1.Introduction

Dam safety is significantly influenced by the seepage behavior of a dam's body,foundation,and bank slope.If an abnormal seepage problem is not detected and treated in a timely manner,it will probably result in seepage failure or even dam failure,as in the case of the failure of the Gouhou Concrete-faced Rockfill Dam in China(Li,1994;Malkawi and Al-Sheriadeh,2000).Therefore,it is important to monitor and predict dam seepage behavior.The frequently used monitoring methods at present mainly include piezometric tubes,osmometers,measurement weirs,and distributed optical fibers.The dam seepage behavior is directly reflected by monitored seepage elements,such as the piezometric tube water level,seepage discharge,seepage pressure,and water clarity(Fu et al.,2014;Su et al.,2015b;Alonso and Pinyol,2016).Considering the structural performance of a dam,the prediction and interpretation of these large sets of available data are very important for dam health monitoring,and are mainly based on mathematical or physical models(Rankovi´c et al.,2014;Wu et al.,2016).

斯诺对 《西行漫记》的人物刻画、环境描写以及叙事的角度几近出神入化的程度，在报告文学创作的艺术手法上成为同类作品的典范。但这本书不仅仅是一部单纯的文学作品，它在政治方面的影响更大。许多中国青年因为读了 《西行漫记》中译本而赶赴延安，积极投身革命，立志报效国家。

The upstream reservoir water level and rainfall are the main factors influencing dam seepage,and previous research results have indicated that the influences of these two factors show a strong time lag effect.Specifically,the change of the piezometric tube water level lags behind the change of the reservoir water level and rainfall(Liu and Sun,2011).To solve this problem,Gu and Wu(2006)established statistical models in different forms for dam seepage monitoring,and the previous segmental average values and equivalent values of the upstream reservoir water level and rainfall were used as model factors.The average value method is frequently used,combined with multiple linear regression(MLR,in which the least squares regression and stepwise regression are frequently used),due to its simplicity(Hu and Ma,2016).However,the influence of the actual time lag effect on dam seepage gradually increases to a great extent and disappears later on,and the time lag effect cannot be appropriately reflected by an average process.As for the equivalent value method,the time lag effect is mostly considered to follow a normal distribution,and the lag time and influencing time are the key parameters that need to be accurately determined.To optimize these two parameters,artificial intelligence algorithms,such as the adaptive linear neuron(Yu et al.,2010)and genetic algorithms(Qiu et al.,2016;Wei et al.,2018),are frequently used.Wang and Bao(2013)also proposed a daily variation monitoring model for seepage elements using the previous equivalent incremental value of reservoir water level.Besides the statistical model,an autoregressive moving average(ARMA)model has also been used to predict dam seepage elements.However,it is incapable of explaining the abnormal phenomenon in the predicted results(Adamowski and Chan,2011).

Artificial intelligence algorithms,such as the artificial neural network(ANN)and the support vector machine(SVM),have been developed in recent years and can be used to establish a nonlinear mapping relationship between the input and output of a system.The essence of the dam safety monitoring model is to establish relationships between a monitored dam effect quantity and its factors,which can be respectively used as the output and input variables.Then,according to the observation data,monitoring models can be established by developing the learning rules between input and output samples with the artificial intelligence algorithm(Kao and Loh,2011;Monjezi et al.,2013;Zheng et al.,2013;Mata,2011).Rankovi´c et al.(2014)developed a nonlinear feedforward neural network(FNN)model to predict the piezometric tube water level in dams,and this approach shows higher prediction accuracy than the traditional MLR method.The SVM is based on the minimization principle of structural risk and can effectively solve the high-dimensional nonlinear problem with small samples;its fitting ability can be optimized by the genetic algorithm(GA),particle swarm optimization(PSO),etc.Thus,SVM models are frequently used for dam safety monitoring.To reduce the modeling time without decreasing the prediction accuracy,Su et al.(2015b)conducted sensitivity analysis of SVM model input factors with respect to output factors.The SVM model is very adaptable to the situation in which the factors of dam effect quantities have a great deal of uncertainty.The aforementioned situation can also be solved through cooperation between the principal component analysis(PCA)and the SVM,in which the main features of the uncertain input factors are primarily extracted by PCA before establishing the SVM model(Chen et al.,2013).

In order to accurately evaluate the dam seepage behavior,the piezometric tube water level was used as an example to establish the monitoring models for the base flow effect and daily variation of dam seepage elements,respectively,in this study.The base flow effect monitoring model was used to separate the time effect component from the monitored seepage elements,so as to avoid the influence of seepage lag effect.The model can be used to predict and evaluate the longterm development trend of the dam seepage behavior.To monitor the daily variation of dam seepage elements,the SVM was used to establish the nonlinear mapping relationship between the daily variation of a seepage element and its factors,and the seepage lag effect-induced uncertainty of the factors was optimized through sensitivity analysis.

2.Time lag effect of monitored dam seepage elements

Due to the water level fluctuation of a reservoir,the dam body and its foundation are in an unsteady seepage state.The resistance caused by the weak permeability of dam materials leads to a change of water pressure in different parts of the dam-foundation system lagging behind the fluctuation of the reservoir water level and the in filtration of rainfall.The relationship between the monitored water level of a piezometric tube embedded behind the impervious curtain and the reservoir water level of the Chencun Arch Dam,with other factors almost remaining the same,is shown in Fig.1.It can be seen that,at the same reservoir water level,the monitored piezometric tube water level at the rising stage of the reservoir water level is lower than that at the dropping stage.This phenomenon is mainly caused by the time lag effect of unsteady seepage.

多西他赛联合替吉奥对晚期胃癌患者CEA、CA199、TAM水平及疗效的影响 ………………………… 吴周姜丽等（18）：2524

As for the factors of dam seepage elements,monitoring results show that the piezometric tube water level is mainly influenced by the upstream and downstream reservoir water levels,rainfall,temperature,and time effect,with their components denoted as h u,h d,h P,h T,and hθ,respectively.To consider the seepage lag effect corresponding to the upstream reservoir water level and rainfall,the previous segmental average values of these two factors have mostly been used.Therefore,the traditional hydraulic,precipitation,temperature,and time effect(HPTT)statistical model for piezometric tube water level h is generally as follows(Gu and Wu,2006):

where Hi（i=0，1，…，m1）is the i th previous segmental average value of the upstream reservoir water level;Pj（j=0，1，…，m2）is the j th previous segmental average value of rainfall;H d is the downstream water level,monitored simultaneously with the seepage element;t is the cumulative monitoring time;θ=t/100;and a u i,a d,bj,c1i,c2i,k1,and k2 are the regression coefficients.

3.Monitoring model for base flow effect

The seepage field of a dam-foundation system continuously changes with the development of environmental factors.The base flow shows the basic effects of the main factors,the hydraulic pressure and time effect,on dam seepage,and its development rule can be used to evaluate the performance of the dam seepage prevention and drainage system.Therefore,to avoid the influences of the reservoir water level change and seepage lag effect,it is necessary to establish a base flow effect monitoring model,which can be used to estimate the time effect component and the long-term seepage behavior.

3.1.Extraction of base flow effect

Generally,the reservoir water level and piezometric tube water level both fluctuate around certain base values,which are respectively denoted as H b and h b.Then,for any time,the reservoir water level and piezometric tube water level can be denoted as H b+ΔH and h b+ Δh,respectively,where ΔH and Δh are the water level variations with respect to H b and h b,respectively.However,a seepage element is monitored as a comprehensive value influenced by different factors.The effect of an individual factor is unknown and can only be separated by mathematical models or other signal separation methods.In general,each factor has a certain frequency.Therefore,according to the frequency of a factor and its seepage component,the monitored comprehensive value of the seepage element can be separated in the frequency domain,and components of different factors can be distinguished by their frequencies(Wang et al.,2018).Generally,the base flow effect is represented by the separated low-frequency component with a cycle period of more than one year.

The Mianhuatan Dam is located on the Tingjiang River of Fujian Province,China.The crest elevation and maximum dam height of this roller-compacted concrete gravity dam are 179.0 m and 113.0 m,respectively.To monitor the seepage behavior at the abutment,seven piezometric tubes were embedded under the left and right dam slopes.Several piezometric tubes were located further downstream,and their water levels changed slightly.Two piezometric tubes with high sensitivity to environmental factors were selected to establish the monitoring models for the base flow effect and daily variation of dam seepage elements.These two piezometric tubes were embedded in front and at the back of the impervious curtain of the right dam abutment,marked as R1 and R3,respectively,in Fig.5.The plane layout of the dam and the locations of the analyzed piezometric tubes are shown in Fig.5.

The wavelet multi-resolution analysis is a signal analysis method in the frequency domain,by which a time series can be decomposed into components of different frequencies,and thus it can be applied to the multi-scale analysis of seepage elements.The decomposition diagram of the wavelet multiresolution analysis is shown in Fig.3.During the decomposition of a time series,only the low-frequency component obtained in the previous step is decomposed again.The decomposition of a time series in the wavelet multi-resolution analysis can be expressed as follows:

where ΔHi（i=0，1，…）is the daily variation of the upstream reservoir water level that is monitored i days before the seepage element monitoring time,and ai is the regression coefficient.

3.2.Base flow effect monitoring model based on wavelet multi-resolution analysis

Of all those dam seepage factors,the environmental temperature and rainfall usually cycle with annual periodicity.However,the variation period of the reservoir water level differs from dam to dam,and it varies from daily regulation to multi-year regulation,even without obvious periodicity.Therefore,to establish a monitoring model for the base flow effect of dam seepage,multi-layer wavelet decompositions are first conducted of seepage elements and the reservoir water level.When the frequency of a decomposed low-frequency component of the monitored time series is lower than that of annual periodicity,the decomposed low-frequency component at this decomposition layer can be determined as the base value,and thus,the monitoring model for the base flow effect is established.

Based on the frequency characteristic,the base flow of dam seepage is mainly caused by the time effect and the long-term hydraulic action.Thus,the base flow effect of the piezometric tube water level can be expressed with the following monitoring model:

where a h is the regression coefficient.

The SVM is based on the statistical learning theory and has a strong nonlinear mapping ability in high dimensional space.Therefore,the SVM was adopted in this study to establish a nonlinear relationship between the daily variation of a seepage element and its factors.

4.Daily variation SVM model for dam seepage elements

In Engineering practice,monitoring models are used for safety analysis and abnormality mechanism interpretation of the dam-foundation system.The abnormality is diagnosed based on the variation of monitored variables,for example,the sudden increase or decrease of the piezometric tube water level usually reveals the failure of the seepage prevention and drainage system or the blockage of the piezometric tube.The base value of the piezometric tube water level mostly reflects the overall quality of the seepage prevention and drainage system,rather than the abnormality diagnosis.Therefore,a monitoring model for the daily variation of the piezometric tube water level with consideration of the time lag effect needs to be established.

IBM开发的Fabric[6]看到了另外一个生存空间，那就是避开以太坊，构造联盟链，在企业中运行。得益于IBM的代码质量和一贯良好的形象，Fabric很快在联盟链中占据了主导地位。Fabric的特点是不用密码货币，转而用节点背书，其中每个节点的身份可以识别，不诚实的节点需要付出代价。目前在大部分所谓落地的应用中，例如：银行、供应链、积分、税务等场景，无一例外地采用了联盟链的模式。

他也许是在说他自己，内心不够纯净，做久了playboy，懒得再相信真心？但是他在努力，他在努力接受她的真心。

4.1.Factors of daily variation of dam seepage elements

The daily variation of the piezometric tube water level is mainly induced by the variations of reservoir water level,rainfall,and temperature.Considering that the downstream reservoir water level changes slightly for most dams,its influence on the daily variation of the piezometric tube water level can be ignored.Therefore,the daily variation of the piezometric tube water level consists of three parts,as follows:

where Δh′, Δh u, Δh P,and Δh T are the daily variation of the piezometric tube water level and its components caused by the variations of upstream hydraulic pressure,rainfall,and temperature,respectively.

To consider the seepage lag effect,the previous daily variations of the upstream reservoir water level are initially determined as the hydraulic factors.Thus,the hydraulic component of the daily variation of the piezometric tube water level can be determined as follows:

A flow chart for establishing the daily variation SVM model of dam seepage elements is shown in Fig.4.

where f0 is the original signal;and fi and di are,respectively,the low-and high-frequency components decomposed from the low-frequency component fi-1 of the previous step.

The influence of rainfall on the daily variation of the piezometric tube water level is not an average process,and it can be evaluated with the previous rainfall Pj（j=0，1，…，）that is monitored j days before the seepage element monitoring time:

whereis the regression coefficient.

民办高校可向政府申请财政支持，或者向当地政府申请制定资助政策，通过政府帮助解决民办高校的财政压力，促进民办高校的发展。例如：向政府申请“互联网+教育”推广专项基金，专门用于采购和维护“互联网+教育”必备硬件设施。同时，民办高校可以借助自身市场运作灵活机制，寻求获得市场资金的投入与支持，通过股份分担制与企业合作、与国外联合办学等各种形式引进资金，吸引国际优质教学资源来增强民办高校的资金与实力；也可以建立社会捐赠制度，大力宣传鼓励捐赠教育事业的优惠政策。

The temperature component in the daily variation of the piezometric tube water level is determined by the periodic function as shown in Eq.(1).

4.2.Optimization of input factors of SVM model

As for a given sample combination ｛（x i，yi），i=1，2，…，n｝,a linear function can be determined to express the relationship between an input vector x i= （xi1，x i2，…，xir）and an output value f（x i）.f（x i）is determined according to the criterion of minimum cumulative square deviation between the output value f（x i）and the actual value yi.When using the nonlinear regression,the input vector is first mapped into a high dimensional space using the nonlinear mapping φ :x i→φ（x i）,and then the nonlinear problem is transformed into a linear problem in the high dimensional space.Thus,the relationship between the SVM input and output vectors can be expressed as f（x i）=ωT·φ（x i）+b,and the corresponding optimization criterion is defined as follows:

where ω is a one-dimensional vector and b is a parameter determined in the optimization process,respectively;ξi andare the slack variables introduced due to the allowance of fitting error;and C is the penalty factor of the sample whose training error is greater than the threshold ε,where C ＞ 0.

黎冰颖的《盛装苗女——山雀》同样也是描绘少数民族形象的代表，她刻画了一位佩戴了各式各样银饰的苗族少女，与《京族渔女》不同，画中人物侧身站立，目光炯炯，少了一丝柔美而多了一份坚定，画面以红白两色为主，红是服饰的颜色，而白则是各种银饰的颜色，画家在纸上用深灰色打底，不仅突出了银饰的质感，不均匀的底色还呈现出变化微妙的自然效果。

Sensitivity analysis is an effective method of studying the importance of each input factor.To reduce the input space dimension of the SVM with high prediction accuracy,the initially determined uncertain input factors of the initial SVM model are optimized through sensitivity analysis,and some unimportant factors can be eliminated.The sensitivity of an SVM input factor to an output factor can be expressed as the partial derivative of the model output factor with respect to the input factor,and the sensitivity of the l th SVM input factor to the output factor f（x i）is defined as follows(Su et al.,2015a):

where n is the total number of training samples,and r is the total amount of the used input factor.

To evaluate the prediction performance of the established SVM model,the mean square error(MSE)is calculated as follows:

4.3.Establishment of daily variation SVM model

Due to the uncertainty of the lag time of the piezometric tube water level with respect to the reservoir water level and rainfall,it is difficult to determine the previous factors in Eqs.(5)and(6),of which the measured time series prior to the monitoring time of the piezometric tube water level may have certain influence on the monitored results.Therefore,to achieve the highest prediction accuracy with the least number of model input factors,a daily variation SVM model for dam seepage elements was developed as follows:

Step 1:The correlation coefficients between the daily variation of the piezometric tube water level and the previously measured time series of rainfall and the daily variation of the reservoir water level are calculated,and the factors that have positive correlation coefficients with the daily variation of the piezometric tube water level are determined.

To study the mechanism of seepage lag effect,assumptions for a piezometric tube are as follows:(1)the upstream reservoir water level H and the piezometric tube water level h are respectively kept constant for a long time in the steady seepage state;and(2)the reservoir water level rises or drops by a certain amplitude and then remains constant,while other environmental variables remain unchanged.Statistical analyses show that the change of the piezometric tube water level occurs later than that of the reservoir water level,and it takes a longer time for the piezometric tube water level to reach a steady state,as shown in Fig.2.During this process,the seepage lag effect is mainly caused by three factors(Wang and Bao,2013):(1)the time interval for water pressure transmission induced by water filling into or discharging from a porous medium,which is the main factor of the seepage lag effect;(2)the time interval induced by monitoring methods,e.g.,for a piezometric tube,a time interval is needed for water to fill into or discharge from its interior,and the delayed time increases with the size of the tube;and(3)the time interval required for squeezing the air and filling the porous medium,which mainly occurs in the seepage transition zone.

Step 2:An initial SVM model with the initially determined input factor set is established.

Step 3:The sensitivities of the input factors to the output factor in the SVM model are calculated with Eq.(8).

Step 4:The input factor with the least amount of sensitivity is eliminated,and a new input factor set with the remaining input factors is developed.

Step 5:A new SVM model is established based on the optimized input factor set.

Step 6:The MSE of the newly established SVM model is calculated,and the MSE is used as the performance evaluation index of the SVM model during the process of eliminating unimportant input factors.

Step 7:It is determined whether the initially removed input factor in Step 4 should be finally eliminated or not.If the MSE of the newly established SVM model decreases or increases with a relative increment of the MSE smaller than the preset threshold ε0,which means that the elimination of this input factor either can increase the prediction accuracy or has little influence on the prediction accuracy of the SVM model,this input factor can be eliminated.Then,the sensitivity analysis should be continued by returning to Step 3.Otherwise,if the MSE of the newly established SVM model increases,with a relative increment of the MSE larger than the preset threshold ε0,which means that the elimination of this input factor will significantly reduce the prediction accuracy of the SVM model,this input factor should be retained,and the optimization process of input factors is stopped.

对2013—2016年全国亲子游网络关注度进行统计分析，如图1所示。自2013年以来，全国31个省市自治区对亲子游的日均网络关注度呈现快速增长态势；2015年亲子游网络关注度达到顶峰；2016年关注度趋势有所减缓，但总体处于上升态势，年均增长率为20.81%。

“当年农家”是20世纪70年代典型的农家居所。建于1973年的茅草房用本地茅草覆盖，墙为泥土拌和稻草砌垒而成，屋内的签字室，展现的是20世纪70年代末开创大包干的场景。

5.Case study

当前关于《史记》英译研究成果斐然，但是研究成果主要集中在翻译学方面，鲜有从传统文献语言学的角度去研究。有中文系学者曾作为倪译“翻译坊”成员之一，参与了倪译某初稿的讨论，认为“严谨地英译《史记》原文，或许会促进《史记》文本本身的进一步研究”（曲景毅2014）。倪豪士在谈及他翻译中遇到的困难时说：“真正了解《史记》的中国人，没有什么英文的能力……问他们英文的一句话，他们都不懂。如果年轻人学了古典文学，也学了英文，那是最好的”（2015年8月22日凤凰江苏网采访）。可见，多视角、多维度、跨学科研究一定会促进翻译本身和对原作的研究。

皇帝在宰相的辅助下行使国家的最高决策权，是封建社会的普遍规律。朱元璋废除了丞相这一官职，却废除不了宰相制度。逐渐发展起来的大学士，成为明代中后期的宰相。决策权包括两部分：做决定之前的议事权和最后的决定权。最后的决定权属于皇帝，而阁臣拥有做决定之前的议事权。阁臣、首辅常分别被称为“辅臣”“元辅”，阁臣辅助皇帝行使决策权在明代得到正式承认。

5.1.Monitoring model for base flow effect

To establish the base flow effect monitoring model,time series of the two piezometric tube water levels were first decomposed with the wavelet multi-resolution analysis method.The dam was constructed together with a reservoir with incomplete annual regulation.When the piezometric tube water levels were decomposed to the eighth layer,the periods of the high-frequency and low-frequency components were respectively 341 days and 512 days,and thus the lowfrequency component could be determined as the base value.Time series of the two piezometric tube water levels and extracted base values are shown in Fig.6.It can be seen that the base values of the piezometric tube water level and reservoir water level are basically in a steady state,the piezometric tube water level changes in significant correlation with the reservoir water level,and the time lag effect clearly exists.Due to the higher embedment elevation under the abutment slope and the location in front of the impervious curtain,the water level of the piezometric tube R1 is higher than the reservoir water level.According to Eq.(3),the monitoring models for the base flow effect of the two piezometric tube water levels are as follows:

(1)Piezometric tube R1:

(2)Piezometric tube R3:

The multi-correlation coefficient between the monitored and fitted base values of the piezometric tube water level and MSE value were 0.91 and 0.084 for the base flow effect monitoring model of R1,and 0.94 and 0.028 for the base flow effect monitoring model of R3,respectively,indicating that the two established models could better reflect the monitored base values.Fig.6 shows that the time effect components of the piezometric tube water level base values increase quickly at the initial stage of the analyzed period and slightly decrease in the long-term operation.The evolution of these two piezometric tube water levels shows that the seepage behavior of the right abutment slope was stable.

5.2.Daily variation SVM model for dam seepage elements

The correlation coefficients between the daily variation of the piezometric tube water level and the previous factors were first calculated,as shown in Fig.7.On this basis,the previous input factors were determined:daily variations of the reservoir water level within the previous 21 days(ΔH1 through ΔH21)and 23 days(ΔH1 through ΔH23),and rainfalls within the previous 23 days(P1 through P23)and 16 days(P1 through P16),were respectively considered in the initial SVM models of R1 and R3.Therefore,taking the temperature factors and the two current factors(ΔH0 and P0)into account,there were in total 50 and 45 input factors,respectively,for the two initial SVM models.

According to Fig.4,the daily variation SVM model was established for the water levels of the two piezometric tubes.The total number of monitoring samples was 630,of which 600 samples were used for training the SVM model and the remaining 30 samples were used for testing.Evolution of the MSE value during the process of optimizing the SVM model input factors is shown in Fig.8,which demonstrates that the minimum MSE values were 7.5×10-3 and 4.46×10-3 for R1 and R3,respectively.The SVM model input factor optimization processes for R1 and R3 are shown in Tables 1 and 2,respectively,in which 29 and 21 input factors were respectively eliminated in turn for the SVM models of R1 and R3.A threshold of the MSE increase ratio,relative to the minimum MSE value of each optimization process,was preset as 5%in this study.Therefore,the 29th model and 21st model,with the optimal MSE values of 7.87×10-3 and 4.49×10-3,were respectively determined to be the best daily variation SVM models for R1 and R3.

With the optimized SVM model and its input factors,the daily variations of the two piezometric tube water levels were respectively fitted,as shown in Fig.9.As can be seen,the daily variations of the monitored value and the fitted value of the SVM model are basically the same,except for a few large errors caused by monitoring errors or some other reasons.Therefore,the established daily variation SVM model can be used to predict and evaluate the fluctuation of the piezometric tube water level with the change of environmental factors.

To evaluate the accuracy of the optimized SVM model,boxplots of residuals were used to conduct the error analysis.The boxplot consisted of the mean,the median,the upper quartile Q3,the lower quartile Q1,and the minimum and maximum values of the residuals.Outliers could be identified when they were beyond the minimum limit and maximum limit.Boxplots of residuals for the optimized SVM model and initial SVM model are shown in Fig.10,in which the small box is used to express the mean value.The figure clearly shows that the optimized SVM models for R1 and R3 have no outliers,and the residuals are in normal distribution.Therefore,the data mining ability of the optimized SVM model is better than that of the initial SVM model.

当前，我国很多电厂企业人员众多且工作分配模糊，导致企业人力成本过高，各个部门虽然员工较多，但是工作效率却难以提升，存在因人设岗的情况。同时，随着科学技术的蓬勃发展，企业为了提升自身的核心竞争力，不断引进高技能以及高素质人才，进一步加剧了人力成本的提升，成为了电厂企业健康发展的重要阻碍。

5.3.Discussion

Traditional HPTT models were also established for R1 and R3 with the stepwise regression method.Regression coefficients in Eq.(1)are shown in Table 3,and time series of the separated components are shown in Fig.11.It needs to be noted that these two piezometric tubes were all located higher than the downstream reservoir water level,so the downstream hydraulic component h d was not considered in the HPTT model.It can be seen that the time effect components separated by the proposed base flow effect monitoring model and the traditional HPTT model are very close,and the maximum values were 2.80 m and 2.83 m for R1,and 1.62 m and 1.38 m for R3,respectively.During the stepwise regression process,the previous factors H1 and H2 were eliminated,while H0,,and were retained.This phenomenon is not consistent with the seepage lag effect,which presents with a continuous normal distribution,indicating that the HPTT model could not fully explain the variation of the piezometric tube water level in this study.

Fig.11 shows that the proportions of rainfall and temperature components are very small,and the base value of the piezometric tube water level is mainly interpreted by the hydraulic component and time effect component.Fig.12 shows the time series of the fitted base values of the piezometric tube water level.It can be seen that the fitted base values obtained by the two methods are almost consistent,and the proposed method can better reflect the tendency change.Therefore,the proposed base flow effect monitoring model is feasible.

Sensitivity analysis of the input factors of the daily variation SVM models for R1 and R3 was conducted.The distributions of sensitivities of the daily variation of the upstream reservoir water level and rainfall with lag time are shown in Figs.13 and 14.The results show the following:(1)Sensitivities of the daily variation of the upstream reservoir water level and rainfall follow normal distribution,especially for the daily variation of the upstream reservoir water level of the SVM model for R1 and the rainfall of the SVM model for R3.It is in accordance with the results that the influences of the reservoir water level and rainfall on seepage elements follow normal distribution(Gu and Wu,2006;Qiu et al.,2016).(2)Sensitivities of rainfall input factors are obviously higher than those of the daily variation of the upstream reservoir water level,e.g.,for the SVM model of R1,the maximum sensitivities of rainfall and the daily variation of the upstream reservoir water level were 0.5705(P1)and 0.1797(ΔH6),and the average sensitivities of rainfall and the daily variation of the upstream reservoir water level were 0.1686 and 0.1115,respectively.This means that rainfall has a more significant impact on the piezometric tube water level.The reason for this is that these two piezometric tubes were buried under the slope of the dam's right bank,which made the rainfall more likely to seep into the piezometric tube than reservoir water.(3)The seepage lag time and influencing period of rainfall are both shorter than those of the daily variation of the upstream reservoir water level.For the rainfall,the seepage lag time and influencing period were one day and three days for R1,and one day and six days for R3,respectively.However,the difference between influences caused by the previous daily variations of the upstream reservoir water level on the two piezometer tube water levels is not clear,and the lag time and influencing period of the daily variation of the upstream reservoir water level for R3 were determined to be four days and eight days,respectively.This phenomenon can be interpreted by the fact that the seepage path of rainfall is shorter than that of the upstream reservoir water.

虽然鱼儿们如此顽皮，但对我这个“钓鱼高手”来说，也只是雕虫小技。我先将一撮没有连着线的鱼食抛进了鱼群，那些鱼儿们过去嗅了嗅，都走开了。他们好像在嘲笑我：“哼！我们才不中计呢！想把我们钓走，没门儿！”可他们不知道，他们已经中计了！刚刚我把鱼食抛得很近，本来就是给他们设的圈套。他们一下子躲到了后面，却不料我的鱼钩上已经放了鱼食在等他们，其中一个调皮的，上去咬了一口，没有被钓起来，他的胆子就变大了，一口将鱼食吞了下去，我熟练的将鱼竿收回，哈！一条黑色的鱼！我用同样的方法，又钓了九条鱼！

The aforementioned analyses can also verify the rationality of the proposed daily variation SVM model.In contrast to the MSE values of the initial SVM model with non-optimized input factors,which were 9.14×10-3 and 5.22×10-3 for R1 and R3,respectively,the MSE values of the optimized SVM model were 7.87×10-3 and 4.49×10-3 for R1 and R3,13.4%and 14.0%lower,respectively.On the other hand,the time intervals between the monitoring time of the remaining input factors after optimization and that of the piezometric tube water level are generally smaller,and the sensitivity distributions of the input factors accord with the lag influence rule of unsteady seepage,which present with continuous normal distribution.

2、屋面结构安全可靠，屋面整体达到正常使用及遭遇地震、暴雪、大风、暴雨时的安全性要求，无漏雨、渗水现象。

6.Conclusions

To investigate the base flow effect and time lag effect of unsteady seepage,monitoring models for the base flow effect and daily variation of dam seepage elements were respectively established to predict and evaluate the long-term trend and short-term fluctuation of the dam seepage behavior in this study.The following conclusions are drawn:

(1)The base flow effect of the piezometric tube water level is mainly interpreted by the hydraulic component and time effect component.The time effect component and fitted base value obtained with the proposed base flow effect monitoring model are close to those obtained with the traditional HPTT model,and the interpretation rationality of the proposed model is better than that of the traditional HPTT model.Therefore,the proposed base flow effect monitoring model is feasible.

(2)Sensitivity analysis of the input factors of the optimized SVM model shows that the influences of the daily variation of the reservoir water level and rainfall on the daily variation of the piezometric tube water level follow normal distribution.The SVM-based sensitivity analysis can also be used to optimize the lag time and influencing period in the equivalent value method of the statistical model.

(3)Under the normal condition of a dam-foundation impervious curtain,the seepage behavior of the dam bank slope is more likely influenced by rainfall,while the difference between the influences caused by the previous daily variations of the upstream reservoir water level on seepage elements is not clear.The time lag effect of external factors on seepage elements should be considered in diagnosing the abnormity of dam seepage behavior.

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