Displacements Prediction in Double-Arch Dam Rock Abutment Using SPSS Software Based on Extensometer Readings Case study: Karun 4 Concrete Dam, Iran

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1 Research Journal of Applied Sciences, Engineering and Technology 4(22): , 2012 ISSN: Maxwell Scientific Organization, 2012 Submitted: March 03, 2012 Accepted: March 24, 2012 Published: November 15, 2012 Displacements Prediction in Double-Arch Dam Rock Abutment Using SPSS Software Based on Extensometer Readings Case study: Karun 4 Concrete Dam, Iran 1 Hadi kamali Bandpey, 1 Kaveh Ahangari and 2 Mirsaeid Hosseini Shirvani 1 Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 3 Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran Abstract: In this study we present a method for Displacements Prediction in Double-Arch Dam Rock Abutment Using SPSS Software Based on Extensometer Readings. Displacement in dams is the most tangible and important parameter which could be crucial in their safety. Different elevation displacements are yielded by various loadings and the thrust force imposed on foundation and abutment. Most concrete dams are constructed on stone foundations. Displacements in foundation and abutment are measured by extensometers. Karun 4 Concrete dam is designed with 11 galleries, from elevation 1016 to 802 m, in the order from top elevation (dam crest elevation 1032) to the bottom elevation (dam foundation elevation 806) within the dam body. As a whole, 19 extensometers in the left bank, 17 in the right, and one more in the middle are implemented in the dam. Karun 4 dam has already been impounded with water up to the elevation Displacements in Karun 4 are recorded by extensometers whence water was leveled in 7 elevations , , , , , In this study, using SPSS we have tried to predict the displacements for a situation in which water will be elevated to the elevations 1013, 1023, 1032 in the future for elevations which are equipped with anchor. The most predicted displacement pertaining to the left bank when water was leveled to the elevation 1013, was 3.65 mms by R 2 = for the implemented anchor. Proceeding further, as water is leveled to the elevations 1023 and 1033, the most predicted displacement respectively would be 4.31 and 5.66 by R 2 = ; and is related to the anchor implemented in the elevation The most predicted displacement for the right bank is , and mms by R 2 = for the elevation m. Keywords: Displacement, extensometer, Karun 4 concrete dam, SPSS INTRODUCTION Displacement in dams is the most tangible parameter which could be easily measured. Displacement involves all directions, but the most critical displacements occur in the horizontal plate. Concrete dams are needed to have equipment in order to measure displacements including relative motion of the interior points of the dam and the dam motion relative to a stationary exterior point. In the current article, to achieve the amount of displacement, extensometer instrumentation data has been utilized in the contact surface between the dam body and the rock mass. Accomplished researches in this field including: Aleksandrovskaya and Urakhchin (1974) predict the displacements of concrete gravity dams on rock foundations and the horizontal displacements of high concrete dams during operation in which most effective analysis is based on use of the so-called method of influence coefficients. The maximum and minimum values of displacements obtained by the monograms can serve this purpose (Aleksandrovskaya and Urakhchin, 1974). Allen and Cluff (2000) considered the effect of active faults in dam foundations (Allen and Cluff, 2000). Wieland et al. (2003) Considered earthquake resilience in large concrete dams (Wieland et al., 2003). Mata and Portela (2007) used neural network for predicting Radial displacement by using pendulum readings in Concrete dam (Mata and Portela, 2007), but in this paper Extensometer readings are used for predicting mentioned displacement by statistical study (SPSS: Statistical Package for the Social Sciences). Wieland et al. (2008b) Studied in potentially active faults in the foundations of large dam and its effect on the selection of sites, dam type and design aspects of dams to resist fault movements (Wieland et al., 2008a, b) predict the relative crest settlement of Concrete-Faced Rock fill Dams (CFRDs) Corresponding Author: Hadi kamali Bandpey, Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 4607

2 sited on marl, marly limestone, cretaceous to Miocene limestone (Fig.1) (Mahab-Ghodss Consulting Engineering Company, 2010a). Fig. 1: A view of Karun 4 dam (mahab-ghodss consulting engineering company) analyzed using an artificial neural network model. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding (Kim and Kim, 2008). Mata (2011) interprets concrete dam behavior with artificial neural network and multiple linear regression models. The results of this study show that NN models can be a powerful tool to be included in assessments of existing concrete dam behavior (Mata, 2011). CASE STUDY Karun 4 dam construction site: Karun 4 dam is located in plaited chains of Zagros Mountain which runs along north-west to south-east. The chief bed of the reservoir is Technical considerations on the extensometers installed in the body of Karun 4 dam: Karun 4 dam is designed with 11 galleries respectively from DG1 (Drainage Gallery) elevation 1016 to DG11 elevation 807 respectively from the top elevation (dam crest elevation 1032) to the bottom elevation (dam foundation elevation 806) within the body of the dam. Galleries elevations and the elevation whose extensometer and anchors are installed, along with their readings (displacement in the same elevation in which the anchor is installed) in left and right abutments are presented in Table 1 and 2. In the right and the left bank, respectively 2 and 4 instruments were refrained as a result of overdue installation and lack of ideal readings and are not applied in predicting (Table 1 and 2 with asterisks). In respective readings, positive displacement depicts the ascent of the rod or the subsidence of the body of dam, and negative displacement indicates downward movement of the rod. Extensometers are installed in dam banks (the contact surface between dam body and rock mass) with determined dip and azimuth. In Karun 4 dam 19 Extensometers are implemented in the left, 17 in the right bank and one in the middle. Figure 2 illustrates the extensometers layout in the concrete double-arch dam, Karun 4. Fig. 2: A view of the extensometers installed in foundation and abutment of Karun 4 dam 4608

3 Table 1: Information on the extensometers installed in the right bank (Karun 4) (Mahab-Ghodss Consulting Engineering Company, 2010b) Displacement readings in 7 water elevation Instrument coding and Anchors start point elevation Rodlength elevation EX30-FBI * EX30-FB * EX / EX / EX / EX / EX / EX / EX / EX / EX / EX / EX / EX / EX / EX /

4 Table 1: (Continue) Displacement readings in 7 water elevation Instrument coding and Anchors start point elevation Rodlength elevation Ex / Table 2: Information on the extensometers installed on the left bank (Karun 4) Displacement readings in 7 water elevation Instrument coding and Anchors start point elevation Rodlength elevation EX / * EX / * EX / * EX / EX / EX / * EX / EX / EX / EX-LDGI left EX / EX / EX /

5 Table 2: (Continue) Displacement readings in 7 water elevation Instrument coding and Anchors start point elevation Rodlength elevation EX / EX / EX / EX / EX / EX / EX / EX / Table 3: Results of displacement prediction in the right bank using cubic, linear, and quadratic models Anchor points ( a1-a60 ) Water elevvation a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a Prediction Prediction Prediction R 2 (linear) Prediction Prediction Prediction R 2 (Quadratic) Prediction Prediction Prediction R 2 (Cubic) a18 a19 a20 a21 a22 a23 a24 a25 a26 a27 a28 a29 a30 a31 a32 a33 a

6 Table 3: (Continue) Anchor points ( a1-a60 ) Water elevvation a18 a19 a20 a21 a22 a23 a24 a25 a26 a27 a28 a29 a30 a31 a32 a33 a34 Prediction Prediction Prediction R 2 (linear) Prediction Prediction Prediction R 2 (Quadratic) Prediction Prediction Prediction R 2 (Cubic) a35 a36 a37 a38 a39 a40 a41 a42 a43 a44 a45 a46 a47 a48 a49 a50 a Prediction Prediction Prediction R 2 (linear) Prediction Prediction Prediction R 2 (Quadratic) Prediction Prediction Prediction R 2 (Cubic) a52 a53 a54 a5500 a56 a57 a58 a59 a Prediction Prediction Prediction R 2 ( linear ) Prediction Prediction Prediction R 2 (Quadratic) Prediction Prediction Prediction R 2 ( Cubic ) Table 4: Model summary and parameter estimates (using SPSS software), Dependent variable: a32 Model summary Parameter estimates Equation R 2 F df1 df2 Sig. Constant b1 b2 b3 Linear Quadratic E-5 Cubic E-7 The independent variable is water elevation Table 5: Model summary and parameter estimates (by using of SPSS software), Dependent variable: a56 Model summary Parameter estimates Equation R 2 F df1 df2 Sig. Constant b1 b2 b3 Linear Quadratic E-5 Cubic E-8 The independent variable is water elevation 4612

7 Table 6: Results of displacement prediction in the right bank using cubic, linear, and quadratic equations Anchor points (a1- a68 ) Water elevvation a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a Prediction Prediction Prediction R 2 (linear) Prediction Prediction Prediction R 2 (Quadratic) Prediction Prediction Prediction R 2 (Cubic) a18 a19 a20 a21 a22 a23 a24 a25 a26 a27 a28 a29 a30 a31 a32 a33 a Prediction Prediction Prediction R 2 (linear) Prediction Prediction Prediction R 2 (Quadratic) Prediction Prediction Prediction R 2 (Cubic) a35 a36 a37 a38 a39 a40 a41 a42 a43 a44 a45 a46 a47 a48 a49 a50 a Prediction Prediction Prediction R 2 (linear) Prediction Prediction Prediction R 2 (Quadratic) Prediction Prediction Prediction R 2 (Cubic) a52 a53 a54 a55 a56 a57 a58 a59 a60 a61 a62 a63 a64 a65 a66 a67 a Prediction Prediction Prediction R 2 (linear) Prediction

8 Table 6: (Continue) Anchor points (a1- a68 ) Water elevvation a52 a53 a54 a55 a56 a57 a58 a59 a60 a61 a62 a63 a64 a65 a66 a67 a68 Prediction Prediction R 2 (Quadratic) Prediction Prediction Prediction R 2 (Cubic) Table 7: Model summary and parameter estimates (using SPSS software), Dependent variable: a56 Model summary Parameter estimates Equation R 2 F df1 df2 Sig. Constant b1 b2 b3 Linear Quadratic E-5 Cubic E-8 The independent variable is water elevation DISPLACEMENT PREDICTION Using the extensometer readings in 7 elevations distancing 10 m from each other, a hidden relation between pieces of data would be unveiled. The interesting point is that the recorded displacements in right-left banks except a series of anchors which are poorly installed or those having improper readings, all prove to have high rate of regression coefficient. Right bank: Taking all the correlation coefficients in the right bank, noting Table 3 among the extensometer anchors, the highest correlation coefficient in anchors (Table 4) a32 and (Table 5) a56 are respectively related to the anchors first installed at a depth of 54 m from extensometers EX /3 and EX /3. Figure 3, 4, 5 and 6 shown results our methods. Considering Table 4, results for the right bank are as follows: Linear: (With correlation coefficient 0.973) Y = 0.o68(x) Quadratic: (With correlation coefficient 0.999) Y= 3.496*10-5 * (x 2 ) Cubic: (With correlation coefficient 0.999) Y = 2.227*10-7 * (x 3 ) (x) Resulted relations using Table 5 are as follows: Linear: (With correlation coefficient 0.988) Y = 0.022(x) Quadratic: (With correlation coefficient 0.997) Y = 1.141*10-5 * (x 2 ) Cubic: (With correlation coefficient 0.997) Y = 4.322*10-8 * (x 3 ) (x) Left bank: Taking all the correlation coefficients in the left bank and bearing in mind Table 6 among extensometer anchors, the highest correlation coefficient belongs to the anchor a56 (Table 7) pertaining to the first anchor installed at the depth of 54 m EX /3. Resulted relations using Table 7 are as follows: Linear: (With correlation coefficient 0.984) Y = 0.027(x) Quadratic: (With correlation coefficient 0.993) Y = 1.371*10-5 * (x 2 ) Cubic: (With correlation coefficient 0.997) Y = 3.185*10-8 * (x 3 ) (x) Displacement (mm) Observed Linear Quadratic Cubic Water elevation (m) Fig. 3: Regression diagram resulted by anchor 32a data, using SPSS 4614

9 Displacement (mm) Observed Linear Quadratic Cubic Water elevation (m) Fig. 4: Regression diagram resulted by anchor 56a data, using SPSS Displacement (mm) Observed Linear Quadratic Cubic Water elevation (m) Fig. 5: Regression diagram resulted from anchor a56 using SPSS software Water elevation: 1013 m Water elevation: 1023 m Water elevation: 1032 m Fig. 6: Predicted displacement diagram for the right bank Water elevation: 1032 m Water elevation: 1032 m Water elevation: 1032 m Displacement (mm) Fig. 7: Predicted displacement diagram for the left bank CONCLUSION With the observation of the high rate of the cubic correlation coefficient, the predicted displacement in the right bank and the left bank are as follows: Among the extensometers installed in the right bank, extensometers with instrument coding EX /2 (a13-a16), EX /2 (a25-a28), EX /2 (a49-a52), and EX /3 (a57-a60) are of no efficient correlation coefficient and could be refrained as 4615

10 a matter of poor installation or improper reading; but the rest are of high rate of correlation coefficient. Therefore, displacements using 44 anchors with a high correlation coefficient are due to occur. Among the 68 extensometer installed in the left flank, only the anchor installed in elevation pertaining to the extensometer with the coding EX /2 has a low correlation coefficient and the rest have a high correlation coefficient. We could conclude that in comparison to the right bank, the left bank benefits from a higher correlation coefficient. So in the case of the left bank, displacement is predicted using 67 anchors with a high correlation coefficient: For instance, taking Fig. 7 into account the most predicted displacement for the left bank when water is leveled at elevation 1013 equals 3.65 mms with R 2 = referring to the anchor installed in elevation Consecutively, when water is leveled at elevations 1023 and 1033, the most predicted displacements are in the order 4.31 and 5.66 mms with R 2 = and refer to the anchor installed in elevation REFERENCES Aleksandrovskaya, É.K. and V.P. Urakhchin, Prediction of the displacements of concrete gravity dams on rock foundations. Power Technol. Eng., 8(5): Allen, C.R. and L.S. Cluff, Active faults in dam foundations: An update. Proceeding of 12 th World Conference on Earthquake Engineering, Auckland, New Zealand. Kim, Y.S. and B.T. Kim, Prediction of relative crest settlement of concrete-faced rock fill dams analyzed using an artificial neural network model. Comput. Geotech., 35: Mahab-Ghodss Consulting Engineering Company, 2010a. Soil mechanics and foundation & abutment geology study report. Karun 4 Dam, Phase 3. Mahab-Ghodss Consulting Engineering Company, 2010b. Instrumentation and behaviography of Karun 4 dam and power station. Appendix No. 2-4, Impounding Report No. 6: The Map of Enlongatometers Layout of the Dam Body and the Results of the Enlongatometers of the Dam Body. Mata, J., Interpretation of concrete dam behavior with artificial neural network and multiple linear regression models. Eng. Struct., 33: Mata, J. and E.T.A. Portela, Application of neural networks to dam safety control. 5the International Conference on Dam Engineering, February, Congress Centre of Line Lisbon, Portugal. Wieland, M., R.P. Brenner, and P. Sommer, Earthquake resilience of large concrete dams: Damage, repair and strengthening concepts. Trans. 21 st International Congress on Large Dams, Montreal, Q83-R10, 3, pp: Wieland, M., R.P. Brenner and A. Bozovic, 2008a. Potentially active faults in the foundations of large dams part I: Vulnerability of dams TO seismic movements in dam foundation. Special Session S13, Proceeding of 14th World Conference on Earthquake Engineering, Beijing, China, October Wieland, M., R.P. Brenner and A. Bozovic, 2008b. Potentially active faults in the foundations of large dams Part 2: Design aspects of dams to resist fault movements. Special Session S13, Proceeding of 14th World Conference on Earthquake Engineering, Beijing, China, October