Method for monitoring stability of deep foundation pit

Abstract

The invention discloses a deep foundation pit stability monitoring method, and relates to the technical field of deep foundation pit monitoring, and the method comprises the steps: carrying out the data monitoring of the geological information and interference factors of a deep foundation pit, preliminarily evaluating the stability of the deep foundation pit and the influence factors of the deep foundation pit, carrying out the prediction evaluation of the stability of the deep foundation pit, and carrying out the maintenance management operation. According to the method, the stability of the deep foundation pit is comprehensively evaluated from the displacement risk, stress risk and water pressure risk of the deep foundation pit, the comprehensiveness of data fusion processing is guaranteed, the influence conditions of rainwater factors, temperature factors and vibration factors on the stability of the deep foundation pit are monitored, and therefore the influence degree of interference factors on the stability of the deep foundation pit is analyzed; according to the method and the system, the interference factors are monitored to generate corresponding early warning prompt signals for processing, and the influence of the interference factors on the stability of the deep foundation pit is monitored, so that early warning and corresponding maintenance management on the stability of the deep foundation pit are realized, the anti-interference capability of the deep foundation pit is improved, the timeliness of stability monitoring management is realized, and the continuous stability of the deep foundation pit is further ensured.

Description

Technical Field

[0001] The invention relates to the technical field of deep foundation pit monitoring, and in particular to a deep foundation pit stability monitoring method. Background Art

[0002] Deep foundation pit engineering is an indispensable part of modern urban construction and is widely used in subway construction, road widening, housing construction and other fields. However, there are many safety hazards in the foundation pit construction process, such as foundation pit collapse and soil collapse. These safety hazards pose a serious threat to the life and property safety of construction workers and surrounding residents. The existing deep foundation pit stability monitoring mainly uses displacement, stress or groundwater and other related parameters for single-angle monitoring, which makes the data fusion capability insufficient and cannot fully reflect the actual status of the entire foundation pit. In addition, the stability of the deep foundation pit will be disturbed by external factors, such as temperature changes, vibration interference, rainfall changes, etc., which will affect the stability of the deep foundation pit, resulting in the problem of insufficient timeliness of stability monitoring management, and the resulting defect of poor continuous stability of the deep foundation pit; In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to solve the problems that the existing deep foundation pit stability monitoring has insufficient data fusion capability, cannot fully reflect the actual status of the entire foundation pit, and the stability of the deep foundation pit may be disturbed by external factors, resulting in insufficient timeliness of stability monitoring management, and the resulting defect of poor continuous stability of the deep foundation pit. The present invention realizes early warning of the stability of the deep foundation pit and corresponding maintenance management, improves the anti-interference ability of the deep foundation pit, ensures the timeliness of stability monitoring management, and thus ensures the continuous stability of the deep foundation pit.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A deep foundation pit stability monitoring method comprises the following steps: Step 1: Perform data monitoring on the geological information and interference factors of the deep foundation pit: set up a multi-source data monitoring module, which includes a geological monitoring submodule and an interference monitoring submodule, collect geological data through the geological monitoring submodule, and collect interference factor data through the interference monitoring submodule; Step 2: Preliminary assessment of the stability of the deep foundation pit through the geological data of the deep foundation pit: Setting up a geological data analysis module, pre-processing the displacement parameters, stress parameters and pore water pressure parameters through the geological data analysis module, and sequentially assessing the displacement risk degree, stress risk degree and water pressure risk degree of the deep foundation pit, and then comprehensively assessing the stability of the deep foundation pit in combination with the time series change trend; Step 3: In-depth evaluation of the influencing factors of deep foundation pit stability through interference factor data: set up an interference factor analysis module, analyze the interference factor data through the interference factor analysis module, construct the influence function of the interference factor on the stability of the deep foundation pit, and fit the correlation function between the interference factor and the stability of the deep foundation pit through rainwater parameters, temperature parameters and vibration parameters, so as to evaluate the influence of rainwater factors, temperature factors and vibration factors on the stability of the deep foundation pit; Step 4: Construct a dynamic early warning model to predict and evaluate the stability of the deep foundation pit: Set up a central server and construct a dynamic early warning model. Through the correlation function between the interference factor and the deep foundation pit stability, obtain the prediction coefficient of the deep foundation pit stability, so as to predict and evaluate the deep foundation pit stability and generate corresponding early warning prompt signals; Step five, performing maintenance and management operations on the deep foundation pit through early warning prompt signals: setting up a maintenance and management module, receiving early warning prompt signals through the maintenance and management module, and thus performing corresponding maintenance and management operations on the deep foundation pit.

[0005] Furthermore, the specific process of preliminarily evaluating the stability of deep foundation pits through geological data of deep foundation pits is as follows: The displacement parameters, stress parameters and pore water pressure parameters are preprocessed through the geological data analysis module to evaluate the displacement risk, stress risk and water pressure risk of the deep foundation pit in turn; Then, combined with the time series change trend, the stability of the deep foundation pit is comprehensively evaluated. The specific process is as follows: Set up a risk time series analysis model, and input a risk curve s into the risk time series analysis model; Mark the number of geological risk monitoring cycles Td of risk curve s as n0, so as to extract n0 points of risk curve s, mark any point as p, mark the coordinates of point p as (Xp, Yp), mark the neighboring points of point p and their coordinates as q (Xq, Yq); obtain the change rate Kp of point p through the coordinates of point p (Xp, Yp) and the coordinates of point q (Xq, Yq); obtain the curve increase rate Ks of risk curve s through the change rates of n0 points; then calculate the standard deviation through the curve increase rate Ks of n0 points to obtain the curve growth fluctuation coefficient σs of risk curve s; combine the curve increase rate Ks of risk curve s with the curve growth fluctuation coefficient σs to obtain the state assessment value Zs of risk curve s; The risk time series analysis model outputs the state assessment value Zs of the risk curve s; The geological risk monitoring period Td is set to regularly calculate the displacement risk coefficient X1, stress risk coefficient X2 and water pressure risk coefficient X3 of the deep foundation pit, so as to construct the change curves between the displacement risk coefficient X1, stress risk coefficient X2 and water pressure risk coefficient X3 of the deep foundation pit and the geological risk monitoring period Td; The change curve is substituted into the risk time series analysis model, and the state assessment value of the corresponding curve is output, and then the comprehensive stability index Stab of the deep foundation pit is comprehensively obtained.

[0006] Furthermore, the displacement parameters are preprocessed and the displacement risk level of the deep foundation pit is evaluated; The displacement parameters include horizontal displacement, vertical displacement and tilt angle; Construct a deep foundation pit BIM three-dimensional model, divide and mark the model into N0 characteristic areas, mark any characteristic area as i, and mark the horizontal displacement, vertical displacement and inclination angle of characteristic area i as , ,θxy; The displacement risk coefficient X1 of the deep foundation pit is obtained through the displacement parameters of N0 characteristic areas.

[0007] Furthermore, stress parameters are preprocessed and the stress risk level of deep foundation pits is evaluated; Stress parameters include the internal force value of the retaining wall, the internal force value of the anchor rod and the internal force value of the support; Extract N1w collection points of internal force of retaining walls, N1m collection points of internal force of anchor rods and N1c collection points of internal force of supports from the BIM stereoscopic model of deep foundation pit, mark any internal force value of retaining walls as Fw, any internal force value of anchor rods as Fm, and any internal force value of supports as Fc; Set the standard range of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support. When the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support are within the corresponding standard range, it means that the stress parameters are normal; An internal force risk analysis model is set, and an internal force index f and its standard interval [Gf1, Gf2] are input into the internal force risk analysis model, so as to obtain and output a risk assessment value Xf of the internal force index f; Substitute the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod, the internal force value Fc of the support and their corresponding standard intervals into the internal force risk analysis model, and output the risk assessment values ​​of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support, and mark them as Xfw, Xfm and Xfc respectively; The stress risk coefficient X2 of the deep foundation pit is obtained by combining the risk assessment value Xfw of N1w retaining wall internal force values ​​Fw, the risk assessment value Xfm of N1m anchor rod internal force values ​​Fm, and the risk assessment value Xfc of N1c support internal force values ​​Fc.

[0008] Furthermore, the pore water pressure parameters are preprocessed and the water pressure risk level of deep foundation pits is evaluated; The pore water pressure parameters include horizontal pore water pressure Gx, vertical pore water pressure Gy and groundwater level Hxy; Extract N2 feature points of the deep foundation pit BIM three-dimensional model, mark any feature point as j, mark the horizontal pore water pressure and vertical pore water pressure of feature point j as Gx and Gy respectively, and mark the groundwater level of the deep foundation pit as Hxy; The water pressure risk coefficient X3 of the deep foundation pit is obtained by combining the horizontal pore water pressure Gx and vertical pore water pressure Gy corresponding to N2 characteristic points and the groundwater level Hxy.

[0009] Furthermore, the specific process of obtaining the comprehensive stability index Stab of the deep foundation pit is as follows: Construct the change curve Swy between the displacement risk coefficient X1 of the deep foundation pit and the geological risk monitoring period Td, substitute the change curve Swy as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the displacement state assessment value Zwy of the deep foundation pit; Construct a change curve Syl between the stress risk coefficient X2 of the deep foundation pit and the geological risk monitoring period Td, substitute the change curve Syl as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the stress state assessment value Zyl of the deep foundation pit; Construct the change curve Sks between the water pressure risk coefficient X3 of the deep foundation pit and the geological risk monitoring period Td, substitute the change curve Sks as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the water pressure state assessment value Zks of the deep foundation pit; The displacement state assessment value Zwy, stress state assessment value Zyl and water pressure state assessment value Zks of the deep foundation pit are combined to obtain the comprehensive stability index Stab of the deep foundation pit.

[0010] Furthermore, the specific process of constructing the influence function of interference factors on the stability of deep foundation pit is as follows: Set the interference factor collection period Tc to collect interference factor data regularly, and construct the rain parameter vector Rvect, temperature parameter vector Wvect and vibration parameter vector Dvect; By monitoring and obtaining the interference factor data of m0 interference factor collection periods Tc, the rain parameter vector Rvect, the temperature parameter vector Wvect and the vibration parameter vector Dvect are combined to construct the interference factor matrix IF: , and synchronously obtain the deep foundation pit comprehensive stability index Stab of m0 interference factor acquisition period Tc; Thus, the correlation function F1 between the rainwater parameter vector Rvect and the comprehensive stability index Stab of the deep foundation pit, the correlation function F2 between the temperature parameter vector Wvect and the comprehensive stability index Stab of the deep foundation pit, and the correlation function F3 between the vibration parameter vector Dvect and the comprehensive stability index Stab of the deep foundation pit are constructed.

[0011] Furthermore, the specific process of constructing a dynamic early warning model to predict and evaluate the stability of deep foundation pits is as follows: By monitoring the rainwater parameter vector Rvect, the temperature parameter vector Wvect and the vibration parameter vector Dvect, the correlation function between the interference factor and the stability of the deep foundation pit is obtained; The prediction coefficient Pdt of the deep foundation pit stability is obtained by combining the correlation function F1 between the rainwater parameter vector Rvect and the deep foundation pit comprehensive stability index Stab, the correlation function F2 between the temperature parameter vector Wvect and the deep foundation pit comprehensive stability index Stab, and the correlation function F3 between the vibration parameter vector Dvect and the deep foundation pit comprehensive stability index Stab; The evaluation interval of the prediction coefficient Pdt of the deep foundation pit stability is set, the prediction degree of the deep foundation pit stability is evaluated by interval comparison, and the corresponding early warning prompt signal is generated.

[0012] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention conducts data monitoring on the geological information and interference factors of the deep foundation pit, conducts a comprehensive assessment of the stability of the deep foundation pit from the perspective of displacement risk, stress risk and water pressure risk of the deep foundation pit, ensures the comprehensiveness of data fusion processing, and monitors the influence of rainwater factor, temperature factor and vibration factor on the stability of the deep foundation pit, thereby analyzing the influence of the interference factors on the stability of the deep foundation pit, generates corresponding early warning prompt signals for processing, and monitors the influence of the interference factors on the stability of the deep foundation pit, thereby realizing early warning of the stability of the deep foundation pit and corresponding maintenance management, thereby improving the anti-interference ability of the deep foundation pit, realizing the timeliness of stability monitoring management, and thus ensuring the continuous stability of the deep foundation pit. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram showing the steps of the solution process of the present invention is shown; Figure 2 A schematic diagram showing the flow of data processing of the present invention is shown; Figure 3 A connection diagram of the device modules of the present invention is shown. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] Embodiment 1: like Figure 1-Figure 3 As shown, a deep foundation pit stability monitoring method is implemented based on a deep foundation pit stability monitoring device, the device is provided with a multi-source data monitoring module, a geological data analysis module, an interference factor analysis module, a central server and a maintenance management module, wherein the multi-source data monitoring module, the geological data analysis module, the interference factor analysis module, the central server and the maintenance management module are connected in communication, and the method specifically includes the following steps: S1, data monitoring of geological information and interference factors of the deep foundation pit: setting a multi-source data monitoring module, the multi-source data monitoring module includes a geological monitoring submodule and an interference monitoring submodule, collecting geological data through the geological monitoring submodule, and collecting interference factor data through the interference monitoring submodule; S1-1, the specific process of collecting geological data is: The geological monitoring submodule includes UAV laser radar, micro force sensor and osmometer, through which geological data are collected. The geological data include displacement parameters, stress parameters and pore water pressure parameters; S1-101, displacement parameters include horizontal displacement , vertical displacement and the tilt angle θxy; The distance of the target object is calculated by the round-trip time of the laser signal emitted by the UAV laser radar. Specifically, the laser radar emits a laser signal to the target object. The laser signal is reflected back after encountering the target object and is received by the laser radar receiving system. By measuring the round-trip time of the laser signal, the distance of the target object can be calculated, and by setting the laser signal emission cycle, the distance of the target position can be measured regularly, so as to obtain the displacement of the target position; the automatic inclinometer is often used to monitor the displacement inside the rock and soil, and is generally arranged in the middle of the periphery of the foundation pit, the positive corner and other parts; S1-102, stress parameters include the internal force of the retaining wall Fw, the internal force of the anchor rod Fm and the internal force of the support Fc; The internal force monitoring points of the retaining wall should be arranged at the position where the bending moment extreme value appears in the retaining wall. The number of monitoring points and the horizontal spacing shall be determined according to the specific situation. In the plane, the mid-span point between two adjacent supports of the retaining wall and the position with a large excavation depth shall be selected; in the vertical section, the monitoring points shall be arranged at the support and the middle position between two adjacent layers of supports; the internal force monitoring points of the anchor rod shall be selected at the position with large force and representativeness. The monitoring points shall be arranged in the middle of each side of the foundation pit, at the positive corner and in the section with complex geological conditions; the monitoring of the internal force of the support shall select different monitoring sensors according to the type of support rod. For concrete support, surface strain gauges are currently mainly used; while for steel support rods, axial force gauges or surface strain gauges are mostly used; S1-103, pore water pressure parameters include horizontal pore water pressure Gx, vertical pore water pressure Gy and groundwater level Hxy; The piezometer is used to monitor the seepage water pressure inside the foundation pit soil, and is generally arranged at the parts of the foundation pit that are subjected to stress, have large deformation or are representative; the groundwater level monitoring of the foundation pit is to monitor the impact of precipitation on the surrounding environment. Water level gauges are arranged along the periphery of the foundation pit and the protected object or between the foundation pit and the protected object, such as adjacent buildings, important pipelines or places where pipelines are densely packed, etc. S1-2, the specific process of collecting interference factor data is as follows: The interference monitoring submodule includes meteorological platform communication equipment, temperature sensors and vibration sensors. The interference factor data is collected through the interference monitoring submodule. The interference factor data includes rain parameters, temperature parameters and vibration parameters. S1-201, rainfall parameters include rainfall amount R1, rainfall intensity R2, rainfall duration R3 and rainfall frequency R4; The rainwater parameters of the deep foundation pit are monitored and collected through the meteorological platform, and the corresponding data are obtained from the meteorological platform through the communication equipment and stored in the interference monitoring submodule; S1-202, temperature parameters include ambient temperature W1 and soil temperature W2; The ambient temperature W1 is monitored through the meteorological platform, and the soil temperature W2 is monitored by setting up temperature sensors; S1-203, vibration parameters include vibration frequency D1 and vibration amplitude D2; Vibration sensors are used to monitor the amplitude and frequency of vibrations in the deep foundation pit retaining walls and strata. The vibration sources include surrounding vehicles and construction equipment, which are affected by the operation of vehicles and other equipment. S2, preliminarily evaluate the stability of the deep foundation pit through the geological data of the deep foundation pit: set up a geological data analysis module, pre-process the displacement parameters, stress parameters and pore water pressure parameters through the geological data analysis module, evaluate the displacement risk degree, stress risk degree and water pressure risk degree of the deep foundation pit in turn, and then comprehensively evaluate the stability of the deep foundation pit in combination with the time series change trend; S2-1, pre-process the displacement parameters, stress parameters and pore water pressure parameters through the geological data analysis module, and evaluate the displacement risk level, stress risk level and water pressure risk level of the deep foundation pit in turn; Sa1, preprocessing displacement parameters and assessing the displacement risk level of deep foundation pits; The displacement parameters include horizontal displacement, vertical displacement and tilt angle; Physically model the deep foundation pit through modeling software, thus constructing a deep foundation pit BIM three-dimensional model, and dividing and marking the model; The deep foundation pit BIM three-dimensional model is divided into N0 feature areas, any feature area is marked as i, and the horizontal displacement, vertical displacement and inclination angle of feature area i are marked as , ,θxy; The displacement risk coefficient X1 of the deep foundation pit is obtained through the displacement parameters of N0 characteristic areas: ; Among them, α1, α2 and α3 are the horizontal displacements , vertical displacement and the weight coefficient of the tilt angle θxy, and the preset α1, α2 and α3 are all greater than 1; when the horizontal displacement , vertical displacement When the inclination angle θxy is higher, the displacement risk coefficient X1 of the deep foundation pit is higher, which means that the offset of each displacement parameter of the N0 characteristic area of ​​the deep foundation pit BIM three-dimensional model is higher, which comprehensively indicates that the displacement risk degree of the deep foundation pit is higher; Sa2, pre-processing stress parameters and assessing the stress risk level of deep foundation pits; Stress parameters include the internal force value of the retaining wall, the internal force value of the anchor rod and the internal force value of the support; Extract N1w collection points of internal force of retaining walls, N1m collection points of internal force of anchor rods and N1c collection points of internal force of supports from the BIM stereoscopic model of deep foundation pit, mark any internal force value of retaining walls as Fw, any internal force value of anchor rods as Fm, and any internal force value of supports as Fc; Set the standard range of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support. When the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support are within the corresponding standard range, it means that the stress parameters are normal; Set up an internal force risk analysis model, input the internal force index f and its standard interval [Gf1, Gf2] into the internal force risk analysis model, and then obtain and output the risk assessment value Xf of the internal force index f: ; in, is the correction coefficient of the internal force index f, the preset correction coefficient It is possible to guarantee A constant value that is always greater than 1. When the internal force index f exceeds the standard interval [Gf1, Gf2] and the higher the range, the higher the risk assessment value Xf, and the higher the risk level of the internal force index f; Substitute the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod, the internal force value Fc of the support and their corresponding standard intervals into the internal force risk analysis model, and output the risk assessment values ​​of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support, and mark them as Xfw, Xfm and Xfc respectively; The internal force value Fw of the enclosure wall is used as the internal force index f, the internal force value Fw of the enclosure wall and its standard interval are input into the internal force risk analysis model, and the risk assessment value Xfw of the internal force value Fw of the enclosure wall is output; The anchor internal force value Fm is used as the internal force index f, the anchor internal force value Fm and its standard interval are input into the internal force risk analysis model, and the risk assessment value Xfm of the anchor internal force value Fm is output; Taking the support internal force value Fc as the internal force index f, inputting the support internal force value Fc and its standard interval into the internal force risk analysis model, and outputting the risk assessment value Xfc of the support internal force value Fc; The stress risk coefficient X2 of the deep foundation pit is obtained by combining the risk assessment value Xfw of the N1w retaining wall internal force values ​​Fw, the risk assessment value Xfm of the N1m anchor internal force values ​​Fm, and the risk assessment value Xfc of the N1c support internal force values ​​Fc: ; in, , , They are the weight coefficients of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support. , , are greater than 0 and ; When the average risk assessment value of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support is higher, the stress risk coefficient X2 of the deep foundation pit is higher, indicating that the magnitude of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc exceeding the standard range is higher, which comprehensively indicates that the stress risk degree of the deep foundation pit is higher; Sa3, preprocessing pore water pressure parameters and assessing the water pressure risk level of deep foundation pits; The pore water pressure parameters include horizontal pore water pressure Gx, vertical pore water pressure Gy and groundwater level Hxy; Extract N2 feature points of the deep foundation pit BIM three-dimensional model, mark any feature point as j, mark the horizontal pore water pressure and vertical pore water pressure of feature point j as Gx and Gy respectively, and mark the groundwater level of the deep foundation pit as Hxy; By combining the horizontal pore water pressure Gx and vertical pore water pressure Gy corresponding to N2 feature points and the groundwater level Hxy, the water pressure risk coefficient X3 of the deep foundation pit is obtained: ; in, , , are the weight coefficients of horizontal pore water pressure Gx, vertical pore water pressure Gy and groundwater level Hxy, and the preset , , are all greater than 1; when the horizontal pore water pressure Gx, vertical pore water pressure Gy and groundwater level Hxy are higher, the water pressure risk coefficient X3 of the deep foundation pit is higher, which means that the horizontal pore water pressure Gx and vertical pore water pressure Gy corresponding to the N2 feature points of the deep foundation pit BIM three-dimensional model are higher, and the groundwater level of the deep foundation pit is higher, which comprehensively indicates that the water pressure risk degree of the deep foundation pit is higher; Sa4, and then comprehensively evaluate the stability of deep foundation pits in combination with the temporal variation trend; Set up a risk time series analysis model, and input a risk curve s into the risk time series analysis model; Mark the number of geological risk monitoring cycles Td of risk curve s as n0, thereby extracting n0 points of risk curve s, marking any point as p, marking the coordinates of point p as (Xp, Yp), and marking the neighboring points of point p and their coordinates as q (Xq, Yq); Through the coordinates of point p (Xp, Yp) and the coordinates of point q (Xq, Yq), obtain the change rate Kp of point p: ; The curve increase rate Ks of the risk curve s is obtained by comprehensively obtaining the change rate of n0 points: ; Then, the standard deviation is calculated through the curve growth rate Ks of n0 points to obtain the curve growth fluctuation coefficient σs of the risk curve s: ; Combine the curve growth rate Ks of the risk curve s with the curve growth fluctuation coefficient σs to obtain and output the state assessment value Zs of the risk curve s: ; in, , They are the conversion coefficients of the curve growth rate Ks and the curve growth fluctuation coefficient σs, and are preset to be 0< <1 and is greater than 0; when the curve increase rate Ks is less than 0 and the smaller the curve increase rate Ks is and the lower the curve growth fluctuation coefficient σs is, the higher the state assessment value Zs of the risk curve s is, indicating that the time series state of the risk curve is in a risk-decreasing trend, and the state assessment stability is better; when the curve increase rate Ks is greater than 0 and the larger the curve increase rate Ks is and the higher the curve growth fluctuation coefficient σs is, the lower the state assessment value Zs of the risk curve s is, indicating that the time series state of the risk curve is in a risk-increasing trend, and the state assessment stability is worse; Set the geological risk monitoring period Td to regularly calculate the displacement risk factor X1, stress risk factor X2 and water pressure risk factor X3 of the deep foundation pit; With the displacement risk coefficient X1 of the deep foundation pit as the ordinate and the geological risk monitoring period Td as the abscissa, a change curve Swy between the displacement risk coefficient X1 of the deep foundation pit and the geological risk monitoring period Td is constructed; Substitute the change curve Swy as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the displacement state assessment value Zwy of the deep foundation pit; With the stress risk coefficient X2 of the deep foundation pit as the ordinate and the geological risk monitoring period Td as the abscissa, a variation curve Syl between the stress risk coefficient X2 of the deep foundation pit and the geological risk monitoring period Td is constructed; Substitute the change curve Syl as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the stress state assessment value Zyl of the deep foundation pit; With the water pressure risk coefficient X3 of the deep foundation pit as the ordinate and the geological risk monitoring period Td as the abscissa, a change curve Sks between the water pressure risk coefficient X3 of the deep foundation pit and the geological risk monitoring period Td is constructed; Substitute the change curve Sks as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the water pressure state assessment value Zks of the deep foundation pit; The displacement state evaluation value Zwy, stress state evaluation value Zyl and water pressure state evaluation value Zks of the deep foundation pit are combined to obtain the comprehensive stability index Stab of the deep foundation pit: ; in, , , are the weight coefficients of the displacement state evaluation value Zwy, the stress state evaluation value Zyl and the water pressure state evaluation value Zk, respectively, and , , The preset values ​​of are all greater than 0; when the displacement state evaluation value Zwy, stress state evaluation value Zyl and water pressure state evaluation value Zks of the deep foundation pit are higher, the deep foundation pit comprehensive stability index Stab is higher, and the comprehensive evaluation of the stability of the deep foundation pit is better; S3, deeply evaluate the influencing factors of deep foundation pit stability through interference factor data: set up an interference factor analysis module, analyze the interference factor data through the interference factor analysis module, construct the influence function of the interference factor on the stability of the deep foundation pit, and fit the correlation function between the interference factor and the stability of the deep foundation pit through rainwater parameters, temperature parameters and vibration parameters, so as to evaluate the influence of rainwater factors, temperature factors and vibration factors on the stability of the deep foundation pit; S3-1, setting the interference factor collection period Tc to collect interference factor data regularly, and constructing the rain parameter vector Rvect, the temperature parameter vector Wvect and the vibration parameter vector Dvect; Construct the rainwater parameter vector Rvect: ; Temperature parameter vector Wvect: ; Among them, the number of collection points marked with ambient temperature W1 is m1, then It refers to the set of ambient temperature W1 of m1 collection points; the number of collection points marking soil temperature W2 is m2, then It refers to the set of soil temperatures W2 at m2 collection points; Vibration parameter vector Dvect: ; Among them, there are m3 collection points marking vibration parameters, then It refers to the set of vibration frequencies D1 of m3 acquisition points, It refers to the set of vibration amplitudes D2 of m3 acquisition points; S3-2, by monitoring and obtaining the interference factor data of m0 interference factor collection periods Tc, the rain parameter vector Rvect, the temperature parameter vector Wvect and the vibration parameter vector Dvect are combined to construct the interference factor matrix IF: , and synchronously obtain the deep foundation pit comprehensive stability index Stab of m0 interference factor acquisition period Tc; S3-201, construct the correlation function F1 between the rainwater parameter vector Rvect and the deep foundation pit comprehensive stability index Stab: ; Among them, ρ1, ρ2, ρ3 and ρ4 are the weight coefficients of rainfall amount R1, rainfall intensity R2, rainfall duration R3 and rainfall frequency R4, respectively, and ρ1, ρ2, ρ3 and ρ4 are all greater than 0; when the rainfall amount R1, rainfall intensity R2, rainfall duration R3 and rainfall frequency R4 are higher, the influence of rain factor on the stability of deep foundation pit is higher, and the comprehensive stability index Stab of deep foundation pit is lower; S3-202, construct the correlation function F2 between the temperature parameter vector Wvect and the comprehensive stability index Stab of the deep foundation pit: ; in, is the set of ambient temperature W1 The average value of refers to the set of soil temperatures W2 The average values ​​of γ1 and γ2 are and The conversion coefficients γ1 and γ2 are both greater than 1. The conversion coefficients γ1 and γ2 are to ensure and If the constant value is always between 0 and π, then with the increase of ambient temperature W1 and soil temperature W2, the comprehensive stability index Stab of deep foundation pit will show a trend of first increasing and then decreasing, that is, the fitted correlation function F2 will show the trend state of the sin function in the interval [0, π], indicating that when the temperature factor is too high or too low, it will affect the stability of the deep foundation pit, thus causing the comprehensive stability index Stab of the deep foundation pit to become lower; S3-203, construct the correlation function F3 between the vibration parameter vector Dvect and the comprehensive stability index Stab of the deep foundation pit: ; in, and are the sets of vibration frequencies D1 and the vibration amplitude D2 The average value of and are the sets of vibration frequencies D1 and the vibration amplitude D2 The variance value of and The vibration mean coefficient is obtained by combining the above two methods to characterize the overall level of the deep foundation pit vibration factor. and Combined with the above, the vibration variance coefficient is obtained to characterize the local difference level of the vibration factor of the deep foundation pit; and are the weight coefficients of the vibration mean coefficient and the vibration variance coefficient, respectively, and and are greater than 0; when and , and When it is higher, the influence of vibration factor on the stability of deep foundation pit is higher, and the comprehensive stability index Stab of deep foundation pit is lower; S4, constructing a dynamic early warning model to predict and evaluate the stability of the deep foundation pit: setting up a central server and constructing a dynamic early warning model, obtaining the prediction coefficient of the deep foundation pit stability through the correlation function between the interference factor and the deep foundation pit stability, thereby predicting and evaluating the stability of the deep foundation pit and generating corresponding early warning prompt signals; S4-1, by monitoring the rainwater parameter vector Rvect, the temperature parameter vector Wvect and the vibration parameter vector Dvect, the correlation function between the interference factor and the stability of the deep foundation pit is obtained; S4-2, by combining the correlation function F1 between the rainwater parameter vector Rvect and the deep foundation pit comprehensive stability index Stab, the correlation function F2 between the temperature parameter vector Wvect and the deep foundation pit comprehensive stability index Stab, and the correlation function F3 between the vibration parameter vector Dvect and the deep foundation pit comprehensive stability index Stab, the prediction coefficient Pdt of the deep foundation pit stability is obtained: ; Among them, Ψ1, Ψ2 and Ψ3 are the weight coefficients of correlation function F1, correlation function F2 and correlation function F3 respectively. It is preset that Ψ1, Ψ2 and Ψ3 are all greater than 0 and ; When the correlation function F1, the correlation function F2 and the correlation function F3 are higher, the prediction coefficient Pdt of the deep foundation pit stability is higher, and the prediction degree of the deep foundation pit stability is higher; S4-3, setting an evaluation interval of the prediction coefficient Pdt of the deep foundation pit stability, evaluating the prediction degree of the deep foundation pit stability through interval comparison, and generating a corresponding early warning signal; There are M0 evaluation intervals of the prediction coefficient Pdt of the stability of the deep foundation pit, and any evaluation interval is marked as Qm. When the prediction coefficient Pdt of the stability of the deep foundation pit is in the evaluation interval Qm, a Vm-level early warning prompt signal is generated. When the prediction coefficient Pdt of the stability of the deep foundation pit is lower, the level of the early warning prompt signal is higher, indicating that the stability of the deep foundation pit is worse; S5, performing maintenance management operations on the deep foundation pit through early warning prompt signals: setting a maintenance management module, receiving early warning prompt signals through the maintenance management module, and thus performing corresponding maintenance management operations on the deep foundation pit; Maintenance and management operations mainly use early warning signals to prompt management personnel to carry out corresponding maintenance and management. From the perspectives of rainwater scheduling, temperature control and vibration reduction, the interference factors of deep foundation pit stability are managed. Specific management operations are combined with actual conditions. For example, when the meteorological forecast shows that the rainfall is high, it is necessary to carry out rainwater scheduling and control in advance to evacuate the rainwater to the preset water storage area to avoid serious impact on the stability of the deep foundation pit.

[0016] In summary, the present invention conducts data monitoring on the geological information and interference factors of the deep foundation pit, conducts a comprehensive assessment of the stability of the deep foundation pit from the perspective of displacement risk, stress risk and water pressure risk of the deep foundation pit, ensures the comprehensiveness of data fusion processing, and monitors the influence of rainwater factor, temperature factor and vibration factor on the stability of the deep foundation pit, thereby analyzing the influence of the interference factor on the stability of the deep foundation pit, generating corresponding early warning prompt signals for processing, and by monitoring the influence of the interference factors on the stability of the deep foundation pit, thereby realizing early warning of the stability of the deep foundation pit and corresponding maintenance management, thereby improving the anti-interference ability of the deep foundation pit, realizing the timeliness of stability monitoring management, and thus ensuring the continuous stability of the deep foundation pit.

[0017] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0018] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A deep foundation pit stability monitoring method, characterized in that: The following steps are involved: Step 1: Perform data monitoring on the geological information and interference factors of the deep foundation pit: set up a multi-source data monitoring module, which includes a geological monitoring submodule and an interference monitoring submodule, collect geological data through the geological monitoring submodule, and collect interference factor data through the interference monitoring submodule; Step 2: Preliminary assessment of the stability of the deep foundation pit through the geological data of the deep foundation pit: Setting up a geological data analysis module, pre-processing the displacement parameters, stress parameters and pore water pressure parameters through the geological data analysis module, and sequentially assessing the displacement risk degree, stress risk degree and water pressure risk degree of the deep foundation pit, and then comprehensively assessing the stability of the deep foundation pit in combination with the time series change trend; Step 3: In-depth evaluation of the influencing factors of deep foundation pit stability through interference factor data: set up an interference factor analysis module, analyze the interference factor data through the interference factor analysis module, construct the influence function of the interference factor on the stability of the deep foundation pit, and fit the correlation function between the interference factor and the stability of the deep foundation pit through rainwater parameters, temperature parameters and vibration parameters, so as to evaluate the influence of rainwater factors, temperature factors and vibration factors on the stability of the deep foundation pit; Step 4: Construct a dynamic early warning model to predict and evaluate the stability of the deep foundation pit: Set up a central server and construct a dynamic early warning model. Through the correlation function between the interference factor and the deep foundation pit stability, obtain the prediction coefficient of the deep foundation pit stability, so as to predict and evaluate the deep foundation pit stability and generate corresponding early warning prompt signals; Step five, performing maintenance and management operations on the deep foundation pit through early warning prompt signals: setting up a maintenance and management module, receiving early warning prompt signals through the maintenance and management module, and thus performing corresponding maintenance and management operations on the deep foundation pit.

2. A deep foundation pit stability monitoring method according to claim 1, characterized in that: The specific process of preliminarily evaluating the stability of deep foundation pits through geological data of deep foundation pits is as follows: The displacement parameters, stress parameters and pore water pressure parameters are preprocessed through the geological data analysis module to evaluate the displacement risk, stress risk and water pressure risk of the deep foundation pit in turn; Then, combined with the time series change trend, the stability of the deep foundation pit is comprehensively evaluated. The specific process is as follows: Set up a risk time series analysis model, and input a risk curve s into the risk time series analysis model; Mark the number of geological risk monitoring cycles Td of risk curve s as n0, so as to extract n0 points of risk curve s, mark any point as p, mark the coordinates of point p as (Xp, Yp), mark the neighboring points of point p and their coordinates as q (Xq, Yq); obtain the change rate Kp of point p through the coordinates of point p (Xp, Yp) and the coordinates of point q (Xq, Yq); obtain the curve increase rate Ks of risk curve s through the change rates of n0 points; then calculate the standard deviation through the curve increase rate Ks of n0 points to obtain the curve growth fluctuation coefficient σs of risk curve s; combine the curve increase rate Ks of risk curve s with the curve growth fluctuation coefficient σs to obtain the state assessment value Zs of risk curve s; The risk time series analysis model outputs the state assessment value Zs of the risk curve s; The geological risk monitoring period Td is set to regularly calculate the displacement risk coefficient X1, stress risk coefficient X2 and water pressure risk coefficient X3 of the deep foundation pit, so as to construct the change curves between the displacement risk coefficient X1, stress risk coefficient X2 and water pressure risk coefficient X3 of the deep foundation pit and the geological risk monitoring period Td; The change curve is substituted into the risk time series analysis model, and the state assessment value of the corresponding curve is output, and then the comprehensive stability index Stab of the deep foundation pit is comprehensively obtained.

3. A deep foundation pit stability monitoring method according to claim 2, characterized in that: Preprocess displacement parameters and assess the displacement risk level of deep foundation pits; The displacement parameters include horizontal displacement, vertical displacement and tilt angle; Construct a deep foundation pit BIM three-dimensional model, divide and mark the model into N0 characteristic areas, mark any characteristic area as i, and mark the horizontal displacement, vertical displacement and inclination angle of characteristic area i as , ,θxy; The displacement risk coefficient X1 of the deep foundation pit is obtained through the displacement parameters of N0 characteristic areas.

4. A deep foundation pit stability monitoring method according to claim 3, characterized in that: Pre-process stress parameters and assess the stress risk level of deep foundation pits; Stress parameters include the internal force value of the retaining wall, the internal force value of the anchor rod and the internal force value of the support; Extract N1w collection points of internal force of retaining walls, N1m collection points of internal force of anchor rods and N1c collection points of internal force of supports from the BIM stereoscopic model of deep foundation pit, mark any internal force value of retaining walls as Fw, any internal force value of anchor rods as Fm, and any internal force value of supports as Fc; Set the standard range of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support. When the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support are within the corresponding standard range, it means that the stress parameters are normal; An internal force risk analysis model is set, and an internal force index f and its standard interval [Gf1, Gf2] are input into the internal force risk analysis model, so as to obtain and output a risk assessment value Xf of the internal force index f; Substitute the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod, the internal force value Fc of the support and their corresponding standard intervals into the internal force risk analysis model, and output the risk assessment values ​​of the internal force value Fw of the retaining wall, the internal force value Fm of the anchor rod and the internal force value Fc of the support, and mark them as Xfw, Xfm and Xfc respectively; The stress risk coefficient X2 of the deep foundation pit is obtained by combining the risk assessment value Xfw of N1w retaining wall internal force values ​​Fw, the risk assessment value Xfm of N1m anchor rod internal force values ​​Fm, and the risk assessment value Xfc of N1c support internal force values ​​Fc.

5. A deep foundation pit stability monitoring method according to claim 4, characterized in that: Pre-process pore water pressure parameters and assess the water pressure risk level of deep foundation pits; The pore water pressure parameters include horizontal pore water pressure Gx, vertical pore water pressure Gy and groundwater level Hxy; Extract N2 feature points of the deep foundation pit BIM three-dimensional model, mark any feature point as j, mark the horizontal pore water pressure and vertical pore water pressure of feature point j as Gx and Gy respectively, and mark the groundwater level of the deep foundation pit as Hxy; The water pressure risk coefficient X3 of the deep foundation pit is obtained by combining the horizontal pore water pressure Gx and vertical pore water pressure Gy corresponding to N2 characteristic points and the groundwater level Hxy.

6. A deep foundation pit stability monitoring method according to claim 5, characterized in that: The specific process of obtaining the comprehensive stability index Stab of deep foundation pit is as follows: Construct the change curve Swy between the displacement risk coefficient X1 of the deep foundation pit and the geological risk monitoring period Td, substitute the change curve Swy as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the displacement state assessment value Zwy of the deep foundation pit; Construct a change curve Syl between the stress risk coefficient X2 of the deep foundation pit and the geological risk monitoring period Td, substitute the change curve Syl as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the stress state assessment value Zyl of the deep foundation pit; Construct the change curve Sks between the water pressure risk coefficient X3 of the deep foundation pit and the geological risk monitoring period Td, substitute the change curve Sks as the risk curve s into the risk time series analysis model, output the state assessment value of the corresponding curve, and mark it as the water pressure state assessment value Zks of the deep foundation pit; The displacement state assessment value Zwy, stress state assessment value Zyl and water pressure state assessment value Zks of the deep foundation pit are combined to obtain the comprehensive stability index Stab of the deep foundation pit.

7. A deep foundation pit stability monitoring method according to claim 6, characterized in that: The specific process of constructing the influence function of interference factors on the stability of deep foundation pit is as follows: Set the interference factor collection period Tc to collect interference factor data regularly, and construct the rain parameter vector Rvect, temperature parameter vector Wvect and vibration parameter vector Dvect; By monitoring and obtaining the interference factor data of m0 interference factor collection periods Tc, the rain parameter vector Rvect, the temperature parameter vector Wvect and the vibration parameter vector Dvect are combined to construct the interference factor matrix IF: , and synchronously obtain the deep foundation pit comprehensive stability index Stab of m0 interference factor acquisition period Tc; Thus, the correlation function F1 between the rainwater parameter vector Rvect and the comprehensive stability index Stab of the deep foundation pit, the correlation function F2 between the temperature parameter vector Wvect and the comprehensive stability index Stab of the deep foundation pit, and the correlation function F3 between the vibration parameter vector Dvect and the comprehensive stability index Stab of the deep foundation pit are constructed.

8. A deep foundation pit stability monitoring method according to claim 7, characterized in that: The specific process of constructing a dynamic early warning model to predict and evaluate the stability of deep foundation pits is as follows: By monitoring the rainwater parameter vector Rvect, the temperature parameter vector Wvect and the vibration parameter vector Dvect, the correlation function between the interference factor and the stability of the deep foundation pit is obtained; The prediction coefficient Pdt of the deep foundation pit stability is obtained by combining the correlation function F1 between the rainwater parameter vector Rvect and the deep foundation pit comprehensive stability index Stab, the correlation function F2 between the temperature parameter vector Wvect and the deep foundation pit comprehensive stability index Stab, and the correlation function F3 between the vibration parameter vector Dvect and the deep foundation pit comprehensive stability index Stab; The evaluation interval of the prediction coefficient Pdt of the deep foundation pit stability is set, the prediction degree of the deep foundation pit stability is evaluated by interval comparison, and the corresponding early warning prompt signal is generated.

Images