CN111042143A - Foundation pit engineering early warning method and system based on analysis of large amount of monitoring data - Google Patents
Foundation pit engineering early warning method and system based on analysis of large amount of monitoring data Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
- E02D17/00—Excavations; Bordering of excavations; Making embankments
- E02D17/02—Foundation pits
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
- E02D33/00—Testing foundations or foundation structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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Abstract
The invention provides a foundation pit engineering early warning method and system based on analysis of a large amount of monitoring data. The method introduces a big data concept, analyzes the risk control index of the risk source through a large amount of monitoring data statistics, and is not limited to a single deformation control quantity; analyzing the risk level of a single underlying risk source and the risk probability corresponding to each level based on a large amount of monitoring data, taking steel support axial force data as an example, and setting the ratio of the actual axial force to the designed axial force to be low risk outside a 70% interval, medium risk outside an 80% interval and high risk outside a 90% interval; the big data statistical analysis method provided by the invention collects the foundation pit information and data and updates the foundation pit data of the system in time, and realizes dynamic adjustment of the control index of the monitoring item through the accumulation of the foundation pit risk information, so that the risk management is more accurate.
Description
Technical Field
The invention relates to the technical field of foundation pit engineering risk early warning of civil engineering, in particular to a foundation pit engineering early warning method and system based on mass monitoring data analysis.
Background
In recent years, foundation pit engineering is continuously developed in the direction of large scale and large depth, and along with different geological conditions, various support forms, complex working condition environment and the like, theoretical research and design construction of the foundation pit engineering face a plurality of problems. Accidents happen in the process of foundation pit engineering construction, and the accidents happen under different geological conditions, different construction levels and different foundation pit grades, so that a plurality of serious consequences are caused. Therefore, risk analysis and early warning are required to be carried out on the foundation pit engineering, and monitoring quantities such as support shaft force, lateral deformation of the building enclosure, settlement of the surface outside the pit and the like are particularly required, so that the accident rate in the foundation pit engineering construction is reduced. Relevant regulations and regulations at home and abroad are more focused on the construction technology implementation level, risk assessment and control are rarely involved, and the risk analysis and early warning level of the foundation pit is lower.
In the aspect of research on foundation pit deformation characteristics, a plurality of scholars at home and abroad study the characteristics of interaction of a supporting structure with a soil body and external factors by counting actual measurement data of foundation pit engineering, and the counted characteristics are used as the standard of risk early warning. Moormann collects deformation data of a large number of foundation pits, analyzes the deformation rule of the support structure under various soil layer conditions, and obtains the relation between the maximum deformation of the foundation pit in soft soil and the excavation depth of the foundation pit; the xu China establishes a database of 315 foundation pit engineering deformation actual measurement cases in the Shanghai region, and comprehensively discloses deformation rules of lateral movement of the deep foundation pit support structure, ground surface settlement behind a wall, pit bottom springback and the like. However, statistical analysis on the stress characteristics of the supporting structure in the deep foundation pit excavation process is less, and the supporting system is an important factor influencing the stability of the foundation pit.
In the research on foundation pit risk control, Matsuo et al researches the mechanism of various failure modes of a foundation pit supporting structure, and finds that the important factor influencing the reliability of the foundation pit is the stability of a supporting system. In actual engineering, a lot of cases of foundation pit collapse caused by failure of steel support effect also occur, a large amount of monitoring data are not effectively utilized, means for evaluating the abnormal level of the steel support axial force in the excavation process of foundation pit engineering are lacked, and the requirements for abnormal investigation of the support axial force and primary risk control of the foundation pit cannot be met.
Through retrieval, the Chinese patent CN208091363U discloses a foundation pit early warning system, which can detect and early warn in real time by judging whether the foundation pit is deformed or not through the inclination of a horizontal connection transparent pipe of two top plates. However, the above patents have the following disadvantages: the risk control index value is obtained without combining a large amount of monitoring data for statistical analysis, and the method can only be used for judging whether the foundation pit is deformed or not and cannot be applied to other monitoring indexes.
Through retrieval, the Chinese patent application CN101838991A discloses a deep foundation pit risk assessment method based on network reasoning, a deep foundation pit engineering risk assessment calculation model is established, and risk probability calculation is performed by combining a risk tree network method and an expert investigation method to obtain the total risk level of the deep foundation pit engineering and the average risk level of each risk event. Chinese patent application CN106022634A discloses a foundation pit risk management method based on big data analysis, comprehensively analyzing the deformation of a foundation pit from multiple aspects of monitoring data and environmental factors in the construction process, and comprehensively judging the risk source existing in the construction process of the foundation pit by carrying out massive data fusion; however, the above scheme has the following disadvantages: because the occurrence probability of the bottom-layer risk event is obtained through the investigation and data analysis of the deep foundation pit engineering accident case, the risk level of the bottom-layer risk event and the probability corresponding to each level are not analyzed; moreover, foundation pit risk assessment based on expert judgment has certain limitation, and objective rules need to be analyzed by combining big data statistics.
Disclosure of Invention
Aiming at the problems and difficulties in the prior art, a foundation pit engineering early warning method and a foundation pit engineering early warning system based on analysis of a large amount of monitoring data are needed to perform single index risk assessment on common risk items in foundation pit engineering, and provide guiding significance for safe construction of foundation pit engineering by combining with BIM (building information modeling) model to feed back foundation pit excavation working conditions and risk early warning information in real time.
According to one aspect of the invention, a foundation pit engineering early warning method based on analysis of a large amount of monitoring data is provided, which comprises the following steps:
s1: establishing a deep foundation pit parameter acquisition and BIM model, and determining soil layer parameters, excavation depth, support level or/and surrounding environment of each foundation pit;
s2: collecting risk monitoring indexes, and analyzing the correlation between the monitoring indexes and the foundation pit parameters;
s3: using the correlation to call corresponding actual monitoring data or theoretical design values to carry out normalization processing on the measured values of the monitoring indexes to obtain the normalized statistical data of each risk monitoring index;
s4: carrying out normal distribution probability analysis on the statistical data, introducing a discrimination method of a confidence interval to provide a basis for setting a risk control value, and obtaining a risk index value;
s5, comparing and analyzing the obtained monitoring data with the risk index value, and sending out early warning information when the measured value does not meet the range of the risk control index value;
s6: and displaying the risk related information in a three-dimensional manner in real time through the BIM model.
Further, in S1, the collection of the deep foundation pit parameters is the excavation depth of the foundation pit.
Further, in S1, the BIM model is used to provide an engineering project selection function, an engineering excavation stage selection function, a working condition date selection function, foundation pit soil layer parameter information, excavation depth and information, supporting structure information, monitoring point layout information, or/and a surrounding building environment.
Further, in S2, the monitoring indicators include a maximum enclosure displacement, an enclosure kick ratio, a maximum enclosure displacement rate, a support shaft force, ground surface settlement, and pit bottom midpoint rebound.
Further, in S2, the correlation between the monitoring index and the foundation pit parameter is analyzed, including the correlation between the maximum lateral displacement of the wall and the excavation depth, the correlation between the maximum ground surface settlement and the excavation depth, the correlation between the support axial force and the foundation pit support level, and the like.
Further, in S2, after determining the deformation and stress monitoring index, performing statistics on the maximum and minimum values of the actual measurement of the index, and performing statistics on the stage data of the key node, where the key node includes an earth excavation completion stage and a bottom plate completion stage.
Further, in S4, the data processing is performed on the normalized monitoring values obtained through statistics, and an average and a standard deviation are obtained to obtain an average distribution and a dispersion degree.
In S4, the normalized transformation of the normal distribution is performed, the probability intervals of the monitored values are obtained using the standard normal distribution percentage table, and the risk control index is obtained by defining a low risk outside the 70% interval, a medium risk outside the 80% interval, and a high risk outside the 90% interval.
Further, in S5, by comparing the current monitoring value of the foundation pit monitoring index with the risk index control value obtained in S4, it is determined whether a risk exists in a certain monitoring item of the foundation pit; and returns this monitored actual value to the statistical data.
According to a second aspect of the present invention, there is provided a foundation pit engineering early warning system based on analysis of a large amount of monitoring data, comprising: the system comprises a preprocessing module, a data processing module and a post-processing module; the pre-processing module is used for collecting basic characteristic parameters of the foundation pit to be managed and related information of risk monitoring indexes, displaying the information of the foundation pit and monitoring point arrangement corresponding to the monitoring indexes by combining a BIM model, visually displaying the whole information of the foundation pit in a three-dimensional mode, and uploading the whole information to the early warning system; the data processing module is used for counting historical data and actual monitoring data of corresponding deformation and stress monitoring items, obtaining a risk control value of a single monitoring index through the core computing module, and comparing, analyzing and judging whether a certain monitoring item of the foundation pit has a risk or not;
and the post-processing module is used for vividly displaying the collected risk information in a three-dimensional mode through the BIM, adopting special marks for the alarm object points and generating a foundation pit risk evaluation report.
Compared with the prior art, the invention has at least one of the following beneficial effects:
the method and the system introduce a big data concept, and analyze the risk control index of the risk source through a large amount of monitoring data statistics, and are not limited to a single deformation control quantity.
The method and the system of the invention carry out statistical analysis on a large amount of monitoring data of different types of monitoring indexes by adopting the same and unified early warning method, such as steel support axial force monitoring data, underground continuous wall body inclination measurement data, surface subsidence monitoring data and the like, can fully utilize the advantages of big data and adopt the same criterion to obtain the risk early warning values of all indexes, and are not limited to the specified early warning values of all indexes; the invention introduces a unified general method, sets a scientific and reasonable confidence interval, obtains the foundation pit early warning index based on big data analysis, and has stronger implementable effect.
Drawings
The above and other features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a foundation pit engineering early warning method based on analysis of a large amount of monitoring data in a foundation pit excavation process according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a foundation pit engineering early warning system based on analysis of a large amount of monitoring data during excavation of a foundation pit according to an embodiment of the invention;
FIG. 3 is a statistical normal distribution curve of the supporting axial force ratio of steel in the earth excavation completion stage in the engineering example verified according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The present invention will be described in more detail below with reference to the accompanying drawings, which illustrate embodiments of the invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Referring now to fig. 1, a detailed description will be given of a process of excavating a foundation pit for analyzing three full conventional supports in the shanghai region according to an embodiment of the present invention: a foundation pit engineering early warning method and system based on steel support axial force monitoring data analysis.
As shown in fig. 1, for the method, in S1, soil layer parameters, excavation depth, support level, surrounding environment, and the like of each foundation pit are determined through the collection of deep foundation pit parameters and the establishment of a BIM model.
In this embodiment, the steel bracing axial force data is obtained from measured values of the cross brace and the diagonal brace axial force of three foundation pits in No. 14 lane wuding road station C area, No. 9 lane bi-cloud road station a area, and No. 9 lane bi-cloud road station E area. The excavation depth of the Wudingmu area C is 17.2m, 1 concrete support and 4 steel supports, the excavation depth of the Biyun area A and the E area is 16.7m, and 1 concrete support and 3 steel supports. And judging that the three foundation pits are all primary foundation pits according to the foundation pit soil information and the surrounding environment, wherein the excavation depth is about 17 m.
Meanwhile, a BIM three-dimensional model is established according to the engineering related drawing information, and the model can accurately express information such as the number, the material, the size and the like of an actual component; the method can provide an engineering project selection function, an engineering excavation stage selection function, a working condition selection function and the like, steel support monitoring items of three foundation pits are selected according to prompts, and the BIM model can display information such as arrangement positions, intervals, sizes and the like of steel supports in the foundation pits; the excavation working condition can also obviously show the excavation scheme of the foundation pit and the arrangement sequence of the corresponding steel supports, and the change rule of the steel supports is comprehensively analyzed from multiple angles of space and time.
As shown in fig. 1, for the method, in S2, the axial force design value of each layer of steel support is calculated according to the envelope bending moment diagram of the building envelope. In step S2, in order to obtain an actual monitoring value of the steel support axial force monitoring index, a proper foundation pit section needs to be selected, support axial force monitoring points are arranged from top to bottom, and field monitoring is performed.
In this embodiment, the arrangement of foundation ditch support axial force monitoring cross-section is decided according to the condition of foundation ditch excavation length and surrounding environment, and 2 bracing +3 stull monitoring points are arranged to wuding railway station C district in this embodiment, and 2 bracing +1 stull monitoring points are arranged to the cloud railway station A district, and 2 bracing +1 stull monitoring points are arranged to the cloud railway station E district.
As shown in fig. 1, for the method, in S2, the measured values of the axial force of the steel support are recorded during the whole excavation process of the foundation pit, and the staged data of the key nodes are extracted, including the maximum axial force value, the minimum axial force value, the axial force value of the stage where the earth excavation is completed, and the axial force value of the stage where the bottom plate is completed. And then providing theoretical support for follow-up by utilizing the correlation between the axial force monitoring value and the theoretical design value of the steel support.
Specifically, in this embodiment, a steel support axial force is taken as an example to perform risk early warning analysis, and a correlation between other common risk monitoring indexes and a foundation pit characteristic parameter or a theoretical design value is explained:
the relationship between the maximum lateral movement of the enclosure wall and the excavation depth of the foundation pit is as follows: the maximum lateral movement of the wall body is increased along with the increase of the excavation depth of the foundation pit, the integrity of the supporting system is better and better along with the increase of the excavation depth of the foundation pit, the relation between the maximum lateral movement of the wall body and the excavation depth of the foundation pit is not kept in linear correlation, but the invention firstly considers the correlation between the deformation of the foundation pit and a single parameter, so that the maximum lateral movement of the enclosure of the foundation pit and the excavation depth of the foundation pit can be determined to meet the linear proportional relation.
The relationship between the maximum settlement of the earth surface and the excavation depth of the foundation pit is as follows: according to the relation between the maximum surface subsidence and the excavation depth obtained by the relevant calculation model, the maximum surface subsidence is rapidly increased along with the increase of the excavation depth of the foundation pit, so that the linear proportional relation between the maximum surface subsidence and the excavation depth of the foundation pit can be determined.
The enclosure skirting ratio is as follows: the method comprises the steps that the risk that the foundation pit does not overturn can be guaranteed only by meeting a certain skirting ratio in the construction process of different foundation pits in foundation pit engineering, therefore, the enclosure skirting ratio can be used as a monitoring index for unifying different foundation pits, the enclosure skirting ratio is equal to wall toe displacement/maximum displacement, the wall toe displacement and the maximum displacement monitoring measured values of different foundation pits are monitored, the probability interval of the enclosure skirting ratio is calculated and analyzed, and then the risk control index of the skirting ratio is used.
Steel support axial force: the actual monitoring value of the axial force of the steel support is related to the excavation depth of the foundation pit, the supporting level, the supporting design and the like in the excavation process of the foundation pit, so that the design value of the axial force of the steel support needs to be determined when the early warning of the risk of the axial force of the support is considered. The theoretical design value of the support axial force comprehensively considers environmental factors such as excavation depth, support level and support design, and the correlation between the measured value and the theoretical value can be established, so that the steel support axial force risk early warning is judged.
The maximum displacement rate of the enclosure is as follows: the common risks of the foundation pit engineering include that the deformation or the stress exceeds the standard design requirement, and the large change rate of the deformation or the stress is also a precursor of the risk of the foundation pit, so that the monitoring of the maximum displacement rate of the enclosure structure is very important, and the maximum lateral movement change rate of the enclosure structure is used as a risk monitoring item to avoid the situations of 'false alarm' and 'false alarm'.
In this embodiment, the three foundation pits have counted up the whole monitoring data of 37 groups of steel shotcrete axle power altogether, including earthwork excavation completion stage axle power value, bottom plate completion stage axle power value, maximum axle power value, minimum axle power value, both can be from the change characteristic of analysis steel shotcrete axle power on the whole, also can be to the time variation effect of single steel shotcrete axle power and carry out the analysis, the axle power change threshold value of the steel shotcrete along with excavation time, excavation operating mode of consideration that can be comparatively accurate.
As shown in fig. 1, in step S3, normalization is performed according to the correlation between the measured axial force value of the steel support and the theoretical design value described in step S2, that is, the axial force ratio statistical data of each support is obtained by calculating the ratio of the maximum axial force value, the minimum axial force value, the axial force value of the earth excavation completion stage, and the axial force value of the bottom plate completion stage to the designed axial force value.
In this embodiment, the normalization processing of the steel support axial force data is "an axial force measured value/theoretical design value"; the axial force ratio statistical data are obtained for statistical analysis, and the independent axial force data are different due to different foundation pits, different working conditions and different excavation environments, so that the independent support axial force data have no analytical value.
In this embodiment, the maximum axial force value, the minimum axial force value, the foundation pit earthwork excavation completion stage axial force value, and the bottom plate completion stage axial force value of 37 groups of steel support axial force data are respectively subjected to ratio with the design axial force value, and the axial force ratio result is obtained through statistics and is stored in the database module, so that the processing in subsequent S4 is facilitated.
As shown in fig. 1, for the method, in S4, normal distribution probability analysis is performed on the statistical data, and the discrimination method introducing the confidence interval provides a basis for setting the risk control value, and obtains the risk index value.
In this embodiment, the frequency analysis is performed on the steel support axial force ratio data range, and a histogram of the relevant data and a corresponding normal distribution curve are made, so as to provide a basis for setting a risk control value through statistical analysis of the frequency curve. Specifically, based on the obtained data, a probability statistics and confidence interval method is introduced to process a single monitoring index value:
the distribution law of the discrete random variable X is:
P{X＝Xk}＝pkand k is 1,2,3
To obtain more probability intervals, a normalized transformation of the normal distribution can be performed using the standard normal distribution, as follows. The normal distribution generally translates to a standard normal distribution:
if X to N (mu, sigma)2) Let us order
U is (0, 1).
According to the data characteristics, the ratio of the measured axial force to the designed axial force is specified to be low risk outside a 70% interval, medium risk outside an 80% interval and high risk outside a 90% interval.
In this example, axial ratio data were processed to obtain a mean and a standard deviation, and then a normal distribution curve function was fitted as a probability function. The method comprises the following specific steps:
①, performing excel form derivation on the cross brace and inclined brace axial force monitoring data of three foundation pits of a Wudingmu road station A getting area, a Biyun road station A area and a Biyun road station E area by using a monitoring data acquisition and derivation function module in the BIM model;
②, selecting axial force value data corresponding to the earth excavation completion date and the bottom plate completion date of the single steel support in the excel table according to the excavation working conditions, extracting the maximum and minimum axial force value data of the whole process of the single steel support, and obtaining the axial force ratio statistical data of the node supports in different stages by calculating the ratio of the maximum and minimum axial force value data to the steel support axial force design value;
③, respectively sequencing 4 groups of steel supporting axial force ratio data obtained by statistics from small to large, and carrying out normal distribution statistical processing on the measured axial force ratio data to obtain characteristic values such as an average value, a standard deviation, a variation coefficient and the like;
④, carrying out standard transformation on the normal distribution in ③ by using a standard normal distribution formula, and obtaining distribution intervals corresponding to 70%, 80% and 90% by combining a standard normal distribution table so as to obtain risk control indexes of each node;
⑤, obtaining multi-level and multi-stage supporting axial force control indexes by fusing 4 groups of control indexes according to the maximum axial force ratio, the minimum axial force ratio, the earth excavation finishing stage axial force ratio and the bottom plate finishing stage axial force ratio which are calculated in ④, as shown in Table 1.
As shown in fig. 1, for the method, in S5 and S6, the three risk levels of high, medium and low of the axial force of the steel support in the excavation process of the foundation pit are determined, corresponding early warning information is sent out when the actual measured axial force value does not meet the risk control index value range, and the risk level corresponding to the support is marked out by a risk early warning module of the system; while historical data is saved, newly monitored steel support axial force data is imported into a database in real time, and the database is continuously updated, so that the data statistics is more reasonable and regular; in addition, the abnormal information is displayed through the BIM model, the integrity of the foundation pit can be grasped, the deformation information of the foundation pit is visually displayed, and meanwhile, the risk information of the foundation pit is comprehensively and integrally processed by combining the conditions of other risk monitoring indexes; generally, the main risk of the axial force of the steel support in the excavation process of the foundation pit is that the axial force of the steel support is too large or too small, the steel support for early warning is coordinated through the comprehensive action of other supports of the foundation pit, and the axial force value of the support is increased when the axial force of the support is too small, so that the steel support can reasonably exert the effect.
In the embodiment, according to the statistical data of the supporting axial force ratio of the existing foundation pit steel, the risk judgment criterion and the early warning system of the axial force in the excavation process of the full-conventional steel supporting foundation pit are finally obtained, the supporting axial force is regulated in multiple levels and multiple stages, and the guiding measures after supporting warning are given, so that the effects of risk evaluation and early warning are achieved.
Table 1 is a risk criterion table of axial force in the excavation process of the fully-conventional support foundation pit obtained by statistics in the engineering embodiment verified by the embodiment of the present invention.
In another embodiment, a foundation pit engineering pre-warning system based on analysis of a large amount of monitoring data is provided, fig. 2 is a schematic diagram of a foundation pit engineering pre-warning system in a foundation pit excavation process according to an embodiment of the present invention, and as shown in fig. 1, the pre-warning system 100 includes a pre-processing module 101, a data processing module 102, and a post-processing module 103.
The pre-processing module 101 comprises the acquisition of foundation pit parameter information and the acquisition of deformation or stress measured values of foundation pit risk monitoring items; the data processing module 102 comprises core calculation, comparative analysis, monitoring data storage and historical data recording; the post-processing module 103 includes risk assessment, information presentation, and the like.
The pre-processing module 101 is configured to collect basic characteristic parameters of the foundation pit to be managed and related information of risk monitoring indexes, display information of the foundation pit and monitoring point arrangement corresponding to the monitoring indexes in combination with the BIM model, visually display overall information of the foundation pit in a three-dimensional manner, and upload the information to the early warning system 100.
The data processing module 102 is used for counting historical data and actual monitoring data of corresponding deformation and stress monitoring items, obtaining a risk control value of a single monitoring index through the core computing module, and comparing, analyzing and judging whether a certain monitoring item of the foundation pit has a risk or not; meanwhile, the database can be continuously updated along with the storage of new monitoring data, and a more reasonable risk control value is calculated by a recycling algorithm; in order to more vividly show the data change rule, the data processing module also provides a numerical value change rate curve of a single monitoring index value and a relation curve of the single monitoring index value and other related indexes, judges the early warning condition of foundation pit deformation and stress in multiple directions and angles, and uploads the result to the early warning system 100.
And the post-processing module 103 is used for vividly displaying the collected risk information in a three-dimensional mode through the BIM, adopting special marks for alarm object points, such as marking different colors for alarm steel support mark points, wall inclination measurement monitoring layout points, earth surface settlement monitoring layout points and the like, wherein the low, medium and high risks are represented by yellow, orange and red colors in sequence, and a decision maker can control foundation pit construction and safety treatment through the BIM more integrally after the special marks, and simultaneously generate a foundation pit risk assessment report.
Specifically, taking steel support axial force as an example, a risk platform hardware system is established, and a corresponding software platform operating environment is deployed at the same time. Basic excavation information, soil layer and hydrology information, supporting structure information, surrounding environment, monitoring point arrangement and the like of the foundation pit are summarized in the BIM. Meanwhile, the early warning system can show the actual excavation working condition of the foundation pit and vividly describe the construction processes of layered excavation, support installation and the like of the foundation pit.
During the excavation of the foundation pit, collecting the measured value of the ground axial force corresponding to the steel support monitoring point or other foundation pit deformation information, and introducing the measured value into a data processing module to analyze and compare the risk condition of the measuring point; meanwhile, alarm points are specially marked in the system, so that the specific excavation working condition and the monitoring point space position of the corresponding foundation pit can be accurately positioned, the risk of the foundation pit is described from two aspects of time and space, and a basis is provided for a decision maker.
The database in the data processing module 102 may include historical data or newly acquired monitoring data, and the early warning system 100 may return the newly acquired data to the database, and recall the algorithm to correct the foundation pit risk control value, so as to provide a more reasonable control value for foundation pit risk early warning in the subsequent excavation process.
According to the method and the system in the embodiment of the invention, the risk grade of a single bottom layer risk source and the risk probability corresponding to each grade are analyzed based on a large amount of monitoring data, and the ratio of the actual axial force to the designed axial force is specified to be low risk outside a 70% interval, medium risk outside an 80% interval and high risk outside a 90% interval by taking steel support axial force data as an example. Different from the existing foundation pit risk management method based on patents, specifications and big data analysis, the embodiment of the invention describes the risk level of a single bottom layer risk event, such as a risk event that the supporting axial force exceeds an allowable value and the lateral deformation of a wall body exceeds an allowable value, and sets low, medium and high risks according to 70%, 80% and 90% confidence intervals. By dividing the risks of the basic events into high, medium and low levels, the low, medium and high early warning measures of the same risk source event are different, so that the risk early warning means is more reasonable. The embodiment of the invention introduces a statistical method of confidence intervals and probabilities, combines standard normalization processing, and can easily extract index values on the confidence intervals to obtain early warning values corresponding to different grades.
Furthermore, the big data statistical analysis method in the above embodiment of the present invention collects the foundation pit information and data and updates the foundation pit data of the system in time; through accumulation of foundation pit risk information, the control indexes of the monitoring items are dynamically adjusted, so that risk management is more accurate.
In some embodiments, a redis cache database is used, millions of data can be stored at the same time, and an automatic acquisition service program is arranged on a server, so that data monitored every day can be automatically acquired into the database and then is called to perform monitoring calculation. The data query progress and efficiency can be improved by using redis, dynamic risk indexes of all monitoring quantities are obtained for newly collected data every day according to a set confidence interval, and the control indexes are dynamically adjusted, so that the risk management is more suitable for the whole process of foundation pit construction excavation. The mass data can be rapidly inquired by utilizing the redis, the calculation is carried out by utilizing an automatic acquisition program arranged on the server, and the control value of the risk index can be more reasonable along with the continuous increase of the data volume.
As those skilled in the art will appreciate, the invention may be embodied in many other specific forms without departing from the spirit or scope of the invention. Although embodiments of the present invention have been described, it is to be understood that the present invention should not be limited to those precise embodiments, and that various changes and modifications can be effected therein by one skilled in the art without departing from the scope or spirit of the invention as defined by the appended claims.
Claims (10)
1. A foundation pit engineering early warning method based on analysis of a large amount of monitoring data is characterized by comprising the following steps: the method comprises the following steps:
s1: establishing a deep foundation pit parameter acquisition and BIM model, and determining soil layer parameters, excavation depth, support level or/and surrounding environment of each foundation pit;
s2: collecting risk monitoring indexes, and analyzing the correlation between the monitoring indexes and the foundation pit parameters;
s3: using the correlation to call corresponding actual monitoring data or theoretical design values to carry out normalization processing on the measured values of the monitoring indexes to obtain the normalized statistical data of each risk monitoring index;
s4: carrying out normal distribution probability analysis on the statistical data, introducing a discrimination method of a confidence interval to provide a basis for setting a risk control value, and obtaining a risk index value;
s5, comparing and analyzing the obtained monitoring data with the risk index value, and sending out early warning information when the measured value does not meet the range of the risk control index value;
s6: risk related information is displayed in a three-dimensional mode through the BIM model, and early warning is achieved.
2. The foundation pit engineering early warning method as claimed in claim 1, wherein in the step S1, the deep foundation pit parameter is collected as a foundation pit excavation depth.
3. The foundation pit engineering early warning method according to claim 1, wherein in S1, the BIM model is used for providing engineering project selection function, engineering excavation stage selection function, working condition date selection function, foundation pit soil layer parameter information, excavation depth and information, supporting structure information, monitoring point layout information or/and surrounding building environment.
4. The foundation pit engineering early warning method according to claim 1, wherein in the step S2, the monitoring indexes include enclosure maximum displacement, enclosure kick ratio, enclosure maximum displacement rate, support axial force, ground surface settlement, and pit bottom midpoint rebound.
5. The foundation pit engineering early warning method of claim 4, wherein in S2, the correlation between the monitoring index and the foundation pit parameter is analyzed, and the correlation comprises the correlation between the maximum lateral displacement of the wall and the excavation depth, the correlation between the maximum ground surface settlement and the excavation depth, the correlation between the supporting axial force and the foundation pit supporting level, and the like.
6. The foundation pit engineering early warning method according to claim 1, wherein in S2, after deformation and stress monitoring indexes are determined, statistics of maximum and minimum actual measurement values of the indexes is performed, and statistics is performed on staged data of key nodes, wherein the key nodes include an earth excavation completion stage and a bottom plate completion stage.
7. The foundation pit engineering early warning method according to claim 1, wherein in S4, the normalized monitoring values obtained through statistics are subjected to data processing to obtain a mean and a standard deviation, and an average distribution condition and a dispersion degree are obtained.
8. The foundation pit engineering early warning method according to claim 7, wherein in S4, a standardized transformation of normal distribution is performed, a probability interval of a monitoring value is obtained by using a standard normal distribution percentage segment table, a low risk is specified outside a 70% interval, a medium risk is specified outside an 80% interval, and a high risk is specified outside a 90% interval, so as to obtain a risk control index.
9. The foundation pit engineering early warning method according to claim 1, wherein in S5, whether a risk exists in a monitoring item of the foundation pit is judged by comparing the current monitoring value of the foundation pit monitoring index with the risk index control value obtained in S4, and the actual value of the monitoring item is returned to the statistical data.
10. A foundation pit engineering early warning system based on analysis of a large amount of monitoring data is characterized by comprising: the system comprises a preprocessing module, a data processing module and a post-processing module;
the pre-processing module is used for collecting basic characteristic parameters of the foundation pit to be managed and related information of risk monitoring indexes, displaying the information of the foundation pit and monitoring point arrangement corresponding to the monitoring indexes by combining a BIM model, visually displaying the whole information of the foundation pit in a three-dimensional mode, and uploading the whole information to the early warning system;
the data processing module is used for counting historical data and actual monitoring data of corresponding deformation and stress monitoring items, obtaining a risk control value of a single monitoring index through the core computing module, and comparing, analyzing and judging whether a certain monitoring item of the foundation pit has a risk or not;
and the post-processing module is used for vividly displaying the collected risk information in a three-dimensional mode through the BIM, adopting special marks for the alarm object points and generating a foundation pit risk evaluation report.
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