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This article discusses the reason and mechanism for uplift of stand column in the foundation pit, proposes the simplified calculation method of estimating the additional bending moment of inner support through uplift of stand column in allusion to two main influence factors of bottom heave and vertical load caused by the excavation of foundation pit, inversely calculates the permissible additional bending moment of inner support based on this, verifies by combining the monitoring data for uplift of stand column in the actual project, and finally, further proposes the project measures corresponding to the uplift of stand column, hoping to provide beneficial reference for the similar project.
Therefore, it is relatively difficult to calculate and forecast whether the stand column pile is lifted or sunk finally through data, and it is more difficult to calculate the final displacement quantitatively, which can be controlled and adjusted through monitoring in real time only.
Up to September 15, 2006, the lifting amount and differential settlement of each support is shown in Table 1 according to the monitoring data.
Lifting Amount of Support/mm Maximum Differential Settlement/mm NT MT Ultimate Bending Moment/kN•m Play Ratio/％ No.4 28.47 23.06 144.430 1 001.87 1 610.399 4 348.840 1 37.03 No.5 24.93 19.52 279.148 1 001.87 3 126.459 5 760.072 0 54.27 No.6 23.00 17.59 335.737 1 001.87 3 760.263 6 632.848 9 56.69 No.7 13.48 8.37 162.079 1 001.87 1 815.294 7 373.298 5 24.63 No.8 6.58 1.17 131.859 1 001.87 344.090 8 143.536 9 4.22 No.9 2.38 -3.03 48.619 1 001.87 544.532 2 104.577 2 25.86 Fig.4 Changing curve for measured value Fig.5 Curve for uplift of stand column Z04 with time Construction measurement of reducing deformation of stand column In the normal state, the vertical deformation of stand column can be effectively controlled by taking measures from the following aspects according to the reason and mechanism that the stand column produces the vertical displacement: l For the bottom heave has big influence to the stand column lifting, the reduction of bottom heave can reduce such influence
l Strengthen the statistical analysis of monitoring data of uplift of stand column, and summarize the change rule and calculation method appropriate for the vertical displacement of stand column of Foundation Pit in Soft Soil from mass data.

The experiment for finding the sampling sizes used statistical values in data analysis.
This means that if the variance value of the data is 100 µm2, the variance value of 95.74 µm2 can be explained with a regression model whereas the remaining volume is not explainable due to uncontrollable variables.
Since the values of feed rate and cutting speed can affect variance of the measured data of surface roughness, it can be concluded that the experiment design is accurate and appropriate.
The mean that if variance value of the data is 100 µm2 the variance value of 96.13 µm2 can be explained with a regression model whereas the remaining volume is not explainable due to uncontrollable variables.
Table 5 Regression analysis: surface roughness values, cutting speed and feed rate Regression Analysis: Ra versus Cutting Speed, Feed rate The regression equation is Ra = 19.8 - 0.00742 Cutting Speed + 3.98 Feed rate Predictor Coef SE Coef T P Constant 19.808 1.064 18.62 0.000 Cutting Speed -0.007422 0.002783 -2.67 0.013 Feed rate 3.980 2.143 1.96 0.046 S = 1.81862 R-Sq = 60.6% R-Sq (adj) = 54.4% The data from Table 5 can form the relation between the main factors and dependent variables as shown in the following linear equations: Ra = 19.8 - 0.00742 Cutting Speed + 3.98 Feed rate (1) 4.

The experimental data regarding the zeolite Y membrane/alpha-alumina correspond to those published by Barbosa et al., (2018) [19].
From the data presented in Fig. 3, it is possible to verify a decrease in permeate concentration when using the zeolite membrane ZSM-5/gamma-alumina over time.
The experimental data obtained for the zeolite Y membrane/alpha alumina correspond to those published by Barbosa et al., (2018) [19].
The following results can be outlined as the most promising output data from this study.
Comparison of permeate flux and rejection data for separation of an oil-in-water emulsion from the present study with other membranes published in the literature.

The system hardware components The system's hardware device mainly are: simulation pipeline, accelerometer, signal conditioning equipment, data acquisition card and PC, etc.
The data acquisition card is used the U.S.
Obviously, a significant reduction in signal noise filtering, it is a good filtering effect.
Leak source localization VI block flow diagram In actual programming, because of the stress wave sensors to detect the signal processed is fluctuating around zero, so begin with a "absolute value" function will be negative into positive, set the threshold so that only when need to set a positive threshold can be easy to understand, then use a "data transfer" function to the signal from the dynamic data type is converted to an array data type, the array elements for each sample point corresponding sample values.
[7] Xiang Ke-feng.based on a number of LabVIEW Data Acquisition System Study [D].

Pang Tiejun (2006)[1] selected and researched the Shenzhen and Shanghai's 1,200 listed companies in the financial data of non-financial from 2000 to 2004.
In this paper, we choose 29 listed tourism companies as the sample and make an analysis using the data of corporation annual reports between 2006 and 2009.
Prior to the analysis of the data, we exclude five companies whose data are not available.
Data Sources.
All the required data of this paper are from the website of China’s listed companies (www.cnlist.com).

The Theory of Gold Investment Risk Management Model Time series refers some kind of statistical indicators chronological record of an orderly form data.
In the economic analysis, one of the important data is commonly used in time series data.
Holding period yield is the measure of volatility and correlation time units, but also to obtain the frequency observed data.
For the whole period of observation is the length of time the yield volatility and correlation observations of a given holding period, the data selected time range, also known as the data window;(3)confidence level.
[5] Ede1,T.Seasonality,Risk and return in daily COMER gold and silver data 1982-2002.

Mixed selection algorithm insertion process After the insertion process shown in Fig. 1, the set T of the element becomes the following sequence: { b11,b32,b21,b33,b12,b34,b22,b41,b13,b42,b23,b43,b14,b24,b15,b25,b16,b31} As can be seen from the above algorithm, when a sufficient number of classes meet the limited number of exam, by the gradual reduction of the number of |Bi|, the ratio of the number of candidates of the same class in the same examination room will gradually increase.
Improved divide-and-conquer mixed selection algorithm As large-scale examination involves multi-lingual (subject category), therefore, we have adopted the idea of divide-and-conquer to divide the enrollment data in accordance with the lingual (subject, category) into a number of separate registration data.
We take a registration data to illustrate the realization process of the improved divide-and-conquer mixed selection algorithm, arrange the data, then the rest of the registration data, and eventually merge into the entire examination schedule data, in order to achieve the purpose of fast solving.
To facilitate the description of the problem, we take a registration data as an example, the following definitions: Definition 1: ZRS：Total number of registration of candidates Definition 2: m：Total enrollment classes Definition 3: KCZWS：Standard examination seat number Definition 4: KCS：The examination room number Definition 5: Bi：Class Name Definition 6: KCi：Examination room No.
According to the following results, we can be clearly seen the process of exam organization that how the algorithm generates a seat number, two digits of figure with box indicates the change of the examination room number: 0101,0201,0301,0401,0501,0102,0202,0302,0402,0502,0103,0203,0303,0403,0503,0104,0204,0304,0404,0504,0105,0205,0305,0405,0505,0106,0206,0306,0406,0506,0107,0207,0307,0407,0507,0108,0208,0308,0408,0508,0109,0209,0309,0409,0509,0110,0210,0310,0410,0510,0111,0211,0311,0411,0511,0112,0212,0312,0412,0512,0113,0213,0313,0413,0513,0114,0214,0314,0414,0514,0115,0215,0315,0415,0116,0216,0316,0416,0117,0217,0317,0417,0118,0218,0318,0418,0119,0219,0319,0419,0120,0220,0320,0420,0121,0221,0321,0421,0122,0222,0322,0422,0123,0223,0323,0423,0124,0224,0324,0424,0125,0225,0325,0425,0126,0226,0326,0426,0127,0227,0327,0427,0128,0228,0328,0428,0129,0229,0329,0429,0130,0230,0330,0430, We just need to put the seat numbers generated into corresponding registration data successively, as sorting

Sample Selection and Data Collection.
Data Analysis.
The data is analyzed right after questionnaire recycling and waste volume processing.
That reflects a high level of reliability and data quality.
In this study, LISREL8.70 is applied to carry out a confirmatory factor analysis on the data to verify the degree of fitting between the data with the model.

The processes are lacking analysis of historical data and assessment of the electricity behavior trust state.
However, those data can reflect a lot of valuable information which can very well complement the management of electrical behavior.
Window size can be set according to the experience of the manager; also can finally be set after anglicizing of the data.
Dynamic data mining based on sliding window.
Research of sliding windows scheme based on data stream.

The experts believe that the inconsistency between curves reduces in case of data expansion and the "steps" randomly appearing on the empirical curves will "decay".
Basic data and research methodology The criterion is a probability Pk of accidental appearance of revealed empirical polymodality in the form [2,8]: , (1) where n is the sample size; !
For work with the data base, a program was written in the programming environment Borland Delphi 7 ã2002 Borland Software Corporation.
The program was designed for the primary analysis of data, calculation and plotting of the probability curves and determination of the annual runoff polymodality degree characteristics.
Based on this data, a series of 74 values of R was obtained and curve of their probability presented in Fig. 4 was constructed.