Analysis of Engineering Properties and Bearing Capacity of the Clay in Nanning Basin Area I

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1 Analysis of Engineering Properties and Bearing Capacity of the Clay in Nanning Basin Area I Kong Xianggang, School of Civil and Architectural Engineering, Guangxi University, Nanning, China Guangxi Polytechnic of Construction, Nanning, China Zhang Xingui * School of Civil and Architectural Engineering, Guangxi University, Nanning, China *Corresponding author( xgzhangchn@foxmall.com) i Zhaoyang, School of Civil and Architectural Engineering, Guangxi University, Nanning, China Guangxi Polytechnic of Construction, Nanning, China u Haili School of Civil and Architectural Engineering, Guangxi University, Nanning, China Abstract The clay in Nanning Basin Area I is the natural bearing stratum for the construction of ordinary buildings and subways in this area. Thus, the discussion of its physical properties and bearing capacity is of great importance for engineering construction. Utilizing the rock and soil testing materials that have been collected through years by Nanning Exploration and Survey Institute and the Designing Institute of Guangxi University, this study has carried out statistical analysis on the physical and mechanics parameters of the bearing capacity of the clay in Naning Basin Area I; provided standard penetration, modulus of compression, pore space ratio, statistic of liquidity index, and variation range; studied the correlation among these indicators and assessed the reliability of these data. In addition, this study also carefully analyzed the correlation among the bearing capacity, liquidity index and pore space ratio of the clay in this area and provided the empirical formula for the calculation of the bearing capacity of clay in Area I. Key words: Clay, Bearing capacity, Correlation analysis. INTRODUCTION Nanning, which will be developed into a metropolis and regional international city, is China s open window to the Association of Southeast Asian Nations. With rapid development of urban construction, utilization of underground space becomes inevitable. The widespread underground clay is the foundation of most buildings and the environment for subway construction and other underground constructions. Therefore, to assess the mechanical properties and bearing capacity index of clay in a reliable, convenient and economic way is quite important for engineering construction. Clay is a plastic soil. With external load on it, clay can become any other shape without crevice and maintain the shape when the external load is removed. So far, there is few reports on the results of engineering property research of Nanning Basin clay. In 99, Junxun iang et al. studied and concluded the engineering geological properties of tertiary system clay rock of lacustrine facies and its slope elurium in Nanning through analysis and statistics of 93 clay samples (Qiaoxin T., Tianjun M., Heng W., 99). In 993, Tianjun M. et al. focused their study on the hardness of some soil layers of first and second terraces (Tianjun M., Heng W., 993). They strove to build the empirical formula for local soil hardness and other properties so that the main geological problems of environmental engineering in urban Nanning can be analyzed and countermeasures can be found. In 999, Yuankun aistudied the engineering geological properties of deposited clayey soil in the upper part of Wanggao Fm of pleistocene series in Nanning (Yuankun., 999). Through the statistical analysis on the results of indoor experiments of 57 undisturbed samples, he concluded the regression equations of liquid limit and plastic limit, water content and pore space ratio for clay and silty clay. With abundant collection of historical geologic maps and geotechnical engineering investigation materials of Nanning Basin, this study analyzed the correlation of parameters of the clay in Naning Basin Area I with focuses on foundation soil bearing capacity, standard penetration, pore space ratio, liquidity index and compression modulus using Nanning Engineering Geology Information System built by Nannning Exploration and Survey Institute and Guangxi University. Through the study, the empirical formula of foundation soil 8

2 bearing capacity was built based on the statistical data, which provides theoretical basis for the analysis and assessment of engineering properties of the clay in this area and offers a good guidance to the engineering planning and construction of cities.. ENGINEERING GEOOGICA AREAS OF NANNING BASIN ocated in the Guanxi Zhuang Autonomous Region, Nanning Basin is a spindle shape in northeast-southwest direction. The center of the basin is flat, surrounded by low mountains and hills. The Yongjiang river zigzags its way from the west to the eat of Nanning Basin. On both sides of the river there are terraces from class I to class VI. According to landform, geological origin, age of deposition, structure of rock and soil mass, engineering geological properties, the basin is divided into five areas including central erosion built terrace engineering geological area (Area I), east hill engineering geological area (Area II), southeast karst monadnock valley engineering geological area ( Area III), southwest hill engineering geological area (Area IV), and north tectonic erosion low mountains and hills engineering geological area ( Area V). 3. FORMATION AND DISTRIBUTION OF THE CAY IN AREA I The clay in Nanning Basin Area I is mostly hard, hard-plastic and plastic. Its formation and distribution have the following features: () Alluvium and proluvium: mainly refer to the alluvium and proluvium of Yongjiang river which cover the whole low-order terraces of Yongjiang river and part of terraces III and IV. The clay of low-order terraces of Yongjiang river is mostly hard to hard-plastic, while the clay of the upper part of some regional areas is plastic. The clay of high-order terraces is basically hard-plastic to plastic, and the lower part is plastic. () Slope deposit: mainly distributed on the contacting zone of high-order terraces and zonally distributed on low-order terraces alongside the slope toe of high-order terraces. Its thickness varies greatly. Apart from clay, it also contents round gravels and fine sands. (3) Eluvial slope deposit: mainly distributed on denudated edge zones of high-order terraces, hill area, karst area and low mountain and hill area. It is mainly the intensely weathered elurium of tertiary system mudstone and silty mudstone. 4. PHYSICA MECHANICA INDICATORS OF CAY The upper part of clay is mostly hard-plastic to hard with its thickness varies between -m. The upper hard-plastic clay gradually turns into hard-plastic silty clay with depth. Due to laterization, the hard-plastic clay mass of terraces I and II has porphyritic texture. The porphyritic texture is especially obvious in the clay of terrace II in that the clay is more deeply laterized because of its older age (Cetin H.,4; ancellotfa R,Preziosi.,997). Hard-plastic clay has good bearing capacity, so it is an ideal natural bearing stratum with shallow foundation (Xia J,Huang G,Yan S.,6).Below hard-plastic clay is plastic clay which is mostly silty clay. It has medium compressibility and its thickness is usually -3cm, indicating that it s a good bearing stratum (Nakaoka K, Yamamotoa S, Hasegawa H, et al. 4; Chang C S, Hicher P Y, Yin Z Y, et al., 9). The statistical mean values of the physical mechanical indicators of the clay in Nanning Basin Area I are shown in Table. Table. Mean values of the physical mechanical indexes of the clay in Nanning Basin Area I Water Unit iquid Plastic Plasticity iquidity Compression Soil indicators penetration content-w weight-r limit-w limit-wp index-ip index-i modulus-es Types N % kn/m 3 % % % MPa Hard to hard-plastic clay Plastic clay Soft clay Hard to hard-plastic silty clay Plastic silty clay Soft to flowing silty clay Conglomeratic clay ANAYSIS OF THE CORREATION OF CAY PARAMETERS According to geological engineering standards in china, there are two ways to determine the bearing capacity of clay: one is using pore space ratio e and liquidity index I to look up the table; another is using SPT blow count N to look up the table. Therefore, it is of great importance to build relationship among SPT blow 8

3 count, liquidity index, pore space ratio, and study their correlation. 5. Analysis of the Correlation between SPT Blow Count and iquidity Index On the basis of original experimental data, the confidence level.5 was adopted; the data with notable errors were removed; and regression analysis of SPT blow count and liquidity index was carried out. inear, logarithmic and polynomial regression curves were selected be compared to each other, as shown in Figure to Figure 3. In addition, the fitting result can be seen in Table.. iquidity index y = -.695x R = Figure. inear regression curve of SPT blow count and liquidity index of clay in Area I. iquidity index y = ln(x) R = Figure. ogarithmic regression curve of SPT blow count and liquidity index of clay in Area I. iquidity index y =.6x -.968x +.56 R = Figure 3. Polynomial regression curve of SPT blow count and liquidity index of clay in Area I 8

4 Table. Regression equation of SPT blow count and liquidity index of clay in Area I Function Regression equation Determination coefficient linear I -.695N R.84 logarithmic I.6335 In( N).6967 R Polynomial I N N R Note: N is SPT blow count; I is liquidity index; the regression formulas in the table are obtained after removal of abnormal values. It can be known from Figure to Figure 3 that for the clay in Area I, the SPT blow count is in negative correlation with liquidity index, which is consistent with the actual engineering condition. The higher the soil liquidity index, the softer the soil, and the lower the SPT blow count. According to Table, all the determination coefficients of the three regression equations are above.8, showing good correlation. Among them, the polynomial regression curve has the highest fitting degree. iquidity index can be used to judge the status of clay. The polynomial regression curve (Figure 3) with better fitting result can help to further analyze the relationship between clay status and SPT blow count. The analyzing result is demonstrated in Table 3. During outdoor recording, clay status can be estimated via SPT blow count according to the statistical relationship shown in Table 3. Table 3. Relation of SPT blow count and status of clay in Area I Status iquidity index-i -N Flowing I. N 3 Soft.75 I. 3 N 5 Plastic.5 I.75 5 N Hard-plastic I.5 N 4 Hard I 4 N 5. Analysis of the Correlation between SPT blow count and Compression Modulus SPT blow count is the indicator for hardness, usually used to assess the properties and bearing capacity of soil foundation. It can be easily acquired on the scene. Compression modulus is the indicator for compressibility related to deformation. It is usually used to calculate settlement. To study the relationship between the two, correlation between hardness indicator and compressibility indicator was built; on the basis of original experimental data, the confidence level.5 was adopted and the data with big errors were removed. The fitted regression curves of SPT blow count and compression modulus are shown in Figure 4 to Figure 6, and the correlation analysis result can be seen in Table 4. c ompression modulu y =.94e.4x R = Figure 4. Exponential regression curve of SPT blow count and compression modulus of clay in Area I 83

5 Figure 5. inear regression curve of SPT blow count and compression modulus of clay in Area I c ompression modulu c ompression modulu y =.84589x R = y = 7.54ln(x) R =.7648 SPT blow c ounts Figure 6. ogarithmic regression curve of SPT blow count and compression modulus of clay in Area I Table 4. Regression equation of SPT blow count and compression modulus of clay in Area I Function Regression equation Determination coefficient Exponential Es.4 N.94e R.878 inear E.84589N S R ogarithmic E 7.54 In( N) R.7648 S Polynomial E N N R.8557 S Power function ES N R.848 Note: N is SPT blow count; E S is compression modulus; the regression formulas in the table are obtained after removal of abnormal values. Table 4 indicates that for the clay in Area I, the SPT blow count is in positive correlation with compression modulus, showing that the larger the SPT blow count, the lager the compression modulus, which is consistent with actual engineering condition. The determination coefficients of the five regression equations of SPT blow count and compression modulus are bigger than.7, showing that the correlation between the two is good. Among them, the determination coefficient of exponential regression equation is the biggest, which means the 84

6 highest fitting degree. Thus, according to the exponential relationship between the SPT blow count and compression modulus of the clay in Area I, preliminary estimation of soil compression modulus can be enables through on-site standard penetration test. 5.3 Two-factor Correlation Analysis of SPT Blow Count, iquidity Index and Pore Space Ratio () Test of Data Correlation The most commonly used Pearson correlation coefficient r is employed to test the linear correlation between variables. The formula is follow: r X X Y Y ( X X ) ( Y Y) i i (4-) Where r is Pearson correlation coefficient; X is the mean value of X; and Y is the mean value of Y. First, correlation of the three variables, STP blow count, liquidity index and pore space ratio, is tested respectively. The result is shown in Figure 5. Table 5. Correlation of SPT blow count, liquidity index and pore space rate of clay in Area I SPT blow count iquidity index Pore space ratio Pearson Correlation -.897** -.56** SPT blow count Significance (two-sided).. N Pearson Correlation -.897**.685** iquidity index Significance (two-sided).. N Pore space ratio Pearson Correlation -.56**.685** Significance (two-sided).. N Note: when the confidence level (two-sided test) is.5, the correlation is significant. Table 5 shows that the significance (two-sided) is all less than.5, indicating that the correlation is significant. The Pearson correlation coefficient between SPT blow count and liquidity index is -.897, a negative correlation, showing extremely strong correlation; the Pearson correlation coefficient between SPT blow count and pore space ratio is -.56, a negative correlation too, showing medium correlation; the Pearson correlation coefficient between pore space ratio and liquidity index is.685, a positive correlation, showing strong correlation. () Test of Fit Goodness of Regression Equations The determination coefficient and regression standard deviation are adopted to test the fit goodness of regression equations, namely the fitting degree of regression and predicted values. The test result is shown in Table 6. Table 6. Model recapitulation Model R R Error of Change statistics Adjusted R Durbin-Wats standard R on(u) estimation change F change df df Significanc e F change Note: predictive variable: (constant), liquidity index, pore space ratio; dependent variable: SPT blow count R is the correlation coefficient that indicates the correlation between independent variables and dependent variable; R is determination coefficient that shows the fit goodness between linear fitting equation and original data; and real data. R is adjusted determination coefficient that shows the corresponding degree of linear equation and Table 6 shows that the regression adjusted determination coefficient R of SPT blow count and pore space ratio is.87, which means that there are only a small amount of unexplained variables in the data and the fit goodness of equation is good. Durbin-Watson (DW) is autocorrelation coefficient, used to test data autocorrelation. The range of DW value is obtained by using the amount of explanatory variables of regression model K, sample size T and test level to look up the Table of Test Critical Value DW Distribution. Through this method the range of DW value (.76,.4) was obtained. The DW value.836 is within this range, so autocorrelation does not exist and the regression is not spurious regression. (3)Test of Regression Equation Significance The t test is adopted to test the significance of regression coefficients, and the result is shown in Table 7. 85

7 Table 7. Coefficient table Unstandardized coefficient Collinearity coefficient Model t Significance B Beta Allowable VIF error (Constant) Pore space ratio iquidity index Note: dependent variable: SPT blow count First, it is assumed that the two independent variables, pore space ratio and liquidity index, have no significant influence on SPT blow count. From Table 7 it can be seen that the significance of pore space ratio and liquidity index is.49 and. respectively, all smaller than.5. This means the two independent variables, pore space ratio and liquidity index, have significant influence on SPT blow count. The variance inflation factor VIF is less than, meaning that there exists no multicollinearity between the two independent variables, pore space ratio and liquidity index. The standardized coefficient Beta is often used to measure the importance of independent variables of test model. To ensure the accuracy of result, the dimension of all the variables has been unified to reduce errors. This has changed the actual situation, so it s not suitable for predictive analysis. Therefore, unstandardized coefficient B is adopted. Through the unstandardized coefficient it can be known that the relationship among pore space ratio e, liquidity index I and SPT blow account N is: N=.88e 3.985I (4)Analysis of Residual Minimum value Table 8. Statistical data of residual Maximum value(x) Mean value deviation Number Predicted value Residual predicted value residual Note: dependent variable: SPT blow count Residual is the part of dependent variable that can not be explained by independent variable. Table 8 demonstrates that the mean value of standard residual is, and the deviation is.993, close to., which means the standard residual is nearly in standard normal distribution, and thus the predicted value of SPT blow count is an unbiased estimation. 6. ANAYSIS OF THE CORREATION AMONG BEARING CAPACITY, IQUIDITY INDEX AND PORE SPACE RATIO In engineering projects, the bearing capacity of clay can be measured through pore space ratio and liquidity index. However, when measuring bearing capacity using the mean value of the data of a number of indoor experiments, the discreteness in a data group can not be reflected by the mean value. Nevertheless, to measure bearing capacity, not only the difference among data groups should be considered but also the discreteness within data group, namely the variable coefficient of the data within a group. Therefore, before determining bearing capacity, the indicators of bearing capacity need to be amended, which is a rather complicated process though. If the empirical relationship among pore space ratio, liquidity index and bearing capacity can be obtained by fitting the data of indoor experiments, it will be of great value of engineering application. 6. Test of Data Correlation The correlation among bearing capacity, liquidity index and pore space ratio of clay was tested, and the result is shown in Table 9. Table 9. Correlation among bearing capacity, liquidity index and pore space ratio of clay in Area I Pore space ratio iquidity index Bearing capacity Pearson correlation.53** -.854** Pore space ratio Significance (two-sided).. 86

8 iquidity index Bearing capacity N Pearson correlation.53** -.598** Significance (two-sided).. N Pearson correlation -.854** -.598** Significance (two-sided).. N Note: when the confidence level (two-sided test) is.5, the correlation is significant. According to Table 9, the Pearson correlation coefficient of pore space ratio and liquidity index is.53, which is positive and medium correlation; the Pearson correlation coefficient of pore space ratio and bearing capacity is -.854, which is negative and extremely strong correlation; the Pearson correlation coefficient of liquidity index and bearing capacity is -.598, which is negative and medium correlation. 6. Test of Fit Goodness of Regression Equations As Table shows, the regression adjusted determination coefficient of bearing capacity, pore space ratio and liquidity index of the clay in Area I is.759, showing high fit goodness. By checking the Table of Test Critical Value DW Distribution, the DW value is.797, which is in the range of (.75,.85). Therefore, autocorrelation does not exists in independent variables, and the regression is not spurious regression. Table. Model recapitulation Error of Change statistics Mode R R l Adjusted R standard R F Significanc df df estimation change change e F change Note: predictive variable: (constant), liquidity index, pore space ratio; dependent variable: bearing capacity Durbin-Wats on(u) 6.3 Test of Regression Equation Significance Table. Variance analysis Model Sum of squares Degree of freedom Mean square F Significance Regression b Residual Sum Note: dependent variable: bearing capacity; predictive variable: (constant), liquidity index, pore space ratio It s demonstrated in Table that the variance significance is., lower than significance level.5. This indicates that at least one of liquidity index and pore space ratio has significant influence on bearing capacity. Table. Coefficients Model Unstandardized Collinearity coefficient coefficient t Significance B Beta Allowable VIF error (Constant) Pore space ratio iquidity index Note: dependent variable: bearing capacity As shown in Table, the significance of pore space ratio and liquidity index is all lower than.5, which means that both the independent variables, pore space ratio and liquidity index, have significant influence on the dependent variable--bearing capacity. The variance inflation factor VIF is less than, indicating that collonearity does not exists between the two independent variables--pore space ratio and liquidity index. For bearing capacity, the influence of pore space ratio is greater than that of liquidity index. The relationship among pore space ratio e, liquidity index I and bearing capacity f ak, e is: fak, e 4.348e 6.89I

9 6.4 Residual Analysis Table 3 Residual statistical table Minimum value Maximum Mean value value(x) deviation Number Predicted value Residual predicted value residual a. Dependent variable: bearing capacity Table 3 shows that the mean value of standard residual is., and the deviation is.99, close to., indicating that the standard residual is nearly in standard normal distribution, and thus the predicted value of bearing capacity of an unbiased estimation. 7. CONCUSIONS () For the clay in Area I, the SPT blow count is in negative correlation with liquidity index, and the correlation is strong. Among all the regression curves, the polynomial regression curve has the best fitting result. It is consistent with actual engineering condition: the higher the liquidity index, the softer the soil, and the lower the SPT blow count. () For the clay in Area I, the SPT blow count is in positive correlation with compression modulus, and the correlation is good. Among all the regression equations, the exponential regression equation has the biggest determination coefficient and the best fitting result. It is also consistent with practical engineering experience: the higher the SPT blow count, the higher the compress modulus. (3) For the clay in Area I, the bearing capacity is well fitted with pore space ratio and liquidity index. iquidity index and pore space ratio have significant influence on bearing capacity respectively, but the influence of pore space ratio is greater than that of liquidity index. Pore space ratio is in negative correlation with bearing capacity, and the correlation is extremely strong; liquidity index is also in negative correlation with bearing capacity, and the correlation is medium. There is no collinearity between pore space ratio and liquidity index. REFERENCES Qiaoxin T., Tianjun M., Heng W. (99) arge Scale Geological Map of City. Exploration and Research Institute of Guangxi Integrated Design College: Nan Ning. Tianjun M., Heng W. (993) City arge Scale Geological Mapping Method, Journal of Guilin Institut -e of Technology, (3), pp Junxun. (99) Engineering geological properties of the clay rock of tertiary system clay and its slope elurium in Nanning, Journal of Exploration Science Technology, (6), pp Yuankun. (999) Engineering Geological Properties of Alluvial Clay of Upper Wanggao Formation ofpleistocene Series in Nannign, Journal of Guangxi Civil Construction, (6), pp.-. Cetin H. (4) Soil-particle and pore orientations during consolidation of cohesion soils, Engineering Geology, 73(-), pp.-. ancellotfa R., Preziosi. (997) A general nonlinear mathmatical model for soil consolidation problems, International Journal of Engineering Science, 35(-) pp Xia J., Huang G., Yan S. (6) Behaviour and engineering implications of recent floodplain soft soil along lower reaches of the Yangtze River in Western Nanjing, China, Engineering Geology, 87(-), pp Nakaoka K., Yamamotoa S., Hasegawa H. et al. (4) ong-term consolidation mechanisms based on micro-macro behavior and in situ XRD measurement of basal spacing of clay minerals, Applied Clay Science. 6(-4), pp Chang C S., Hicher P Y., Yin Z Y., et al.(9) Elastoplastic Model for Clay with Microstructural Consideration, Journal of Engineering Mechanics,35(9), pp