On the influence of the soil and groundwater to the subsidence of houses in Van Quan, Hanoi

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This paper applied the regression method to study the effect of soil and groundwater on the residential constructions in Van Quan urban area, Hanoi. Subsidence monitoring was carried out for 4 consecutive years, from 2005 to 2009, including over 500 subsidence monitoring points with high-precision Ni007 and INVAR gauges.

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Nội dung Text: On the influence of the soil and groundwater to the subsidence of houses in Van Quan, Hanoi

VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 Original Article On the Influence of the Soil and Groundwater to the Subsidence of Houses in Van Quan, Hanoi Dinh Xuan Vinh Hanoi University of Natural Resources and Environment, 41 Phu Dien, Tu Liem, Hanoi, Vietnam Received 11 January 2020 Revised 14 April 2020; Accepted 22 August 2020 Abstract: The area of Van Quan, Hanoi before 2004 was the rice field. Nearby, Ha Dinh water plant has well-drilled underground water for residential activities. Van Quan's new urban area after being formed has detected many subsidences. The objective of this study is to assess the main causes of the subsidence of the houses, based on groundwater and soil. This paper applied the regression method to study the effect of soil and groundwater on the residential constructions in Van Quan urban area, Hanoi. Subsidence monitoring was carried out for 4 consecutive years, from 2005 to 2009, including over 500 subsidence monitoring points with high-precision Ni007 and INVAR gauges. A groundwater observation well is 30 meters deep at the site of the settlement. The results show a small effect of groundwater on subsidence. The characteristics of the young sediment area and the soil consolidation process are the main causes leading to serious subsidence in residential constructions in Van Quan urban area. This paper provides a different perspective on the impact of groundwater on the subsidence of residential structures within approximately 100 ha. Keywords: monitoring, subsidence, residential houses, groundwater, soil. 1. Introduction activities, and construction process. urban floor. In this paper, we want to explore the impact of The situation of land subsidence in the groundwater on the upper floor and the region due to various subjective and objective consolidation process of soil on shallow causes that many scientists as Tuong The Toan, foundation constructions, in particular, houses Tu Van Tran, Ty Van Tran [1-3] agreed as under 5 floors in Van Quan urban area, Hanoi. follows: Characteristics of sedimentary basins We have built a groundwater monitoring well during consolidation, denudation or accretion of with a depth of 30 meters in the survey area. topographic surfaces, groundwater extraction Observation data of groundwater and subsidence ________  Corresponding author. E-mail address: dxvinh@hunre.edu.vn https://doi.org/10.25073/2588-1094/vnuees.4539 42
D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 43 of residential houses of Van Quan urban area type can be interpreted as internal causes and were conducted regression analysis. Thereby we results within the system. This format includes assess the influence of each cause to the multiple regression (MR) model, stepwise settlement of the houses on the young regression (SR), principal component regression sedimentary basin. (PCR), partial least square regression (PLSR) Some studies use the method of Terzaghi as and artificial neural network (ANN). The second Ty Van Tran, Hiep Van Huynh [3], or the Finite model is based on the statistical rule of Element method as Tu Van Tran et al [2], based dependent variables ie using linear statistical on groundwater monitoring data to forecast models themselves, not by other environment ground subsidence. In this study, we use the variables. They do not establish a model between groundwater monitoring data in the subsidence cause and effect. This type includes Time series area (about 100 hectares) and the subsidence (TS series), Gray system (GS). The deformation monitoring data of the houses according to prediction model is based on information drawn national Class II leveling Regulation. from the deformation monitoring data series, Conducting the regression analysis for each these processes are performed in different ways. cause of subsidence. The first is groundwater. Parameter model based on the analysis of The second is the during consolidation monitoring data by continuous mechanical rules. subsidence of the soil because Van Quan urban First, determine the relationship between the area is located on a young sedimentary basin [2]. dependent variables and the independent variables built on mechanical rules. Next, linear statistics are applied to correct the assumed 2. Research Methods and Data calculation values or parameters throughout the The raw monitoring data including calculation. This model type has a Kalman filter appropriate measurements is a very important [4]. part of the building safety data. Based on the Regression analysis is a statistical method monitoring data, one can recheck the design plan where the expected value of one or more random as well as the construction process and the variables is predicted based on the condition of operation of the building. The raw data provide other (calculated) random variables. Regression valuable information that sheds light on the analysis is not just about curve matching stability of the building. However, the raw data (choosing a curve but best matching a set of data cannot reveal the shifting field or the points); it must also coincide with a model of deformation trend of the building. A deterministic and stochastic components. The comprehensive analysis is therefore needed to defined component is called the predictor and the accurately and comprehensively identify various random component is called the error term. deformations from a large volume of raw data. Regression analysis is both a mathematical- Two types of dynamic models are formulated to statistical method and a deformation physics analyze deformation monitoring test data, non- explanation, so it can be used to predict parametric models based on mathematical- deformation. Calculation of univariate or statistical theory, and principles-based parametric multivariate regressions is the solution a system models major of continuous mechanics. of linear equations based on the least-squares Non-parametric model based on principle the functional model is represented as mathematical - statistical prediction algorithms. a matrix. The first model is based on a functional 𝑌 = 𝑋𝛽 + 𝜀 (1) relationship between the independent variables In this model, Y is a dependent variable, that (the environment variables) and the dependent is, the vector of deformation measurement, variables (are the deformations). Models of this matrix representing the component of the
44 D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 dependent variable is 𝑌 𝑇 = (𝑦1 , 𝑦2 , … , 𝑦𝑛 ), n is For multivariate linear regression equations, the amount of measurement; Equation (1) has we find the estimate 𝛽̂ by the least-squares many variables x and each variable has a parameter method so that β that needs to be estimated; The vector of random 2 error ε is the deviation of measured value (RMS ∑(𝑦𝑖 − 𝑦̂𝑖 )2 = ‖𝑌 − 𝑌̂‖ = ‖𝑒‖2 = 𝑚𝑖𝑛 measured value), 𝜀 𝑇 = (𝜀1 , 𝜀2 , … , 𝜀𝑛 ). Where 𝑖 the measurements are random components and We obtain vector follow the standard distribution rule 𝑁(0, 𝜎 2 ), we can apply the Gauss - Markov procedure. The 𝛽̂ = (𝑋 𝑇 𝑋)−1 𝑋 𝑇 𝑌 random model is and posterior accuracy ∑ 𝜀𝜀 = 𝐸{𝜀. 𝜀 𝑇 } = 𝜎 2 𝑄𝜀𝜀 ∑𝛽̂𝛽̂ = 𝜎02 𝑄𝛽̂𝛽̂ = 𝜎02 . (𝑋 𝑇 𝑋)−1 } (2) 𝑄𝜀𝜀 = 𝐼 Elements on the diagonal of the covariance X is a matrix of the form matrix ∑𝛽̂𝛽̂ are the variances of the estimates 𝛽𝑗 1 𝑥11 𝑥12 ⋯ 𝑥1𝑚 ie 𝑞𝛽̂𝛽̂ = 𝑆𝛽2𝑗 . 1 𝑥21 𝑥22 ⋯ 𝑥2𝑚 𝑋=[ ] (3) Post-regression values ⋮ ⋮ ⋮ ⋮ ⋮ 1 𝑥𝑛1 𝑥𝑛2 ⋯ 𝑥𝑛𝑚 𝑌̂ = 𝑌 + 𝑉 = 𝑋𝛽̂ = 𝑋(𝑋 𝑇 𝑋)−1 𝑋 𝑇 𝑌 = 𝐻𝑌 Matrix (3) shows m deformation-causing The H-matrix is called the "hat" matrix [5]. factors, each deformation-causing factor The principles of a multivariate linear represents a measure of an independent variable regression model and solutions are consistent or its function, they form the elements of the with the indirect adjustment model and the matrix X, similar for the dependent variable there common solution in surveying, but different in are all n groups; that: the number of causes of deformation β is the regression coefficient vector, 𝛽 𝑇 = influence in the multivariate linear regression (𝛽0 , 𝛽1 , … , 𝛽𝑚 ). Where: model has not been predetermined, it is 𝛽0 is the coordinate origin coefficient; necessary to use a certain method to defined regression, making the optimal regression model. 𝛽1 is the slope coefficient of Y according to In linear regression analysis, we include the the variable 𝑥1 and keeping the variables following concept: Residual Sum of Square (Q), 𝑥2 , 𝑥3 , … , 𝑥𝑚 constant; Total Sum of Square (S) and Explained Sum of 𝛽2 is the slope coefficient of Y according to Squares (U). We have the variable 𝑥2 and keeping the variables 𝑌− = +(𝑌̂ − 𝑌̄) (4) 𝑥1 , 𝑥3 , . .. , 𝑥𝑚 constant; ,... The concepts are defined as follows: 𝑛 𝛽𝑚 is the slope coefficient of Y according to 𝑆 = (𝑌 − 𝑌̄)𝑇 (𝑌 − 𝑌̄) = ∑(𝑦𝑖 − 𝑦̄ )2 the variable 𝑥𝑚 and keeping the variables 𝑖=1 𝑥1 , 𝑥2 , . .. , 𝑥𝑚−1 constant. 𝑛 𝑇 The slope coefficient 𝛽1 represents the 𝑄 = (𝑌 − 𝑌̂) (𝑌 − 𝑌̂) = ∑(𝑦𝑖 − 𝑦̂)2 = 𝑉 𝑇 𝑉 change in the mean of Y per unit of change of 𝑥1 𝑖=1 𝑛 regardless of the change of 𝑥2 , 𝑥3 , . .. , 𝑥𝑚 , so the 𝑇 𝛽𝑗 is also called partial regression coefficients. 𝑈 = (𝑌̂ − 𝑌̄) (𝑌̂ − 𝑌̄) = ∑(𝑦̂𝑖 − 𝑦̄ )2 𝑖=1 }
D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 45 Where the deformation-cause factor and the measured 𝑛 deformation values are not related to each other. 1 If the coefficient is close to -1 or +1, the 𝑦̄ = ∑ 𝑦𝑖 𝑛 deformation-cause factor and measured strain 𝑖=1 𝑦̂𝑖 is the regression value of the dependent value have a great relationship. We have variable. 𝑅 2 = 𝑈⁄𝑆 Can prove that: 𝑆 = 𝑄 + 𝑈 We have conducted a groundwater In regression, the correlation coefficient (R) monitoring of a well built in the urban area of is a statistical index that measures the degree of Van Quan (Figure 1). Simultaneously with correlation between deformation-cause factors monitoring the subsidence time of the houses and measured deformation values [6]. The (Figure 2), we conduct monitoring the correlation coefficient is close to 0, meaning that groundwater level (Figure 3). Figure 1. Groundwater monitoring well. Figure 2. Cracks on Van Quan houses. -10,300 -10,350 -10,400 -10,450 -10,500 -10,550 -10,600 -10,650 9/12/05 2/14/06 11/4/06 11/6/06 05/09/2005 23/09/2005 28/10/2005 12/11/2005 25/11/2005 23/12/2005 08/01/2006 3/3/06 12/02/2007 (26/11/2007) (21/01/2008) (25/03/2008) (26/05/2008) (26/07/2008) (26/09/2008) (24/11/2008) 23/1/2006 11/10/2006 11/12/2006 11/8/2006 (1/10/2007) (31/3/2009) 09/10/05 (15/05/07) (27/07/07) (5/2/2009) Figure 3. Groundwater level in Van Quan area during monitoring of subsidence.
46 D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 Figure 4. The groundwater monitoring well, the points of measurements and the boreholes. Monitoring data from May 2005 to March The regression model we build is based on a 2009. Subsidence monitoring is done by high- finite set of measurement data, so it may be precision leveling Ni007 and Invar gauges. The affected by measurement errors ε. We have the measurement technique complies with the following hypothesis national grade II standard. Monitoring the 𝐻0 : 𝛽0 = 𝛽1 = 𝛽2 = ⋯ = 𝛽𝑚 = 0 groundwater level with the Piezometer gauge. (Figure 4). 𝐻1 : Have at least one coefficient 𝛽𝑗 ≠ 0 If the assumption H0 is true, that is, all slope coefficients are zero, then the regression model 3. Theory and Calculation built has no effect in predicting or describing the Methods of assessing the conformity of the dependent variable. Formulation regression model according to mathematical 𝑈 statistics include: Calculating the correlation 𝐹𝑡𝑡 = 𝑚 (5) 𝑄 coefficient R, using statistical tests to evaluate (𝑛 − 𝑚 − 1) the overall model, calculating standard errors of estimates, statistical tests list each individual In this formula, U and Q are known, n and m independent variable. In geodesy, we are are sample size (number of measurements) and interested in testing the overall regression model independent variable (number of factors and testing the dominance of each deformation affecting deformation into the model), effect factor (such as temperature, time, respectively. The degree of freedom of the pressure,...) on the dependent variable numerator f1 = m, the degree of freedom of the (deformation values). denominator f2 = (n-m-1). Select the confidence level for the F statistic with 95%, that is, the
D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 47 alpha level for the test is 5%. Look up ∆𝑄 = 𝑄𝑚 − 𝑄𝑚+1 distribution table F to find the limit value 𝐹𝑓1 ,𝑓2 ,𝛼 . {∆𝑈 = 𝑈𝑚 − 𝑈𝑚+1 If Ftt > F limited, reject the H0 hypothesis. The F ∆𝑄 = ∆𝑈 statistic must be used in combination with the Thus, the residual sum of squares increases significance level value when you are deciding if by the reduction of the explained sum of squares your overall results are significant. after increasing the deformation-cause factor xm Test the dominance of each factor affecting + 1, through which the regression equation also deformation (such as temperature, pressure, reflects the contribution of the additional time,... ) to the dependent variable (is the increase factor with the regression effect. The measured deformation value). We have the predominance test for the added deformation- following hypothesis cause factor is as follows 𝐻0 : 𝐸(𝛽̂𝑗 ) = 0 𝐻0 : 𝐸(𝛽̂ ′𝑚+1 ) = 0 𝐻𝐴 : 𝐸(𝛽̂𝑗 ) = 𝛽̂𝑗 ≠ 0 𝐻𝐴 : 𝐸(𝛽̂ ′𝑚+1 ) = 𝛽̂ ′𝑚+1 ≠ 0 Create the following statistics according to Forming the F statistical distribution ∆𝑄 the T distribution 𝐹= 𝛽̂𝑗2 𝑄𝑚+1 ⁄(𝑛 − 𝑚 − 2) 𝑞𝛽̂𝑗𝛽̂𝑗 𝑇= < 𝑇𝑛−𝑚−1,𝛼 (6) ∆𝑄 𝑄 ⁄(𝑛 − 𝑚 − 2) 2 (𝑛 − 𝑚 − 1) = ~𝐹1,𝑛−𝑚−2 (7) 𝑄𝑚+1 qβ̂ β̂ is the jth element on the main diagonal of Taking the significance level of 5%, when j j the matrix 𝑄𝛽̂𝛽̂ , where 𝑞𝛽̂𝑗𝛽̂𝑗 is the variance of 𝐹> 𝐹1, 𝑛 − 𝑚 − 2, 𝛼, the original hypothesis is accepted, that is, the increased deformation- the regression coefficient estimates (𝑆𝛽2𝑗 ); Q is cause factor has a significant effect on the the residual sum of square. Look at the house's deformation, in contrast. it should not be distribution table of T, get significance level of added. In the regression equation, the influence 5%, dominance of deformation influence factors of deformation often correlate with each coefficient 𝛽̂𝑗 is 95% respectively. If 𝑇 < other, that is, there is some relation to each other. 𝑇𝑛−𝑚−1,𝛼, then the corresponding deformation- The close correlation between the variables in 2 the regression model created a multicollinearity cause factor 𝑥𝑗 has a very small effect on phenomenon, making the variance of the deformation, which can be removed from the regression coefficient estimates big valuable. regression equation. The multicollinearity phenomenon also reverses In the regression model, we must put the the regression coefficient, instead of positive deformation-cause factors into the regression coefficients, that is, the high water level causes equation. In the process of testing their the deformation of the dam to be large, resulting dominance, if any factors do not pass the test, in negative results, the high water level makes they will be removed, and other factors must be the dam less deformed. included in the evaluation model. Assume a Based on the above test steps, it is possible following multivariate linear regression equation to induce the following step regression: 𝑦̂ = 𝛽̂0 + 𝛽̂1 𝑥1 + ⋯ + 𝛽̂𝑚 𝑥𝑚 a) Prequalification of independent variables affecting the deformation The residual sum of squares and the explained sum of squares is Qm + 1, Um + 1, now we b) Determine the first univariate linear have regression equation. Assuming that m
48 D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 independent variables affect deformation, each 𝑌̂ = 𝛽0 + 𝛽1 𝑥𝛾 of these independent variables creates a The linear regression equation for time univariate linear regression equation, for a total of m equations. Calculate the residual sum of 𝑌̂ = 𝛽0 + 𝛽2 𝑥𝜃 + 𝛽3 𝑥2𝜃 squares Q of each equation. If the regression Based on the observed data series we have equation with 𝑄𝑘 = 𝑚𝑖𝑛{𝑄𝑖 }, 𝑖 = ̅̅̅̅̅̅1, 𝑚, then the following regression equation the regression equation with Qk is collected after - For the effect of groundwater on the testing its according to equations (6) and (7). subsidence of houses c) Determine the best two-variable regression 𝑌̂ = 9876.1124 + 309.3856 𝑥𝛾 + 56.5974 equation based on the univariate linear regression The correlation coefficient 𝑅 2 = 0.0628 = equation, in turn increasing the independent 6.28%, that is, the water table affects only 6.28% variables affect deformation, and have (m-1) two to the subsidence of the structure. The posterior linear regression equations. Calculate (m-1) the error of regression is 56.5974 mm. The posterior residual sum of squares ΔQ, consider the error of the estimated coefficient 𝛽1 is 𝑆𝛽1 = difference ∆Q j = max{∆Q i }, i = ̅̅̅̅̅ 1, m. 158.29. The test value according to (6) for 𝛽1 is The jth incremental independent variable is T = - 1.9545, corresponding to the significance the “waiting” independent variable, conducting level of 5.55%. The correlation coefficient is too its test, if adopted, it will be included in the low and the post-estimation error 𝑆𝛽1 is too high, equation. It is the best two-variable linear so we remove the groundwater element from the regression equation. If not, then stop at the regression model. univariate regression equation. - For the effect of soil consolidation time on d) If two independent variables affecting the subsidence of the houses deformation are dominant for dependent variable 𝑌̂ = 6694.9641-1.4108 𝑥𝜃 +0.0024 𝑥2𝜃 + 6.7862 Y (amount of deformation), then according to the above method, continue to select independent The correlation coefficient 𝑅 2 = 0.9809 = variables to affect the third and fourth 98.09%, ie the time of soil consolidation affects deformation,... So on until it is impossible to 98% of the settlement of the building. The slope increase the new independent variable and can coefficient 𝛽2 indicates the settlement rate and not remove any independent variables selected, 𝛽3 indicates the settlement acceleration is 0.0024 then stop. As a result, we have the best mm2 /week. The posterior error of the regression regression model. is 6.7862 mm. The posterior error of the estimated coefficient 𝛽2 is 𝑆𝛽2 = 0.0422, the The independent variable affecting coefficient 𝛽3 is 𝑆𝛽3 = 0.0002. The test value deformation is groundwater and time. The observation time characterizes the deformation according to (6) for 𝛽2 is T = - 33,4372, of the test point over time, so its first-order corresponding to the significance level of 6.6.10- differential is the subsidence rate, its second- 74%, and 𝛽3 is T = 9.8588, corresponding to the degree differential is the subsidence significance level of 2.3.10-16%, the value This acceleration. Simultaneous time represents the is very small by our standards (5%). level of consolidation of the soil under the construction. It can be said that: the consolidation 4. Results and Discussion subsidence time lasts correspondingly the soil Based on the results of regression analysis of belongs to young sediments. the causes of subsidence of residential houses, Develop a regression equation for the groundwater level and the time of consolidation groundwater variable γ and for time variable θ. of the soil from 2005 to 2009, we can draw a We have a linear regression equation for regression line of subsidence according to the groundwater consolidation time of the soil background.
D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 49 6750 6700 6650 mm (Subsidence) 6600 6550 6500 6450 Actual Regression 6400 28/10/05 19/12/06 12/11/07 25/12/07 11/11/08 25/12/08 5/9/05 3/5/06 2/8/06 3/2/07 5/5/07 (25/06/07) 4/2/08 7/5/08 12/9/05 23/1/06 17/6/06 18/9/06 3/11/06 19/3/07 11/8/07 1/10/07 29/6/08 10/8/08 26/9/08 12/2/09 25/3/09 17/3/06 25/3/08 Figure 5. Soil consolidation plays a major role in subsidence of the Van Quan houses. Although some scientific studies suggest that in the period 2005-2009. The fluctuations are the groundwater level strongly affects the mainly recorded during the rainy and dry background subsidence. But to consider specific seasons. Because of the relatively stable residential constructions, when the soil groundwater level in Van Quan, it cannot cause background is loaded with the houses under 5 the subsidence of residential houses. floors with the foundation structure without For the young sedimentary areas, the reinforced concrete piles. This case has shown consolidation element subsided over time, that the cohesive subsidence factor of the soil is constructions from three floors should have the main cause of the subsidence of the houses. reinforced concrete foundation piles, constructed The underground water observation well in by the method of pressing piles. The depth of Van Quan urban area is made of Tien Phong reinforced concrete piles should exceed the fill plastic pipe with a diameter of 90 mm, a depth of and soft soil layers, for Van Quan area is about 30 m from the protective steel pipe mouth on the 15 m depth, based on the geological survey ground, the bottom of the tube is in direct contact drilling boreholes (Figure 6). with the soil and is not prevented way. Due to In fact, after 2008, most of the residential insufficient funds, we could not build a deeper houses in VanQuan's new urban area have to groundwater monitoring well, or have a higher reinforce their foundations with piles, increasing standard. This aquifer is at the top of the construction costs, but ensuring stable and safe aquifers, not surface water or affected by surface houses for a long time. This is also an experience water. Monitoring data of groundwater level for civil engineering designers in delta areas with directly at the well did not notice much change weak soil.
50 D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 Sheet number: 1/2 CYLINDRICAL BOREHOLE BOREHOLE No.3 Construction Residential houses Coordination:X Position Van Quan - Ha Noi - Y: Start day 25/01/2006 End day: 26/01/2006 The height of borehole,m: 0,000 Groundwater level, m : The depth of borehole,m: 53,90 Soil layer Samples SPT Depth, m Thickness,m Height, m Layer Depth, m Number of Number Number Desc r i pt i o n Ex per i men t al c h a r t Depth, m hammers ` From To 15 15 15 N 0 20 40 60 80 100 0 1 Land fill: Sand, clay Number, N 1 mixed with 1 2 construction waste 2 3 3,70 -3,70 3,70 3 4 U1 3,8 4,00 4,0 4,45 1 1 1 2 4 2 5 U2 5,80 6,00 5 6 6,00 6,45 1 1 2 3 6 3 7 U3 7,80 8,00 7 8 8,00 8,45 1 2 2 4 8 4 9 Clay, clay mixed with U4 9,80 10,00 9 10 10,00 10,45 1 1 2 3 dark gray color, mixed 10 3 11 2 with plant organic matter, plasticity U5 11,80 12,00 11 12 flowing 12,00 12,45 1 2 2 4 12 4 13 U6 13,80 14,00 13 14 14,00 14,45 1 1 2 3 14 3 15 U7 15,80 16,00 15 16 16,00 16,45 1 2 2 4 16 4 17 U8 17,80 18,00 17 18 18,00 -18,00 14,30 18,00 18,45 3 4 3 7 18 7 19 19 20 D2 20,00 20,45 5 7 7 14 20 14 21 21 22 D3 22,00 22,45 5 9 11 20 22 20 23 23 Depth, m Fine grained sand ash 24 3 D4 24,00 24,45 7 11 12 23 gray, gray, sometimes 24 23 25 mixed with organic, medium compacted 25 26 state D5 26,0 26,45 7 10 12 22 26 27 27 Note: - M : Original form - D : Disturbance form Figure 6. Cylindrical of Borehole No. 3 at the Van Quan residential houses.
D.X. Vinh / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 4 (2020) 42-51 51 5. Conclusions References Regression model is a traditional analytical [1] T.T. Toan. Forecast of land surface subsidence method to evaluate the impact of independent due to lowering of groundwater level. Final report of the project RD 9505, Hanoi. 1999. (in causes on measured values. Groundwater level Vietnamese). and soil consolidation process over time are [2] T.V. Tu, H.N. Anh, D.D. Minh, N.M. Tung, factors to consider when designing a building. Forecast of ground deformation in Ha Dong area The study showed that the groundwater level in due to urbanization and groundwater extraction. the upper floor fluctuated very small and 98% of Journal of Earth Sciences 35(1)(2013) 29-35. (in subsidence of residential houses in VanQuan's Vietnamese). new urban area was due to the weak soil. [3] T.V. Ty, H.V. Hiep. Current status of groundwater This study case is only for residential extraction and correlation between water level buildings from 3 to 5 floors with non-reinforced lowering and land subsidence: Research in Tra Vinh and Can Tho city. Can Tho University concrete foundation and only consider the top Journal of Science. Topics: Environment and aquifer. For buildings under 3 floors are not Climate Change 1 (2017) 128-136. (in covered by this study. Buildings above 5 floors Vietnamese). often have foundations made of reinforced [4] D.X. Vinh, N.T. Nhung, N.V. Quang. concrete piles up to a depth of 20 to 60 meters, Determination of Deformation of Construction so they may be affected by deeper aquifers. More Using Parametric Modeling-Kalman Filter comprehensive studies are needed on this issue Application and NonParametric Modeling-Time to be clear about the impact of groundwater on Series Application. VNU Journal of Science: the subsidence of buildings. Earth and Environmental Sciences 34(3) (2018) 1- 3. https://doi.org/10.25073/2588-1094/vnuees.4274. Acknowledgments (in Vietnamese). [5] P.J. Huber, E.M. Ronchetti. Robust Statistics. The author thanks the support for monitoring Second Edition. Published by John Wiley & Sons, data of Van Quan of HUDCIC Consulting Inc. Canada. 1981. Investment and Construction Joint Stock [6] R.A. Maronna, R.D. Martin, V.J. Yohai. Robust Company. The author also thanks the comments Statistics: Theory and Methods, John Wiley & of reviewers who helped improve the content of Sons, Ltd. England. 2006. this article.