not only improves the interpretation of the prediction model for deformation but also makes the prediction of deformation more accurate, and it can improve the evaluation indexes such as RMSE by 50%, the nMAPE by 40%, and R2 by 10% compared with the traditional prediction model. The combined prediction model is more capable of mining the hidden features of the data and has a deeper picture of the overall peak and local extremes of the deformation data, which provides a new way of thinking for the dam deformation prediction model.
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