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.
Highlights
Sluice is one kind of low-head water retaining structures for purposes of power generation, flood control, irrigation, or water supply
Specific combinations are shown in the orthogonal design table, as shown in Table 2. en, the seepage pressures in the foundation are computed using the finite element models shown in Figures 13 and 14 with different combinations of permeability coefficients. e regression analysis module of SPSS software is adopted to obtain the coefficients of eight response surface equations for the seepage pressures at four monitoring nodes corresponding to the measuring points UP2, UP3, UP6, and UP7 in flood and dry seasons, respectively. e multiple correlation coefficients of the regression analysis for the eight response surface equations are all greater than 0.9
Seepage monitoring could re ect realistic seepage state in the sluice foundation, its drawbacks are that measuring points are limited and just local seepage state could be observed
Summary
Sluice is one kind of low-head water retaining structures for purposes of power generation, flood control, irrigation, or water supply. E foundations of these sluices are similar in material compositions, seepage characteristics, antiseepage measures, and seepage monitoring. E seepage in the sluice foundation is typically monitored with osmometers buried behind the cutoff wall. E monitoring data of seepage pressure can reflect the real seepage behavior in sluice foundation, whereas the shortcomings are that just local seepage states could be observed due to limited measuring points. E seepage field in the whole concerned area can be analyzed using numerical simulation techniques, such as finite element method (FEM), and the effects of material composition, seepage characteristics of various materials, seepage control. E values of seepage parameters such as permeability coefficients have a great influence on the numerical simulation results of foundation seepage. Seepage parameters could be calibrated with seepage monitoring data, and numerical simulation using the calibrated parameters would yield more realistic results. Typical calibration methods for geotechnical parameters include simulated annealing technique [9, 10], particle swarm optimization [11,12,13], neural network and genetic algorithm [14,15,16,17], Nelder–Mead algorithm [13, 18], response surface method (RSM) [19,20,21], and support vector machine [22]
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