Analysis of Sluice Foundation Seepage Using Monitoring Data and Numerical Simulation

Abstract

For sluices built on soil foundations, seepage safety of the foundation is one of the most concerns during operation of sluices. Monitoring data could reflect the real seepage behavior in the foundation, but of which the shortcoming is that generally only the local seepage states can be measured. The seepage field in the whole foundation can be analyzed by numerical simulation. The permeability coefficients of the foundation materials significantly affect the numerical simulation results; however, it is difficult to accurately determine the values of permeability coefficients. In this paper, an approach based on response surface method (RSM) for calibration of permeability coefficients was proposed, and the efficiency of parameter calibration is improved by constructing the response surface equation instead of time-consuming finite element calculation of foundation seepage. The seepage in a sluice foundation was analyzed using monitoring data and numerical simulation. The monitoring data showed that the seepage pressure in the foundation periodically varies with high value in flood season and low value in dry season. After calibration of the permeability coefficients of the foundation materials using the measured seepage pressure, the seepage fields in the foundation for different water levels were numerically simulated to investigate the cause for the periodical variation of the seepage pressure and the seepage safety of the foundation was assessed with the calculated seepage gradients. The methods adopted in this study could be applied to seepage analysis for sluice foundations with similar geologic conditions and antiseepage measures.

Dynamic Risk Evaluation and Early Warning of Crest Cracking for High Earth-Rockfill Dams through Bayesian Parameter Updating

“…The individual locations of the monitoring points are indicated in Figure 1. Response surface method (RSM) was utilized to replace the time-consuming finite element calculations of the displacements at the monitoring points [23], and particle swarm optimization (PSO) method was used to minimize the objective function and calibrate the parameters [7]. Figure 2 shows that there was a period from 20 December 2009 to 20 May 2010, during which the water level was relatively stable, and the monitoring data of this period was selected for back analysis of creep parameters by RSM-PSO method.…”

A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM

“…Sluice is a common hydraulic engineering, and its main role is to regulate the flow of water. A low-head water retaining structure can be used to generate electricity, control flooding, provide irrigation, or provide water [1], and ensure the rational use of water resources and water environment protection [2]. Once the sluice has an accident, the damage to the traffic and urban infrastructure and the casualties caused by the accident are far more serious than the consequences caused by general public facilities [3].…”

“…1 , and c 2 are regression coefficients for each component (i = 1, 2); α 1 is the adjustment parameter related to the water level weight distribution function, at a fixed observation time t = t 0 , it is a constant; α 2 is the adjustment parameter related to rainfall weight distribution function, at a fixed observation time t = t 0 , it is a constant; x 1 is the water level's lag hours of; x 2 is the number of hours affected by water level; H(t) is the time t's water level; x 3 is the rainfall factor's lag hours; x 4 is the number of hours affected by the rainfall factor; p(t) is rainfall factor values at time t extracted by factor analysis; and θ is divided by 2400, which is the number of hours since the monitoring began until the initial observation began 1.…”

A New Approach for Seepage Parameters Inversion Analysis Using Improved Whale Optimization Algorithm and Support Vector Regression

“…This can lead to a reduction in the structural strength of the dam, triggering serious catastrophes such as dam failure. Seepage analysis based on monitoring data of dams can effectively understand the working condition of dams [1][2][3][4][5][6]. One of the most important parameters in seepage calculations is the hydraulic conductivity [7].…”