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CHAPTER 6 CONCLUSION AND RECOMQMENDATION . 1. In general, actually the experimental semivariogram Created the bad experimental semivariogram showed that some of those parameters were hardly any relationship. It might because of the wide of study area while the data were limited. 2. Semivariogram model could still be forced in this case. Because the estimation block kriging which was produced by averaging a kriging from whole study However, it was more conservative and definitely not always right. 3. Data from other project were very useful for increasing the reliability of the estimation. Unfortunately, sometimes data available is much too far from the study area. So, it make the bad pattern of the experimental semivariogram. It will have implication to the kriging estimation in which less reliability might be occurred. SO, more focus analyses have to be done before using the far data even in the same geological area. 4. Data processing, produce in the laboratory, more or less affected the result of semivariogram and kriging. The limited number of data from laboratory can cause the consistency of the data thus data could be improper to use. 5. Result from the single layer settlement model shown inconsistencies output. ln In some place, the total settlement produced almost half of the total settlement in the multi layer settlement model while in the other place produced almost the same result. this inconsistencies result might be because the uncertainty that was occurred during the calculation process. For instance the determination of the boundary level for weak soil layer which was selected in the border/transition between Clay layer and Sandy Clay layer.

CHAPTER 6

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CHAPTER 6CONCLUSION AND RECOMQMENDATION .

1. In general, actually the experimental semivariogram Created the badexperimental semivariogram showed that some of those parameters were hardly anyrelationship. It might because of the wide of study area while the data were limited.

2. Semivariogram model could still be forced in this case. Because the estimationblock kriging which was produced by averaging a kriging from whole study

However, it was more conservative and definitely not always right.

3. Data from other project were very useful for increasing the reliability of the estimation.Unfortunately, sometimes data available is much too far from the study area. So, it

make the bad pattern of the experimental semivariogram. It will have implication to thekriging estimation in which less reliability might be occurred. SO, more focus analyses

have to be done before using the far data even in the same geological area.

4. Data processing, produce in the laboratory, more or less affected the result of semivariogramand kriging. The limited number of data from laboratory can cause the consistency

of the data thus data could be improper to use.

5. Result from the single layer settlement model shown inconsistencies output. lnIn some place, the total settlement produced almost half of the total settlement in the multi layersettlement model while in the other place produced almost the same result. this inconsistencies

result might be because the uncertainty that was occurred during the calculation process.For instance the determination of the boundary level for weak soil layer

which was selected in the border/transition between Clay layer and Sandy Clay layer.It was not totally correct. Another uncertainty came in the process when we were averagingthe data in vertical direction. Even thought there was a theory that proofs the method, we

could not totally agree that the soil layer in the top layer of sub grade profile in that area ishomogenous. We (again) just assumed it to be homogenous profile. So I suggested several

other options that may give a good approach to the settlement model.

a. using a multi layer settlement model with a modification in determining the soilproperties data in each soil layer. This method could give the better soil properties estimation

for each layer since it used the geostatistical approach. In the same time, theprocess wlll not create a "new” uncertainty because the determination of the boundary

level of each layer has been standardized. But be aware of the availability of theIf a number of the data set rs too few, the “new” uncertainty will easily appear.

b. Separate the layer into three layer model. This option rs just applicable for this project.We noticed that there are three big group of soil layer in this case. Clay layer, SandyClay layer and Stiff Sand. We can improve the process by taking a sandy clay layerinto the “moderate layer” instead of adjusting it into stiff layer. We could define thesoil properties for each layer by using a geostatistical as well, but the different is we

separate the data set into three groups. With this approach, the uncertainty of the layer, which give the most influence in settlement calculation, will be reduced.

6. The uncertainty could not be reduced easily by using a geostatistical method, especially for this study

case. Even though the the bandwidth of settlement prediction is always lower for single

Page 2: CHAPTER 6

layer model, there were other uncertainties to produce that result. It mean that the geostatisticalmethod actually could reduce the uncertainty, but in the same time it still

remains the other uncertainty. To solve these problems, the data which we analyzedshould be larger and the study area should be narrower. Another way is to decrease the

uncertainty by calibrating the analyses with the actual result in the field. So, the analyseswill be more accurate.

6.2 Recommendation1. Continue research is highly important to get the better view of properness of applied

geostatistical for geotechnical method. It is also a good credit, if the comparison refers tothe real result in the field.

2. Study is better to use the geotechnical analysis which provides more continues data withless variability. For instance, CPT interpretation analysis. It is recommended to do the next

research in this field due to the high continues of the data set.