Estimating cement take and grout efficiency on foundation

improvement for Li-Yu-Tan dam

Yang , Chau-Ping

Department of Civil Engineering, Chuag-Hau University,

30 Tung Shiang, Hsinchu, Taiwan, 30067

Fax: +886-3-5372188; E-mail address: ycp@chu.edu.tw

Abstract

The cement take needed for dam foundation improvement with grout-curtain is difficult to

estimate due to the complexity of the rock foundation and the great number of Lugeon tests

involved in the analysis. Therefore, this study adopted the mean method, the linear regression

method, and the back-propagation neural network (BPN) method to analyze the grout-curtain

construction data of the Li-Yu-Tan dam, Taiwan, in order to estimate the cement take needed.

The samples analyzed included data from 3,532 grout sections. The data from the first half of

the grout-curtain construction were used to derive the parameters of the predictive schemes,

and then the second half of the grout-curtain construction’s data were used to test the

accuracy of those schemes. The accuracy levels estimated by these three methods on gross

cement take were 71.8%, 59.8%, and 75.3% for the mean method, the linear regression

method and the BNP method respectively. All accuracy levels estimated by these three

methods were higher than the original design level of 43.4%. Furthermore, the efficiency of

the grout improvement in the studied cases were confirmed by observing the changes of the

distribution curve of the following each grout sequence. The method proposed

is intelligible and can be applied in other situations.

Keywords: Dam foundation; Grouting; Cement take; Estimation; BPN

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1. Introduction

Taiwan is located on a sedimentary rock with rugged terrain and complicated geological

properties. The bedrock inherently has discontinuities such as faults, folds, beddings, joints,

and fractures, which are the major factors that affect the engineering properties of rock

foundations such as permeability, shear strength, and deformation. When a dam is located on

bedrock that has unknown discontinuities, the underlying foundation needs to be improved to

raise its engineering properties and ensure a watertight reservoir. Using cement grouting to

improve bedrock has been quite common (Baker, 1982; Jaroslavl, 1989; Houlsby, 1990;

JSIDRE, 1994), and there are numerous examples of its application to the engineering of

dam-foundation improvement (Ewert, 1985; Weaver, 1991). However, since the dam

foundation is below the surface of the ground, the expense for cement grouting is the most

difficult construction expense to estimate. The expense of cement grouting mainly includes

the operational part and the material part. The expense of materials is calculated based on the

cement take. Then the expense of the grouting operation is determined based on the

material’s expense. Therefore, it is necessary to study various methods of estimating the

cement take of the grouting based on actual construction data.

In general, the status of the discontinuities in the dam foundation is indirectly expressed by

the determined from the Lugeon tests. The information gained from the Lugeon

tests can also be used to design the water to cement ratio and the injection pressure used in

the grouting process. Eq. (1) is the definition of the . Generally speaking, if a

dam foundation has a high , it will have more discontinuities with high

permeability and more cement take is needed for the grout improvement.

= = (1)

Where is the water take ( ), is the standard injection pressure (981 ), is the

injection time ( ), is the injection pressure used ( ), is the length of grout section

( ).

The is the best physical parameter to express the status of discontinuities in a

dam foundation. Theoretically, it is quite difficult to define the relationship between cement

take and the (Yamaguchi and Matsumoto, 1989; Hirota et al., 1990).

Additionally, when researchers estimate the cement take needed for a new dam foundation

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from past experiences, they still encounter the problems of different geological properties for

the proposed dam site. For example, the cement take designed for the improvement of the

foundation of the Li-Yu-Tan dam, Miao-Li County, Taiwan, was 50 . However, the

average reading of cement take from the construction records of Li-Yu-Tan dam was 115

(Taiwan Water Resources Agency, 1993). This difference resulted in a doubling of the

amount of gross cement take from what was required in the original design. This experience

illustrates the difficulty in cement take estimation.

Consequently, this study focuses on the practical application of cement take estimation by

adopting the mean method, the linear regression method, and the back-propagation neural

network (BPN) method to analyze the construction data from the grout- curtain improvement

of the Li-Yu-Tan dam’s foundation, and indicate how to estimate the cement take needed. The

efficiency of the grouting for this dam site is also addressed in this article.

As shown in Fig.1, the Li-Yu-Tan dam is located at about 500 in the upper stream of the

Jing-San brook, a tributary of the Da-An river, which is in the mountainous regions of

northwestern Taiwan. The dam is a zone-type-earth-dam with a height of 96 , a bottom

width along the foundation of the river of about 500 and a gross volume of 3,700,000 .

The major terrain includes gravelly terra rossa and some riverbank outcrops. There are no

faults or obvious folds on either side of the river. The major discontinuities in the foundation

of the dam site are dozens of developed shear zones. Most shear zones are distributed in the

right side of the abutments of the dam with slips of 2 ~5 above. (Taiwan Water

Resources Agency, 1986a).

2. Factors affecting cement take

Theoretically, there are many factors that affect the cement take needed for improving dam

foundations. Moreover, since some factors may have combined effects, it is not possible to

clearly define the role of each factor. Some factors that can be categorized or quantified are

the strata, zone of dam foundation, depth of grout section, injection pressure, and the

.

2.1. Strata

This category covers properties such as the rock layers, the nature of discontinuities, the

rock strength, the mineral components, and the cementation. All of these properties may have

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combined effects on cement take. If a dam foundation consists of different rock layers, it may

have more hidden discontinuities. Shallow bedrock tends to have a high density of cracks or

openings and is subjected to grout leakage and hole collapse. If a rock foundation has little

strength, the grout hole will be less independent. The disadvantages of bedrock mentioned

above increase the amount of cement take needed for grout improvement.

As shown in Fig. 2, the strata in the dam site vicinity are northeastwards and meet the river

valley at 28~34 degrees. All the strata are leaning towards the upper stream at 30~34 degrees.

The strata of the Li-Yu-Tan dam’s foundation include clean sandstone (CS), mudstone (MS),

and alternations with sandstone and shale (AL). The major formation of clean sandstone

contains quartz sand, which has a tensile strength of about 1,050 and a coefficient of

permeability about . However, since quartz sand has a poor cementation

quality, the seepage paths are more likely to cause a loss of fine material. Mudstone contains

different amounts of mud; therefore, its tensile strength ranges from 1,140 to 2,010 ,

and the average coefficient of permeability is . If the mudstone has good

cementation and low permeability, it is considered as the bedrock layer because of the better

engineering properties. Alternations with sandstone and shale have intertwined clean

sandstone and shale or mudstone and shale in small alternating thickness. The thickness of

mud accumulation between layers can reach 30 above. On the surface layer, seepage paths

can form that cause deterioration of the shale into fragments or even seams. The width of

fragments is about 20 ~30 above.

2.2. Zone of dam foundation

Runoffs flush weak parts of the ground to form river valleys. When the pressure of ground

is relieved, riverbanks will move inwards, and tensile fractures will occur in the banks. This

development will result in more cracks on the upper half of the dam abutments and induce

greater permeability. For this reason, the cement takes needed for the grout improvement in

the right zone, left zone, and the valley are different. This research has divided the dam

foundation into the riverbed, the left upper zone, the left lower zone, the right upper zone and

the right lower zone, as shown in Fig.3 and Fig. 4, according to the tunnel locations for the

grout-curtain construction. However, because the riverbed has been dug to the level of fresh

bedrock with a permeability lower than 10 , there are only a few in-place grout holes.

Thus, the analytical extent of this research covers only the left upper zone, the left lower

zone, the right upper zone, and the right lower zone. The shaded part in Fig. 4 is the outcome

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of the grout-curtain in the Li-Yu-Tan dam’s foundation. For the shallower parts, grouting can

be performed from the top, but, in the deeper areas, the grouting will have to be performed

from tunnels.

2.3. Depth of grout section

In a rock layers deeper into the underground, the cracks are narrow and comparatively do

not take in grout because of the greater tectonic stresses in lower elevation. When the tectonic

stress is taken into consideration, the depth of the grout section is considered as one of the

factors that affect cement take. As to the grout-curtain construction in the Li-Yu-Tan dam, the

diameter of the grout holes was 3.8 and the greatest vertical depth of a grout hole was

limited to 50 . Inside of each grout hole, there were several grout sections, and the grout

process was conducted from the bottom to the top of the grout hole. If the depth of the grout

section was smaller than 30 , the grout section length was 5 . When the depth of a grout

section was greater than 30 , the section length was 10 .

2.4. Injection pressure

The injection pressure is the major technical factor affecting cement take. If during the

grouting process, the operator increases injection pressure to fill the cracks with more grout,

this action may cause the loosening and cracking of bedrock. As a result, the extent of the

grout area may become larger. Theoretically, the injection pressure should be smaller than the

tectonic stress corresponding to the depth of a grout section, which is obtained from the

hydraulic fracturing test. Moreover, the injection pressure should be smaller than the tensile

strength of the rocks (Kutzner, 1985; Shibata, 1989). In Taiwan, the field of dam engineering,

considers that the injection pressure is determined based on the principle of additional

pressure increasing about 30 per meter depth. The injection pressure adopted for the

grout-curtain construction of the Li-Yu-Tan dam was 150 to 1200 from top to the

bottom of the grout hole (Taiwan Water Resources Agency, 1986b).

2.5.

The is the only physical parameter that the researcher could obtain to

evaluate the multiple factors that affect cement take. This value shows the degree of

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permeability in the dam foundation. Basically, in grout improvement, a dam foundation that

has a high requires more cement take.

3. Data analysis

In the Li-Yu-Tan dam’s grout-curtain construction, the grout holes were of the split-

spacing type. Split-spacing means that the grout holes were arranged in the sequence of

primary holes, secondary holes, tertiary holes, and quaternary holes. Supplementary holes

may be added to enhance the locations with more discontinuities in the bedrock or near the

holes that required more cement take. Basically, the arrangement of grout holes was based on

the quality of bedrock. In the Li-Yu-Tan dam, the grout holes were arranged at intervals of 1

to 3 . When the grouting process of a specific hole lasts for 60 minutes, but the amount of

cement take does not reach 70 , the grouting for this section should be stopped. Finally, the

drill inspection holes used for performing the Lugeon test to check the permeability of the

dam foundation were improved. The process of grouting in each grout section was arranged

in the following sequence: drilling, washing, water testing, and grouting. During water

testing, the Lugeon tests need to be performed to obtain the , which gives the

permeability of that specific grout section, and determines the water to cement ratio.

Table 1 lists the data analyzed for 469 grout holes and 3,532 grout sections. Each grout

section had data such as zone, sequence, hole depth, length of grout section, rock nature,

, injection pressure, and cement take. All of the data were collected from the

inspection chart of the grout-curtain construction for the Li-Yu-Tan dam in 1993. Then, all

the data were entered into an Excel application program for calculations before the analysis

began.

For the convenience of analysis, this study has adopted the symbol to

represent the of a specific grout section. In addition, because the

lengths of the grout sections analyzed were not the same (between 5 and 10

), the cement take of a grout section was divided by its length to obtain the

cement take per unit length ( ). There were three reasons to use

cement take instead of cement mortar take to define . First, the voids in the

cracks were fi lled by solid cement. Secondly, the major material expense in

grout construction is the quantity of cement. Thirdly, many documents related

to grout ing refer to cement take in place of cement mortar take (Ennto, 1988;

Tano, 1988). Generally speaking, a grout section with a higher needs a

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greater amount of for grout improvement.

4. Estimation method of cement take

4.1. The mean method

The relationship between and is complicated because it is affected by many factors

including geological properties, injection pressure, and grout operation. Nowadays, in

practical application, researchers still think that is a major factor affecting . Since there

are enough samples gathered in this study, either the middle value method or the mean

method can be adopted to find the regression formula that expresses the relationship of

and .

Hirota, et al. (1990) tried to use the middle value of and as a representative value to

observe their relationship. was defined as the middle value in the distribution curve

(refer to Fig. 14 and Fig. 15) and as the middle value in the distribution curve . Next,

and were determined for primary holes and secondary holes etc. Then, regression

was applied at over for different sequences to derive the formula in Eq. (2).

(2)

However, Hirota, et al. (1990) also pointed out that the accuracy of the estimation derived

from Eq. (2) was based on the frequency distribution pattern of the samples. That is, samples

with normal distribution were more applicable to Eq. (2). Furthermore, Fig.5 and Fig.6 are

the histograms of the samples used in this study. The histograms clearly show that the

samples were not normally distributed but skewed. Most of the samples fell into zones of a

small value of <200 . Therefore, it was not appropriate to use the middle value

method to estimate cement take in this case. Consequently, in place of the middle value

method, this study adopted the mean method to regress the relationship of and , then

defined as the mean of , and as the mean of for each zone in the dam

foundation.

Since different dam foundations rarely have the same geological properties, a predictive

scheme may be applicable only to the specific dam foundations under analysis and cannot be

applied to other cases. However, in the same dam foundation, since the operation load for

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grout improvement is usually in large quantities and the construction period is usually long, it

is possible to use the data collected from the completed grout sections to estimate the cement

take needed for the remaining sections. Therefore, this study used the data collected from the

first half of the grout-curtain construction in four zones to calculate its and . Then,

the four sets of ( , ) points were regressed to derive Eq. (3).

(3)

The relationship of and in Fig. 7 is obtained from different zones that are highly

correlative (with a correlation coefficient as 0.95). It was found that the accuracy of the

estimation derived from Eq. (3) was mainly affected by the similarity of the distribution

pattern of the samples instead of the normal distribution. The distribution patterns of the

samples of the two upper zones were similar (see Fig. 5 and Fig. 6). Therefore, the mean

method (i.e. Eq. (3)) was adopted as one of preference scheme to estimate cement take in this

case.

4.2. The linear regression method

Yang (2002) observed the corresponding relationship of spatial distribution between

and in this case, and found that the spatial distribution of the contours of and

corresponded. Thus, this study attempted to find the linear regression equation that directly

expressed the relationship of and . The - relationship collected from the first half

of the grout-curtain construction is shown in Fig. 8 and Fig. 9. As a whole, had a tendency

to increase along with the increase of , but there was little correlation between Lg and Lu.

With the help of ms-Excel software, several types of equations were tried. Among these types

of equations, although the correlation demonstrated by the quadratic type of Eq. (4.a), Eq.

(4.b), Eq. (4.c), Eq. (4.d) was higher, still the values of were small. However, because the

geological properties of a dam foundation are complicated, it is hard to observe the factors

that might affect . Accordingly, the linear regression method is still often used (Ewert,

1985; Yamaguchi and Matsumoto, 1989; Hirota, et al., 1990).

Left upper zone =0.36 (4.a)

Left lower zone =0.43 (4.b)

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Right upper zone =0.41 (4.c)

Right lower zone =0.49 (4.d)

4.3. Back-propagation neural network (BPN) method

The BPN is a branch of artificial neural networks (ANN). The growing interest in ANN

among researchers is due to its excellent performance in learning ability, fault tolerance,

pattern recognition, and the modeling of nonlinear relationships especially involving a

multitude of non-digital variables in place of conventional techniques. Generally, a complex

domain is characterized by a number of interacting factors. Yet, such factors are often

incomplete or unreliable. If ANN is used to analyze complex engineering systems, it can

alleviate noise interference and raise the accuracy level of the analysis (Goh, 1995). ANN has

been widely applied to research in the field of geotechnical engineering in recent years. For

example, Goh (1994) used ANN to evaluate the liquefaction potential of soil. The

comparisons indicated that such a model was more reliable than the conventional dynamic

stress method. Schaap et al. (1998) used the hierarchical neural network model for the

prediction of water retention parameters and saturated hydraulic conductivity from basic soil

properties. Such a model is attractive because of high accuracy and because it permits a

considerable degree of flexibility toward available input data.

Huang and Wanstedt (1998) applied BPN to the categorization of rocks and found that the

categorizing ability of BPN was much better than statistical methods. Additionally, a

conventional method for modeling the stress-strain behavior of soil is the constitutive law.

However, such law is characterized by the difficulties in obtaining correct parameters,

conducting mathematical calculations, and the oversimplification of the hypothesis. In a quite

different way of research thinking the constitutive law was replaced with BPN to simulate the

stress-strain behavior of soil (Ellis et al., 1995; Zhu et al., 1998; Imad, 2000; Yang, 2002).

4.3.1. Mechanism of BPN

The typical architecture of BPN used in this study is shown in Fig. 10. The input layer uses

linear transfer functions to handle the input variables in the network. The number of

processing elements in the input layer depends on the problem. In the hidden layer, it learns

how each processing element in the input layer affects the others through association of the

connection weights. In the output layer, an S-shaped sigmoid transfer function is used to

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handle output variables to make the domain to be [0, 1]. The number of processing elements

in the output layer depends on the problem. BPN learns by modifying the connection weights

of the elements in response to the errors between the actual output values and the target

output values. This is carried out through the gradient descent on the sum of squared error for

all the training patterns.

The learning algorithm of BPN requires the following steps:

a. Use the connection weight to show the correlation between the input variable and

each processing element. Meanwhile, biases and activity function value will come

out. Then, convert the value to either the target output value in the hidden layer

and to the target output value in the output layer.

b. As to the processing elements in the output layer, use and the actual output value to

calculate the offset . The calculation of the processing elements in the hidden layer also

adopts , and to calculate the offset .

c. In the input layer and the hidden layer, use the learning rate , and to calculate the

correction value of the connection weight . In the hidden layer and the output layer,

use the learning rate , and to calculate . Then, update the in each processing

element to complete the learning of one cycle.

d. Repeat the computation described above until convergence or approximately 3,000

learning cycles are reached.

The BPN software used in this research was PC-Neuron, written in language (Yeh,

1997). With the assistance of the original programmer, a new subprogram was written to

return to the target output value from the original domain [0, 1]. Then, this value was

converted to a data file that Excel software can treat.

4.3.2. Architecture of BPN for estimating cement take

This study focuses on the practical application of research related to the input variables that

were taken from the data collected in the grout-curtain construction phase. The input

variables which needed to be fed into the BPN program were the zone of the dam foundation,

the type of rock layers, the injection pressure, the depth of grout section, and . The output

variable was the of each grout section. Among these variables, both and are

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measured digital data and the others are represented by the classification codes. The codes of

these input variables are listed in Table 2.

The learning algorithm of BPN can be divided into the training phase and the testing phase.

Similar to the mean method and the linear regression method mentioned above, the learning

samples for these phases were also collected from the first half of the grout construction in

the four zones. The samples were randomly categorized into the training set and testing set in

the first phase of data processing. The initial learning rate, the initial inertial factor, and the

initial connection weight were set to be 5.0, 0.5 and 0.3 respectively. After a number of

different hidden layers were tried, one hidden layer was used in the BPN model employed

here. In the preliminary task, a network with different elements ranging from 2 to 8 in the

hidden layer was trained for the same number of 3,000 cycles. It was found that the value of

the average sum squared error ( ) would reach the minimum value of 0.11 when the

number of elements was equal to 5. Eq. (5) is used to calculate :

(5)

Where is the actual output value of processing element j in example p, is the target

output value of processing element j in example p, is the number of example, is the

number of processing element in the output layer.

So, a 5 5 1 network was set up as shown in Fig.11. The learning process was performed

with a Pentium 586 computer, which took about 110 min. of CPU time. Finally, BPN was

applied to the training set and produced the connection weights and biases. Then, the

architecture of BPN for estimating cement take was built (see Fig.11).

4.3.3. Performance of BPN for estimating cement take

In the learning process, a change in the with respect to the number of the learning

cycle is shown in Fig. 12. The results indicate that convergence was achieved for the training

and testing phase. That means the amounts of the samples were sufficient and there was some

degree of correlation between the input variables and the output variables. The performance

of BPN is shown in Fig. 13. The scatter of the target output values versus the actual

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output values were assessed using regression analysis and its degree of correlation of 0.82

was an acceptable one.

5. Estimated results of cement take

5.1. The mean method

The procedure requires that one, first, changes the in Eq. (3) to , and the to

to get Eq. (6).

(6)

Then, according to the different zones of the dam foundation, one replaces the value of

each grout section of the second half of the grout-curtain construction into Eq. (6) to get .

Next, multiply this value by the length of that grout section to get the cement take.

Calculate the sum of cement take for all grout sections to get the estimated gross cement take.

The estimated accuracy was defined as a ratio of estimated gross cement take to gross cement

take of grout-curtain construction. The estimated accuracy levels of the mean method are

68.0%, 74.0%, 71.0% and 76.0% for the left upper zone, the left lower zone, the right upper

zone, and the right lower zone respectively. The average estimated accuracy for the four

zones is 71.8% (see Table 3).

5.2. The linear regression method

Put the values of each grout section of each zone in the second half of the grout

construction into Eq. (4.a), Eq. (4.b), Eq. (4.c) and Eq. (4.d) to get values, and further to

get the estimated gross cement take. The estimated accuracy levels of the linear regression

method are 64.9%, 69.8%, 57.9% and 50.1% for the left upper zone, the left lower zone, the

right upper zone, and the right lower zone respectively. The average estimated accuracy for

the four zones is 59.8% (see Table 3).

5.3. The BPN method

According to the different zones of the dam foundation, use the data of grout sections in

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the second half of the grout-curtain construction as the input variables. Key the input

variables of each grout section into the BPN program with the architecture as shown in Fig.

10 to predict the value of that grout section and further to obtain its cement take. Repeat

the prediction process described above one by one until all of the grout sections have been

covered. Then, calculate the sum of cement take for all the grout sections to get the estimated

gross cement take. The estimated accuracy levels of BPN method are 78.2%, 81.4%, 71.9%

and 75.6% for the left upper zone, the left lower zone, the right upper zone, and the right

lower zone respectively. The average estimated accuracy for all the zones is 75.3% (see Table

3).

6. Grout efficiency

When the grout improvement is finished, one uses the changes of the distribution curve

and its middle value to observe the efficiency of the grout improvement. From the

distribution curves of plotted in Fig. 14 and Fig. 15, it is clear that after a sequence of

grout improvement, the values of the grout sections in each zone were reduced. Then,

after the fourth sequence of grout improvement was performed, 90% of the grout sections had

a permeability less than 4 , 3 , 9 , 8 , for the left upper zone, the

left lower zone, the right upper zone and the right lower zone, respectively. Furthermore, one

uses the changes of plotted in Fig. 16 to show the efficiency of the grout improvement

in the four zones. Along with the increase of the grout sequence, tends to become

smaller. For example, the obtained from the left upper zone decreased from 7.12 to

1.96, and the same value obtained from the right upper zone decreased from 9.36 to 2.52.

That is to say, the efficiency of the grout improvement in the Li-Yu-Tan dam’s foundation

was confirmed.

7. Summary and conclusions

This study adopted the mean method, the linear regression method, and the BPN method to

estimate the cement take needed for the grout improvement on the Li-Yu-Tan dam’s

foundation. The levels of average estimated accuracy on gross cement take are 71.8%, 59.8%

and 75.3% for the mean value method, the regression method and the BPN method

respectively. All of these levels were higher than the designed level of 43.4%.

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As to the mean method, because the mean of the samples analyzed was calculated with the

intention to draw on the strength of each to offset the weakness of the other, the mean can

supplement the low correlation between and . Therefore, the level of average estimated

accuracy obtained is 71.8%, higher than 59.8% from the linear regression method and slightly

lower than 75.3% from the BPN method. In comparison, the analytical process of the mean

method was simpler than BPN method, whereas its level of estimated accuracy is acceptable.

In addition, because BPN method takes into consideration the effects of factors on , such a

structure, which naturally increases the level of estimated accuracy. However, the

construction of the network, testing and data input process still tend to be more time-

consuming. Moreover, its estimation tool is a network program instead of just a regression

equation such as Eq. (3) and Eq. (4). It must be declared that the coefficients in Eq. (3), Eq.

(4) and Fig. 10 are only suitable to the Li-Yu-Tan dam. The three methods mentioned above

can be applied in other situations only when using the data collected from the completed parts

of the grouting to estimate the rest of the grout take at the same site.

The efficiency of the grout improvement in the studied case was confirmed by observing

the changes of the distribution curve and its middle value following each grout

sequence. This method is intelligible and can be applied in other situations.

Acknowledgements

Thanks are expressed to the National Science Council, Taiwan (NSC85-2211-E-216-004),

for research funding and to the Water Resources Agency, Ministry of Economic Affairs,

Taiwan, for the data collection.

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grouting on Li-Yu-Tan Dam, Water Resources Agency, Ministry of Economic Affairs,

Taiwan, Ch.4. (Chinese)

15

19. Tano, S. (1988), Foundation grouting in TENZAN dam. The Dam Digest, Japan Dam

Foundation Society, No. 520-4, 53-83. (Japanese)

20. Weaver, K. (1991), Dam foundation grouting, Library of Congress Catalog, Card No. 91-

34635, American Society of Civil Engineers.

21. Yamaguchi, Y. and Matsumoto, N. (1989), Permeability and Lugeon values of dam

foundation, Journal of Japan Society of Civil Engineering, 12(412), 51-60.

22. Yang, C.P. (2002), Modeling of shear behavior of saturated OTTAWA sands with back-

propagation networks, Journal of Chinese Institute of Civil and Hydraulic Engineering,

14(2), 175-180. (Chinese)

23. Yeh, I. C. (1997), Application of artificial neural network. Ju-lin Ltd., Taiwan, Ch.1~Ch.4.

(Chinese)

24. Zhu, J.H., Zaman, M.M. and Anderson, S.A. (1998), Modeling of shearing behavior of a

residual soil with recurrent neural network, Journal of Numerical and Analytical Methods

in Geomechanics, John Wiley & Sons Ltd., 22, 671-687.

16

Table 1Number of grout holes and grout sections at each zone of the grout-curtain of the Li-Yu-Tan dam.

Zone Sequence Number of grout holes

Total length of grout holes

Number of grout sections

Left upper zone

Primary 11 591 83Secondary 9 543 76Tertiary 20 1,158 163Quaternary 36 1,869 262Supplementary 11 540 76Inspection 14 739 104

Left lower zone

Primary 15 828 108Secondary 15 826 108Tertiary 30 1,655 216Quaternary 51 2,326 303Supplementary 3 166 22Inspection 14 772 101

Right upper zone

Primary 16 976 133Secondary 15 987 134Tertiary 30 1,949 265Quaternary 52 3,029 412Supplementary 28 1,422 194Inspection 20 1,193 162

Right lower zone

Primary 9 480 79Secondary 9 472 78Tertiary 17 906 150Quaternary 26 879 145Supplementary 4 221 37Inspection 14 749 124

Sum 469 25,276 3,532

Table 2The codes of input variables for BPN analysis.

Zone of dam foundation

Code Type of rock layer Code Injection pressure ()

Code Depth of grout section ( )

Code

Left upper zone

1 Clean sandstone 1 0~200 1 0~20 1

Right upper zone

2 Mudstone 2 201~400 2 21~40 2

Left lower zone

3 Alternation with sandstone and shale

3 401~600 3 41~60 3

Right lower zone

4 601~800 4 61~80 4

801~1,000 5 81~100 51,001~1,200 6

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Table 3Amount of gross cement take estimated by three methods at each zone for the second half of the grout-curtain construction.

Item ZoneLeft upper

zoneLeft lower

zoneRight upper

zoneRight lower

zoneSum for

four zones

Total length of grout holes ( )

(1)2,721 3,287 4,778 1,854 12,638

Gross cement take ()

(construction)(2)

296,126 228,512 670,533 262,223 1,457,393

Gross cement take ()

(1) 50( )(design)

(3)

136,050 164,350 238,900 92,700 631,900

Gross cement take ()

(mean method)(4)

201,366 169,099 476,079 199,290 1,045,832

Gross cement take ()

(regression method)(5)

192,169 159,477 388,266 131,362 871,273

Gross cement take ()

(BPN method)(6)

231,570 186,007 482,034 198,156 1,097,767

Estimatedaccuracy

levels

(%)45.9 71.9 35.6 35.3 43.4

(%)68.0 74.0 71.0 76.0 71.8

(%)64.9 69.8 57.9 50.1 59.8

(%)78.2 81.4 71.9 75.6 75.3

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(Kcy: Muddy sandstone, Siltstone and shale; l: gravelly terra rossa)

Fig. 1. The location and geological map of Li-Yu-Tan dam

Fig. 2. Longitudinal section of the Li-Yu-Tan dam indicating the rock layers in dam foundation (CS=clean sandstone, MS=mudstone, AL=alternation of sandstone and shale).

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Fig. 3. Characteristic Zones of the grout-curtain in the dam foundation.

Original ground surface Crest

Dam Grouting tunnel

Design excavation surface

Grouting tunnel

Grouting hole

Fig. 4. Longitudinal section of the Li-Yu-Tan dam indicating the extent of thegrout-curtain.

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Left upper zone

0100200300400500600

0-200 201-400 401-600 601-800 801-1000Lg (kgf/m)

No.of

secti

ons i

n grou

t hole

Fig. 5. Histogram of in the left upper zone.

Right upper zone

0

200

400

600

800

1000

0-200 201-400 401-600 601-800 801-1000Lg (kgf/m)

No.of

secti

ons i

n grou

t hole

Fig. 6. Histogram of in the right upper zone.

40

60

80

100

120

140

160

0 2 4 6 8 10 12 14 16

Luav(Lugeon)

Lgav

(kgf

/m)

y=8.0569x+36.273 R2=0.9486 Right lower

zone

Right upper zone

Left upper zone

Left lower zone

Fig. 7. Linear regression of over in the left and right zones of the abutments of the dam.

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Fig. 8. Relationship between and in the left upper zone.

Fig. 9. Relationship between and in the right upper zone.

22

Fig. 10. Typical BPN architecture.

Weights and Biasesitem node 0

weightnode 1weight

node 2weight

Node3 weight

node 4 weight

node 10 weight

biases

node 5 0.34 0.59 0.92 -0.52 1.57 -0.79 0.95node 6 0.18 -0.81 0.50 -1.09 1.74 -0.63 0.04node 7 0.23 -1.24 -0.71 -0.14 1.48 -0.35 0.79node 8 -0.22 2.01 -1.24 0.45 1.74 -0.36 0.84node 9 -0.17 2.32 0.63 -0.16 1.36 -0.36 0.36node 10 0.50

Fig. 11. Architecture of BPN for estimating , indicating the connection weights, and the biases.

23

Training phase

Testing phase

Fig. 12. Convergence characteristics of BPN for estimating cement take.

Fig. 13. Comparison of actual and target values.

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Fig. 14. The changes of distribution curve after each grout sequence (left upper zone).

Fig.15. The changes of distribution curve after each grout sequence (right upper zone).

Fig.16. Grout efficiency in the four zones.

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26