CN111401768A - Subway station safety construction early warning method - Google Patents

Subway station safety construction early warning method Download PDF

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CN111401768A
CN111401768A CN202010216696.7A CN202010216696A CN111401768A CN 111401768 A CN111401768 A CN 111401768A CN 202010216696 A CN202010216696 A CN 202010216696A CN 111401768 A CN111401768 A CN 111401768A
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张拥军
杨文祥
胡同旭
唐世斌
聂闻
刘洪治
阎明东
马天辉
夏煌帅
王盛
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Abstract

The invention belongs to the technical field of construction safety, and provides a subway station safety construction early warning method aiming at the problems that the existing subway construction safety evaluation method cannot effectively reduce adverse effects caused by subjectivity and cannot be effectively used for practice. The method comprises the following specific steps: collecting the scoring results of experts, calculating a credibility distribution function, and taking the average value of confidence intervals with higher credibility; calculating a standard deviation; solving a variation coefficient; and (3) evaluating the construction safety by combining a principal component analysis method and a linear regression least square method. The invention innovatively introduces a reduction control method adopted due to adverse effects generated by expert scoring, so that the result is more reasonable and accurate, and in addition, the principal component analysis method can more comprehensively, comprehensively and effectively evaluate the tunnel construction safety, discover hidden dangers in time and reduce the occurrence of safety accidents.

Description

Subway station safety construction early warning method
Technical Field
The invention belongs to the technical field of construction safety, and particularly relates to a subway station safety construction early warning method.
Background
Along with the rapid economy, the urbanization level and the increasing urban population, the accompanying people flow and the traffic flow are also rapidly increased, and the traditional ground traffic can not meet the requirements of people. Therefore, the development and construction of urban subways are favored by people and are rapidly developed, but in the construction of subways, emergencies occur continuously, so that not only is great property loss caused, but also the life safety of people is seriously threatened. Therefore, a safety evaluation measure is required to reduce the occurrence of accidents. The safety evaluation method is not always independent of participation of people, people have the advantages of flexibility, comprehensiveness and the like as evaluation main bodies, and the safety evaluation method cannot be replaced, but meanwhile, the subjectivity brings accuracy. For example, in the comprehensive evaluation of subway system safety based on principal component analysis, an expert scoring method is adopted, so that the expert scoring inevitably generates subjectivity, and corresponding reduction measures are not pointed out and taken, so that the evaluation method is difficult to guide practice. The existing method can not effectively reduce the adverse effect caused by subjectivity, and the invention is provided based on the method.
Disclosure of Invention
Aiming at the problems that the existing subway construction safety evaluation method cannot effectively reduce adverse effects caused by subjectivity and cannot be effectively used for practice, the invention provides a subway station safety construction early warning method, innovatively introduces a reduction control method adopted due to the adverse effects caused by expert scoring, and enables the result to be more reasonable and accurate.
A subway station safety construction early warning method comprises the following steps:
step one, summoning the tunnel and constructCollecting 5 scoring results C of one index, calculating distribution probability α of 5C values, and determining confidence interval [ a, b ] by at least 5 experts in each aspect of industry, scoring, wherein the scoring content comprises five aspects of ambient environment condition, building envelope risk, foundation pit instability and collapse risk, safety behavior of constructors and management factors](ii) a Computing confidence distribution function
Figure BDA0002424721110000011
And taking a scoring average value of a confidence interval with higher reliability; calculating a scoring average value corresponding to each index according to the method;
calculating a risk index scoring average value:
Figure BDA0002424721110000012
in the formula, kijDenotes the score, n, of expert i on index jjRepresents the number of scoring experts and,
Figure BDA0002424721110000013
an average value representing the index score of the expert;
step three, calculating sigmajFor the standard deviation of index j, the calculation formula is:
Figure BDA0002424721110000014
step four, solving a variation coefficient:
Figure BDA0002424721110000015
in the formula, vjRepresenting the variation coefficient of the index j, namely the fluctuation condition of the index j marked by the expert; v. ofjThe value of (A) is bounded by 0.16, and the smaller the value is than 0.16, the higher the consistency of experts is, and the more suitable the value is used as the evaluation index of the subway construction safety risk; conversely, the larger the value ratio is 0.16, the less suitable this evaluation index is, and vjIf more than 0.16, replacing the new scoring index, and repeating the steps until v is higher than vj≤0.16；
Step five, extracting the mean value of the confidence interval with higher credibility, and setting an original data matrix
Figure BDA0002424721110000021
Wherein xij(i is more than or equal to 1 and less than or equal to q, j is more than or equal to 1 and less than or equal to p) represents the evaluation score of the safety evaluation factors at the time point, namely p safety evaluation factors exist in the original data, and q evaluation time points exist in the original data;
step six, carrying out data standardization on the original data matrix (1), and setting the standardized data matrix S as (S)ij)q×p
Figure BDA0002424721110000028
Step seven, the normalized data matrix S is equal to (S)ij)q×pEstablishing a correlation coefficient matrix R ═ R (R) of the variablesij)q×p
Figure BDA0002424721110000022
Step eight, solving the characteristic value lambda 1 of the correlation coefficient matrix R is more than or equal to lambda 2 and more than or equal to …, more than or equal to lambda p and more than 0, and calculating the contribution rate H and the accumulated contribution rate TH of the characteristic value, wherein the calculation formula is as follows:
Figure BDA0002424721110000023
Figure BDA0002424721110000024
calculating the accumulated contribution rate according to the formula (5), selecting the first a principal components to ensure that the accumulated contribution rate is more than 96%, and obtaining the linear combination of the first a principal components as follows
Figure BDA0002424721110000025
Wherein Fki(k is more than or equal to 1 and less than or equal to q, i is more than or equal to 1 and less than or equal to a) is the ith principal component of the time point k, and Za × p is taken from the unit characteristic matrix Zq × p of the correlation coefficient matrix R；
Step ten, solving a comprehensive evaluation function
wk＝H1Fk1+H2Fk2+…+HaFka(1≤k≤q)；
Step eleven, drawing the W value obtained in the step eleven in an X-Y coordinate system according to different time points by adopting a linear regression least square method to form a scatter diagram, wherein the X axis represents time, the Y axis represents a comprehensive evaluation function, and a linear regression equation is set as
Figure BDA0002424721110000026
The experimental value Y and the regression value
Figure BDA0002424721110000027
The deviation (C) is Y- (KX + b), and the mean of the squares of the deviations is calculated as E (C)2)＝E[(Y-(KX+b))2]＝E[|E-E(Y)-K(X-E(X)+(E(Y)-KE(X)-b))|2]＝σ2(Y)+K2σ2(X)-2KE[(X-E(X))(Y-E(Y))+(E(Y)-KE(X)-b)2]Wherein sigma2(X)＝E{[X-E(X)]2}σ2(X) is a variance of
Figure BDA0002424721110000031
Determining
Figure BDA0002424721110000032
b＝E(Y)-KE(X)；
Bringing K and b into
Figure BDA0002424721110000033
In the formula, a linear regression equation is obtained, with safety above the line and danger below the line.
Furthermore, the method is combined with other dynamic monitoring systems adopted in the construction process, such as a GPS monitoring system, when the numerical value is below, safety investigation should be timely carried out to retrieve data monitored by the GPS, the reason is analyzed, and hidden dangers are eliminated.
The method introduces the confidence concept of unknown rational numbers into the evaluation calculation, so that the result is more reasonable and accurate; the evaluation object which accords with actual construction is modified, the accuracy of the expert scoring is improved, the main component analysis method can be used for evaluating the safety construction of the subway tunnel construction more comprehensively, comprehensively and effectively, hidden dangers are found in time, safety accidents are reduced, and the life and property safety of people is guaranteed.
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FIG. 1 shows a method for early warning of tunnel safety construction;
fig. 2 illustrates an embodiment of a comprehensive evaluation function.
Detailed Description
The present invention will be described in further detail with reference to the following embodiments and the accompanying drawings.
Embodiment safety construction early warning method for tunnel with special stratum with soft top and hard bottom
Construction site: this patent takes a certain subway station in Qingdao as an example, and this tunnel cave body is mainly located in slightly weathering volcanic rock and metamorphic rock, and the stratum is soft on the top and hard down, and the stable difference is great, the incident appears more easily. The safety evaluation process of the invention in the whole tunnel excavation process is explained in detail.
The method comprises the following steps: reference is made to FIG. 1
Step one, 5 experts in the aspect of tunnel safety construction are summoned and scored; the scoring content comprises five aspects of the ambient environment condition, the risk of the building enclosure, the instability and collapse risk of the foundation pit, the safety behavior of constructors and management factors; each full score is 10 points, the scoring period is once a week, and specific reference contents in each aspect are as follows:
1.1 ambient conditions: the settlement of the earth surface, the cracking inclination degree of surrounding buildings, the damage and leakage of underground pipelines, the smoothness of drainage and exhaust, the large-area descending of the vault of the tunnel and the damage degree of primary lining.
1.2 envelope risks: piping sand, fracture damage, overall instability, cracking and leakage.
1.3 risk of instability and collapse of foundation pit: whether the foundation pit detection scheme is reasonable in design, whether the load around the foundation pit is overlarge, whether the excavation sequence is correct, whether the excavation exceeds the standard, whether the support erection is timely and whether the support dismantling is consistent and appropriate.
1.4 safety behavior of constructors: whether the operation of constructors is accurate comprises the operation and the placement of the machine, the construction sequence, whether workers wear safety protection articles or not, whether the workers work in a fatigue way or not, whether the treatment of dangerous goods such as inflammable and explosive materials is proper or not.
1.5 management factors: supervising whether to be qualified or not, safety education and training, implementation of various regulations and regulations, establishment of emergency plans, implementation of safety organization work and the like.
Collecting 5 scoring results C of one index; calculating the credibility ab of 5C values, and determining the confidence interval [ a, b](ii) a Computing confidence distribution function
Figure BDA0002424721110000034
Taking a scoring average value x of a confidence interval with higher confidence; calculating a scoring mean value x corresponding to each index according to the method;
for example, a score for ambient conditions as in table 1.
TABLE 1 Scoring of ambient conditions
Figure BDA0002424721110000041
So, take x to 7.
Calculating a risk index scoring average value:
Figure BDA0002424721110000042
in the formula, kijDenotes the score, n, of expert i on index jjRepresents the number of scoring experts and,
Figure BDA0002424721110000043
an average value representing the index score of the expert;
step three, calculating sigmajFor the standard deviation of index j, the calculation formula is:
Figure BDA0002424721110000044
step four, solving a variation coefficient:
Figure BDA0002424721110000045
in the formula, vjRepresenting the variation coefficient of the index j, namely the fluctuation condition of the index j marked by the expert; v. ofjThe value of (A) is bounded by 0.16, and the smaller the value is than 0.16, the higher the consistency of experts is, and the more suitable the value is used as the evaluation index of the subway construction safety risk; conversely, the larger the value ratio is 0.16, the less suitable this evaluation index is, and vjIf more than 0.16, replacing the new scoring index, and repeating the steps until v is higher than vj≤0.16；
Coefficient of variation vjThe determination of (a) is shown in table 2.
TABLE 2 determination of the coefficient of variation
Figure BDA0002424721110000046
Thus the first set of coefficients of variation passes and the second set fails. A first set of scoring data is employed.
Step five, extracting the mean value of the confidence interval with higher credibility, and setting an original data matrix
Figure BDA0002424721110000047
Specific data are shown in table 3.
TABLE 3 original data matrix
Figure BDA0002424721110000048
Figure BDA0002424721110000051
Wherein xij(i is more than or equal to 1 and less than or equal to q, j is more than or equal to 1 and less than or equal to p) represents the evaluation score of the safety evaluation factors at the time point, namely p safety evaluation factors exist in the original data, and q evaluation time points exist in the original data;
step six, carrying out data standardization on the original data matrix (1), and setting the standardized data matrix S as (S)ij)q×p
Figure BDA0002424721110000052
Specific data are shown in table 4.
Table 4. normalized data matrix S ═ S (S)ij)q×p
0.5536 0.4816 0.2176 0.6736 1.584
1.192 6.592 1.312 2.752 1.8
0.192 2.736 0.784 0.48 0.816
1.832 4.112 0.992 8.592 3.4
1.3376 4.1936 1.0496 1.7616 3.136
Step seven, the normalized data matrix S is equal to (S)ij)q×pEstablishing a correlation coefficient matrix R ═ R (R) of the variablesij)q×p
Figure BDA0002424721110000053
Specific data are shown in table 5.
TABLE 5 correlation coefficient matrix R
39 38 38.6 32.2 36.8
38 40.2 39 32.4 37
38.6 39 39 32.8 36.4
32.2 32.4 32.8 29.2 30.2
36.8 37 36.4 30.2 37.6
Step eight, solving the characteristic value lambda 1 of the correlation coefficient matrix R is more than or equal to lambda 2 and more than or equal to …, more than or equal to lambda p and more than 0, and calculating the contribution rate H and the accumulated contribution rate TH of the characteristic value, wherein the calculation formula is as follows:
Figure BDA0002424721110000054
Figure BDA0002424721110000055
specific data are shown in table 6.
TABLE 6. contribution rate and cumulative contribution rate
Figure BDA0002424721110000056
Figure BDA0002424721110000061
Calculating the accumulated contribution rate according to the formula (5), selecting the first a principal components to ensure that the accumulated contribution rate is more than 80%, and obtaining the linear combination of the first a principal components as follows
Figure BDA0002424721110000062
Wherein Fki(k is more than or equal to 1 and less than or equal to q, i is more than or equal to 1 and less than or equal to a) is the ith principal component of the time point k, and Za × p is taken from a unit feature matrix Zq × p of the correlation coefficient matrix R;
step ten, solving a comprehensive evaluation function
wk＝H1Fk1+H2Fk2+…+HaFka(1≤k≤q)；
Specific data are shown in table 7.
TABLE 7 evaluation of the comprehensive evaluation function
w1 w2 w3 w4 w5
12.616 13.09 14.19 13.08 11.658
Step eleven, drawing the W value obtained in the step eleven in an X-Y coordinate system according to different time points by adopting a linear regression least square method to form a scatter diagram, wherein the X axis representsTime and Y axis represent a comprehensive evaluation function, and a linear regression equation is set as
Figure BDA0002424721110000063
The experimental value Y and the regression value
Figure BDA0002424721110000064
The deviation (C) is Y- (KX + b), and the mean of the squares of the deviations is calculated as E (C)2)＝E[(Y-(KX+b))2]＝E[|E-E(Y)-K(X-E(X)+(E(Y)-KE(X)-b))|2]＝σ2(Y)+K2σ2(X)-2KE[(X-E(X))(Y-E(Y))+(E(Y)-KE(X)-b)2]
Wherein sigma2(X)＝E{[X-E(X)]2}σ2(X) is a variance of
Figure BDA0002424721110000065
Can determine
Figure BDA0002424721110000066
b ═ e (y) -ke (x); bringing K and b into
Figure BDA0002424721110000067
In the formula, a linear regression equation is obtained, with safety above the line and danger below the line.
Specific data are shown in table 8.
TABLE 8 calculation results
k b y
-0.47 14.3 y＝-0.47x+14.3
As shown in FIG. 2, w1、w2And w5Unsafe, w3And w4And (4) safety. This means that the safety criteria in the first, second and fifth week are not met, and that the checking should be done in time according to the scoring data, e.g. an index with a score of less than five. The case does not reach the standard in the aspect of management in the first week, does not reach the standard in the aspect of safety behaviors of constructors in the second week, and does not reach the standard in the aspect of an enclosure structure in the fifth week. Although there were deficiencies in the individual indicators for the third and fourth weeks, the overall W value met the safety standards. Corresponding safety measures are taken in time for the substandard indexes, hidden dangers are eliminated, and smooth construction of the project is guaranteed.
The method is also combined with other dynamic monitoring systems adopted in the construction process, such as a GPS monitoring system, when the W numerical value display is unsafe, the data monitored by the GPS is called, safety investigation should be carried out in time, reasons should be analyzed, hidden dangers are eliminated, and smooth proceeding of the engineering is guaranteed.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.

Claims (1)

1. A safety construction early warning method for a subway station is characterized by comprising the following steps:
the method comprises the steps of firstly, collecting at least 5 experts in all aspects of tunnel construction, scoring, collecting 5 scoring results C of an index, calculating the distribution probability α of 5C values, and determining confidence intervals [ a, b ]](ii) a Computing confidence distribution function
Figure FDA0002424721100000018
And taking a scoring average value of a confidence interval with higher reliability; calculating a scoring average value corresponding to each index according to the method;
calculating a risk index scoring average value:
Figure FDA0002424721100000011
in the formula, kijDenotes the score, n, of expert i on index jjRepresents the number of scoring experts and,
Figure FDA0002424721100000012
an average value representing the index score of the expert;
step three, calculating sigmajFor the standard deviation of index j, the calculation formula is:
Figure FDA0002424721100000013
step four, solving a variation coefficient:
Figure FDA0002424721100000014
in the formula, vjRepresenting the variation coefficient of the index j, namely the fluctuation condition of the index j marked by the expert; v. ofjThe value of (A) is bounded by 0.16, and the smaller the value is than 0.16, the higher the consistency of experts is, and the more suitable the value is used as the evaluation index of the subway construction safety risk; conversely, the larger the value ratio is 0.16, the less suitable this evaluation index is, and vjIf more than 0.16, replacing the new scoring index, and repeating the steps until v is higher than vj≤0.16；
Step five, extracting the mean value of the confidence interval with higher credibility, and setting an original data matrix
Figure FDA0002424721100000019
Wherein xij(i is more than or equal to 1 and less than or equal to q, j is more than or equal to 1 and less than or equal to p) represents the evaluation score of the safety evaluation factors at the time point, namely p safety evaluation factors exist in the original data, and q evaluation time points exist in the original data;
step six, aligning the original numberCarrying out data standardization according to the matrix (1), and setting a standardized data matrix S as (S)ij)q×p
Figure DEST_PATH_IMAGE001
Step seven, the normalized data matrix S is equal to (S)ij)q×pEstablishing a correlation coefficient matrix R ═ R (R) of the variablesij)q×p
Figure DEST_PATH_IMAGE002
Step eight, solving the characteristic value lambda 1 of the correlation coefficient matrix R is more than or equal to lambda 2 and more than or equal to …, more than or equal to lambda p and more than 0, and calculating the contribution rate H and the accumulated contribution rate TH of the characteristic value, wherein the calculation formula is as follows:
Figure FDA0002424721100000017
Figure FDA0002424721100000021
calculating the accumulated contribution rate according to the formula (5), selecting the first a principal components to ensure that the accumulated contribution rate is more than 96%, and obtaining the linear combination of the first a principal components as follows
Figure FDA0002424721100000022
Wherein Fki(k is more than or equal to 1 and less than or equal to q, i is more than or equal to 1 and less than or equal to a) is the ith principal component of the time point k, and Za × p is taken from a unit feature matrix Zq × p of the correlation coefficient matrix R;
step ten, solving a comprehensive evaluation function
wk＝H1Fk1+H2Fk2+···+HaFka(1≤k≤q)；
Step eleven, adopting a linear regression least square method to obtain the W value obtained in the step eleven according to different timeDrawing the intermediate points in an X-Y coordinate system to form a scatter diagram, wherein the X axis represents time, the Y axis represents a comprehensive evaluation function, and a linear regression equation is set as
Figure FDA0002424721100000023
The experimental value Y and the regression value
Figure FDA0002424721100000027
The deviation (C) is Y- (KX + b), and the mean of the squares of the deviations is calculated as E (C)2)＝E[(Y-(KX+b))2]＝E[|E-E(Y)-K(X-E(X)+(E(Y)-KE(X)-b))|2]＝σ2(Y)+K2σ2(X)-2KE[(X-E(X))(Y-E(Y))+(E(Y)-KE(X)-b)2]
Wherein sigma2(X)＝E{[X-E(X)]2} σ2(X) is a variance of
Figure FDA0002424721100000024
And
Figure FDA0002424721100000025
determining
Figure FDA0002424721100000026
b＝E(Y)-KE(X)；
Bringing K and b into
Figure FDA0002424721100000028
In the formula, a linear regression equation is obtained, with safety above the line and danger below the line.
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