CN109931109B - Tunnel construction dynamic collapse safety comprehensive early warning method based on multi-metadata - Google Patents
Tunnel construction dynamic collapse safety comprehensive early warning method based on multi-metadata Download PDFInfo
- Publication number
- CN109931109B CN109931109B CN201910317156.5A CN201910317156A CN109931109B CN 109931109 B CN109931109 B CN 109931109B CN 201910317156 A CN201910317156 A CN 201910317156A CN 109931109 B CN109931109 B CN 109931109B
- Authority
- CN
- China
- Prior art keywords
- early warning
- information
- construction
- monitoring
- risk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Lining And Supports For Tunnels (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
A tunnel construction dynamic collapse safety comprehensive early warning method based on multi-element data comprises the steps of constructing a tunnel construction dynamic collapse engineering database by collecting data, adopting the multi-element data to identify construction risks according to dynamic parameters of the tunnel construction dynamic collapse engineering database, analyzing unit data in the multi-element data respectively, and judging the risk level of the unit data; and finally, the comprehensive early warning evaluation information is informed to constructors and monitoring personnel in a prompting and displaying mode, a perfect early warning system is provided for tunnel collapse safety early warning, the process is shared in real time, the construction and monitoring of the workers are facilitated, the reliability and the safety are high, and the economic practicability is high.
Description
Technical Field
The invention relates to a tunnel construction dynamic collapse safety comprehensive early warning method based on multivariate data, and belongs to the technical field of tunnel construction safety monitoring.
Background
With the rapid development of tunnel construction, tunnel construction safety is more and more concerned. For a long time before the new Olympic method appears, a plurality of tunnels are worked according to the previous construction case. Because of the particularity of the tunnel engineering, the collapse disaster situation cannot be predicted before excavation, so that the engineering is planned only by the experience of former mobile workers or the mobile workers must have some blindness. Since the new Olympic method appears, the method can realize timely monitoring, namely, the collected surrounding rock information is put into the continuously changed design and operation scope while the tunnel is tunneled, and the tunnel construction safety is improved to a great extent. However, at present, a dynamic landslide safety comprehensive early warning system which can completely consider multivariate data does not exist, and the existing system is single and depends too much on monitoring and measuring, so that the working boundary of disaster-specific monitoring and early warning and monitoring and measuring early warning is fuzzy. In actual engineering, after a disaster is discovered through monitoring and measuring, disaster pre-warning is generally performed by continuing monitoring and measuring, and the disaster is not monitored and pre-warned under the condition that a disaster body objectively exists.
Disclosure of Invention
The invention aims to solve the technical problem of providing a tunnel construction dynamic collapse safety comprehensive early warning method based on multi-metadata, and overcoming the defects of the prior art.
In order to solve the technical problems, the invention is realized by the following technical scheme: a tunnel construction dynamic collapse safety comprehensive early warning method based on metadata comprises the following steps:
s1, collecting data, and constructing a tunnel construction dynamic collapse engineering database;
collecting basic information before tunnel construction through investigation, design and construction files, and collecting dynamic information in tunnel construction;
s2, risk identification;
according to dynamic parameters of a tunnel construction dynamic collapse engineering database, adopting multivariate data to carry out construction risk identification, wherein the multivariate data comprises geological risk early warning before construction, geological risk early warning during construction, advance forecast early warning, precursor information early warning and monitoring information early warning;
s3, unit data analysis;
respectively carrying out unit data analysis on the geological risk early warning, advance forecast early warning, precursor information early warning and monitoring information early warning, and judging the risk level of the geological risk early warning, advance forecast early warning, precursor information early warning and monitoring information early warning;
s4, fusing the multi-element data to obtain construction dynamic collapse safety comprehensive early warning evaluation information;
fusing according to the parallelity and relevance of the multi-element data to obtain the total risk level before tunnel construction, the comprehensive possibility level of collapse in construction and the comprehensive probability scale level;
s5, issuing results, and sharing the early warning process in real time;
and early warning is carried out on the construction dynamic collapse safety comprehensive early warning evaluation information in a prompting and displaying mode through terminal equipment.
In the step s1, the constructor performs overall design and overall survey by combining with the surveying instrument, and collects the basic information before tunnel construction; in the construction process, the survey and the real-time monitoring of the sections and the sections are carried out, and the dynamic information in the tunnel construction is collected.
In the step s2, the data of the geological risk early warning, the advance forecast early warning and the precursor information early warning are managed through section and section information; and monitoring information early warning data is managed and issued through section information.
The geological risk early warning comprises risk assessment before construction, safety assessment before construction and safety assessment during construction, wherein the risk assessment before construction comprises overall risk and special risk which are assessed according to a scoring method; before-construction safety evaluation and in-construction safety evaluation define tunnel collapse risk influence factor weights according to an analytic hierarchy process, and then a fuzzy evaluation matrix is constructed for evaluation;
the advanced forecasting early warning is to obtain the scale grade and the possibility grade of the possible collapse disaster according to the advanced geological forecasting result in the tunnel construction dynamic collapse engineering database;
the precursor information early warning is that the collapse grade and the collapse result are artificially given according to the tunnel collapse precursor representation observed in the monitoring and measuring process;
the monitoring information early warning comprises monitoring measurement early warning and special monitoring early warning, and the monitoring information early warning outputs an early warning threshold value and judges and outputs an early warning grade according to geological information of a tunnel section monitored by a measuring instrument.
In the aforementioned step s3, the unit data analysis includes processing the data of the monitoring points inside the unit, regression analysis of the data, and comparison and determination of the data and the threshold, where the prediction time point is advanced in the regression analysis, and the prediction result is not included in the multi-metadata fusion.
In the above-mentioned analysis of the unit data,
geological risk early warning: the risk is not processed or the maximum value is neglected, the risk is reevaluated after the risk is processed, and the reevaluated value exists all the time and participates in the dynamic monitoring evaluation;
advanced forecasting and early warning: when the secondary evaluation is the maximum value, the risks are not processed and always participate in dynamic monitoring evaluation, and the risks need to be reevaluated after sections are communicated;
precursor information early warning: when the maximum value is obtained in the secondary evaluation, the precursor information early warning item can be artificially judged to be the maximum value, the precursor information early warning result is only reserved for a short time period, and the time period is 1-7 days or is eliminated after the time period is run through;
monitoring and measuring early warning, and special monitoring and early warning: and when the secondary evaluation takes the maximum value, the next piece of data arrives for continuous evaluation, and when a plurality of pieces of data are in one day, the maximum value of the evaluation results of the plurality of pieces of data is taken to participate in dynamic evaluation.
In the step s4, when the multivariate data is fused, the same section information is fused during the same section evaluation, the section-contained relation is fused by searching the section-contained relation with the section when the section-contained section evaluation is performed, the independent section is mainly used when the information is incomplete, and finally the comprehensive early warning grade is output by monitoring the section management.
When the multi-metadata are fused, the overall risk assessment result is not fused;
before construction, taking a larger value of the special risk assessment and construction safety assessment results as a comprehensive early warning result;
in construction, the results of special risk assessment and construction safety assessment are taken as large values to participate in dynamic comprehensive monitoring and early warning; when the advanced geological forecast, the precursor information, the monitoring measurement and the special monitoring exist, the priority of the precursor information is the highest; when the system does not adopt precursor information, the comprehensive grade is a large value for the multi-element data grade comparison.
When the collapse risk is less than grade III, monitoring and early warning are carried out by fusing monitoring, measuring and early warning information and other multivariate data information without special monitoring and early warning.
The unit data analysis and the multivariate data fusion conclusion are all calibrated by red, orange, yellow and blue, wherein the red represents the highest grade IV of the collapse possibility degree or the collapse possibility scale degree, the orange represents the extremely high grade III, the yellow represents the medium grade II, and the blue represents the low grade I.
Compared with the prior art, the invention discloses a tunnel construction dynamic collapse safety comprehensive early warning method based on multivariate data, which comprises the steps of constructing a tunnel construction dynamic collapse engineering database by collecting data, adopting the multivariate data to identify construction risks according to dynamic parameters of the tunnel construction dynamic collapse engineering database, analyzing unit data in the multivariate data respectively, and judging the risk level of the unit data; and finally, the comprehensive early warning evaluation information is informed to constructors and monitoring personnel in a prompting and displaying mode, so that a perfect early warning system is provided for tunnel collapse safety early warning.
The invention enables the tunnel construction monitoring and early warning to be digital, and the tunnel construction monitoring and early warning system fuses the overall risk and special risk early warning information before tunnel construction and various early warning information and monitoring information in construction to obtain perfect comprehensive early warning and evaluation information of dynamic collapse safety of construction, shares the process in real time, facilitates the construction and monitoring of workers, and has high construction efficiency, reliability and safety.
In addition, the construction risk identification is carried out by adopting the multivariate data, the defect caused by only using monitoring measurement in the existing tunnel construction is overcome, the multivariate data is analyzed, monitoring measurement early warning or special monitoring early warning or two monitoring early warnings can be adopted in the tunnel construction, the monitoring resources can be reasonably distributed, a large amount of manpower and material resources are saved, the convenience and the flexibility are high, and the economic practicality is strong.
Drawings
Fig. 1 is a general flow chart of the present invention.
FIG. 2 is a flow chart of multivariate data analysis according to the invention.
FIG. 3 is a flow chart of multivariate data fusion in accordance with the invention.
FIG. 4 is a flow chart of the geological risk early warning analysis of the present invention.
Fig. 5 is a flow chart of the monitoring information early warning analysis according to the present invention.
Detailed Description
The technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiment of the present invention:
as shown in fig. 1 to 5, a comprehensive early warning method for tunnel construction dynamic collapse safety based on metadata includes the following steps:
s1, collecting data, and constructing a tunnel construction dynamic collapse engineering database;
collecting basic information before tunnel construction through investigation, design and construction files, and collecting dynamic information in tunnel construction;
s2, risk identification, wherein the risk identification comprises pre-construction evaluation and in-construction evaluation;
according to dynamic parameters of a tunnel construction dynamic collapse engineering database, adopting multivariate data to carry out construction risk identification, wherein the multivariate data comprises geological risk early warning before construction; the evaluation in construction comprises geological risk early warning, advance forecast early warning, precursor information early warning and monitoring information early warning;
s3, unit data analysis;
respectively carrying out unit data analysis on the geological risk early warning, advance forecast early warning, precursor information early warning and monitoring information early warning, and judging the risk level of the geological risk early warning, advance forecast early warning, precursor information early warning and monitoring information early warning;
s4, fusing the multi-element data to obtain construction dynamic collapse safety comprehensive early warning evaluation information;
fusing according to the parallelity and relevance of the multi-element data to obtain the total risk level before tunnel construction, the comprehensive possibility level of collapse in construction and the comprehensive probability scale level;
s5, issuing results, and sharing the early warning process in real time;
and displaying the comprehensive early warning and evaluation information of the construction dynamic collapse safety to constructors, managers and monitoring personnel through terminal equipment.
In step s1, the constructor combines the surveying instrument to carry out overall design and overall survey, and collects the basic information before tunnel construction; in the construction process, the survey and the real-time monitoring of the sections and the sections are carried out, and the dynamic information in the tunnel construction is collected.
Step s2, combining the foundation information before tunnel construction to identify risks before construction; and (4) combining dynamic information in tunnel construction to identify risks in construction.
In the risk identification, data of geological risk early warning, advance forecast early warning and precursor information early warning are managed through section and section information, and monitoring information early warning results are managed and issued through the section information.
The geological risk early warning comprises risk assessment before construction, safety evaluation before construction and safety evaluation during construction. The data of the geological risk early warning are derived from investigation, design and construction files, wherein the investigation is a detailed investigation file, the design is a construction drawing design file, and the construction is a construction organization design file.
The risk assessment before geological risk early warning construction comprises a total risk and a special risk, and both the total risk and the special risk are assessed according to a scoring method; and (4) performing risk assessment before construction to obtain the total risk level (I-IV) and the special collapse risk level (I-IV) of the tunnel construction.
The overall risk assessment data index is specifically shown in the following table:
the overall risk size calculation formula is as follows:
R＝G(A+L+S+C)，
wherein G refers to geological assigned values around the tunnel, vertical shaft and inclined shaft routes; a refers to the assigned value of a standard excavation section; l is the value assigned by the length from the tunnel population to the exit (the lengths of the tunnel vertical shaft and the inclined shaft are calculated when the length of the tunnel is calculated); s is a value assigned in the form of a tunnel exit group which becomes a channel; and C is the assigned value of the topographic condition of the tunnel portal.
The overall risk assessment before construction is assessed according to scoring standards in safety risk assessment guidelines (trial) for construction of highway bridges and tunnel projects, assessment grades (I-IV) are divided according to interval probability values to which ranges of assessment results belong, the assessment results are obtained according to an assessment grade table, the assessment results are calibrated by red, orange, yellow and blue, and the overall risk grade is as follows: red for very high risk (iv), orange for high risk (iii), yellow for medium risk (ii), blue for low risk (i), as shown in the following table:
the evaluation data of the special risk comprise collapse accident possibility evaluation indexes and safety management evaluation indexes; the collapse accident possibility evaluation index is specifically shown in the following table:
collapse accident possibility evaluation index
The safety management evaluation indexes are specifically shown in the following table:
the calculation formula of the special risk is as follows:
P＝γ·(C×A+B+D+E+F)，
wherein, gamma is a reduction coefficient, the evaluation index score is calculated by a formula M ═ a + b + c + d + e + f + g + h, and the gamma value can be obtained according to a reduction coefficient comparison table.
Reduction coefficient comparison table
| Calculating the score M | Reduction factor gamma |
| M＞12 | 1.2 |
| 9≤M≤12 | 1.1 |
| 6≤M≤8 | 1 |
| 3≤M≤5 | 0.9 |
| 0≤M≤2 | 0.8 |
The evaluation method comprises the following steps: evaluating the possibility grade, the severity grade and the special risk grade of the collapse accident by adopting the calculated score P of the special risk, wherein the evaluation grades are IV-I grades from high to low according to the severity degree, and are calibrated by red, orange, yellow and blue, and the following table shows that:
the safety evaluation of the safety evaluation before and during geological risk early warning construction is carried out, tunnel collapse risk influence factor weight is defined according to an analytic hierarchy process, and then a fuzzy evaluation matrix is constructed for evaluation; the safety evaluation before and during construction is carried out by dividing fault broken zone collapse and non-fault broken zone collapse; and (4) before and during construction, safety evaluation is carried out to obtain the risk grade (I-IV) and the probability scale grade (I-IV) of collapse possibly occurring in tunnel construction.
The evaluation procedure was as follows: (which is prior art and not described in detail herein.)
Firstly, determining an index system, as shown in the following table:
fracture zone collapse-overall evaluation index and grade division
Non-fault fracture zone collapse-overall evaluation index and grade division
Secondly, determining the weights of the first-level index system and the second-level index system according to the index system in the first step, and referring to the following steps:
fracture zone collapse-overall evaluation index weight
Non-fault broken zone collapse-overall evaluation index weight
Thirdly, determining the membership degree and the membership degree matrix of the index,
according to the evaluation indexes in the step two, referring to the relation table of the evaluation indexes and the evaluation grades, and combining the membership function to obtain evaluation factor indexes:
relationship between evaluation index and evaluation grade
Finally, respectively calculating the evaluation factors mu according to the total collapse evaluation membership function calculation formula1，μ2，μ3，μ4The score of (a).
The calculation formula of the total collapse evaluation membership function is as follows:
① when ak，1＜ak，3The method comprises the following steps:
② when ak，1＞ak，3The method comprises the following steps:
fourthly, calculating a fuzzy vector
Respectively giving evaluation factors mu according to the membership function1，μ2，μ3，μ4Assigning a score of 1, 2, 3, 4 to obtain a fuzzy vectorF：
Step five, grading
The calculated blur vector F is compared with the following table to obtain a collapse probability rating (the scale rating procedure is the same).
Obtaining the probability grade of the tunnel disaster according to the evaluation value F
Dividing collapse possibility grade and collapse scale grade of safety evaluation before and during geological risk early warning construction, and calibrating by red, orange, yellow and blue:
tunnel collapse safety early warning grade standard
The evaluation information index of the advanced forecast early warning is an advanced geological forecast result, and a scale grade and a possibility grade of a possible collapse disaster are given by combining a relevant standard rule and relevant data and adopting an engineering comparison method, an experience judgment method, a quantitative analysis method and the like, and are shown in the following table:
rating of evaluation
The evaluation information indexes of the precursor information early warning are as follows:
precursor of collapse
1. The phenomenon of accelerated mutation occurs when the monitoring value of vault settlement or hole periphery convergence exceeds the limit or the deformation curve is out of limit.
2. The sprayed concrete surface exhibits transverse, longitudinal or uneven cracks.
3. The bedding, joint seams, fissures, etc. of the surrounding rock become large or open.
4. The number of rocks around the crack is sprayed out or the dust in the hole flies upward.
5. The arch crown or the arch shoulder has the appearance of falling blocks and slag, the inverted arch has the appearance of cracking and the like.
6. The support structure is distorted or invaded the limit, and the wrong pole of lock foot cuts off.
7. The tunnel seeps water and drips water and suddenly worsens or becomes muddy.
Note: if the collapse grade is judged through other phenomena, the evaluation basis can be noted.
Evaluation suggestion: and (3) evaluating the possibility grade and scale grade of the collapse disaster by adopting an engineering comparison method, an experience judgment method, a quantitative analysis method and the like according to the collected precursor information, the relevant standard rules and the relevant data.
According to the tunnel collapse precursor representation observed in the tunnel monitoring and measuring process, the engineering classification method, the experience judgment method, the quantitative analysis method and the like are adopted in combination with relevant standard rules and relevant data, and collapse grade and collapse results are given artificially.
The precursor information is judged by monitoring professionals or related professionals according to experience and a precursor information criterion for determining tunnel collapse in advance. The final grade judgment of the precursor information is equivalent to the judgment of the acceptance condition of the whole set of evaluation method by a professional, and is used for making up the mistake or deficiency of the evaluation system.
The monitoring information early warning comprises monitoring measurement early warning and special monitoring early warning.
The monitoring index of the monitoring information early warning is tunnel section geological information, and an engineering comparison method, an experience judgment method, a quantitative analysis method and the like are adopted by combining relevant standard rules and relevant data to obtain an early warning result.
The special monitoring and early warning is that measuring instruments such as a convergence meter, a level meter, a total station, a measuring robot, a soil pressure box, a steel bar meter, a strain gauge, a triangular weir, a rectangular weir and the like are adopted to monitor information such as tunnel section stress, deformation and the like, and early warning grades are judged and output according to input early warning threshold values, and early warning results are obtained as shown in the following table:
the monitoring measurement and the special monitoring early warning evaluation are evaluated according to the percentage that the measured value reaches the threshold value, and the percentage early warning is as follows: the early warning (IV) is red when the measured value reaches more than 80% of the threshold value, the early warning (III) is orange when the measured value reaches more than 60% of the threshold value, the early warning (II) is yellow when the measured value reaches more than 40% of the threshold value, and the early warning (I) is blue when the measured value reaches 0-40% of the threshold value. The early warning modes comprise accumulative threshold early warning, rate threshold early warning and change value threshold early warning, and when an actual value exceeds the accumulative threshold, only the rate or change value early warning can be selected.
In step s3, the unit data analysis includes processing the data of the monitoring points inside the unit, regression analysis of the data, and comparison and determination of the data and the threshold, where the prediction time point of the regression analysis is advanced, and the prediction result is not included in the multi-element data fusion.
In the analysis of the unit data,
geological risk early warning: the risk is not processed or the maximum value is neglected, the risk is reevaluated after the risk is processed, and the reevaluated value exists all the time and participates in the dynamic monitoring evaluation;
advanced forecasting and early warning: when the secondary evaluation is the maximum value, the risks are not processed and always participate in dynamic monitoring evaluation, and the risks need to be reevaluated after sections are communicated;
precursor information early warning: when the maximum value is obtained in the secondary evaluation, the precursor information early warning item can be artificially judged to be the maximum value, the precursor information early warning result is only reserved for a short time period, and the time period is 1-7 days or is eliminated after the time period is run through;
monitoring and measuring early warning, and special monitoring and early warning: and when the secondary evaluation takes the maximum value, the next piece of data arrives for continuous evaluation, and when a plurality of pieces of data are in one day, the maximum value of the evaluation results of the plurality of pieces of data is taken to participate in dynamic evaluation.
In step s4, when the multivariate data are fused, the multivariate data are managed by using uniform sections and zones, the monitoring information is managed by using the sections, when the same sections are evaluated, the same section information is fused, when the zones comprise the sections, the inclusion relationship of the zones is searched by using the sections for fusion, when the information is not complete, the independent sections are used as the main, and finally, the comprehensive early warning grade is output by monitoring the section management.
During the multi-data fusion, according to the results of the unit data analysis and early warning, the following table is referred to for multi-data fusion, and finally, the possibility grade and the scale grade of the tunnel construction dynamic collapse safety comprehensive early warning under the multi-data are obtained, which are shown in the following table:
when the multi-metadata is fused, the overall risk assessment result is not fused; before construction, taking a larger value of the special risk assessment and construction safety assessment results as a comprehensive early warning result; in the construction process, safety assessment before construction is omitted, and the special risk assessment and construction safety assessment result takes a larger value to participate in dynamic comprehensive monitoring and early warning.
When the advanced geological forecast, the precursor information, the monitoring measurement and the special monitoring exist, the priority of the precursor information is the highest, and when the system does not adopt the precursor information, the comprehensive grade is that the grade of the multivariate data is compared and the value is larger.
When the collapse risk evaluated before construction is less than level III, special monitoring and early warning can be omitted, and monitoring and measuring information is fused with other information to carry out monitoring and early warning. The monitoring measurement execution standard is a monitoring measurement control standard in road tunnel construction technical specification (JTGF60-2009), railway tunnel monitoring measurement technical specification (Q/CR9218-2015) and a reserved deformation standard in road tunnel design specification (JTG _ D70-2004), and the peripheral displacement and the vault subsidence are selected according to the design reserved deformation or the reserved deformation in the specification.
The unit data analysis and the multivariate data analysis conclusion are respectively marked by red, orange, yellow and blue, wherein the red represents the degree of collapse possibility or the scale degree of the collapse possibility is highest (IV), the orange represents extremely high (III), the yellow represents medium (II), and the blue represents low (I).
In step s5, the result is issued by two parts of prompt and display, wherein the terminal equipment is a computer, a mobile phone and other display equipment, and the prompt mode is short message, mailbox and public number; the display mode is divided into indoor and on-site information display.
Claims (6)
1. A tunnel construction dynamic collapse safety comprehensive early warning method based on metadata is characterized by comprising the following steps:
s1, collecting data, and constructing a tunnel construction dynamic collapse engineering database;
performing overall design and overall investigation by a constructor in combination with a surveying instrument, and collecting basic information before tunnel construction; in the construction process, performing section and section exploration and real-time monitoring, and collecting dynamic information in tunnel construction;
s2, risk identification;
according to dynamic parameters of a tunnel construction dynamic collapse engineering database, adopting multivariate data to carry out construction risk identification, wherein the multivariate data comprises geological risk early warning, advance forecast early warning, precursor information early warning and monitoring information early warning; the data of the geological risk early warning, the advance forecast early warning and the precursor information early warning are managed through section and section information; monitoring information early warning data is managed and issued through section information;
s3, unit data analysis;
respectively carrying out unit data analysis on the information of geological risk early warning, advance forecast early warning, precursor information early warning and monitoring information early warning, and judging the risk level of the geological risk early warning, wherein:
geological risk early warning: the risk is not processed or the maximum value is neglected, the risk is reevaluated after the risk is processed, and the reevaluated value exists all the time and participates in the dynamic monitoring evaluation;
advanced forecasting and early warning: when the secondary evaluation is the maximum value, the risks are not processed and always participate in dynamic monitoring evaluation, and the risks need to be reevaluated after sections are communicated;
precursor information early warning: when the maximum value is obtained in the secondary evaluation, the precursor information early warning item can be artificially judged to be the maximum value, the precursor information early warning result is only reserved for a short time period, and the time period is 1-7 days or is eliminated after the time period is run through;
monitoring information early warning: when the secondary evaluation takes the maximum value, the next piece of data arrives for continuous evaluation, and when a plurality of pieces of data are in one day, the maximum value of the evaluation result of the plurality of pieces of data is taken to participate in dynamic evaluation;
s4, fusing the multi-element data to obtain construction dynamic collapse safety comprehensive early warning evaluation information;
fusing according to the parallelity and relevance of the multi-element data to obtain the total risk level before tunnel construction, the comprehensive possibility level and the comprehensive probability scale level of collapse in construction,
when the multivariate data is fused, the same section information is fused during the same section evaluation, the section containing section evaluation is fused by searching the containing relation of the section by the section, the information is mainly independent section when not complete, and finally the comprehensive early warning grade is output by monitoring the section management;
s5, issuing results, and sharing the early warning process in real time;
and early warning is carried out on the construction dynamic collapse safety comprehensive early warning evaluation information in a prompting and displaying mode through terminal equipment.
2. The comprehensive early warning method for tunnel construction dynamic collapse safety based on the multivariate data as claimed in claim 1, is characterized in that: in step s2, the geological risk early warning comprises pre-construction risk assessment, pre-construction safety assessment and in-construction safety assessment, wherein the pre-construction risk assessment comprises overall risk and special risk which are assessed according to a scoring method; before-construction safety evaluation and in-construction safety evaluation define tunnel collapse risk influence factor weights according to an analytic hierarchy process, and then a fuzzy evaluation matrix is constructed for evaluation;
the advanced forecasting early warning is to obtain the scale grade and the possibility grade of the possible collapse disaster according to the advanced geological forecasting result in the tunnel construction dynamic collapse engineering database;
the precursor information early warning is that the collapse grade and the collapse result are artificially given according to the tunnel collapse precursor representation observed in the monitoring and measuring process;
the monitoring information early warning comprises monitoring measurement early warning and special monitoring early warning, and the monitoring information early warning outputs an early warning threshold value and judges and outputs an early warning grade according to geological information of a tunnel section monitored by a measuring instrument.
3. The comprehensive early warning method for tunnel construction dynamic collapse safety based on the multivariate data as claimed in claim 1, is characterized in that: in step s3, the unit data analysis includes processing the data of the monitoring points inside the unit, regression analysis of the data, and comparison and determination of the data and the threshold, where the prediction time point of the regression analysis is advanced, and the prediction result is not included in the multi-element data fusion.
4. The comprehensive early warning method for tunnel construction dynamic collapse safety based on the multivariate data as claimed in claim 1, is characterized in that: in step s4, when the metadata are fused, the overall risk assessment result is not fused;
before construction, taking a larger value of the special risk assessment and construction safety assessment results as a comprehensive early warning result;
in construction, the results of special risk assessment and construction safety assessment are taken as large values to participate in dynamic comprehensive monitoring and early warning; when the advanced geological forecast, the precursor information, the monitoring measurement and the special monitoring exist, the priority of the precursor information is the highest; when the system does not adopt precursor information, the comprehensive grade is a large value for the multi-element data grade comparison.
5. The comprehensive early warning method for tunnel construction dynamic collapse safety based on the multivariate data as claimed in claim 4, wherein the early warning method comprises the following steps: when the collapse risk is less than grade III, monitoring and early warning are carried out by fusing monitoring, measuring and early warning information and other multivariate data information without special monitoring and early warning.
6. The comprehensive early warning method for tunnel construction dynamic collapse safety based on the multivariate data as claimed in claim 1, is characterized in that: the unit data analysis and the multivariate data fusion conclusion are all calibrated by red, orange, yellow and blue, wherein red represents the highest level IV of collapse possibility degree or collapse possible scale degree, orange represents the highest level III, yellow represents the middle level II, and blue represents the low level I.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910317156.5A CN109931109B (en) | 2019-04-19 | 2019-04-19 | Tunnel construction dynamic collapse safety comprehensive early warning method based on multi-metadata |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910317156.5A CN109931109B (en) | 2019-04-19 | 2019-04-19 | Tunnel construction dynamic collapse safety comprehensive early warning method based on multi-metadata |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109931109A CN109931109A (en) | 2019-06-25 |
| CN109931109B true CN109931109B (en) | 2020-09-01 |
Family
ID=66990431
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910317156.5A Active CN109931109B (en) | 2019-04-19 | 2019-04-19 | Tunnel construction dynamic collapse safety comprehensive early warning method based on multi-metadata |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109931109B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110243335B (en) * | 2019-07-16 | 2024-03-22 | 贵州省交通规划勘察设计研究院股份有限公司 | Automatic monitoring and early warning device and method for loose ring deformation of surrounding rock in tunnel construction |
| CN112330002B (en) * | 2020-10-27 | 2023-12-08 | 合肥泽众城市智能科技有限公司 | Urban ground collapse disaster comprehensive monitoring and early warning method and system |
| CN112418645B (en) * | 2020-11-18 | 2024-02-02 | 云南航天工程物探检测股份有限公司 | Tunnel engineering full life cycle safety evaluation method |
| CN112610277B (en) * | 2020-12-08 | 2022-04-12 | 中铁隧道局集团有限公司 | Method for rapidly predicting geological disaster risk of water-rich weak surrounding rock tunnel |
| CN112863119B (en) * | 2020-12-31 | 2022-04-01 | 中铁建设集团基础设施建设有限公司 | Engineering construction safety risk early warning system and method |
| CN113359185B (en) * | 2021-05-17 | 2023-12-29 | 中交第二公路工程局有限公司 | Tunnel comprehensive advanced geological forecast intelligent early warning system and implementation method thereof |
| CN113640896A (en) * | 2021-07-07 | 2021-11-12 | 中交第二公路工程局有限公司 | Tunnel unfavorable geology risk early warning display system |
| CN113822227A (en) * | 2021-10-19 | 2021-12-21 | 杭州交通工程监理咨询有限公司 | Tunnel engineering construction risk monitoring method, device and medium |
| CN114446014B (en) * | 2021-12-30 | 2023-11-21 | 中铁十七局集团城市建设有限公司 | Highway tunnel earthquake early warning method and system |
| CN115062809A (en) * | 2022-01-26 | 2022-09-16 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Tunnel advanced geological forecast risk early warning method, system and storage medium |
| CN116562656B (en) * | 2023-07-07 | 2023-11-03 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Tunnel construction geological disaster early warning and prevention and control intelligent decision method and auxiliary platform |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8005100B2 (en) * | 2006-09-21 | 2011-08-23 | Active Control Technology Inc. | Network for confined hazardous or other extreme environments |
| CN101458069B (en) * | 2008-12-30 | 2011-02-23 | 中铁二十四局集团有限公司 | Tunnel wall rock deformation monitoring method and monitoring system thereof |
| CN103903088B (en) * | 2014-03-12 | 2018-11-20 | 王峰 | A kind of high speed railway construction engineering drawing lessons control system and method |
| CN105736050A (en) * | 2014-12-08 | 2016-07-06 | 中国水利水电第四工程局有限公司 | Tunnel construction safety monitor method and system |
| CN108399483B (en) * | 2018-01-18 | 2021-10-08 | 山东大学 | Underground engineering water inrush and mud inrush risk evaluation method based on multi-precursor information |
-
2019
- 2019-04-19 CN CN201910317156.5A patent/CN109931109B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109931109A (en) | 2019-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109931109B (en) | Tunnel construction dynamic collapse safety comprehensive early warning method based on multi-metadata | |
| CN110210776B (en) | Tunnel construction dynamic risk assessment method based on monitoring measurement | |
| CN111074954A (en) | Deep foundation pit engineering safety monitoring system based on BIM | |
| CN110610285A (en) | Underground metal mine goaf risk grading evaluation method | |
| CN111160644B (en) | Railway route selection method and device based on geological disaster risk assessment | |
| CN111042143A (en) | Foundation pit engineering early warning method and system based on analysis of large amount of monitoring data | |
| Lin et al. | Risk assessment of tunnel construction based on improved cloud model | |
| CN105718658A (en) | Large-size bridge online evaluating system | |
| CN105654398A (en) | Side slope construction assessment method based on centesimal system and multistage index system | |
| Ji et al. | Modified Q-index for prediction of rock mass quality around a tunnel excavated with a tunnel boring machine (TBM) | |
| CN112926110A (en) | Real-time visual early warning method for risks in construction process of subway station | |
| CN107091085B (en) | Multi-parameter discrimination method for stratum stability of shallow-buried and underground-excavated tunnel | |
| CN115700666A (en) | Intelligent comprehensive evaluation method for maintenance and investigation of mountain highway subgrade | |
| CN114997671A (en) | Foundation pit deformation safety risk assessment method based on artificial neural network and entropy method | |
| CN101520820A (en) | Method for evaluating hazard of gas tunnel and device for implementing same | |
| Han et al. | Topological mapping of complex networks from high slope deformation time series for landslide risk assessment | |
| CN114493295B (en) | Method for determining tunnel monitoring project under salt rock geology and monitoring system | |
| Fang et al. | RETRACTED ARTICLE: Excavation and support method of tunnel with high ground stress and weak surrounding rock based on GIS | |
| JP4152423B2 (en) | Soundness evaluation system based on a score type data sheet that can be used for inspection work | |
| Yang et al. | Probabilistic model of disc-cutter wear in TBM construction: A case study of Chaoer to Xiliao water conveyance tunnel in China | |
| CN115994398A (en) | Method for evaluating collapse risk of deep-buried granite tunnel | |
| Li et al. | AHP‐FSE‐Based Risk Assessment and Mitigation for Slurry Balancing Shield Tunnel Construction | |
| CN115310748A (en) | Soft soil settlement risk fuzzy evaluation method based on combined empowerment | |
| Hassanpour et al. | Introduction of an empirical classification system for evaluating tunneling impact on the discharge of springs (TIS) in the surrounding areas | |
| CN110909976A (en) | Outburst mine extraction mining deployment rationality judgment improvement method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |