A deep foundation pit deformation early warning method and system

Abstract

The present application 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: obtaining displacement settlement data of a deep foundation pit through a monitoring device; constructing a bearing capacity correction model to obtain a bearing capacity correction factor of a supporting structure according to the bearing capacity correction model; analyzing the anti-slide stability of the deep foundation pit soil according to the soil characteristics of the deep foundation pit soil; calculating a 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-slide stability and the seepage influence factor to correct the displacement settlement data, and completing deep foundation pit deformation early warning using the corrected displacement settlement data. The present application corrects the monitored displacement settlement data according to the actual conditions of the deep foundation pit before deformation early warning, can adapt to different geological conditions, construction stages and environmental factors, and solves the problem of efficiently and accurately early warning of foundation pit deformation.

Description

Technical Field

[0001] This invention relates to the field of foundation pit engineering safety monitoring technology, specifically to a method and system for early warning of deep foundation pit deformation. Background Technology

[0002] In construction engineering, foundation pit construction, especially deep foundation pit construction, is a critical and inherently risky task. Foundation pit safety, as a crucial component of underground engineering safety, directly impacts the overall safety of the project during construction. Deformation during foundation pit construction is affected by numerous unstable factors such as water pressure, temperature, and aging. Therefore, effectively predicting potential foundation pit settlement based on surface settlement monitoring data, guiding the development of safety protection measures, and minimizing property damage and adverse social impacts are of paramount importance. Foundation pit deformation can trigger serious safety accidents such as the tilting of surrounding buildings and ground collapse; thus, real-time and accurate monitoring and early warning of foundation pit deformation are essential.

[0003] Existing methods for monitoring and early warning of foundation pit deformation mainly rely on machine learning models such as neural networks or support vector machines. These models are trained using sample data to predict foundation pit deformation. However, due to the limited amount of monitoring data relevant to actual conditions, training is often done using large amounts of data from other, unrelated foundation pits. Furthermore, alarm thresholds are set by experts with extensive experience in foundation pit engineering, resulting in poor adaptability of the prediction models. Therefore, to more accurately and comprehensively reflect the deformation status of foundation pits, improve the safety of foundation pit construction, and solve the problem of efficiently and accurately providing early warnings of foundation pit deformation, a new approach is needed. Summary of the Invention

[0004] To address the shortcomings of existing methods and the needs of practical applications, and 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, this invention provides a method for early warning of deep foundation pit deformation, comprising the following steps: acquiring displacement and settlement data of the deep foundation pit through monitoring equipment; constructing a bearing capacity correction model and obtaining a bearing capacity correction factor for the support structure based on the bearing capacity correction model; analyzing the anti-sliding stability of the deep foundation pit soil based on its soil characteristics; calculating the seepage influence factor of the deep foundation pit based on its seepage characteristics; and correcting the displacement and settlement data by combining the bearing capacity correction factor, the anti-sliding stability, and the seepage influence factor, and using the corrected displacement and settlement data to complete the early warning of deep foundation pit deformation.

[0005] This invention corrects the real-time monitored displacement and settlement data based on the support structure data, soil characteristics, and seepage characteristics of deep foundation pits, and then performs deformation early warning. It can adapt to different geological conditions, construction stages, and environmental factors, effectively solving the problem of efficient and accurate early warning of foundation pit deformation. This helps to avoid accidents such as foundation pit collapse and ensures the safety of construction personnel and surrounding buildings, underground pipelines, etc.

[0006] Optionally, the deep foundation pit deformation early warning method further includes noise reduction processing of the displacement and settlement data. By performing noise reduction processing on the displacement and settlement data, this invention helps improve data quality and enhance the reliability of analysis, further ensuring engineering safety and optimizing decision-making efficiency.

[0007] Optionally, the bearing capacity correction model satisfies the following formula:

[0008] ,

[0009] in, This indicates the bearing capacity correction factor of the support structure. Indicates the bending moment coefficient. This represents the actual bending moment of the support structure. This indicates the design maximum bending moment of the support structure. Indicates the depth at which the support structure is embedded in the soil. Indicates the height of the support structure. Indicates the axial force coefficient. This represents the actual axial force of the support structure. This indicates the maximum design axial force of the support structure. This indicates the additional load on the ground at the top of the support structure. This represents the active earth pressure exerted on the structure by the soil outside the support structure. This invention maps the stress conditions of the support structure determined by bending moment and axial force to displacement and settlement data, overcoming the limitations of single displacement monitoring and facilitating early and accurate warnings for foundation pit safety.

[0010] Optionally, obtaining the bearing capacity correction factor of the support structure based on the bearing capacity correction model includes the following steps:

[0011] Based on the design mechanical parameters of the support structure, a first judgment threshold is set; the bending moment coefficient and axial force coefficient are determined using the first judgment threshold and the measured mechanical parameters of the support structure; and 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. This 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 invention.

[0012] Optionally, the step of analyzing the anti-sliding stability of the deep foundation pit soil based on its soil properties includes the following steps:

[0013] This invention utilizes soil moisture content and resistivity to determine the degree of softening of the deep foundation pit soil; identifies plastic deformation zones in the soil through strain distribution; and analyzes the anti-sliding stability of the deep foundation pit soil based on the degree of softening within these plastic deformation zones. By analyzing the anti-sliding stability of deep foundation pit soil through its properties, this invention maps the influence of soil properties onto displacement and settlement data, further overcoming the limitations of single displacement monitoring and facilitating early and accurate warnings for foundation pit safety.

[0014] Optionally, the anti-sliding stability of the deep foundation pit soil is analyzed based on the degree of soil softening within the plastic deformation region, satisfying the following formula:

[0015] ,

[0016] in, This indicates the anti-sliding stability of the soil in the deep foundation pit. This indicates the number of soil strips into which the sliding soil mass is divided. Indicates the degree of softening of the soil in deep foundation pits. Indicates the initial cohesion of the soil. This represents the arc length of the sliding surface of the i-th soil strip. Indicates the first The weight of a single earthen strip, Indicates the first The angle between the sliding surface of the soil strip and the horizontal plane Indicates the angle of internal friction. Indicates the area of ​​the plastic deformation zone. This represents the total area of ​​the sliding surface.

[0017] Optionally, calculating the seepage influence factor of the deep foundation pit based on its seepage characteristics includes the following steps:

[0018] The first seepage coefficient of the deep foundation pit is obtained by using the seepage velocity and turbidity; based on Darcy's law, the seepage influence factor of the deep foundation pit is calculated using the first seepage coefficient.

[0019] Optionally, the seepage influence factor of the deep foundation pit, calculated based on Darcy's law and using the first seepage coefficient, satisfies the following formula:

[0020] ,

[0021] in, This indicates the seepage influencing factor of deep foundation pits. , Indicates the monitoring time. Indicates the first seepage coefficient. Indicates the permeability coefficient. Indicates the initial groundwater level. Indicates the first The groundwater level at any given time. This indicates the depth of the foundation pit. This invention calculates the seepage influence factor and comprehensively analyzes the impact of environmental factors such as rainfall on displacement and settlement monitoring, which helps improve the accuracy of the early warning system.

[0022] Optionally, the displacement settlement data is corrected by combining the bearing capacity correction factor, the anti-sliding stability factor, and the seepage influence factor, satisfying the following formula:

[0023] ,

[0024] in, This indicates the corrected displacement and settlement data. Indicates the weighting factor. This indicates the bearing capacity correction factor of the support structure. This represents the original displacement and settlement data. This indicates the anti-sliding stability of the soil in the deep foundation pit. This indicates the seepage influencing factor in deep foundation pits.

[0025] This invention corrects the displacement and settlement data using model formulas, making it objective and accurate, and further facilitating precise assessment and early warning.

[0026] Secondly, to efficiently execute the deep foundation pit deformation early warning method provided by this invention, this invention also provides a deep foundation pit deformation early warning system, including a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory stores a computer program containing program instructions. The processor is configured to call the program instructions to execute the deep foundation pit deformation early warning method as described in the first aspect of this invention. The deep foundation pit deformation early warning system of this invention has a compact structure and stable performance, and can stably execute the deep foundation pit deformation early warning method provided by this invention, further improving the overall applicability and practical application capability of this invention. Attached Figure Description

[0027] Figure 1 A flowchart of a deep foundation pit deformation early warning method provided in an embodiment of the present invention;

[0028] Figure 2 A framework diagram of a deep foundation pit deformation early warning system provided in an embodiment of the present invention. Detailed Implementation

[0029] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0030] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0031] Please see Figure 1 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, this invention provides a method for early warning of deep foundation pit deformation, such as... Figure 1 As shown, in one embodiment, the method includes the following steps:

[0032] S1. Obtain displacement and settlement data of deep foundation pits through monitoring equipment.

[0033] Various types of monitoring equipment, including but not limited to displacement sensors, settlement meters, and inclinometers, are deployed in and around the deep foundation pit to collect data on displacement, settlement, and tilt of the foundation pit in real time.

[0034] Taking a large-scale deep foundation pit as an example, displacement sensors are installed every 10-15 meters at the top of the pit slope, stress and strain sensors are installed at key nodes of the supporting structure, and settlement meters and inclinometers are deployed on the foundations of surrounding buildings and the ground surface. Furthermore, after calibration, all monitoring equipment collects data hourly and uploads the data to the data processing center in real time via a wireless transmission module.

[0035] Furthermore, the process of collecting the data also includes noise reduction processing of the displacement and settlement data.

[0036] In one embodiment, the 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 from 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, thus removing random noise from the data.

[0037] In another embodiment, wavelet transform algorithm can also be used for noise reduction. Specifically, applying wavelet transform algorithm to the monitoring data for noise reduction typically follows these steps:

[0038] Wavelet decomposition: Choosing appropriate wavelet basis functions and the number of decomposition levels is crucial, as the selection of wavelet basis functions significantly impacts noise reduction performance. Commonly used wavelet bases include the db series and sym series. The number of decomposition levels also needs to be determined based on the signal characteristics and noise reduction requirements. Wavelet decomposition of the monitoring data yields wavelet coefficients at different scales and frequencies, which encapsulate the signal's characteristics at different resolutions.

[0039] Thresholding: Choosing a suitable threshold directly affects the noise reduction effect. Common thresholding methods include global thresholding, layer thresholding, and adaptive thresholding. Thresholding is performed on 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 it can be set to 0 (hard threshold) or reduced to close to 0 (soft threshold).

[0040] Wavelet reconstruction: The signal is reconstructed by performing an inverse wavelet transform on the thresholded wavelet coefficients. This step converts the processed signal back from the wavelet domain to the time domain, obtaining the denoised monitoring data.

[0041] Verification and Adjustment: Verify the denoised monitoring data to check if the denoising effect meets the requirements. If the denoising effect is not ideal, adjust parameters such as wavelet basis function, number of decomposition layers or threshold, and perform denoising again.

[0042] S2. Construct a bearing capacity correction model and obtain the bearing capacity correction factor of the support structure based on the bearing capacity correction model.

[0043] In this embodiment, the bearing capacity correction model constructed in step S2 satisfies the following formula:

[0044]

[0045] in, This indicates the bearing capacity correction factor of the support structure. Indicates the bending moment coefficient. This represents the actual bending moment of the support structure. This indicates the design maximum bending moment of the support structure. Indicates the depth at which the support structure is embedded in the soil. Indicates the height of the support structure. Indicates the axial force coefficient. This represents the actual axial force of the support structure. This indicates the maximum design axial force of the support structure. This indicates the additional load on the ground at the top of the support structure. This represents the active earth pressure exerted on the structure by the soil on the outside of the supporting structure.

[0046] Further, obtaining the bearing capacity correction factor of the support structure based on the bearing capacity correction model includes the following steps:

[0047] S21. Set a first judgment threshold based on the design mechanical parameters of the support structure.

[0048] When the actual mechanical parameters of the support structure approach the maximum design mechanical parameters, it will have a series of adverse effects on the support structure and the surrounding environment, including:

[0049] Increased structural deformation can cause significant flexural deformation in the support structure, manifested as the walls or piles bending inwards towards the pit, which may lead to noticeable settlement and cracks in the ground around the pit. For internal support systems, the support components may experience significant axial deformation and flexural deformation, resulting in loosening and deformation of the support connection nodes, which can affect the overall stability of the support system.

[0050] When the properties of structural materials change, and 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 load-bearing capacity gradually reaches the limit state. If loading continues, the steel may experience necking, fracture, or other damage.

[0051] When concrete approaches its critical bending moment or axial force, numerous cracks will appear in the tension zone, and the crack width will continue to increase, causing a sharp decline in the tensile strength of the concrete. The concrete in the compression zone may also experience crushing, preventing the concrete from fully exerting its compressive strength.

[0052] Reduced foundation pit stability and near-limit resistance of the support structure weaken the constraint on the soil on the pit sidewalls. The lateral pressure on the soil may exceed the bearing capacity of the support structure, leading to local collapse or overall instability of the soil on the pit sidewalls. The soil at the bottom of the foundation pit may bulge due to deformation of the support structure, further damaging the stability of the foundation pit and affecting construction safety within the pit and the normal use of surrounding buildings and underground pipelines.

[0053] Furthermore, based on the design mechanical parameters of the support structure, a first judgment threshold is set, and an early warning is issued before the actual mechanical parameters of the support structure reach close to the maximum design mechanical parameters, which is more conducive to improving the safety of foundation pit construction.

[0054] Specifically, a first judgment threshold is set according to management requirements. In this embodiment, the first judgment threshold is 90% of the design mechanical parameters.

[0055] S22. Determine the bending moment coefficient and axial force coefficient using the first judgment threshold and the measured mechanical parameters of the support structure.

[0056] In the embodiments, the bending moment coefficient and axial force coefficient are used to characterize the impact of bending moment and axial force overload on the support structure, satisfying the following formula:

[0057] , ,in, Indicates the bending moment coefficient. This represents the actual bending moment of the support structure. This represents the first threshold for judging bending moment. Indicates the axial force coefficient. This represents the actual axial force of the support structure. This represents the first threshold for judging axial force.

[0058] S23. By combining the bearing capacity correction model, the bending moment coefficient, and the axial force coefficient, the bearing capacity correction factor of the support structure is obtained.

[0059] In this embodiment, strain gauges are pasted at appropriate locations on the support structure according to the requirements of the monitoring point layout, and moisture-proof and waterproof protection measures are taken. Then, the strain gauges are connected to a strain measuring instrument. During the construction process, the resistance change of the strain gauges is measured periodically or in real time to obtain strain data, and then the actual bending moment and actual axial force are calculated.

[0060] Furthermore, the design bending moment and design axial force are obtained according to the design drawings, and the corresponding first judgment threshold is set, thereby determining the corresponding bending moment coefficient and axial force coefficient. Finally, the bearing capacity correction factor of the support structure is obtained through the bearing capacity correction model.

[0061] S3. Based on the soil characteristics of the deep foundation pit, analyze the anti-sliding stability of the deep foundation pit soil.

[0062] In this embodiment, step S3, which involves analyzing the anti-sliding stability of the deep foundation pit soil based on its soil properties, includes the following steps:

[0063] S31. The degree of softening of the soil in the deep foundation pit is obtained by using the soil moisture content and resistivity.

[0064] Moisture content directly affects the pore water pressure and effective stress of soil, while resistivity can indirectly reflect the pore structure, density and ion concentration of soil. The combination of the two can more comprehensively assess the degree of soil softening.

[0065] Specifically, the mechanical properties of the soil can be directly measured on-site through standard penetration tests or static cone penetration tests to assess its strength after softening; stress and strain sensors can also be embedded in the foundation pit wall or soil to monitor the stress and strain changes of the soil in real time, thereby indirectly assessing its strength after softening.

[0066] Furthermore, since the resistivity of soil is closely related to its physical properties (such as water content and void ratio), and changes in these properties can affect the strength of the soil, the strength of the softened soil can be indirectly assessed by monitoring its resistivity.

[0067] Furthermore, the degree of softening of the deep foundation pit soil is defined as the ratio of the softened strength to the initial strength.

[0068] In other embodiments, resistivity imaging (ERT) can be used to monitor groundwater distribution in real time, and combined with water content sensor data, to indirectly assess the soil softening trend.

[0069] S32. Identify the plastic deformation zone of the soil by observing the strain distribution.

[0070] First, based on strain monitoring points arranged in the soil, horizontal and vertical strain data at different locations are obtained using strain gauge method, fiber optic grating method or borehole inclinometer method.

[0071] Then, based on these data, a strain contour map is drawn. In the map, the areas with dense contour lines usually represent areas with large strain changes, which may be the location of the plastic deformation zone.

[0072] For example, if in a certain region the horizontal strain contour lines change rapidly from 0.1% to 0.5%, while the strain changes in other regions are relatively small, then this region needs to be focused on, as it may be a plastic deformation zone.

[0073] Next, observe the strain gradient and calculate the strain gradient at each point in the soil, which is the rate of change of strain in space. Regions with larger strain gradients indicate drastic strain changes and are more likely to be plastic deformation zones.

[0074] For example, regarding vertical strain, if the rate of change of vertical strain from top to bottom is significantly greater than in other areas within a certain depth range, then plastic deformation may exist within this depth range. The strain gradient can be calculated using numerical methods such as the finite difference method to analyze the principal strain direction. Principal strain refers to the maximum and minimum strain at a point in the soil in a specific direction.

[0075] By calculating the direction and magnitude of the principal strain, we can understand the deformation trend of the soil. In the plastic deformation zone, the direction of the principal strain often changes and is different from that 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 there is a significant deflection or concentration of the principal strain direction in a certain area, accompanied by a large strain value, then the area may be a plastic deformation zone.

[0076] Finally, a comprehensive judgment should be made by considering other factors, including:

[0077] Considering the stress state, plastic deformation is closely related to the stress state of the soil. By combining data such as earth pressure and pore water pressure, the stress state of the soil can be analyzed to determine whether the yield condition has been reached, thus helping to identify the plastic deformation zone. For example, when the earth pressure in a certain area exceeds the shear strength of the soil, accompanied by a large strain change, then this area is likely to be a plastic deformation zone.

[0078] By comparing the strain at different locations, the horizontal and vertical strains at different locations can be analyzed. If the vertical strain at a certain point at the bottom of the foundation pit is significantly greater than that at surrounding points, and there are also abnormal changes in the horizontal strain, then the area where that point is located may be a plastic deformation zone. At the same time, the strain at different depths can also be compared. Generally speaking, in the plastic deformation zone, the strain variation with depth will be different from that in the elastic deformation zone.

[0079] Geological conditions have a significant impact on the deformation characteristics of soil. When identifying plastic deformation zones, factors such as soil type and soil layer distribution must be considered. For example, soft clay is more prone to plastic deformation. Under the same stress, the strain in soft clay areas may be much greater than that in sandy areas. Therefore, the occurrence of plastic deformation should be a key focus in areas where soft clay is distributed.

[0080] S33. Analyze the anti-sliding stability of the deep foundation pit soil based on the degree of soil softening within the plastic deformation area.

[0081] Specifically, the anti-sliding stability of the deep foundation pit soil is analyzed based on the degree of soil softening within the plastic deformation region, satisfying the following formula:

[0082] ,

[0083] in, This indicates the anti-sliding stability of the soil in the deep foundation pit. This indicates the number of soil strips into which the sliding soil mass is divided. Indicates the degree of softening of the soil in deep foundation pits. Indicates the initial cohesion of the soil. This represents the arc length of the sliding surface of the i-th soil strip. Indicates the first The weight of a single earthen strip, Indicates the first The angle between the sliding surface of the soil strip and the horizontal plane Indicates the angle of internal friction. Indicates the area of ​​the plastic deformation zone. This represents the total area of ​​the sliding surface. In this embodiment, the sliding soil mass is divided using the Swedish slice method.

[0084] S4. Based on the seepage characteristics of the deep foundation pit, calculate the seepage influence factor of the deep foundation pit.

[0085] In this embodiment, calculating the seepage influence factor of the deep foundation pit based on its seepage characteristics includes the following steps:

[0086] S41. Utilize the seepage velocity and turbidity of the deep foundation pit to obtain the first seepage coefficient of the deep foundation pit.

[0087] In this embodiment, a flow meter is installed in the deep foundation pit drainage system to quantify the seepage flow rate and 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 the turbidity meter for measurement, yielding an accurate turbidity value, typically in NTUs (NTUs of scattering turbidity). In other embodiments, an online monitoring system can also be installed. This system typically consists of sensors, a data acquisition unit, and transmission equipment. The sensors measure the turbidity of the seepage in real time and transmit the data to the data acquisition unit. The data is then transmitted wirelessly or via wired connection to a monitoring center, enabling remote real-time monitoring and allowing for timely understanding of changes in seepage turbidity.

[0088] Furthermore, by utilizing the seepage velocity and turbidity of the deep foundation pit, the first seepage coefficient of the deep foundation pit is obtained, satisfying the following formula:

[0089] ,in, Indicates the first seepage coefficient. This indicates the seepage velocity in a deep foundation pit. Indicates the rate of change of water level difference. Indicates the length of the seepage path. This indicates the rate of change in turbidity.

[0090] S42. Based on Darcy's law, calculate the seepage influence factor of the deep foundation pit using the first seepage coefficient.

[0091] Specifically, the seepage influence factor of the deep foundation pit, calculated based on Darcy's law and using the first seepage coefficient, satisfies the following formula:

[0092] ,

[0093] in, This indicates the seepage influencing factor of deep foundation pits. , Indicates the monitoring time. Indicates the first seepage coefficient. Indicates the permeability coefficient. Indicates the initial groundwater level. Indicates the first The groundwater level at any given time. This indicates the depth of the foundation pit.

[0094] S5. Combining the bearing capacity correction factor, the anti-sliding stability and the seepage influence factor, correct the displacement settlement data, and use the corrected displacement settlement data to complete the early warning of deep foundation pit deformation.

[0095] In this embodiment, the displacement settlement data is corrected by combining the bearing capacity correction factor, the anti-sliding stability factor, and the seepage influence factor, satisfying the following formula:

[0096]

[0097] in, This indicates the corrected displacement and settlement data. Indicates the weighting factor. This indicates the bearing capacity correction factor of the support structure. This represents the original displacement and settlement data. This indicates the anti-sliding stability of the soil in the deep foundation pit. This represents the seepage influence factor of deep foundation pits. In the embodiment, the weighting factor can be determined based on a small amount of historical monitoring data through sensitivity analysis or regression fitting.

[0098] Furthermore, a deep foundation pit deformation early warning system is established in accordance with relevant national or regional standards. Based on the deep foundation pit deformation early warning system, the corrected displacement and settlement data are used to conduct deep foundation pit deformation early warning.

[0099] This invention adapts to the early warning system built by relevant national or regional standards by adaptively correcting the displacement and settlement data of deep foundation pits. It does not require training a foundation pit deformation prediction model based on a large amount of unrelated sample data. The model quantification reduces human interference and greatly improves adaptability.

[0100] Please see 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, including: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory contains program instructions used for 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, and can stably execute the deep foundation pit deformation early warning method of the present invention, further improving the overall applicability and practical application capability of the present invention.

[0101] In embodiments, the processor may be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Input devices can be used to acquire data information. Output devices can be used to output the results obtained by storing program instructions contained in a computer program in the memory provided by this invention. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory.

[0102] In one possible implementation, the memory may include a stored program area and a stored data area. The stored program area may store the operating system and applications required for at least one function; the stored data area may store data created during use. Furthermore, the memory may include read-only memory and random access memory, providing instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores the operating system and operating instructions, executable modules, or data structures, or subsets thereof, or extended sets thereof. The operating instructions may include various operation instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and handling hardware-based tasks.

[0103] The embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described deep foundation pit deformation early warning method.

[0104] The storage medium can include various media that can store program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0105] In summary, this invention corrects the real-time monitored displacement and settlement data based on the support structure data, soil characteristics, and seepage characteristics of deep foundation pits, and then performs deformation early warning. It can adapt to different geological conditions, construction stages, and environmental factors, effectively solving the problem of efficient and accurate early warning of foundation pit deformation. This helps to avoid accidents such as foundation pit collapse and ensures the safety of construction personnel, surrounding buildings, underground pipelines, etc.

[0106] Therefore, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. A method for early warning of deformation in deep foundation pits, characterized in that, Includes the following steps: Displacement and settlement data of deep foundation pits are obtained through monitoring equipment; Construct a bearing capacity correction model, and obtain the bearing capacity correction factor of the support structure based on the bearing capacity correction model; Based on the soil properties of the deep foundation pit, analyze the anti-sliding stability of the soil in the deep foundation pit; Based on the seepage characteristics of the deep foundation pit, the seepage influence factor of the deep foundation pit is calculated; By combining the bearing capacity correction factor, the anti-sliding stability factor and the seepage influence factor, the displacement settlement data is corrected, and the corrected displacement settlement data is used to complete the early warning of deep foundation pit deformation. The bearing capacity correction model satisfies the following formula: in, This indicates the bearing capacity correction factor of the support structure. Indicates the bending moment coefficient. This represents the actual bending moment of the support structure. This indicates the design maximum bending moment of the support structure. Indicates the depth at which the support structure is embedded in the soil. Indicates the height of the support structure. Indicates the axial force coefficient. This represents the actual axial force of the support structure. This indicates the maximum design axial force of the support structure. This indicates the additional load on the ground at the top of the support structure. This represents the active earth pressure exerted on the structure by the soil on the outside of the supporting structure. The analysis of the anti-sliding stability of the deep foundation pit soil based on its soil properties includes the following steps: The degree of softening of the soil in the deep foundation pit is obtained by using the soil moisture content and resistivity. Identify the plastic deformation zones of soil by analyzing its strain distribution; The anti-sliding stability of the deep foundation pit soil is analyzed based on the degree of soil softening within the plastic deformation area. The displacement and settlement data are corrected by combining the bearing capacity correction factor, the anti-sliding stability factor, and the seepage influence factor, satisfying the following formula: in, This indicates the corrected displacement and settlement data. Indicates the weighting factor. This indicates the bearing capacity correction factor of the support structure. This represents the original displacement and settlement data. This indicates the anti-sliding stability of the soil in the deep foundation pit. This indicates the seepage influencing factor in deep foundation pits.

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

3. The deep foundation pit deformation early warning method according to claim 1, characterized in that, The step of obtaining the bearing capacity correction factor of the support structure based on the bearing capacity correction model includes the following steps: Based on the design mechanical parameters of the support structure, a first judgment threshold is set; The bending moment coefficient and axial force coefficient are determined by using the first judgment threshold and the measured mechanical parameters of the support structure. By combining the bearing capacity correction model, the bending moment coefficient, and the axial force coefficient, the bearing capacity correction factor of the support structure is obtained.

4. The deep foundation pit deformation early warning method according to claim 1, characterized in that, The anti-sliding stability of the deep foundation pit soil is analyzed based on the degree of soil softening within the plastic deformation region, satisfying the following formula: in, This indicates the anti-sliding stability of the soil in the deep foundation pit. This indicates the number of soil strips into which the sliding soil mass is divided. Indicates the degree of softening of the soil in deep foundation pits. Indicates the initial cohesion of the soil. This represents the arc length of the sliding surface of the i-th soil strip. Indicates the first The weight of a single earthen strip, Indicates the first The angle between the sliding surface of the soil strip and the horizontal plane Indicates the angle of internal friction. Indicates the area of ​​the plastic deformation zone. This represents the total area of ​​the sliding surface.

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

6. The deep foundation pit deformation early warning method according to claim 5, characterized in that, Based on Darcy's law, the seepage influence factor of the deep foundation pit is calculated using the first seepage coefficient, satisfying the following formula: in, This indicates the seepage influencing factor of deep foundation pits. , Indicates the monitoring time. Indicates the first seepage coefficient. Indicates the permeability coefficient. Indicates the initial groundwater level. Indicates the first The groundwater level at any given time. This indicates the depth of the foundation pit.

7. 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, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory includes program instructions for executing the deep foundation pit deformation early warning method according to any one of claims 1-6.

Images