Keywords

1 Introduction

With the continuous development of urban construction, high-rise residential buildings and large buildings are increasing, and the pile foundation is being promoted because of its large bearing capacity. The superstructure transfers the load to the surrounding soil through the pile foundation, effectively reducing the settlement of the pile body and meeting the seismic requirements of the building. Circular excavation bored pile has the advantages of strong adaptability, no noise and high bearing capacity, and is widely used in pile foundation construction, but in practical engineering, geology conditions are complex and changeable, and quality problems emerge in endlessly. The bearing capacity of pile foundation plays a decisive role in engineering safety, and the slurry quality directly determines the quality of bored pile foundation [1].

Slurry mainly plays the role of protecting the hole wall, slag discharge and hole cleaning in the construction. At present, the research on the relationship between hole forming (pile) and slurry has made some results [2,3,4,5,6]. However, there is not much research on the design and analysis of slurry mix ratio, especially the optimization of different soil layers. Wang X P determined the proportion of the main component of the pile wall protection slurry suitable for the ultra-thick sand layer through the ratio test [7]; Wang J et al. determined the optimal quality ratio suitable for the volcanic ash deposition areas through the slurry performance test [8]; Li et al. adopted the uniform design test method for the mix ratio test, and obtained the optimal slurry mix ratio through analysis and optimization [9]. Most of the above scholars determine the slurry mix ratio through the analysis of the test results, but there are very few studies to determine the optimal mix ratio through further optimization and comparative verification.

Slurry preparation is a crucial part of the construction of bored pile. For different geological conditions, different proportions of slurry should be prepared. Combined with the station project of Huizhou North Railway Station, the range of bentonite and additives was determined according to the actual formation conditions, and then the orthogonal test was designed, and SPSS and MATLAB were analyzed to determine the optimal slurry ratio.

2 Project Overview

Huizhou North Station Railway is a line side flat station with elevated waiting room, including station room, platform canopy, underground contact passage, subway section, elevated ramp bridge, etc., with a total construction area of 49,998 m2. The foundation form is bored cast-in-place pile, the foundation design grade is grade A, and the design grade of the building pile foundation is grade A. The revealed strata in the proposed site are mainly Quaternary strata, and the conditions of each stratum are shown in Table 1.

Table 1 Stratigraphic distribution
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As can be seen in Table 1, the sand layer of the site is deep and thick, and the rotary excavation into the hole in the deep sand layer can easily cause engineering problems such as poor hole wall integrity, fast sand precipitation, excessive sediment and buried drilling. According to the distribution of on-site strata, refer to the Technical Specification for Highway Bridge and Culvert Construction (JTG/T 3650-2020) and relevant documents [10,11,12], The basic performance parameters of the slurry are determined as shown in Table 2.

Table 2 Basic performance parameters of slurry
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3 Trial

3.1 Test Materials

The raw material of the slurry is calcium bentonite with large mesh and strong stability, and the admixture is selected from Carboxymethyl Cellulose (CMC) which can increase the slurry viscosity and form a film on the soil surface to prevent the hole wall from peeling; Sodium Carbonate (Na2CO3), Can control slurry PH, increase the thickness of hydration film, improve slurry stability, reduce the pollution of slurry by calcium ions and groundwater; Partially Hydrolyzed Polyacrylamide (PHP), can make the slurry colloid form a chemical film in fine sand, coarse sand and gravel soil, close the hole wall, and keep the hole wall stable.

3.2 Test Instrument

The test and measurement content and the required instruments are shown in Table 3.

Table 3 Measurement indicators and related instruments
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3.3 Test Design

Orthogonal tests were determined by orthogonal tables, and the results were analyzed statistically. Among them, the same number of tests is performed at the level of either or both factors, which makes them very representative according to the orthogonal table and reduces the number of trials. The orthogonal test method in the ratio design can avoid unnecessary calculation and optimize the design process. Using four levels and four factors orthogonal test, the factors and level distribution are shown in Table 4. Among these, a is bentonite, b is Na2CO3, c is CMC and d is PHP.

Table 4 Factor levels table
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16 trials were designed according to the orthogonal test table as shown in Table 5.

Table 5 Orthogonal test table
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3.4 Test Method

According to the orthogonal test table, the bentonite, Na2CO3, CMC, PHP in addition to 1000 mL water in turn, using JJ-1 electric mixer to stir and mix at a speed of 850 r/min, hydrate and expand for a certain period of time at room temperature, and test the data with reference to the measurement method given in the relevant specifications.

4 Analysis of the Test Results

4.1 Test Results

According to the 16 sets of test ratio shown in Table 4, the performance test of each formula slurry was tested separately, and the calculated mean value of the measured test data is shown in Table 6. Among them, φ300 and φ600 are the readings of the rotating viscosimeter at 300 r/min and 600 r/min, respectively, and the plastic viscosity and yield point are calculated from the two sets of data.

$$ \eta = \varphi_{600} - \varphi_{300} $$
(1)
$$ \tau_{{\text{d}}} = 0.511\left( {\varphi_{300} - \eta } \right) $$
(2)
Table 6 Slurry test data table of each group
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4.2 Correlation Analysis

Correlation analysis is one of the very mature basic theories in statistics. The interdependence between various phenomena can be manifested as functional relations or correlations among variables [13].

SPSS analysis software was used to carry out bivariate correlation analysis on the test results [14], analyze the correlation degree between each factor and the performance of the slurry, and obtain the correlation coefficient. In this paper, the four factors: bentonite, Na2CO3, CMC, PHP, slurry six indexes: specific gravity, viscosity, plastic viscosity, yield point, water loss, filter cake thickness were statistically analyzed, and the correlation coefficient of the factors and each performance of slurry was obtained, as shown in Table 7.

Table 7 Correlation coefficient between various factors and various slurry performance
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In correlation analysis, positive correlation: correlation coefficient r > 0; negative correlation: r < 0; high correlation: | r | 0.8; moderate correlation: 0.8 > | r | 0.5; low correlation: 0.5 > | r | 0.3; weak correlation: | r | <0.3.

As shown from Table 7:

  1. (1)

    Positive correlation with bentonite: specific gravity, viscosity, plastic viscosity, yield point, water loss, and negative correlation with bentonite: slurry cake thickness. Among them, bentonite is weakly correlated with water loss and slurry cake thickness, moderately related to viscosity, plastic viscosity and yield point, and highly related to specific gravity, which shows that bentonite can significantly increase the proportion of slurry and increase the strength of the internal gel mesh structure when slurry flows. https://baike.baidu.com/item/%E7%BD%91%E7%8A%B6%E7%BB%93%E6%9E%84/5106492.

  2. (2)

    Positive correlation with Na2CO3: water loss, slurry cake thickness, and negative correlation with Na2CO3: specific gravity, viscosity, plastic viscosity, and yield point. Among them, Na2CO3 is weakly correlated with viscosity, plastic viscosity, yield point and slurry cake thickness, is lowly related to specific gravity and moderately related to water loss, indicating that Na2CO3 energy decomposes slurry particles, which can reduce the rate of Na2CO3 and improve the rate of slurry stability.

  3. (3)

    Positive correlation with CMC: specific gravity, viscosity, plastic viscosity, yield point, and negative correlation with CMC: water loss, filter cake thickness. Among them, CMC is weakly related to the specific gravity of slurry, moderately related to plastic viscosity, yield point, and water loss, and highly related to viscosity and filter cake thickness, indicating that It shows that CMC can significantly increase the viscosity of the slurry, enhance the internal friction between the suspended particles of the slurry and between the slurry particles and the liquid phase, improve the slag carrying capacity of the slurry, and reduce the water loss.

  4. (4)

    Positive correlation with PHP: slurry specific gravity, viscosity, plastic viscosity, water loss, filter cake thickness, and negative correlation with PHP: yield point. Among them, PHP is weakly correlated with slurry viscosity, specific gravity, yield point and water loss, and is weakly correlated with filter cake thickness and plastic viscosity, indicating that PHP mainly plays the role of flocculation. In this test, the lifting effect was not obvious, perhaps because the slurry PH value was not increased to above 10, and the PHP was not fully effective.

4.3 Regression Analysis

As a screening method, the stepwise regression introduces the regression equations one by one according to the effect of each performance indicator, while the non-significant factors are always not introduced [15].

The independent variables were bentonite, Na2CO3, CMC and PHP, and the specific gravity, viscosity, plastic viscosity, yield point, water loss and slurry skin thickness of the slurry were the dependent variables. To determine the regression equation between the respective variables and the dependent variables, the multiple stepwise regression analysis was conducted using SPSS software, and the results are shown in Table 8.

Table 8 Regression equations for each factor
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Comparing Tables 7 and 8, the magnitude of the significance of the influence of the independent variable on the dependent variable in the regression equation coincides with the correlation analysis, e.g., The regression equation of the specific gravity of slurry is composed of x1 (bentonite) and x2 (Na2CO3), which is consistent with the large correlation coefficient between bentonite, soda ash and slurry proportion in the correlation analysis. Therefore, the correlation analysis and regression analysis are suitable for this test analysis and meet the actual requirements.

4.4 Regression Equation Test

To determine the fitting effect of the regression equation and the slurry performance index, the regression equation test is required. Given the significance level α = 5%, a one-way ANOVA was performed on the dependent variable versus the independent variable composing the regression equation. For example, in the specific gravity analysis, F1, P1, F2, and P2 in the table are the results of the variance analysis of specific gravity and x1 (bentonite) and x2 (Na2CO3). The cut-off value F is obtained through the F distribution table F0.05(3,12) = 3.49, the F value obtained in the Table is compared with the F value found in Table 9, it can be seen that the value of F1 and F2 is less than 3.49 but close to the critical value, indicating that the respective variable had a significant influence on the dependent variable. And the coefficient of determination R2 is close to 1, indicating that the regression equation fits well and meets the requirements of this experiment.

Table 9 Analysis of variance of the regression equations
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5 Mix Ratio Optimization

Further, slurry specific gravity, viscosity, plastic viscosity, yield point, water loss, filter cake thickness six indicators are the standard to measure the performance of slurry. To determine the optimal slurry mix ratio, the function is optimized and the following objective function is established:

$$ Z = y_{n} $$
(3)

In formula: n = 1~6. The constraints on y and x in the objective function are:

$$ \left\{ {\begin{array}{*{20}l} {1.08 \le y_{1} \le 1.15} \hfill \\ {22 \le y_{2} \le 30} \hfill \\ {8 \le y_{3} \le 13} \hfill \\ {1.5 \le y_{4} \le 3.0} \hfill \\ {y_{5} \le 15} \hfill \\ {0.5 \le y_{6} \le 2.0} \hfill \\ \end{array} } \right. $$
(4)
$$ \left\{ {\begin{array}{*{20}l} {128 \le x_{1} \le 188} \hfill \\ {4.4 \le x_{2} \le 5.6} \hfill \\ {2.7 \le x_{3} \le 4.5} \hfill \\ {0.05 \le x_{4} \le 0.20} \hfill \\ \end{array} } \right. $$
(5)

According to the function relationship of y and x determined by the regression equation, the objective function was optimized by MATLAB software. After calculation, after calculation, when the material dosage is 148 g of bentonite, 5.2 g of Na2CO3, 3.5 g of CMC, and 0.05 g of PHP, the function has an optimal solution, and each index is tested. The screening results of orthogonal tests (148 g of bentonite, 5.2 g of Na2CO3, CMC 3.9 g, and PHP 0.05 g), software calculation results and test results are shown in Table 10.

Table 10 Orthogonal test screening values, software calculation values and test values
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According to the table, the orthogonal test screening value, software calculation value and test value do not differ much. Taking the test value as a reference, the smallest difference is the specific gravity: the deviation of the screening value is 1.77%, and the deviation of the calculated value is 0.88%; the largest difference is the thickness of the slurry: the deviation of the screening value is 21.43%, and the deviation of the calculated value is 7.14%. All of the above meet the requirements, indicating that the orthogonal test is the method of finding the principal contradiction and selecting the better scheme in this test. The optimized treatment results of MATLAB software meet the requirements of various slurry indexes, so this method is applicable for the optimization analysis of slurry mix ratio.

The slurry ratio is specifically applied to Huizhou North Railway Station project and detected by low strain and acoustic wave transmission method: 1181 Class I piles, accounting for 98.504% of the tested piles; 18 class piles, accounting for 1.495% of the tested piles; There are no III and IV piles. The proportion of pile foundation integrity testing type I pile has reached more than 98%, which has ensured the project quality and project progress, achieved good results, and accumulated certain engineering experience.

6 Conclusion

  1. (1)

    Orthogonal test screening value, software calculation value and test value are not much different. With the test value as the reference, the smallest difference is the proportion: The deviation of the screening value is 1.77%, and the deviation of the calculated value is 0.88%; the biggest difference is the thickness of the slurry skin: the deviation of the screening value is 21.43%, and the deviation of the calculated value is 14.29%. All the above meet the requirements, indicating that the orthogonal test, as a method of finding the main contradiction from multiple factors and multiple levels and selecting better schemes, has achieved good results in this test.

  2. (2)

    Bentonite and CMC have a significant impact on various slurry indexes, while Na2CO3 and PHP can be adjusted as additives to meet the standard of slurry use. The optimal mix ratios were 148 g of bentonite, 5.2 g of Na2CO3, 3.5 g of CMC, and 0.05 g of PHP.

  3. (3)

    Using SPSS statistical software for correlation analysis and regression analysis, and the optimized treatment through MATLAB software is suitable for slurry ratio test analysis and treatment, and the optimization mix ratio sought is feasible and reasonable, which can meet the actual engineering needs.