Deep foundation pit deformation early warning method and system

Abstract

The invention relates to the technical field of foundation pit engineering safety monitoring, in particular to a deep foundation pit deformation early warning method and system. The method comprises the following steps: acquiring displacement settlement data of the deep foundation pit through monitoring equipment; constructing a bearing capacity correction model, and obtaining a bearing capacity correction factor of the supporting structure according to the bearing capacity correction model; according to the soil body characteristics of the deep foundation pit soil body, the anti-sliding stability of the deep foundation pit soil body is analyzed; based on the seepage characteristics of the deep foundation pit, seepage influence factors of the deep foundation pit are calculated; and in combination with the bearing capacity correction factor, the anti-sliding stability and the seepage influence factor, the displacement settlement data are corrected, and deformation early warning of the deep foundation pit is completed through the corrected displacement settlement data. According to the method, the monitored displacement settlement data is corrected according to the actual conditions of the deep foundation pit, then deformation early warning is performed, the method can adapt to different geological conditions, construction stages and environmental factors, and the problem of efficiently and accurately performing early warning on deformation of the foundation pit is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of foundation pit engineering safety monitoring, and in particular to a deep foundation pit deformation early warning method and system. Background Art

[0002] In construction projects, foundation pit construction, especially deep foundation pit construction, is a critical and risky task. As an important component of underground engineering safety, foundation pit safety and its safety control during the construction process directly affect the safety of the entire project. Deformation during foundation pit construction is affected by many unstable factors such as water pressure, temperature, and time. It is particularly important to effectively predict possible foundation pit settlement during construction based on foundation pit surface settlement monitoring data, guide construction and formulate safety protection measures, and minimize property losses and adverse social impacts. Foundation pit deformation may cause serious safety accidents such as tilting of surrounding buildings and ground collapse. Therefore, real-time and accurate monitoring and early warning of foundation pit deformation are crucial.

[0003] Existing methods for monitoring and early warning of foundation pit deformation primarily rely on machine learning models, such as neural networks or support vector machines, which are trained using sample data to construct a foundation pit deformation prediction model. However, due to the limited sample size of monitoring data specific to actual conditions, training can only be performed using a large amount of sample data not related to the corresponding foundation pit. Furthermore, the alarm thresholds are set by experts with extensive foundation pit engineering experience, resulting in poor adaptability of the prediction model. Therefore, in order to more accurately and comprehensively reflect the deformation status of foundation pits and improve the safety of foundation pit construction, it is necessary to solve the problem of providing efficient and accurate early warning of foundation pit deformation. Summary of the Invention

[0004] In view of the shortcomings of existing methods and the needs of practical applications, in order to more accurately and comprehensively reflect the deformation status of foundation pits, improve the safety of foundation pit construction, and solve the problem of efficient and accurate early warning of foundation pit deformation. On the one hand, the present invention provides a deep foundation pit deformation early warning method, comprising the following steps: obtaining the displacement and settlement data of the deep foundation pit through monitoring equipment; constructing a bearing capacity correction model, and obtaining the bearing capacity correction factor of the support structure according to the bearing capacity correction model; analyzing the anti-sliding stability of the deep foundation pit soil according to the soil characteristics of the deep foundation pit; calculating the seepage influence factor of the deep foundation pit based on the seepage characteristics of the deep foundation pit; combining the bearing capacity correction factor, the anti-sliding stability and the seepage influence factor, correcting the displacement and settlement data, and using the corrected displacement and settlement data to complete the deep foundation pit deformation early warning.

[0005] The present invention corrects the real-time monitored displacement and settlement data according to the support structure data, soil properties and seepage characteristics of the deep foundation pit, and then performs deformation early warning. It can adapt to different geological conditions, construction stages and environmental factors, and effectively solves the problem of efficient and accurate early warning of foundation pit deformation, which is conducive to avoiding accidents such as foundation pit collapse and ensuring the safety of construction personnel and surrounding buildings, underground pipelines, etc.

[0006] Optionally, the deep foundation pit deformation early warning method further includes performing noise reduction processing on the displacement and settlement data. By performing noise reduction processing on the displacement and settlement data, the present invention is conducive to improving data quality and enhancing analysis reliability, further ensuring project safety and optimizing decision-making efficiency.

[0007] Optionally, the bearing capacity correction model satisfies the following formula: , in, represents the bearing capacity correction factor of the supporting structure, represents the bending moment coefficient, represents the actual bending moment of the supporting structure, represents the design maximum bending moment of the supporting structure, Indicates the depth of the supporting structure embedded in the soil. Indicates the height of the supporting structure, represents the axial force coefficient, represents the actual axial force of the supporting structure, represents the design maximum axial force of the supporting structure, Indicates the additional ground load on the top of the supporting structure. Represents the active earth pressure exerted by the soil outside the support structure. This method maps the influence of bending moment and axial force on the support structure to displacement and settlement data, overcoming the limitations of single displacement monitoring and facilitating early and accurate warning of foundation pit safety.

[0008] Optionally, obtaining the bearing capacity correction factor of the support structure according to the bearing capacity correction model comprises the following steps: A first judgment threshold is set based on the design mechanical parameters of the support structure; a bending moment coefficient and an axial force coefficient are determined using the first judgment threshold and the measured mechanical parameters of the support structure; and a bearing capacity correction factor of the support structure is obtained by combining the bearing capacity correction model, the bending moment coefficient, and the axial force coefficient. The present invention designs a judgment threshold based on the design mechanical parameters of the support structure, thereby adaptively obtaining the bearing capacity correction factor of the support structure, further improving the adaptability of the present invention.

[0009] Optionally, analyzing the anti-sliding stability of the deep foundation pit soil according to soil properties of the deep foundation pit soil comprises the following steps: The softening degree of the deep foundation pit soil is determined by measuring its moisture content and resistivity. The soil's strain distribution identifies areas of plastic deformation. Based on the softening degree within these areas, the anti-sliding stability of the deep foundation pit soil is analyzed. This method analyzes anti-sliding stability based on the soil's characteristics, implicitly mapping the influence of these characteristics to displacement and settlement data. This method overcomes the limitations of single-displacement monitoring and facilitates early and accurate early warning of foundation pit safety.

[0010] Optionally, the anti-sliding stability of the deep foundation pit soil is analyzed based on the softening degree of the soil in the plastic deformation area, and the following formula is satisfied: , in, Indicates the anti-sliding stability of deep foundation pit soil. represents the number of strips into which the sliding soil is divided, Indicates the softening degree of deep foundation pit soil. represents the initial cohesion of soil, represents the arc length of the sliding surface of the i-th soil strip, Indicates the The weight of a piece of soil, Indicates the The angle between the sliding surface of the soil strip and the horizontal plane is represents the internal friction angle, represents the area of plastic deformation zone, Represents the total sliding surface area.

[0011] Optionally, the calculating the seepage influencing factor of the deep foundation pit based on the seepage characteristics of the deep foundation pit comprises the following steps: A first seepage coefficient of the deep foundation pit is obtained by using the seepage velocity and turbidity of the deep foundation pit; and a seepage influence factor of the deep foundation pit is calculated based on Darcy's law using the first seepage coefficient.

[0012] Optionally, the seepage influence factor of the deep foundation pit is calculated by using the first seepage coefficient based on Darcy's law, and satisfies the following formula: , in, represents the seepage influence factor of deep foundation pit, 、 Indicates the monitoring time, represents the first seepage coefficient, represents the permeability coefficient, represents the initial groundwater level, Indicates the The groundwater level at the moment, The present invention calculates the seepage influence factor and comprehensively analyzes the influence of environmental factors such as rainfall on displacement and settlement monitoring, which is conducive to improving the early warning accuracy of the present invention.

[0013] Optionally, the displacement and settlement data are corrected by combining the bearing capacity correction factor, the anti-sliding stability, and the seepage influence factor to satisfy the following formula: , in, represents the corrected displacement and settlement data, represents the weight factor, represents the bearing capacity correction factor of the supporting structure, represents the original displacement and settlement data, Indicates the anti-sliding stability of deep foundation pit soil. Represents the seepage influencing factor of deep foundation pit.

[0014] The present invention corrects the displacement and settlement data through a model formula, which is objective and accurate, and further facilitates accurate assessment and early warning.

[0015] In a second aspect, to efficiently implement the deep foundation pit deformation early warning method provided by the present invention, the present invention further provides a deep foundation pit deformation early warning system, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to call the program instructions to execute the deep foundation pit deformation early warning method described in the first aspect of the present invention. The deep foundation pit deformation early warning system of the present invention has a compact structure and stable performance, and can stably implement the deep foundation pit deformation early warning method provided by the present invention, further enhancing the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a deep foundation pit deformation early warning method provided by an embodiment of the present invention; Figure 2 A framework diagram of a deep foundation pit deformation early warning system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0018] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0019] See also Figure 1 In order to more accurately and comprehensively reflect the deformation of the foundation pit, improve the safety of foundation pit construction, and solve the problem of efficient and accurate early warning of foundation pit deformation. The present invention provides a deep foundation pit deformation early warning method, such as Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Obtain the displacement and settlement data of the deep foundation pit through monitoring equipment.

[0020] Various types of monitoring equipment are arranged in and around the deep foundation pit, including but not limited to displacement sensors, settlement meters, inclinometers, etc., to collect real-time data on the displacement, settlement, inclination, etc. of the foundation pit.

[0021] For example, a large-scale deep foundation pit was used. Displacement sensors were placed every 10-15 meters along the top of the pit slope, stress and strain sensors were installed at key points in the supporting structure, and settlement meters and inclinometers were deployed on the foundations of surrounding buildings and on the ground. Furthermore, all monitoring equipment was calibrated to collect data every hour, which was then uploaded to a data processing center in real time via a wireless transmission module.

[0022] Furthermore, after collecting the data, the method further includes performing noise reduction processing on the displacement and settlement data.

[0023] In one embodiment, a Kalman filter algorithm is used for noise reduction. Taking displacement monitoring data as an example, a state-space model is established. Based on the displacement estimate at the previous moment and the observed value at the current moment, the optimal displacement estimate at the current moment is calculated through the prediction and update steps of the Kalman filter, thereby removing random noise from the data.

[0024] In another embodiment, a wavelet transform algorithm may be used for noise reduction. Specifically, the wavelet transform algorithm is used to perform noise reduction on the monitoring data, generally following the steps below: Wavelet decomposition: Select an appropriate wavelet basis function and decomposition level. The choice of wavelet basis function has a significant impact on the noise reduction effect. Common wavelet bases include the DB series and the SYM series. The number of decomposition levels also needs to be determined based on the characteristics of the signal and the noise reduction requirements. Wavelet decomposition is performed on the monitoring data to obtain wavelet coefficients of different scales and frequencies. These coefficients contain the characteristics of the signal at different resolutions.

[0025] Thresholding: Select an appropriate threshold. The choice of threshold directly affects the noise reduction effect. Common threshold selection methods include global thresholding, layer thresholding, and adaptive thresholding. Thresholding is performed on the wavelet coefficients at each scale. Generally, if the absolute value of a wavelet coefficient is less than the set threshold, it is considered that the coefficient is mainly contributed by noise and can be set to 0 (hard thresholding) or reduced to close to 0 (soft thresholding).

[0026] Wavelet reconstruction: Using the thresholded wavelet coefficients, we perform an inverse wavelet transform to reconstruct the signal. This step converts the processed signal from the wavelet domain back to the time domain to obtain denoised monitoring data.

[0027] Verification and adjustment: Verify the denoised monitoring data to check whether the noise reduction effect meets the requirements. If the noise reduction effect is not ideal, you can adjust parameters such as the wavelet basis function, number of decomposition layers or threshold, and perform noise reduction again.

[0028] S2. Construct a bearing capacity correction model, and obtain a bearing capacity correction factor of the support structure according to the bearing capacity correction model.

[0029] In an embodiment, the bearing capacity correction model constructed in step S2 satisfies the following formula:

[0030] in, represents the bearing capacity correction factor of the supporting structure, represents the bending moment coefficient, represents the actual bending moment of the supporting structure, represents the design maximum bending moment of the supporting structure, Indicates the depth of the supporting structure embedded in the soil. Indicates the height of the supporting structure, represents the axial force coefficient, represents the actual axial force of the supporting structure, represents the design maximum axial force of the supporting structure, Indicates the additional ground load on the top of the supporting structure. It represents the active earth pressure of the soil outside the supporting structure on the structure.

[0031] Furthermore, obtaining the bearing capacity correction factor of the support structure according to the bearing capacity correction model includes the following steps: S21. Set a first judgment threshold according to the design mechanical parameters of the support structure.

[0032] When the actual mechanical parameters of the support structure approach the maximum designed mechanical parameters, a series of adverse effects will be produced on the support structure and the surrounding environment, including: As structural deformation intensifies, the support structure will produce larger flexural deformation, which will manifest as the wall or pile bending inward of the foundation pit, which may cause obvious settlement and cracks in the ground around the foundation pit; for the internal support system, the supporting components may undergo large axial deformation and flexure, causing the support connection nodes to loosen and deform, affecting the overall stability of the support system.

[0033] The performance of structural materials changes. When steel approaches the critical design axial force or bending moment, it will enter the plastic deformation stage. The elastic modulus of the material decreases, the deformation capacity increases, and the bearing capacity gradually reaches the limit state. If the load continues, the steel may suffer from necking, fracture and other damage phenomena.

[0034] When concrete materials approach critical bending moment or axial force, a large number of cracks will appear in the tension zone, and the crack width will continue to increase, causing the tensile capacity of the concrete to drop sharply. The concrete in the compression zone may be crushed, so that the compressive strength of the concrete cannot be fully exerted.

[0035] The stability of the foundation pit is reduced, the resistance of the support structure is close to the limit state, the restraining ability of the soil on the side walls of the foundation pit is weakened, and the lateral pressure of the soil may exceed the bearing capacity of the support structure, resulting in local collapse or overall instability of the soil on the side walls of the foundation pit; the soil at the bottom of the foundation pit may bulge due to the deformation of the support structure, further destroying the stability of the foundation pit, affecting the construction safety in the foundation pit and the normal use of surrounding buildings, underground pipelines, etc.

[0036] Furthermore, a first judgment threshold is set according to the design mechanical parameters of the support structure, and an early warning is issued before the actual mechanical parameters of the support structure reach close to the design maximum mechanical parameters, which is more conducive to improving the safety of foundation pit construction.

[0037] Specifically, the first judgment threshold is set according to management requirements. In the embodiment, the first judgment threshold is 90% of the design mechanical parameters.

[0038] S22. Determine the bending moment coefficient and the axial force coefficient using the first judgment threshold and the measured mechanical parameters of the support structure.

[0039] In the embodiment, the bending moment coefficient and the axial force coefficient are used to characterize the influence of the bending moment and axial force overload on the support structure, and satisfy the following formula: , ,in, represents the bending moment coefficient, represents the actual bending moment of the supporting structure, represents the first judgment threshold of the bending moment, represents the axial force coefficient, represents the actual axial force of the supporting structure, Indicates the first judgment threshold of the axial force.

[0040] S23. Combining the bearing capacity correction model, the bending moment coefficient and the axial force coefficient, obtain a bearing capacity correction factor of the support structure.

[0041] In the embodiment, according to the layout requirements of the monitoring points, a strain gauge is pasted at a suitable location on the support structure, and moisture-proof and waterproof protection measures are taken; then, the strain gauge is connected to the strain measuring instrument. During the construction process, the resistance change of the strain gauge is measured regularly or in real time to obtain strain data, and then the actual bending moment and actual axial force are calculated.

[0042] Furthermore, the design bending moment and design axial force are obtained according to the design drawings and the corresponding first judgment threshold is set, and then the corresponding bending moment coefficient and axial force coefficient are determined, and finally the bearing capacity correction factor of the support structure is obtained through the bearing capacity correction model.

[0043] S3. Analyze the anti-sliding stability of the deep foundation pit soil according to its soil properties.

[0044] In the embodiment, the step S3 of analyzing the anti-sliding stability of the deep foundation pit soil according to the soil properties of the deep foundation pit soil comprises the following steps: S31. Utilize the water content and resistivity of the soil to obtain the softening degree of the deep foundation pit soil.

[0045] Water content directly affects the pore water pressure and effective stress of the soil, while resistivity can indirectly reflect the pore structure, density and ion concentration of the soil. The combination of the two can more comprehensively evaluate the degree of soil softening.

[0046] Specifically, through standard penetration tests or static penetration tests, the mechanical properties of the soil can be directly measured on site, and then its strength after softening can be evaluated. Stress and strain sensors can also be buried in the foundation pit wall or soil to monitor the stress and strain changes of the soil in real time, thereby indirectly evaluating its strength after softening.

[0047] Furthermore, since the resistivity of soil is closely related to its physical properties (such as water content, porosity, etc.), and changes in these properties will affect the strength of the soil, its strength after softening can be indirectly evaluated by monitoring the resistivity of the soil.

[0048] Furthermore, the softening degree of the deep foundation pit soil is determined by taking the ratio of the strength after softening to the initial strength as the softening degree.

[0049] In other embodiments, electrical resistivity tomography (ERT) may be used to monitor groundwater distribution in real time, and combined with water content sensor data, the soil softening trend may be indirectly assessed.

[0050] S32. Identify the plastic deformation area of the soil through the strain distribution of the soil.

[0051] First, based on the strain monitoring points arranged in the soil, the horizontal and vertical strain data at different positions are obtained through the strain gauge method, fiber Bragg grating method or borehole inclinometer method.

[0052] Then, a strain contour map is drawn based on these data. In the map, areas with dense contour lines usually indicate areas with large strain changes, which may be the location of the plastic deformation zone.

[0053] For example, if the horizontal strain contour changes rapidly from 0.1% to 0.5% in a certain area, while the strain changes in other areas are relatively small, then this area needs special attention and may be a plastic deformation zone.

[0054] Next, we observe the strain gradient and calculate the strain gradient at each point in the soil, that is, the rate of change of strain in space. Areas with large strain gradients indicate that the strain changes drastically and are more likely to be plastic deformation areas.

[0055] For example, for vertical strain, if the rate of change of vertical strain from top to bottom within a certain depth range is significantly greater than that in other areas, then plastic deformation may exist in this depth range. Numerical methods such as the finite difference method can be used to calculate the strain gradient and analyze the principal strain direction. The principal strain refers to the maximum and minimum strain at a point in the soil in a specific direction.

[0056] By calculating the direction and magnitude of the principal strain, the deformation trend of the soil can be understood. In the plastic deformation zone, the direction of the principal strain often changes and is different from the principal strain direction in the elastic deformation zone. Generally, methods such as the strain Mohr circle can be used to determine the magnitude and direction of the principal strain. If the principal strain direction shows obvious deflection or concentration in a certain area, accompanied by a large strain value, then this area may be a plastic deformation zone.

[0057] Finally, a comprehensive judgment is made based on other factors, including: Considering the stress state, plastic deformation is closely related to the stress state of the soil. Combining data such as earth pressure and pore water pressure can be used to analyze the stress state of the soil and determine whether the yield condition has been reached, thereby assisting in identifying the plastic deformation zone. For example, when the earth pressure in a certain area exceeds the shear strength of the soil and is accompanied by a large strain change, this area is likely to be a plastic deformation zone.

[0058] Compare the strain at different locations, and analyze the horizontal and vertical strains at different locations. If the vertical strain at a point at the bottom of the foundation pit is significantly greater than that at surrounding points, and the horizontal strain also changes abnormally, then the area at that point may be a plastic deformation zone. At the same time, you can also compare the strain at different depths. Generally speaking, in the plastic deformation zone, the pattern of strain change with depth will be different from that in the elastic deformation zone.

[0059] Consider geological conditions, which significantly influence soil deformation characteristics. When identifying plastic deformation zones, factors such as soil type and soil layer distribution should be considered. For example, soft clay is more susceptible to plastic deformation. Under the same stress, the strain in soft clay areas may be much greater than that in sandy soil areas. Therefore, the occurrence of plastic deformation in areas with soft clay is of particular concern.

[0060] S33. Analyze the anti-sliding stability of the deep foundation pit soil according to the degree of soil softening in the plastic deformation area.

[0061] Specifically, the anti-sliding stability of the deep foundation pit soil is analyzed based on the softening degree of the soil in the plastic deformation area, and the following formula is satisfied: , in, Indicates the anti-sliding stability of deep foundation pit soil. represents the number of strips into which the sliding soil is divided, Indicates the softening degree of deep foundation pit soil. represents the initial cohesion of soil, represents the arc length of the sliding surface of the i-th soil strip, Indicates the The weight of a piece of soil, Indicates the The angle between the sliding surface of the soil strip and the horizontal plane is represents the internal friction angle, represents the area of plastic deformation zone, In the embodiment, the sliding soil body is divided by the Swedish strip division method.

[0062] S4. Calculate the seepage influencing factor of the deep foundation pit based on the seepage characteristics of the deep foundation pit.

[0063] In an embodiment, the calculation of the seepage influencing factor of the deep foundation pit based on the seepage characteristics of the deep foundation pit includes the following steps: S41. Utilize the seepage velocity and turbidity of the deep foundation pit to obtain a first seepage coefficient of the deep foundation pit.

[0064] In one embodiment, a flow meter is installed in the deep foundation pit drainage system to quantify the seepage volume and seepage velocity. A turbidity meter is used to determine the turbidity of the water by measuring the degree of light scattering in the water. Water samples from the seepage in the deep foundation pit drainage system are collected in a suitable container and then placed in a turbidity meter for measurement to obtain an accurate turbidity value, typically expressed in NTUs (nephelometric turbidity units). In other embodiments, an online monitoring system can also be installed. This system typically consists of sensors, a data collector, and transmission equipment. The sensors measure the turbidity of the seepage in real time and transmit the data to the data collector. The data is then transmitted wirelessly or wired to a monitoring center, enabling remote real-time monitoring, allowing for timely monitoring of changes in the seepage turbidity.

[0065] Furthermore, the first seepage coefficient of the deep foundation pit is obtained by using the seepage velocity and turbidity of the deep foundation pit, which satisfies the following formula: ,in, represents the first seepage coefficient, represents the seepage velocity of the deep foundation pit, Indicates the speed of change of water level difference, represents the seepage path length, Indicates the rate of change of turbidity.

[0066] S42. Based on Darcy's law, calculate the seepage influence factor of the deep foundation pit using the first seepage coefficient.

[0067] Specifically, the seepage influence factor of the deep foundation pit is calculated based on Darcy's law using the first seepage coefficient, and satisfies the following formula: , in, represents the seepage influence factor of deep foundation pit, 、 Indicates the monitoring time, represents the first seepage coefficient, represents the permeability coefficient, represents the initial groundwater level, Indicates the The groundwater level at the moment, Indicates the depth of the foundation pit.

[0068] S5. Combining the bearing capacity correction factor, the anti-sliding stability, and the seepage influencing factor, the displacement and settlement data are corrected, and deep foundation pit deformation early warning is completed using the corrected displacement and settlement data.

[0069] In an embodiment, the displacement and settlement data are corrected by combining the bearing capacity correction factor, the anti-sliding stability, and the seepage influence factor to satisfy the following formula:

[0070] in, represents the corrected displacement and settlement data, represents the weight factor, represents the bearing capacity correction factor of the supporting structure, represents the original displacement and settlement data, Indicates the anti-sliding stability of deep foundation pit soil. In the embodiment, the weight factor can be set based on a small amount of historical monitoring data through sensitivity analysis or regression fitting to determine the seepage impact factor of the deep foundation pit.

[0071] Furthermore, a deep foundation pit deformation early warning system is established according to relevant national or regional standards. Based on the deep foundation pit deformation early warning system, the corrected displacement and settlement data are used to perform deep foundation pit deformation early warning.

[0072] The present invention adaptively corrects the displacement and settlement data of deep foundation pits to adapt to the early warning system constructed in accordance with relevant national or regional standards. It does not need to train the foundation pit deformation prediction model based on a large amount of irrelevant sample data. The interference of human factors is reduced through model quantification, which greatly improves adaptability.

[0073] See also Figure 2 In an embodiment, to efficiently execute the deep foundation pit deformation early warning method provided by the present invention, the present invention also provides a deep foundation pit deformation early warning system, comprising: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory contains program instructions for performing the steps of the deep foundation pit deformation early warning method. The deep foundation pit deformation early warning system of the present invention has a compact structure and stable performance, capable of stably executing the deep foundation pit deformation early warning method of the present invention, further enhancing the overall applicability and practical application capabilities of the present invention.

[0074] In an embodiment, the processor may be a central processing unit (CPU), which may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The input device may be used to obtain data information. The output device may be used to output the results obtained by storing the program instructions contained in the computer program in the memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0075] In one possible implementation, the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0076] An embodiment further provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned deep foundation pit deformation early warning method are implemented.

[0077] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0078] To sum up, the present invention corrects the real-time monitored displacement and settlement data according to the support structure data, soil properties and seepage characteristics of the deep foundation pit, and then performs deformation warning. It can adapt to different geological conditions, construction stages and environmental factors, and effectively solves the problem of efficient and accurate early warning of foundation pit deformation, which is conducive to avoiding the occurrence of accidents such as foundation pit collapse and ensuring the safety of construction personnel and surrounding buildings, underground pipelines, etc.

[0079] Therefore, the present invention effectively overcomes various shortcomings of the prior art and has high industrial utilization value.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope described in the present invention.

Claims

1. A deep foundation pit deformation early warning method, characterized in that: The following steps are involved: Obtain the displacement and settlement data of deep foundation pits through monitoring equipment; Constructing a bearing capacity correction model, and obtaining a bearing capacity correction factor of the support structure according to the bearing capacity correction model; Analyzing the anti-sliding stability of the deep foundation pit soil according to its soil properties; Calculating a seepage influencing factor of the deep foundation pit based on the seepage characteristics of the deep foundation pit; The displacement and settlement data are corrected by combining the bearing capacity correction factor, the anti-sliding stability and the seepage influencing factor, and the deep foundation pit deformation early warning is completed using the corrected displacement and settlement data.

2. The deep foundation pit deformation early warning method according to claim 1, characterized in that: The method also includes performing noise reduction processing on the displacement and settlement data.

3. The deep foundation pit deformation early warning method according to claim 1, characterized in that: The bearing capacity correction model satisfies the following formula: ， in, represents the bearing capacity correction factor of the supporting structure, represents the bending moment coefficient, represents the actual bending moment of the supporting structure, represents the design maximum bending moment of the supporting structure, Indicates the depth of the supporting structure embedded in the soil. Indicates the height of the supporting structure, represents the axial force coefficient, represents the actual axial force of the supporting structure, represents the design maximum axial force of the supporting structure, Indicates the additional ground load on the top of the supporting structure. It represents the active earth pressure of the soil outside the supporting structure on the structure.

4. The deep foundation pit deformation early warning method according to claim 1, characterized in that: The method of obtaining the bearing capacity correction factor of the support structure according to the bearing capacity correction model comprises the following steps: Setting a first judgment threshold according to the design mechanical parameters of the support structure; Determining a bending moment coefficient and an axial force coefficient using the first judgment threshold and the measured mechanical parameters of the support structure; The bearing capacity correction factor of the support structure is obtained by combining the bearing capacity correction model, the bending moment coefficient and the axial force coefficient.

5. The deep foundation pit deformation early warning method according to claim 1, characterized in that: The method of analyzing the anti-sliding stability of the deep foundation pit soil according to the soil properties of the deep foundation pit soil comprises the following steps: The softening degree of the deep foundation pit soil is obtained by using the soil moisture content and resistivity; Identify the plastic deformation area of the soil through the strain distribution of the soil; The anti-sliding stability of the deep foundation pit soil is analyzed according to the softening degree of the soil in the plastic deformation area.

6. The deep foundation pit deformation early warning method according to claim 5, characterized in that: The anti-sliding stability of the deep foundation pit soil is analyzed based on the softening degree of the soil in the plastic deformation area, and the following formula is satisfied: , in, Indicates the anti-sliding stability of deep foundation pit soil. represents the number of strips into which the sliding soil is divided, Indicates the softening degree of deep foundation pit soil. represents the initial cohesion of soil, represents the arc length of the sliding surface of the i-th soil strip, Indicates the The weight of a piece of soil, Indicates the The angle between the sliding surface of the soil strip and the horizontal plane is represents the internal friction angle, represents the area of plastic deformation zone, Represents the total sliding surface area.

7. The deep foundation pit deformation early warning method according to claim 1, characterized in that: The method of calculating the seepage influencing factor of the deep foundation pit based on the seepage characteristics of the deep foundation pit comprises the following steps: The first seepage coefficient of the deep foundation pit is obtained by using the seepage velocity and turbidity of the deep foundation pit; Based on Darcy's law, the seepage influence factor of the deep foundation pit is calculated using the first seepage coefficient.

8. The deep foundation pit deformation early warning method according to claim 7, characterized in that: The seepage influence factor of the deep foundation pit is calculated based on Darcy's law using the first seepage coefficient, and satisfies the following formula: , in, represents the seepage influence factor of deep foundation pit, 、 Indicates the monitoring time, represents the first seepage coefficient, represents the permeability coefficient, represents the initial groundwater level, Indicates the The groundwater level at the moment, Indicates the depth of the foundation pit.

9. The deep foundation pit deformation early warning method according to claim 1, characterized in that: The displacement and settlement data are corrected by combining the bearing capacity correction factor, the anti-sliding stability, and the seepage influence factor to satisfy the following formula: , in, represents the corrected displacement and settlement data, represents the weight factor, represents the bearing capacity correction factor of the supporting structure, represents the original displacement and settlement data, Indicates the anti-sliding stability of deep foundation pit soil. Represents the seepage influencing factor of deep foundation pit.

10. A deep foundation pit deformation early warning system, characterized in that: The deep foundation pit deformation early warning system includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the deep foundation pit deformation early warning method described in any one of claims 1-9.

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