CN113887019A - Foundation pit integral early warning state evaluation method based on grey correlation - Google Patents
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Abstract
The invention relates to an automatic monitoring technology, and provides a foundation pit integral early warning state evaluation method based on grey correlation, which comprises the following steps: according to the specific requirements of an entrusting party or a construction party and related units, a monitoring scheme is formulated, and monitoring point positions are determined; confirming monitoring point positions on site, and installing monitoring sensors and wireless gateways; performing index association on the sensor for monitoring the point location, and calculating the importance PX of the index X associated with the sensorj(ii) a Collecting sensor data and transmitting the sensor data to a management platform, and determining whether the sensor gives an early warning according to an early warning threshold value of the sensor; scoring the early warning state of the early warning sensor, calculating the index importance probability and obtaining the structural state scoreValue Ns(ii) a Performing overall early warning by integrating the structural state score of the foundation pit; according to the invention, the state is scored according to the rule through the importance of the early warning index of the foundation pit, so that the state score of the structure is obtained, and the whole early warning of the foundation pit is carried out according to the rule, so that the whole early warning method of the foundation pit structure is provided.
Description
Technical Field
The invention relates to an automatic monitoring technology, in particular to a foundation pit integral early warning state evaluation method.
Background
With the rapid advance of urbanization and the increasing number of urban engineering projects, the safety problem of foundation pit engineering gradually draws attention of people, and due to the complexity and uncertainty of the foundation pit engineering, foundation pit monitoring becomes an important means for preventing foundation pit safety accidents.
Traditional foundation ditch engineering monitoring is generally detected through manual operation instrument, and the detection condition is influenced by the environment greatly, the timeliness is poor, adopts automatic monitoring can effectively avoid the unfavorable environment of artifical detection, higher monitoring frequency can guarantee the real-time of parameter early warning, avoids the instantaneous accident of collapsing, can use manpower sparingly simultaneously the cost, guarantees the security of monitoring engineering to can realize data automatic analysis and report to the police, guarantee construction safety. However, the existing alarm mechanism for automatic monitoring of the foundation pit engineering mainly alarms according to measured parameters of a single sensor. Chinese patent document CN112686566A discloses an early warning method, device, system, computer device and storage medium for a deep foundation pit, which compares target monitoring data corresponding to a foundation pit collecting device with a preset alarm threshold value to determine that the foundation pit collecting device is in an alarm state; however, the above patent can only be used for judging the alarm state of a single collecting device, and cannot judge the alarm state of the whole foundation pit. The existing method for the overall alarm state of the foundation pit is mainly an engineering structure safety assessment method and comprises an expert assessment method, a fuzzy evaluation method, an analytic hierarchy process and the like. Chinese patent document CN110807576A discloses safety of an ultra-deep soft soil foundation pit based on a fuzzy comprehensive evaluation method, and a three-level fuzzy comprehensive evaluation method is adopted to establish a safety evaluation model of a soft soil deep foundation pit project, but the method is too complex, more calculation parameters are required in practical application, and the applicability to a large number of projects needs to be discussed.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide a practical evaluation method for an overall early warning state of a foundation pit based on gray correlation, which is used to solve the technical problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides a method for evaluating the overall early warning state of a foundation pit based on gray correlation, which comprises the following steps:
(1) according to the specific requirements of an entrusting party or a construction party and related units, a monitoring scheme is formulated, and monitoring point positions are determined;
(2) confirming monitoring point positions on site, and installing monitoring sensors and wireless gateways;
(3) performing index association on the sensor for monitoring the point location, and calculating the importance PX of the index X associated with the sensorj;
(4) Collecting sensor data and transmitting the sensor data to a management platform, and determining whether the sensor gives an early warning according to an early warning threshold value of the sensor;
(5) scoring the early warning state of the early warning sensor, calculating the index importance degree probability and obtaining the structural state score Ns;
(6) And (5) integrating the structural state score of the foundation pit to perform integral early warning.
Further, the index X comprises horizontal displacement of the top of the enclosure wall or the side slope, vertical displacement of the top of the enclosure wall or the side slope, horizontal displacement of the bottom of the enclosure wall or the side slope, deep horizontal displacement, uplift or rebound of the pit bottom, vertical displacement of a soil body layer, lateral soil pressure, water level or bearing water level of an observation well, underground water level, pore water pressure, vertical displacement of an upright post, internal force of an anchor rod, internal force of an upright post, internal force of a support, internal force of the enclosure wall, internal force of a soil nail, vertical displacement of a peripheral building, inclination of the peripheral building, horizontal displacement of the peripheral building, ground surface cracks, vertical displacement of the peripheral ground surface and deformation of a peripheral pipeline.
Preferably, the monitoring point position sensors are arranged according to technical building foundation pit engineering monitoring specifications (GB50497-2009), the sensors are associated with the indexes, for example, a fixed inclinometer sensor is associated with deep horizontal displacement of the indexes, and the association between the sensors and the indexes is associated according to common knowledge.
The specific process of the step (3) is as follows:
(3.1) building a foundation pit early warning fault tree;
establishing a foundation pit fault tree, determining a minimal cut set Q by a Boolean algebra method, wherein n bottom events are provided in totaliM, i.e. 1, 2, …, m;
(3.2) calculating the probability of a bottom event PBottom;
PBottom=PSystem+PSpecially for cleaning(formula 1)
Wherein, PSystemCalculating and counting the occurrence probability of the bottom event according to the actual engineering case;
Pspecially for cleaningProbability obtained by expert survey method; the probability of the expert survey method is obtained according to an effective questionnaire;
the effective questionnaire is scored according to the importance evaluation of the experts on the foundation pit early warning according to indexes, wherein the important is 4 points, the important is 3 points, the more important is 2 points, the general important is 1 point, and the unimportant is 0 point, the total score of each index is counted and is divided by the sum of all index scores to obtain the index expert survey probability PSpecially for cleaning;
(3.3) calculating the probability of occurrence of a top event PTop roof;
According to the bottom event probability PBottomObtaining the minimum cut set probability P according to the fault tree series-parallel probability calculation principlei;
According to the minimum cut set probability PiObtaining the probability P of occurrence of the top eventTop roof;
PTop roof=1-∏(1-Pi) (formula 2)
Wherein i is the serial number of the minimal cut set, i is 1, 2, …, m;
(3.4) calculating the structural importance E of the bottom eventj,
Ej=∑Pi/PTop roof(formula 3)
Wherein j is the serial number of the bottom event, and j is 1, 2.
∑PiThe sum of the probabilities of all the minimal cut sets containing the jth bottom event is obtained;
(3.5) calculating the bottom event to form a fault feature vector [ XR];
The feature vector [ X ]R]At all minimal cut sets QiIn the corresponding ith row, the composed bottom event j corresponds to the element X at the positionRi(j) Is 1, the elements at the other positions are 0, then the bottom event constitutes a fault feature vector [ XR]Comprises the following steps:
(3.6) the bottom event structure importance EjForm a vector [ X ] to be examinedT],
[XT]={E1,E2,...,En} (equation 5)
Wherein j is the serial number of the bottom event, and j is 1, 2.
(3.7) calculating the Grey correlation degree gamma of the bottom eventj;
According to the characteristic vector and the vector to be detected,
wherein j is the serial number of the bottom event, and j is 1, 2.
m is the minimum cut set number;
ρ is the resolution coefficient;
εi(j) is the gray correlation coefficient;
Δi(j) is the column element difference;
Δi(j)=|XRi(j)-Ejl (equation 8)
Andrespectively representing the maximum value in the vector row and the maximum value in the vector column; (ii) a
(3.8) correlating the bottom events with a degree of gammajNormalizing to obtain importance PXj:
Further, in the step (4), the early warning of the sensor adopts a third-level early warning, the third-level early warning includes a yellow early warning, an orange early warning, and a red early warning, the early warning value of the red early warning is a maximum limit value specified in building foundation pit engineering monitoring technical specification (GB 50497-.
Further, in the step (5), the early warning state scoring standards include 0 score of safety state, 1 score of yellow early warning, 2 scores of orange early warning and 4 scores of red early warning.
Further, the number q of the early warning sensors is set, and the q early warning sensors are subjected to early warning scoring according to the early warning state scoring standard, and the number N of the early warning sensors is setkWherein k is the serial number of the early warning sensor, and k is 1, 2, … and q;
when the number q of the early warning sensors is more than or equal to 2, the index importance PX associated with the early warning sensorskNormalization processing is carried out to obtain index importance degree probability PXk':
Structural State score NsComprises the following steps:
when the number q of the early warning sensors is 1 and the indexes associated with the early warning sensors are not lateral soil pressure, selecting the index importance PX associated with the early warning sensors, and scoring the early warning sensors according to the pre-warning state scoring standard as N, wherein the importance probability PX' is as follows:
structural State score NsComprises the following steps:
NsPX' × N (formula 11.1)
When the number l of the early warning sensors is 1 and the index associated with the early warning sensors is lateral soil pressure, the structural state score N is obtainedsComprises the following steps:
Nsequal to N (formula 11.2)
I.e. structural state score NsNamely the early warning state score N of the sensor.
In step (6), according to the structural state score NsCarrying out integral early warning:
1) according to the invention, the state is scored according to the rule through the importance of the early warning index of the foundation pit, so that the state score of the structure is obtained, and the whole early warning of the foundation pit is carried out according to the rule, so that the whole early warning method of the foundation pit structure is provided.
2) The operation process of the invention is simple and convenient, and the integral quick early warning of the project can be realized.
3) By combining with engineering practice, the method is applied to the actual foundation pit engineering project, provides reference and reference significance for enterprises in the integral early warning of the actual foundation pit engineering project, and has good popularization value.
Drawings
FIG. 1 is a flow chart of the evaluation method of the present invention.
Fig. 2 is a foundation pit early warning fault tree established by the invention.
Fig. 3 is a diagram of the location arrangement of a certain pit project monitoring point.
Detailed Description
The following detailed description of the embodiments of the present invention is provided in conjunction with the accompanying drawings, and it should be noted that the embodiments are merely illustrative of the present invention and should not be considered as limiting the invention, and the purpose of the embodiments is to make those skilled in the art better understand and reproduce the technical solutions of the present invention, and the protection scope of the present invention should be subject to the scope defined by the claims.
As shown in fig. 1, the invention provides a method for evaluating the overall early warning state of a foundation pit based on gray correlation, which comprises the following steps:
s1, formulating a monitoring scheme according to the specific requirements of the entrusting party or the construction party and the related units, and determining monitoring point positions;
s2, confirming the monitoring point location on site, and installing a monitoring sensor and a wireless gateway;
s3, performing index correlation on the sensor for monitoring the point location, and calculating the importance PX of the index X related to the sensorj;
The index X comprises horizontal displacement of the top of the enclosure wall or the side slope, vertical displacement of the top of the enclosure wall or the side slope, horizontal displacement of the bottom of the enclosure wall or the side slope, deep horizontal displacement, pit bottom uplift or rebound, vertical displacement of soil body layering, lateral soil pressure, water level or pressure-bearing water level of an observation well, underground water level, pore water pressure, vertical displacement of a stand column, internal force of an anchor rod, internal force of the stand column, internal force of a support, internal force of the enclosure wall, internal force of a soil nail, vertical displacement of a peripheral building, inclination of the peripheral building, horizontal displacement of the peripheral building, ground surface cracks, vertical displacement of the peripheral ground surface and deformation of a peripheral pipeline.
Preferably, the monitoring point position sensors are arranged according to the technical specification of building foundation pit engineering monitoring (GB 50497-.
The specific process of step S3 is:
s3.1, building a foundation pit early warning fault tree;
as shown in fig. 2, a foundation pit fault tree is established, the number of bottom events is 22, and the minimum cut sets are determined to be 6 by using a boolean algebra method, that is, the minimum cut set is QiThe value of i in (1) is as follows: 1, 2, …, 6;
specifically Q1={X1,X2,X3,X4,X5,X6};
Q2={X7};
Q3={X8,X9,X10};
Q4={X11};
Q5={X12,X13,X14,X15,X16};
Q6={X17,X18,X19,X20,X21,X22};
S3.2 calculating the bottom event probability PBottom;
PBottom=PSystem+PSpecially for cleaning(formula 1)
Wherein, PSystemCalculating and counting the occurrence probability of the bottom event according to the actual engineering case;
Pspecially for cleaningThe probability is obtained by an expert survey method, and the probability is obtained according to an effective questionnaire;
the effective questionnaire is scored according to the importance evaluation of the experts on the foundation pit early warning according to indexes, wherein the important is 4 points, the important is 3 points, the more important is 2 points, the general important is 1 point, and the unimportant is 0 point, the total score of each index is counted and is divided by the sum of all index scores to obtain the index expert survey probability PSpecially for cleaning;
S3.3 calculating the probability of occurrence of a Top event PTop roof;
According to the bottom event probability PBottomObtaining the minimum cut set probability P according to the fault tree series-parallel probability calculation principlei;
Obtaining the minimum cut set probability according to the bottom event probability as shown in a graph 1;
TABLE 1 minimum cut set probability
According to the minimum cut set probability PiObtaining the probability P of occurrence of the top eventTop roof;
PTop roof=1-∏(1-Pi) (formula 2)
Wherein i is the serial number of the minimal cut set, i is 1, 2, …, 6;
s3.4 calculating the bottom event structure importance Ej;
Ej=∑Pi/PTop roof(formula 3)
Where j is the sequence number of the bottom event, j is 1, 2, 3.
∑PiThe sum of the probabilities of all the minimal cut sets containing the jth bottom event is obtained;
s3.5 calculating bottom event to form fault feature vector XR];
The feature vector [ X ]R]At all minimal cut sets QiIn the corresponding ith row, the composed bottom event j corresponds to the element X at the positionRi(j) Is 1, the elements at the other positions are 0, then the bottom event constitutes a fault feature vector [ XR]Comprises the following steps:
s3.6 bottom event structure importance EjForm a vector [ X ] to be examinedT],
[XT]={E1,E2,...,En} (equation 5)
Wherein j is the serial number of the bottom event, and j is 1, 2.
S3.7 calculating the Grey relevance Gamma of the bottom eventj;
According to the characteristic vector and the vector to be detected,
wherein j is the serial number of the bottom event, and j is 1, 2.
m is the minimum cut set number;
ρ is the resolution coefficient;
εi(j) is the gray correlation coefficient;
Δi(j) is the column element difference;
Δi(j)=|XRi(j)-Ejl (equation 8)
Andrespectively representing the minimum value in the vector row and the minimum value in the vector column;
andrespectively representing the maximum value in the vector row and the maximum value in the vector column;
s3.8 relating bottom event to degree gammajNormalizing to obtain importance PXj:
According to the technical specification of monitoring of building foundation pit engineering (GB 50497-.
TABLE 2 monitoring index and Grey correlation
S4, collecting sensor data and transmitting the sensor data to a management platform, and determining whether the sensor gives an early warning according to an early warning threshold value of the sensor;
the early warning of the sensor adopts three levels of early warnings, wherein the three levels of early warnings comprise a yellow early warning, an orange early warning and a red early warning, the early warning value of the red early warning is the maximum limit value specified in building foundation pit engineering monitoring technical specification (GB 50497-.
S5, scoring the early warning state of the early warning sensor, calculating the index importance degree probability and obtaining the structural state score Ns;
The early warning state scoring standards are that the safety state is 0 score, the yellow early warning is 1 score, the orange early warning is 2 score, and the red early warning is 4 score.
Setting the number q of early warning sensors to carry out early warning scoring on the q early warning sensors according to the early warning state scoring standard, and NkWherein k is the serial number of the early warning sensor, and k is 1, 2, … and q;
when the number q of the early warning sensors is more than or equal to 2, the early warning sensors are usedIndex importance PX associated with a devicekNormalization processing is carried out to obtain index importance degree probability PXk':
Structural State score NsComprises the following steps:
when the number q of the early warning sensors is 1 and the indexes associated with the early warning sensors are not lateral soil pressure, selecting the index importance PX associated with the early warning sensors, and scoring the early warning sensors according to the pre-warning state scoring standard as N, wherein the importance probability PX' is as follows:
structural State score NsComprises the following steps:
NsPX' × N (formula 11.1)
When the number q of the early warning sensors is 1 and the index associated with the early warning sensors is lateral soil pressure, the structural state score N is obtainedsComprises the following steps:
Nsequal to N (formula 11.2)
I.e. structural state score NsNamely the early warning state score N of the sensor.
And S6, performing overall early warning by integrating the structural state score of the foundation pit.
Score by structural state NsCarrying out integral early warning:
example (b):
the project address of a commercial office building of the junior political affairs stock out (2018) No. 8 plot is located in the Suo Tailu No. 128 street of the Shushan mountain in Hangzhou city, Zhejiang province. Entrusted by the owner, the foundation pit project is automatically monitored.
Firstly, a monitoring scheme is formulated, monitoring point positions are determined, as shown in figure 3, 8 fixed inclinometers, 8 static levels, 4 inclinometers, 8 stressometers and 1 water level meter are installed at the monitoring point positions.
Index correlation was performed on the installed sensors as shown in table 3.
TABLE 3 sensor index correlation
Data are collected and monitored for a long time, in the monitoring process, a fixed inclinometer CXYCH08AD1001 generates orange early warning, an inclinometer 2106RB0902 generates yellow early warning, a stress meter 2106RB0907 generates yellow early warning, early warning states of early-warning sensors are scored, the scores and the associated index importance are shown in a table 4, the sensor importance probability is calculated according to a formula (10), and the calculation result is shown in the table 4.
TABLE 4 sensor index correlation
Since the number q of the early warning sensors is 4 and is more than 2, the method is based onCalculated to obtain the structural state score Ns=2×0.284+1×0.284+1×0.432=1.284;
ByIt can be seen that the structural status score Ns1.283, in the yellow warning range, because the whole structure carries out yellow warning.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
Claims (7)
1. A method for evaluating the overall early warning state of a foundation pit based on gray correlation is characterized by comprising the following steps:
(1) according to the specific requirements of an entrusting party or a construction party and related units, a monitoring scheme is formulated, and monitoring point positions are determined;
(2) confirming monitoring point positions on site, and installing monitoring sensors and wireless gateways;
(3) performing index association on the sensor for monitoring the point location, and calculating the importance PX of the index X associated with the sensorj;
(4) Collecting sensor data and transmitting the sensor data to a management platform, and determining whether the sensor gives an early warning according to an early warning threshold value of the sensor;
(5) scoring the early warning state of the early warning sensor, calculating the index importance degree probability and obtaining the structural state score Ns;
(6) And (5) integrating the structural state score of the foundation pit to perform integral early warning.
2. The method for evaluating the overall early warning state of the foundation pit based on the grey correlation according to claim 1, wherein the index X comprises horizontal displacement of the top of the enclosure wall or the side slope, vertical displacement of the top of the enclosure wall or the side slope, horizontal displacement of the bottom of the enclosure wall or the side slope, horizontal displacement of a deep layer, uplift or rebound of the pit bottom, vertical displacement of a soil body layer, lateral soil pressure, water level or pressure bearing water level of an observation well, underground water level, pore water pressure, vertical displacement of a stand column, internal force of an anchor rod, internal force of a stand column, internal force of a support, internal force of the enclosure wall, internal force of a soil nail, vertical displacement of a peripheral building, inclination of the peripheral building, horizontal displacement of the peripheral building, cracks of the ground surface, vertical displacement of the peripheral ground surface and deformation of a peripheral pipeline.
3. The method for evaluating the overall early warning state of the foundation pit based on the grey correlation according to claim 1, wherein the specific process of the step (3) is as follows:
(3.1) building a foundation pit early warning fault tree;
establishing a foundation pit fault tree, determining a minimal cut set Q by a Boolean algebra method, wherein n bottom events are provided in totaliM, i.e. 1, 2, …, m;
(3.2) calculating the probability of a bottom event PBottom;
PBottom=PSystem+PSpecially for cleaning(formula 1)
Wherein, PSystemCalculating and counting the occurrence probability of the bottom event according to the actual engineering case;
Pspecially for cleaningThe probability is obtained by an expert survey method, and the probability is obtained according to an effective questionnaire;
the effective questionnaire is scored according to the importance evaluation of the experts on the foundation pit early warning according to indexes, wherein the important is 4 points, the important is 3 points, the more important is 2 points, the general important is 1 point, and the unimportant is 0 point, the total score of each index is counted and is divided by the sum of all index scores to obtain the index expert survey probability PSpecially for cleaning;
(3.3) calculating the probability of occurrence of a top event PTop roof;
According to the bottom event probability PBottomObtaining the minimum cut set probability P according to the fault tree series-parallel probability calculation principlei;
According to the minimum cut set probability PiObtaining the probability P of occurrence of the top eventTop roof;
PTop roof=1-∏(1-Pi) (formula 2)
Wherein i is the serial number of the minimal cut set, i is 1, 2, …, m;
(3.4) calculating the structural importance E of the bottom eventj;
Ej=∑Pi/PTop roof(formula 3)
Wherein j is the serial number of the bottom event, and j is 1, 2.
∑PiThe sum of the probabilities of all the minimal cut sets containing the jth bottom event is obtained;
(3.5) calculating the bottom event to form a fault feature vector [ XR];
The feature vector [ X ]R]At all minimal cut sets QiIn the corresponding ith row, the composed bottom event j corresponds to the element X at the positionRi(j) Is 1, the elements at the other positions are 0, then the bottom event constitutes a fault feature vector [ XR]Comprises the following steps:
(3.6) the bottom event structure importance EjForm a vector [ X ] to be examinedT],
[XT]={E1,E2,...,En} (equation 5)
Wherein j is the serial number of the bottom event, and j is 1, 2.
(3.7) calculating the Grey correlation degree gamma of the bottom eventj;
According to the characteristic vector and the vector to be detected,
wherein j is the serial number of the bottom event, and j is 1, 2.
m is the minimum cut set number;
ρ is the resolution coefficient;
εi(j) is the gray correlation coefficient;
Δi(j) is the column element difference;
Δi(j)=|XRi(j)-Ejl (equation 8)
Andrespectively representing the minimum value in the vector row and the minimum value in the vector column;
andrespectively representing the maximum value in the vector row and the maximum value in the vector column;
(3.8) correlating the bottom events with a degree of gammajNormalizing to obtain importance PXj;
4. The method as claimed in claim 1, wherein in the step (4), the early warning of the sensor is performed by using a three-level early warning, the three-level early warning includes a yellow early warning, an orange early warning, and a red early warning, the early warning value of the red early warning is a maximum limit value specified in building pit engineering monitoring technical specification (GB 50497-.
5. The method for evaluating the overall early warning state of the foundation pit based on the grey correlation according to claim 1, wherein in the step (5), the early warning state scoring standards are that the safety state is scored 0, the yellow early warning is scored 1, the orange early warning is scored 2, and the red early warning is scored 4.
6. The method for evaluating the overall early warning state of the foundation pit based on the grey correlation according to claim 1, wherein the specific process of the step (5) is as follows:
setting the number q of early warning sensors, and carrying out early warning scoring on the q early warning sensors according to the early warning state scoring standard NkWherein k is the serial number of the early warning sensor, and k is 1, 2, … and q;
when the number q of the early warning sensors is more than or equal to 2, the index importance PX associated with the early warning sensorskNormalization processing is carried out to obtain index importance degree probability PXk':
Structural State score NsComprises the following steps:
when the number q of the early warning sensors is 1 and the indexes associated with the early warning sensors are not lateral soil pressure, selecting the index importance PX associated with the early warning sensors, and scoring the early warning sensors according to the pre-warning state scoring standard as N, wherein the importance probability PX' is as follows:
structural State score NsComprises the following steps:
NsPX' × N (formula 11.1)
When the number q of the early warning sensors is 1 and the index associated with the early warning sensors is lateral soil pressure, the structural state score N is obtainedsComprises the following steps:
Nsequal to N (formula 11.2)
I.e. structural state score NsNamely the early warning state score N of the sensor.
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CN117057632A (en) * | 2023-10-11 | 2023-11-14 | 北京城建勘测设计研究院有限责任公司 | Method for evaluating precipitation recharging feasibility of pebble layer deep foundation pit |
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117057632A (en) * | 2023-10-11 | 2023-11-14 | 北京城建勘测设计研究院有限责任公司 | Method for evaluating precipitation recharging feasibility of pebble layer deep foundation pit |
CN117057632B (en) * | 2023-10-11 | 2024-01-19 | 北京城建勘测设计研究院有限责任公司 | Method for evaluating precipitation recharging feasibility of pebble layer deep foundation pit |
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