Seepage Monitoring Models Study of Earth-Rock Dams Influenced by Rainstorms

Abstract

For earth-rock dams influenced by rainstorms, seepage status monitoring is very important and provides the basis for the safe and effective operation of earth-rock dams. The most influential factors concerning the seepage of earth-rock dams are the reservoir water level, precipitation, temperature, and timeliness, and the influence of the reservoir water level and precipitation on the seepage of an earth-rock dam exhibits hysteretic effects. The reservoir water level of an earth-rock dam abruptly increases and may exceed the historically highest water level, therein causing new deformations of the earth-rock dam or even plastic deformation. Thus, the permeability coefficient for parts of an earth-rock dam changes, and we present the exceeded water level factor. Considering the complexity of the seepage monitoring of earth-rock dams, based on the hysteretic reservoir water level and precipitation, temperature, timeliness, and the exceeded water level factor, a statistical model based on an explicit function and an artificial wavelet neural network model based on an implicit function are established. Based on these two models, an integrated monitoring model based on maximum entropy theory is established. At the end of this paper, three monitoring models are used for the seepage monitoring of a measuring point of an earth-rock dam influenced by rainstorms, and the results show that the three monitoring models obtain satisfactory predication accuracy.

Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm

“…Dam seepage monitoring variables are generally divided into two categories: uplift pressure and leakage. Taking the uplift pressure as an example, it is mainly affected by factors including water pressure, temperature, and time-effect [2,[20][21][22][23][24][25], which can be shown as…”

“…e change of reservoir water levels has a great influence on the seepage of the dam and has a certain delay [2]. e equivalent water level can be calculated according to the method in the literature [21][22][23] to express the delay effect of seepage. δ H has a linear relationship with water level, so the water pressure component can be expressed as…”

Seepage Comprehensive Evaluation of Concrete Dam Based on Grey Cluster Analysis

“…To date, studies [7][8][9] on the evaluation of dam seepage state have been mainly aimed at a single evaluation indicator, which can only reflect partial information of the dam's seepage state and will therefore lead to inaccuracy and incompleteness of the evaluation results. In practical engineering, the seepage state of concrete dams should be reflected by several indicators (dam seepage pressure, dam leakage, and seepage around dam) [10], and multiple monitoring points of each indicator distributed at different elevations in different dam sections.…”

Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis

“…Meanwhile, the statistical models are dam safety monitoring models and built on the basis of measured data (water pressure, temperature, and dam effect) and some regression methods, including multivariate linear regression [5,6], stepwise regression [7,8], partial least squares regression [9,10], and kernel function partial least squares regressions [11]. Other than the deformation monitoring, statistical models have been extensively applied in other aspects of dam safety monitoring, such as seepage and crack [12][13][14].…”