Deep foundation pit deformation sensitive area identification monitoring method based on stress redistribution characteristics

Abstract

The application discloses a deep foundation pit deformation sensitive area identification monitoring method based on stress redistribution characteristics, relates to the technical field of construction safety, and comprises the following steps: step S1, establishing a refined three-dimensional model based on finite element numerical simulation software; step S2, constructing a model to calculate the deformation sensitivity index of each unit and judging the deformation sensitive area; step S3, dividing three-level control areas according to the deformation sensitivity index normalized value and formulating differentiated construction methods; and step S4, dynamically deploying the YL-ASS intelligent sensor network density according to the deformation sensitive area. The method proposes a partitioned and graded excavation control strategy based on deformation sensitive area identification, forms a solution scheme from structure design, excavation control to risk early warning, and significantly improves the foundation pit construction safety, resource utilization efficiency and environmental protection level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction safety, and particularly relates to a deep foundation pit deformation sensitive area identification and monitoring method based on stress redistribution characteristics. BACKGROUND

[0002] Deep foundation pits play a crucial role in urban underground space construction, supporting sustainable development and high-quality construction of cities, and playing an irreplaceable role in promoting the development and progress of human society. With the continuous emergence of large underground complexes and subway stations and other projects, the number, scale and complexity of deep foundation pit projects have shown a significant growth trend.

[0003] Geological conditions are an important factor in determining the difficulty of deep foundation pit support engineering design and construction. Geological factors including soil type, groundwater distribution, geological structure, rock properties, etc. will directly affect the selection of support structure, calculation of support force and determination of construction method. Poor geological conditions may increase construction risks, leading to project delays, cost increases and even safety accidents. Common geological risks and possible consequences include: soft soil layer: prone to quicksand and piping phenomenon, leading to foundation pit instability. Areas with abundant groundwater: prone to seepage damage, leading to support structure failure. Fault and joint development areas: rock is broken and unstable, requiring higher support structure. These geological risks may lead to foundation pit collapse, damage to surrounding buildings, injury to personnel and other serious consequences.

[0004] A deep foundation pit construction safety management system based on parameterized modeling and dynamic BIM is disclosed in Chinese patent CN117787914A. The system includes: a multi-source construction safety information fusion module for deep foundation pit construction safety management, a deep foundation pit construction area spatial geometric structure parameterized dynamic adjustment module, a construction progress and excavation image monitoring module, an automatic risk identification and early warning module based on a deep foundation pit safety risk knowledge base, and a deep foundation pit construction dynamic BIM visualization module based on parameterized three-dimensional modeling. The invention provides an efficient collaboration and intuitive visualization tool for construction safety management, and also reduces decision-making errors or work delays caused by information asymmetry and low transmission efficiency.

[0005] A large-area deep foundation holographic monitoring method based on image recognition technology is disclosed in Chinese Patent No. CN118278251A. The method uses soil parameter sensors for obtaining environmental information of large-area deep foundation, and multifunctional environmental monitoring instruments. The method steps are: pre-processing the collected image information; collecting the environmental information of the surrounding and local construction area of the foundation pit at the same time; extracting features related to soil displacement from the image, including surface texture, edge information, and color change; the obtained image information is stored through an image storage module, and the environmental information is managed through a data management module, ensuring that each obtained image and each environmental data has a time stamp for subsequent comparison and analysis; the obtained information is used for monitoring and calculating the surrounding soil of the foundation pit, and the deformation of the supporting structure is further predicted through the inverse analysis of the soil layer parameters by the finite element model and the BP neural network algorithm; the obtained deformation data is converted into real-time visual data, and the real-time visual data is used for on-site early warning. The image recognition technology used in the invention can provide faster real-time monitoring, and through continuous analysis of image data, changes and abnormal conditions in foundation pit engineering can be detected in a timely manner, so that problems can be discovered and handled earlier.

[0006] The above patents all have the problems raised in the background art: the cross-transfer subway station area deep foundation has the problems of deformation sensitivity, low efficiency of artificial monitoring, and lagging risk response. SUMMARY

[0007] The technical problem to be solved by the present application is to provide a deep foundation deformation sensitive area identification and monitoring method based on stress redistribution characteristics to overcome the deficiencies of the prior art.

[0008] To achieve the above purpose, the technical scheme adopted by the present application is:

[0009] The deep foundation deformation sensitive area identification and monitoring method based on stress redistribution characteristics comprises the following steps:

[0010] Step S1, a refined three-dimensional model is established based on a finite element numerical simulation software;

[0011] Step S2, the model is constructed to calculate the deformation sensitivity index of each element and determine the deformation sensitive area;

[0012] Step S3, three-level control areas are divided according to the normalized value of the deformation sensitivity index, and differentiated construction methods are developed;

[0013] Step S4, the YL-ASS intelligent sensor network density is dynamically deployed according to the deformation sensitive area.

[0014] Further, the step S1 specifically comprises the following steps:

[0015] Step S1.1, a three-dimensional geometric model is established according to the actual engineering size, and the model is meshed;

[0016] Step S1.2, a material constitutive model is defined and material parameters are inputted;

[0017] Step S1.3, boundary conditions are set for simulating actual working conditions;

[0018] Step S1.4, a final displacement field, stress field and volume strain are outputted by software simulation.

[0019] Further, in the step S1.2, the constitutive model is a modified Mohr-Coulomb model, and the specific calculation formula is:

[0020] ;

[0021] wherein, and respectively represent the spatial direction index of the stress tensor component, wherein represents the normal direction of the stress action surface, represents the action direction of the stress, represents the stress component of the stress tensor in the i direction and the j direction, and respectively represent the first parameter and the second parameter of the Lame constant, represents the Kronecker function, represents the trace of the strain tensor, i.e. the volume strain, the repeated index represents summation, represents the stress component of the strain tensor in the i direction and the j direction, represents the yield function, represents the second deviatoric stress invariant, represents the Lode angle, represents the first stress invariant, represents the internal friction angle, represents the cohesion.

[0022] Further, in the step S1.3, the boundary conditions specifically include: displacement boundary conditions and stress boundary conditions;

[0023] The displacement boundary condition is that the z-direction displacement component is fixed to 0 at the position of the model bottom surface, i.e. z=0;

[0024] The stress boundary condition is that the dot product of the stress tensor and the normal vector at the upper boundary of the model is equal to the distributed force vector acting on the boundary.

[0025] Further, in the step S2, the calculation formula of the deformation sensitivity index is:

[0026] ;

[0027] wherein, denotes the deformation sensitivity index of the mth unit, denotes the displacement change vector of the mth unit during the process of foundation pit excavation, denotes the maximum value of the displacement change modulus of all units in the entire model, denotes the change amount of the second deviatoric stress invariant of the mth unit during the process of foundation pit excavation, denotes the maximum value of the change amount of the second deviatoric stress invariant of all units in the entire model, denotes the volumetric strain of the mth unit, denotes the second derivative of the volumetric strain of the mth unit, denotes taking the absolute value, denotes the norm of a vector.

[0028] Further, the step S2 includes the following steps:

[0029] calculating the deformation sensitivity index threshold value of each unit;

[0030] when the deformation sensitivity index of the mth unit is greater than the deformation sensitivity index threshold value , it is determined as a deformation sensitive unit;

[0031] all deformation sensitive units are fused and output to determine the deformation sensitive area.

[0032] Further, the step S3 includes the following: greater than 0.7 is determined as a first-level control area, i.e., a high-risk area; greater than 0.4 and less than or equal to 0.7 is determined as a second-level control area, i.e., a medium-risk area; less than 0.4 is determined as a third-level control area, i.e., a low-risk area; wherein, denotes the normalized deformation sensitivity index of the mth unit.

[0033] Further, the step S3 includes the following: the first-level control area adopts block excavation, the excavation thickness of each layer is not more than 1m, and prestressed anchor cables are applied; the second-level control area has an excavation thickness not more than 2m and a support spacing reduced based on the original design; the third-level control area is excavated according to the conventional design;

[0034] wherein, the prestress design value of the prestressed anchor cable is equal to the safety factor multiplied by the maximum horizontal stress obtained by simulation, wherein the safety factor is usually taken as 1.2;

[0035] The support spacing of the secondary control area is equal to the original design basic support spacing multiplied by a coefficient 0.85, that is, the support spacing is reduced by 15% based on the original design.

[0036] Further, in the step S4, the specific formula of dynamically deploying the YL-ASS intelligent sensor network density is:

[0037]

[0038] wherein, represents the sensor density, represents the spatial coordinates, represents the construction time sequence, represents the conventional area density, represents the normalized deformation sensitivity index under the coordinates and the construction time sequence t, represents the sensitive area centroid under the construction time sequence t, represents the sensitive area range radius.

[0039] Further, the YL-ASS intelligent sensor network sampling frequency is dynamically set, the sensors in the deformation sensitive area adopt a first sampling frequency, and the sensors in the conventional area adopt a second sampling frequency, wherein the first sampling frequency is greater than the second sampling frequency.

[0040] Compared with the prior art, the beneficial effects of the present application are as follows:

[0041] 1. The present application develops a three-dimensional dynamic mechanical model and a zoned and graded excavation strategy to accurately identify high-risk deformation areas and develop differentiated construction methods, aiming at the problems of cross-shaped "mouth-shaped" area deformation sensitivity, low artificial monitoring efficiency and risk response lag.

[0042] 2. The present application realizes accurate simulation of the whole process of deep foundation pit excavation, and obtains the three-dimensional displacement field, stress field and strain field evolving with time, providing a high-precision numerical basis for deformation sensitive area identification.

[0043] 3. The present application constructs a sensor network density function, realizes intelligent allocation of sensor resources in the time and space dimensions through the coupling mechanism of risk sensitivity linear amplification and spatial attenuation, and effectively reduces resource consumption in the edge area while ensuring high-density monitoring in the core area.

[0044] 4. The present application constructs a dynamic excavation control model, significantly improves the safety, resource utilization efficiency and environmental protection level of foundation pit construction, improves the safety of construction, and ensures the stability of the whole process of super-large deep foundation pit construction. BRIEF DESCRIPTION OF DRAWINGS

[0045] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments thereof, when read in conjunction with the accompanying drawings:

[0046] Figure 1 Flowchart of an embodiment of the application;

[0047] Figure 2 Flowchart of a sensitive unit determination of an embodiment of the application;

[0048] Figure 3 YL-ASS sensor density function diagram of an embodiment of the application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the application more clear, the application is described in detail below in conjunction with the drawings and specific embodiments.

[0050] As shown in Figure 1 the deep foundation deformation sensitive area identification monitoring method based on stress redistribution characteristics includes the following steps:

[0051] Step S1, a refined three-dimensional model is established based on finite element numerical simulation software;

[0052] Step S2, the model is constructed to calculate the deformation sensitivity index of each unit and determine the deformation sensitive area;

[0053] Step S3, the three-level control area is divided according to the deformation sensitivity index normalized value and the differentiated construction method is developed;

[0054] Step S4, the YL-ASS intelligent sensor network density is dynamically deployed according to the deformation sensitive area.

[0055] Modeling according to Abaqus:

[0056] 1. Geometric modeling: build the structure geometry through the built-in tools of Abaqus or import the CAD model.

[0057] 2. Material definition: input the elastic modulus, Poisson's ratio, internal friction angle, cohesion and constitutive model.

[0058] 3. Boundary and load setting: apply pressure, displacement constraint and other boundary conditions to simulate the actual working condition.

[0059] 4. Result analysis: output displacement, stress, plastic strain and other data.

[0060] As shown in Table 1, the input parameters of Abaqus are as follows:

[0061] Table 1

[0062]

[0063] The step S1 specifically comprises the following steps:

[0064] Step S1.1, establishing a three-dimensional geometric model according to actual engineering size and meshing the model;

[0065] Step S1.2, defining a material constitutive model and inputting material parameters;

[0066] Step S1.3, setting boundary conditions for simulating actual working conditions;

[0067] Step S1.4, outputting displacement field, stress field and volume strain through software simulation.

[0068] In the step S1.2, the constitutive model is a modified Mohr-Coulomb model, and the specific calculation formula is:

[0069] ;

[0070] wherein, and respectively represent the spatial direction index of the stress tensor component, wherein represents the normal direction of the stress action surface, represents the action direction of the stress, represents the stress component of the i direction and the j direction of the stress tensor, and respectively represent the first parameter and the second parameter of the Lame constant, represents the Kronecker function, represents the trace of the strain tensor, i.e. the volume strain, the repeated index represents summation, represents the stress component of the i direction and the j direction of the strain tensor, represents the yield function, represents the second deviatoric stress invariant, represents the Lode angle, represents the first stress invariant, represents the internal friction angle, represents the cohesive force.

[0071] In mathematics, the Kronecker function is a binary function, named after the German mathematician Leopold Kronecker. When the independent variable ij of the Kronecker function is equal, the output value is 1, otherwise it is 0.

[0072] kk represents repeated index summation, which is the Einstein summation convention in tensor analysis:

[0073] ;

[0074] The specific illustration of the stress tensor components is shown in Table 2:

[0075] Table 2

[0076]

[0077] Practical engineering significance:

[0078] 1. Analysis of foundation pit support structure:

[0079] : Vertical earth pressure at depth z

[0080] : Shear stress on the interface between the diaphragm wall and the soil

[0081] 2. Identification of deformation sensitive areas:

[0082] : Horizontal strain in the long direction of the foundation pit

[0083] : Heave deformation at the bottom of the pit

[0084] 3. Design of support structure:

[0085] : Shear stress at the connection between the support and the surrounding purlin

[0086] : Horizontal deformation of the support wall.

[0087] In step S1.3, the specific formula of the boundary condition is:

[0088]

[0089] where, z is the displacement component in the z direction, is equal to 0 fixed, is the action position, is the model bottom surface, is the stress tensor, is the normal vector, is the distributed force vector acting on the boundary, is the upper boundary of the model.

[0090] In the boundary condition: displacement boundary constraint movement freedom degree (such as fixed bottom), stress boundary simulates external load (such as ground surface overload).

[0091] In step S2, the calculation formula of the deformation sensitivity index is:

[0092]

[0093] wherein, denotes the deformation sensitivity index of the mth unit, denotes the displacement change vector of the mth unit during the excavation of the foundation pit, denotes the maximum value of the displacement change modulus of all units in the entire model, denotes the change amount of the second deviatoric stress invariant of the mth unit during the excavation of the foundation pit, denotes the maximum value of the change amount of the second deviatoric stress invariant of all units in the entire model, denotes the volumetric strain of the mth unit, denotes the second derivative of the volumetric strain of the mth unit, denotes taking the absolute value, denotes the norm of a vector.

[0094] The formula obtains the sensitivity index by multiplying three normalized indexes:

[0095] Displacement ratio: reflects the relative size of the displacement change of the unit (relative to the maximum displacement change); stress variation: reflects the relative size of the deviatoric stress change of the unit (relative to the maximum deviatoric stress change); strain gradient: reflects the degree of severe change of the volumetric strain near the unit (high strain gradient usually indicates strain concentration, which is prone to failure). The meaning of multiplication is that only the region that simultaneously satisfies large displacement change, large stress change, and large strain gradient is considered as a high sensitivity area.

[0096] In the step S2, judging the deformation sensitive area specifically includes:

[0097] calculating the deformation sensitivity index threshold, and the specific formula is:

[0098]

[0099] wherein, denotes the deformation sensitivity index threshold, denotes the deformation sensitivity mean value, denotes the deformation sensitivity standard deviation, denotes the deformation sensitive area determination threshold coefficient;

[0100] when the deformation sensitivity index of the mth unit is greater than the deformation sensitivity index threshold , it is determined as a deformation sensitive unit;

[0101] fusing and outputting all the deformation sensitive units to judge the deformation sensitive area.

[0102] The coefficient for controlling the threshold range is 1.5-2.0. This coefficient is selected according to engineering experience and is used to adjust the range of the sensitive area. A larger will make the sensitive area range smaller (more stringent), and the smaller will make the sensitive area range larger (more relaxed).

[0103] As shown in Figure 2 , a sensitive area determination flowchart, when the deformation sensitivity index of the unit is greater than the deformation sensitivity index threshold, it is determined as a deformation sensitive area, otherwise it is determined as a regular area.

[0104] In step S3, the three-level control area specifically includes: greater than 0.7 is determined as a first-level control area, i.e. a high-risk area; greater than 0.4 and less than or equal to 0.7 is determined as a second-level control area, i.e. a medium-risk area; less than 0.4 is determined as a third-level control area, i.e. a low-risk area; wherein, denotes the normalized deformation sensitivity index of the mth unit.

[0105] In step S3, the differential process specifically includes: block excavation is used in the first-level control area, the excavation thickness of each layer is not more than 1m, and prestressed anchor cables are applied; the excavation thickness of the second-level control area is not more than 2m, and the support spacing is reduced based on the original design; the third-level control area is excavated according to the regular design;

[0106] wherein the prestress design value of the prestressed anchor cable is equal to the safety factor multiplied by the maximum horizontal stress obtained by simulation, wherein the safety factor is usually taken as 1.2, providing additional safety margin;

[0107] The support spacing of the second-level control area is equal to the original design-based support spacing multiplied by a coefficient of 0.85, i.e. the support spacing is reduced by 15% based on the original design, to improve the support strength.

[0108] In step S4, the specific formula for dynamically deploying the YL-ASS intelligent sensor network density is:

[0109]

[0110] wherein, denotes the sensor density, denotes the spatial coordinates, denotes the construction sequence, denotes the regular area density, denotes the normalized deformation sensitivity index under the coordinates and t construction sequence, denotes the sensitive area centroid under t construction sequence, denotes the sensitive area range radius.

[0111] The sampling frequency of the YL-ASS intelligent sensor network is dynamically set according to the region. Sensors in deformation-sensitive areas use a first sampling frequency, while those in normal areas use a second sampling frequency. The first sampling frequency is greater than the second sampling frequency.

[0112] High sampling frequency is used in the sensitive area, with a typical value range of 10-500 Hz, while low sampling frequency is used in the stable area, with a typical value range of 0.1-10 Hz.

[0113] like Figure 3 As shown, the 3D image (density-location-time relationship):

[0114] Surface characteristics: The distribution of density in space and time forms a "mountain"-shaped surface;

[0115] Solid line: The trajectory of density change of fixed spatial points over time;

[0116] Dashed line: The movement trajectory of the centroid of the sensitive area (always in the region of highest density);

[0117] Color mapping: Warm colors represent high density, and cool colors represent low density.

[0118] Dynamic response characteristics:

[0119] When the center of mass approaches a fixed point, the density increases to its maximum value;

[0120] When the center of mass is far from the fixed point, the density drops to its minimum.

[0121] This dynamic deployment allows for the concentrated deployment of more sensors in deformation-sensitive areas (high-risk areas), thereby improving monitoring accuracy; while in non-sensitive areas, the number of sensors can be reduced, saving costs. The entire sensor network can adapt to changes in the risk area during the excavation process.

[0122] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.

Claims

1. A method for identifying and monitoring the deformation sensitive area of a deep foundation pit based on stress redistribution characteristics, characterized in that, It comprises the following steps: Step S1, establishing a refined three-dimensional model based on finite element numerical simulation software; Step S2, constructing a model to calculate the deformation sensitivity index of each unit and judging the deformation sensitive area; Step S3, dividing three-level control areas according to the normalized value of the deformation sensitivity index and formulating differentiated construction methods; Step S4, dynamically deploying the YL-ASS intelligent sensor network density according to the deformation sensitive area; In the step S2, the calculation formula of the deformation sensitivity index is: ； wherein, denotes the deformation sensitivity index of the mth element, denotes the displacement change vector of the mth element during the excavation of the foundation pit, denotes the maximum value of the displacement change modulus of all elements in the entire model, denotes the change amount of the second deviatoric stress invariant of the mth element during the excavation of the foundation pit, denotes the maximum value of the change amount of the second deviatoric stress invariant of all elements in the entire model, denotes the volumetric strain of the mth element, denotes the second derivative of the volumetric strain of the mth element, denotes taking the absolute value, denotes the norm of a vector; In the step S4, the specific formula of dynamically deploying the YL-ASS intelligent sensor network density is: ； wherein, denotes the sensor density, denotes the spatial coordinate, denotes the construction timing, denotes the regular area density, denotes the normalized deformation sensitivity index under the X coordinate and t construction timing, denotes the sensitive zone centroid under the t construction timing, denotes the sensitive zone range radius.

2. The method of claim 1, wherein, The step S1 specifically comprises the following steps: Step S1.1, establishing a three-dimensional geometric model according to the actual engineering size and performing mesh division on the model; Step S1.2, defining the material constitutive model and inputting material parameters; Step S1.3, setting boundary conditions for simulating actual working conditions; Step S1.4, outputting displacement field, stress field and volume strain through software simulation.

3. The method of claim 2, wherein, In the step S1.2, the constitutive model is a modified Mohr-Coulomb model, and the specific calculation formula is: ； wherein and denote the spatial direction indices of the stress tensor components, respectively, wherein denotes the surface normal direction of the stress, denotes the direction of action of the stress, denotes the stress component of the stress tensor in i- and j-direction, and denote the first and second parameter of the Lame constants, respectively, denotes the Kronecker function, denotes the trace of the strain tensor, i.e. the volumetric strain, repeated indices denote summation, denotes the stress component of the strain tensor in i- and j-direction, denotes the yield function, denotes the second deviatoric stress invariant, denotes the Lode angle, denotes the first stress invariant, denotes the internal friction angle, denotes the cohesion.

4. The method of claim 3, wherein, In the step S1.3, the boundary conditions specifically include displacement boundary conditions and stress boundary conditions; The displacement boundary condition is that the z-direction displacement component is fixed at 0 at the bottom surface position of the model, i.e. z=0; The stress boundary condition is that the dot product of the stress tensor and the normal vector at the upper boundary of the model is equal to the distributed force vector acting on the boundary.

5. The method of claim 4, wherein, In the step S2, judging the deformation sensitive area specifically comprises the following steps: Calculating the deformation sensitivity index threshold value of each unit; When the deformation sensitivity index of the mth unit is greater than the deformation sensitivity index threshold value the mth unit is determined as a deformation-sensitive unit.​ Fusing all deformation sensitive units to output the judged deformation sensitive area.

6. The method of claim 5, wherein, The step S3 specifically includes: greater than 0.7 is determined as a first control area, i.e. a high-risk area; greater than 0.4 and less than or equal to 0.7 is determined as a second control area, i.e. a medium-risk area; less than 0.4 is determined as a third control area, i.e. a low-risk area; wherein, denotes the normalized deformation sensitivity index of the mth unit.

7. The method of claim 6, wherein, In the step S3, the differentiated construction method specifically includes: the first-level control area adopts block excavation, the excavation thickness of each layer is not more than 1m, and prestressed anchor cables are applied; the second-level control area has an excavation thickness not more than 2m, and the support spacing is reduced based on the original design; the third-level control area is excavated according to the conventional design; The prestress design value of the prestressed anchor cable is equal to the safety factor multiplied by the maximum horizontal stress obtained by simulation, and the safety factor is 1.2; The support spacing of the second-level control area is equal to the original design-based support spacing multiplied by the coefficient 0.85, i.e. the support spacing is reduced by 15% based on the original design.

8. The method of claim 7, wherein, The sampling frequency of the YL-ASS intelligent sensor network is dynamically set according to the area, the sensors in the deformation sensitive area adopt a first sampling frequency, and the sensors in the conventional area adopt a second sampling frequency, wherein the first sampling frequency is greater than the second sampling frequency.

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