Loess roadbed slope assembly type supporting structure deformation monitoring method and system

Abstract

The invention provides a deformation monitoring method and system for a loess roadbed slope fabricated supporting structure, and relates to the technical field of slope monitoring. The method comprises the following steps: arranging a three-dimensional displacement sensor array; panel specification parameters of the supporting panel are obtained; obtaining loess density; according to the panel specification parameters and the loess density, the distance between adjacent sensors of a three-dimensional displacement sensor array is determined; acquiring node displacement vectors of a plurality of nodes of the plurality of support panels; determining a local curvature distortion coefficient of the supporting structure according to the node displacement vector and the distance between the adjacent sensors; and according to the local curvature distortion coefficient of the supporting structure, the panel specification parameter and the node displacement vector, determining a deformation early warning condition. According to the invention, the deformation condition of the supporting structure can be monitored by adjusting the distance between the adjacent sensors and combining the local curvature distortion and the displacement vector included angle of the supporting structure, so that the reliability and accuracy of deformation early warning are improved, and the monitoring cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of slope monitoring, and in particular to a deformation monitoring method and system for an assembled support structure of a loess roadbed slope. Background Art

[0002] Although current related technologies can monitor support deformation and slope data in real time, they do not take into account the sensor layout method and the influence of the local curvature distortion and displacement vector angle of the support structure on the deformation of the support structure. In other words, it is impossible to monitor the deformation condition of the support structure by adjusting the spacing between adjacent sensors and combining the local curvature distortion and displacement vector angle of the support structure.

[0003] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0004] The present invention provides a deformation monitoring method and system for an assembled support structure of a loess roadbed slope, which can solve the technical problem in related technologies that it is impossible to monitor the deformation condition of a support structure by adjusting the spacing between adjacent sensors and combining the local curvature distortion and displacement vector angle of the support structure.

[0005] According to a first aspect of the present invention, a method for monitoring deformation of an assembled support structure for a loess roadbed slope is provided, comprising: arranging a three-dimensional displacement sensor array at a plurality of support panels of the assembled support structure, wherein the three-dimensional displacement sensor array is distributed in a rectangular grid; obtaining panel specification parameters of the support panels, wherein the panel specification parameters include yield strength, panel elastic modulus and panel thickness; drilling holes in the loess roadbed slope to obtain loess samples and obtain loess density; determining the adjacent sensor spacing of the three-dimensional displacement sensor array based on the panel specification parameters and the loess density; obtaining node displacement vectors of a plurality of nodes of a plurality of support panels based on the three-dimensional displacement sensor array; determining a local curvature distortion coefficient of the support structure based on the node displacement vector and the adjacent sensor spacing; and determining a deformation warning condition based on the local curvature distortion coefficient of the support structure, the panel specification parameters and the node displacement vector.

[0006] Furthermore, the spacing between adjacent sensors of the three-dimensional displacement sensor array is determined based on the panel specification parameters and the loess density, including: obtaining the slope inclination angle of the loess roadbed slope where multiple support panels are located; and determining the spacing between adjacent sensors of the three-dimensional displacement sensor array based on the slope inclination angle, the panel specification parameters and the loess density.

[0007] Furthermore, the adjacent sensor spacing of the three-dimensional displacement sensor array is determined according to the slope inclination, the panel specification parameters and the loess density, including: according to the formula Determine the adjacent sensor spacing of the three-dimensional displacement sensor array of the nth support panel , where E is the elastic modulus of the panel, H is the thickness of the panel, is the density of loess, is the acceleration due to gravity, is the slope inclination angle of the loess roadbed slope where the nth support panel is located.

[0008] Furthermore, the local curvature distortion coefficient of the support structure is determined based on the node displacement vector and the adjacent sensor spacing, including: determining the node displacement vector modulus of multiple nodes of multiple support panels based on the node displacement vector; determining the local curvature distortion coefficient of the support structure based on the node displacement vector modulus and the adjacent sensor spacing.

[0009] Furthermore, according to the node displacement vector modulus and the adjacent sensor spacing, the local curvature distortion coefficient of the support structure is determined, including: according to the formula Determine the local curvature distortion coefficient of the support structure at the node of the i-th row and j-th column of the n-th support panel ,in, is the displacement vector modulus of the node at the i-th row and j-th column of the n-th support panel, is the displacement vector modulus of the node at the i+1th row and jth column of the nth support panel, is the displacement vector modulus of the node at the i-th row and j+1-th column of the n-th support panel, is the adjacent sensor spacing of the three-dimensional displacement sensor array of the nth support panel, and n, i and j are all positive integers.

[0010] Furthermore, deformation warning conditions are determined based on the local curvature distortion coefficient of the support structure, the panel specification parameters and the node displacement vector, including: determining the local curvature distortion coefficient threshold of the support structure based on the panel specification parameters; determining the displacement vector angle based on the node displacement vector; and determining the deformation warning conditions based on the local curvature distortion coefficient of the support structure, the local curvature distortion coefficient threshold of the support structure and the displacement vector angle.

[0011] Further, according to the node displacement vector, the displacement vector angle is determined, including: according to the formula Determine the displacement vector angle of the node in the i-th row and j-th column of the n-th support panel ,in, is the displacement vector of the node at the i-th row and j-th column of the n-th support panel, is the displacement vector of the node at the i+1th row and jth column of the nth support panel, is the displacement vector of the node in the i-th row and j+1-th column of the n-th support panel, and n, i and j are all positive integers.

[0012] Furthermore, according to the local curvature distortion coefficient of the support structure, the local curvature distortion coefficient threshold of the support structure and the displacement vector angle, the deformation warning condition is determined, including: according to the formula Determine the deformation warning condition C, where: is the local curvature distortion coefficient of the support structure at the node of the i-th row and j-th column of the n-th support panel, is the threshold value of the local curvature distortion coefficient of the support structure, is the displacement vector angle of the node in the i-th row and j-th column of the n-th support panel, N is the number of support panels, I is the number of rows of the three-dimensional displacement sensor array, J is the number of columns of the three-dimensional displacement sensor array, n≤N, i≤I, j≤J, and n, i, j, N, I and J are all positive integers.

[0013] According to a second aspect of the present invention, a deformation monitoring system for an assembled support structure of a loess roadbed slope is provided, comprising: a three-dimensional displacement sensor array module for arranging a three-dimensional displacement sensor array at a plurality of support panels of an assembled support structure, wherein the three-dimensional displacement sensor array is distributed in a rectangular grid; a panel specification parameter module for obtaining panel specification parameters of the support panels, wherein the panel specification parameters include yield strength, panel elastic modulus, and panel thickness; a loess density module for drilling holes in the loess roadbed slope to obtain loess samples and obtain loess density; an adjacent sensor spacing module for determining the adjacent sensor spacing of the three-dimensional displacement sensor array based on the panel specification parameters and the loess density; a node displacement vector module for obtaining node displacement vectors of a plurality of nodes of a plurality of support panels based on the three-dimensional displacement sensor array; a support structure local curvature distortion coefficient module for determining the local curvature distortion coefficient of the support structure based on the node displacement vector and the adjacent sensor spacing; and a deformation warning condition module for determining a deformation warning condition based on the local curvature distortion coefficient of the support structure, the panel specification parameters, and the node displacement vector.

[0014] Technical effect: According to the present invention, by arranging a three-dimensional displacement sensor array in the form of a rectangular grid at multiple support panels of the prefabricated support structure, the displacement of the support panels can be comprehensively monitored from three directions and multiple positions. The spacing between adjacent sensors is reasonably set according to the panel specification parameters of the support panel and the physical properties of the loess roadbed, so as to more accurately capture the deformation characteristics of the support structure, improve monitoring efficiency, and reduce monitoring costs. The local curvature distortion coefficient can reflect the degree of local bending deformation of the support structure, identify potential slip surfaces, and determine the deformation warning conditions in combination with the displacement vector angle, thereby reducing the limitations of single parameter judgment, improving the reliability and accuracy of deformation warning, and helping to timely detect abnormal deformation of the support structure. When determining the spacing between adjacent sensors of the three-dimensional displacement sensor array, the spacing between adjacent sensors of the three-dimensional displacement sensor array can be determined by the slope inclination, panel specification parameters and loess density. By setting a reasonable spacing between adjacent sensors, effective monitoring of the deformation of the support structure can be achieved. At the same time, the excessive configuration of the number of sensors is reduced, and the monitoring cost is reduced. When determining the local curvature distortion coefficient of the support structure, the node displacement vector modulus and the spacing between adjacent sensors can be used to determine the local curvature distortion coefficient of the support structure. The horizontal and vertical displacement differences between the target node and the adjacent nodes can more comprehensively reflect the local bending and twisting deformation of the support structure. Normalization can eliminate the influence of the rectangular grid size, making the data of different monitoring areas comparable. Dynamic spacing and normalization can amplify the local curvature distortion coefficient of the support structure in the high-risk area of ​​the slope top, which is in line with the characteristics of the slope top being more prone to instability in actual projects and reducing misjudgments. When determining the displacement vector angle, the node displacement vector angle can be determined by the node displacement vectors of three adjacent nodes. By analyzing the direction of the adjacent node displacement vectors, the dangerous phenomenon of overall slip and the normal phenomenon of local deformation can be more accurately distinguished. By analyzing the spatial distribution of the displacement vector angle, the area of ​​inharmonious deformation can be quickly located, providing a target for reinforcement design. When determining the deformation warning conditions, the local curvature distortion coefficient of the support structure can be compared with the local curvature distortion coefficient threshold of the support structure to determine whether the node has entered the plastic stage. Combined with the angle between the displacement vectors of adjacent nodes, invalid warnings can be reduced, the false alarm rate can be further reduced, and the accuracy and comprehensiveness of the deformation warning conditions can be improved.

[0015] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and not limiting of the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can derive other embodiments based on these drawings without inventive efforts. Figure 1 A schematic flow chart of a method for monitoring deformation of a prefabricated support structure for a loess roadbed slope according to an embodiment of the present invention is exemplarily shown; Figure 2 The flowchart of calculating the distance between adjacent sensors according to an embodiment of the present invention is exemplarily shown; Figure 3 The following is an exemplary flowchart of calculating the local curvature distortion coefficient of the support structure according to an embodiment of the present invention; Figure 4 A flowchart for determining deformation warning conditions according to an embodiment of the present invention is exemplarily shown; Figure 5 A block diagram of a deformation monitoring system for a loess roadbed slope prefabricated support structure according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0018] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0019] Figure 1A flow chart of a deformation monitoring method for a prefabricated support structure of a loess roadbed slope according to an embodiment of the present invention is exemplarily shown, and the method includes: step S1, arranging a three-dimensional displacement sensor array at multiple support panels of the prefabricated support structure, wherein the three-dimensional displacement sensor array is distributed in the form of a rectangular grid; step S2, obtaining panel specification parameters of the support panel, wherein the panel specification parameters include yield strength, panel elastic modulus and panel thickness; step S3, drilling holes in the loess roadbed slope to obtain loess samples and obtain loess density; step S4, determining the adjacent sensor spacing of the three-dimensional displacement sensor array based on the panel specification parameters and the loess density; step S5, obtaining the node displacement vectors of multiple nodes of multiple support panels based on the three-dimensional displacement sensor array; step S6, determining the local curvature distortion coefficient of the support structure based on the node displacement vector and the adjacent sensor spacing; step S7, determining the deformation warning condition based on the local curvature distortion coefficient of the support structure, the panel specification parameters and the node displacement vector.

[0020] According to the deformation monitoring method of the prefabricated support structure of the loess roadbed slope of the embodiment of the present invention, by arranging a three-dimensional displacement sensor array in the form of a rectangular grid at multiple support panels of the prefabricated support structure, the displacement of the support panels can be comprehensively monitored from three directions and multiple positions. The spacing between adjacent sensors is reasonably set according to the panel specification parameters of the support panel and the physical properties of the loess roadbed, so as to more accurately capture the deformation characteristics of the support structure, improve monitoring efficiency, and reduce monitoring costs. The local curvature distortion coefficient can reflect the degree of local bending deformation of the support structure, identify potential slip surfaces, and determine deformation warning conditions in combination with the displacement vector angle, thereby reducing the limitations of single parameter judgment, improving the reliability and accuracy of deformation warning, and helping to timely detect abnormal deformation of the support structure.

[0021] According to one embodiment of the present invention, in step S1, an array of three-dimensional displacement sensors (e.g., a total station, a GNSS receiver, or a MEMS inertial unit) is distributed in the form of a rectangular grid, that is, the support panels of the prefabricated support structure are divided into an I×J rectangular grid, where I is the number of rows of the three-dimensional displacement sensor array, and J is the number of columns of the three-dimensional displacement sensor array. Three-dimensional displacement sensors can be set at key positions of the support panels of the prefabricated support structure, for example, at the connections of the support panels that are subjected to greater pressure or are prone to deformation, at the edges of the structure, and the like.

[0022] According to one embodiment of the present invention, in step S2, the yield strength, panel elastic modulus and panel thickness of the support panel are obtained based on the design drawings of the prefabricated support structure. The yield strength is the stress value at which the support panel material begins to undergo plastic deformation, and the panel elastic modulus is a physical quantity that measures the ability of the support panel material to resist elastic deformation. For example, the yield strength of Q235 steel is 235 MPa, and the panel elastic modulus is 200 GPa.

[0023] According to one embodiment of the present invention, in step S3, a drilling point can be selected at the bottom of the loess roadbed slope. When the drilling reaches a predetermined depth, the drilling is stopped and a soil sampler (e.g., a thin-walled soil sampler, a thick-walled soil sampler, etc.) is used to collect loess samples, thereby determining the loess density by the ring knife method. For example, the loess density of the loess roadbed is close to 1.8 tons / cubic meter.

[0024] According to one embodiment of the present invention, in step S4, the distance between adjacent sensors of the three-dimensional displacement sensor array is determined according to the panel specification parameters and the loess density.

[0025] Figure 2 The flowchart for calculating the distance between adjacent sensors according to an embodiment of the present invention is exemplarily shown.

[0026] According to one embodiment of the present invention, step S4 includes: step S41, obtaining the slope inclination angle of the loess roadbed slope where multiple support panels are located; step S42, determining the adjacent sensor spacing of the three-dimensional displacement sensor array based on the slope inclination angle, the panel specification parameters and the loess density.

[0027] According to one embodiment of the present invention, the slope inclination angles at the top and the bottom of the loess roadbed slope are different. Therefore, the slope inclination angles at the loess roadbed slope positions where different support panels are located are different. The slope inclination angles at the loess roadbed slope positions where multiple support panels are located can be measured using an electronic theodolite. The panel elastic modulus reflects the stiffness of the support structure. The larger the panel elastic modulus, the less likely the structure is to deform. The spacing between adjacent sensors can be increased. For a support panel with a panel thickness of H, its bending stiffness expression is: , where E is the elastic modulus of the panel, H is the thickness of the panel, The Poisson's ratio is the Poisson's ratio of the material used to support the panel. In engineering applications, the influence of the Poisson's ratio is often ignored (i.e. ), which is simplified to Therefore, bending stiffness is proportional to the cube of the panel thickness. The thicker the support panel, the stronger its deformation resistance, and the spacing between adjacent sensors can be appropriately increased. Loess density affects the self-weight stress of the slope. The greater the loess density, the greater the potential deformation, and the spacing between adjacent sensors needs to be reduced to improve monitoring accuracy. The greater the slope inclination, the higher the slip risk, and the spacing between adjacent sensors needs to be reduced accordingly. The spacing between adjacent sensors can be determined based on the relationship between the panel elastic modulus, panel thickness, loess density, and the spacing between adjacent sensors. Based on the rectangular grid distribution of the three-dimensional displacement sensor array and the spacing between adjacent sensors, the three-dimensional displacement sensors are deployed on each support panel.

[0028] According to one embodiment of the present invention, the adjacent sensor spacing of the three-dimensional displacement sensor array is determined according to the slope inclination, the panel specification parameters and the loess density, including: determining the adjacent sensor spacing of the three-dimensional displacement sensor array of the nth support panel according to formula (1): , (1), Where, E is the elastic modulus of the panel, H is the thickness of the panel, is the density of loess, is the acceleration due to gravity, is the slope inclination angle of the loess roadbed slope where the nth support panel is located.

[0029] According to one embodiment of the present invention, in formula (1), the numerator The greater the bending stiffness, the stronger the support panel's ability to resist deformation, and the greater the distance between adjacent sensors. The product of loess density and gravity acceleration determines the self-weight stress of the slope soil. The greater the density or gravity, the more dense the sensors are needed to monitor the deformation caused by soil pressure. is the sliding force component of the loess roadbed slope where the nth support panel is located, that is, the slope sliding load. The larger the slope sliding load, the more likely shear slip will occur, and the spacing between adjacent sensors needs to be reduced. In order to unify the units on both sides of the equation, The fourth power, that is, , the adjacent sensor spacing of the 3D displacement sensor array can be obtained. Different support panels have different slope inclinations. Therefore, the adjacent sensor spacing of the 3D displacement sensor array on each support panel is different. The adjacent sensor spacing of the 3D displacement sensor array of the nth support panel needs to be calculated based on the slope inclination of the loess roadbed slope where the nth support panel is located.

[0030] In this way, the spacing between adjacent sensors of the three-dimensional displacement sensor array can be determined by the slope inclination, panel specification parameters and loess density. By setting a reasonable spacing between adjacent sensors, effective monitoring of the deformation of the support structure can be achieved. At the same time, the excessive configuration of the number of sensors can be reduced, thereby reducing the monitoring cost.

[0031] According to one embodiment of the present invention, in step S5, the installation position of each three-dimensional displacement sensor represents a node, which can fully reflect the deformation state of the support panel and is distributed in the form of a rectangular grid.

[0032] According to one embodiment of the present invention, in step S6, the local curvature distortion coefficient of the support structure is determined based on the node displacement vector and the adjacent sensor distance.

[0033] Figure 3 A flow chart for calculating the local curvature distortion coefficient of a supporting structure according to an embodiment of the present invention is exemplarily shown.

[0034] According to one embodiment of the present invention, step S6 includes: step S61, determining the node displacement vector modulus of multiple nodes of multiple support panels based on the node displacement vector; step S62, determining the local curvature distortion coefficient of the support structure based on the node displacement vector modulus and the adjacent sensor spacing.

[0035] According to one embodiment of the present invention, the node displacement vector is used to describe the displacement of the node on the support panel in three-dimensional space, which is usually expressed as ,in, is the node displacement vector, 、 and are the displacement components of the node in the directions of the X, Y, and Z coordinate axes, respectively. The displacement components can be collected and processed by a three-dimensional displacement sensor array. The node displacement vector modulus is ,in, is the node displacement vector modulus. On the support panel, based on the layout of the 3D displacement sensor array, a node is selected. The nodes adjacent to it in the transverse and longitudinal directions form a local node group. The local curvature distortion coefficient of the support structure is determined based on the displacement vector moduli of the three adjacent nodes and the spacing between adjacent sensors.

[0036] According to one embodiment of the present invention, the local curvature distortion coefficient of the support structure is determined based on the node displacement vector modulus and the adjacent sensor spacing, including: determining the local curvature distortion coefficient of the support structure at the node of the i-th row and j-th column of the n-th support panel according to formula (2): , (2), in, is the displacement vector modulus of the node at the i-th row and j-th column of the n-th support panel, is the displacement vector modulus of the node at the i+1th row and jth column of the nth support panel, is the displacement vector modulus of the node at the i-th row and j+1-th column of the n-th support panel, is the adjacent sensor spacing of the three-dimensional displacement sensor array of the nth support panel, and n, i and j are all positive integers.

[0037] According to one embodiment of the present invention, in formula (2), is the difference between the displacement vector modulus of the node (target node) at the i-th row and j-th column of the n-th support panel and the displacement vector modulus of the node at the i+1-th row and j-th column of the n-th support panel, representing the displacement difference along the lateral direction of the support panel. It is the difference between the displacement vector modulus of the node at the i-th row and j-th column of the n-th support panel and the displacement vector modulus of the node at the i-th row and j+1-th column of the n-th support panel, which represents the displacement difference along the longitudinal direction of the support panel. The sum of the above two represents the total displacement difference of the node. The larger the total displacement difference, the more serious the local curvature distortion of the support structure. Through bidirectional (transverse + longitudinal) displacement difference analysis, it is easier to discover potential slip surfaces and reduce misjudgment. It is the ratio of the total displacement difference to the square of the spacing between adjacent sensors, which represents the displacement change rate per unit area, that is, the local curvature distortion coefficient of the support structure. Normalization processing eliminates the influence of the rectangular grid size and makes the data of different monitoring areas comparable. The larger the local curvature distortion coefficient of the support structure, the more serious the local curvature distortion, which indicates the formation of a slip surface. When the local curvature distortion coefficient of the support structure is close to 0, it means that the displacement distribution is uniform and the structure is stable.

[0038] In this way, the local curvature distortion coefficient of the support structure can be determined by the node displacement vector modulus and the spacing between adjacent sensors. The local bending and twisting deformation of the support structure can be more comprehensively reflected through the lateral and longitudinal displacement differences between the target node and the adjacent nodes. The influence of the rectangular grid size is eliminated through normalization processing, making the data of different monitoring areas comparable. The local curvature distortion coefficient of the support structure in the high-risk area of ​​the slope top is amplified through dynamic spacing and normalization, which is in line with the characteristics of the slope top being more prone to instability in actual projects and reduces misjudgment.

[0039] According to one embodiment of the present invention, in step S7, a deformation warning condition is determined based on the local curvature distortion coefficient of the support structure, the panel specification parameters and the node displacement vector.

[0040] Figure 4 A flow chart for determining deformation warning conditions according to an embodiment of the present invention is exemplarily shown.

[0041] According to one embodiment of the present invention, step S7 includes: step S71, determining the local curvature distortion coefficient threshold of the support structure according to the panel specification parameters; step S72, determining the displacement vector angle according to the node displacement vector; step S73, determining the deformation warning condition according to the local curvature distortion coefficient of the support structure, the local curvature distortion coefficient threshold of the support structure and the displacement vector angle.

[0042] According to one embodiment of the present invention, if the cross section is perpendicular to the neutral axis before bending, it remains flat and perpendicular to the neutral axis after bending. The strain at any point on the cross section is ,in, is the strain at any point on the cross section, y is the vertical distance from the stress point to the neutral axis, the maximum value of y is H / 2, H is the panel thickness, R is the radius of curvature, K is the curvature, combined with Hooke's law , we can get ,in, is the stress, E is the elastic modulus of the panel, when the maximum stress reaches the yield strength, the support structure begins to yield, that is, plastic deformation begins to occur. and When the local curvature distortion coefficient threshold of the support structure is obtained, ,in, is the maximum stress, is the yield strength, is the maximum vertical distance from the stress point to the neutral axis. The displacement vector angle reflects the relative change in displacement direction between adjacent nodes.

[0043] According to one embodiment of the present invention, determining the displacement vector angle according to the node displacement vector includes: determining the displacement vector angle of the node of the i-th row and j-th column of the n-th support panel according to formula (3): , (3), in, is the displacement vector of the node at the i-th row and j-th column of the n-th support panel, is the displacement vector of the node at the i+1th row and jth column of the nth support panel, is the displacement vector of the node in the i-th row and j+1-th column of the n-th support panel, and n, i and j are all positive integers.

[0044] According to one embodiment of the present invention, in formula (3), is the relative displacement vector between the node in the i-th row and j-th column of the n-th support panel and the node in the i+1-th row and j-th column of the n-th support panel, is the relative displacement vector between the node in the i-th row and j-th column of the n-th support panel and the node in the i-th row and j+1-th column of the n-th support panel, is the cosine value of the displacement vector angle between the above two relative displacement vectors. The smaller the cosine value, the larger the displacement vector angle, which means that the direction difference between the two relative displacement vectors is greater. , indicating that the displacement directions are almost the same, which is a coordinated deformation (for example, temperature expansion is a normal phenomenon). , indicating that the displacement directions are quite different, which is an uncoordinated deformation (for example, slip or structural failure occurs, which is a dangerous phenomenon). , indicating that the displacement direction is orthogonal, which is a shear deformation (for example, the support panel is dislocated, which is a dangerous phenomenon).

[0045] In this way, the displacement vector angle can be determined through the node displacement vectors of three adjacent nodes. By analyzing the direction of the displacement vectors of adjacent nodes, the dangerous phenomenon of overall slip and the normal phenomenon of local deformation can be distinguished more accurately. By analyzing the spatial distribution of the displacement vector angle, the area of ​​inharmonious deformation can be quickly located, providing a target for reinforcement design.

[0046] According to one embodiment of the present invention, a deformation warning condition is determined based on the local curvature distortion coefficient of the support structure, the local curvature distortion coefficient threshold of the support structure, and the displacement vector angle, including: determining the deformation warning condition C according to formula (4), (4), in, is the local curvature distortion coefficient of the support structure at the node of the i-th row and j-th column of the n-th support panel, is the threshold value of the local curvature distortion coefficient of the support structure, is the displacement vector angle of the node in the i-th row and j-th column of the n-th support panel, N is the number of support panels, I is the number of rows of the three-dimensional displacement sensor array, J is the number of columns of the three-dimensional displacement sensor array, n≤N, i≤I, j≤J, and n, i, j, N, I and J are all positive integers.

[0047] According to one embodiment of the present invention, in formula (4), The local curvature distortion coefficient of the support structure at the node of the i-th row and j-th column of the n-th support panel is greater than the local curvature distortion coefficient threshold of the support structure, indicating that the support structure has entered the plastic deformation stage and is at risk of instability. The displacement vector angle of the node in the i-th row and j-th column of the n-th support panel is greater than 60°, indicating that the displacement directions of adjacent nodes are quite different and overall slip may occur. When , it can only indicate that the local impact causes the local curvature distortion coefficient of the support structure at the node to exceed the threshold, and cannot indicate that the support structure is unstable as a whole. When , it is the difference in displacement direction, which may be caused by non-destructive factors (such as vibration), that is, elastic deformation, and the support structure does not undergo plastic deformation. At this time, the support structure is still in a safe state. It means that the local curvature distortion coefficient of the support structure is greater than the local curvature distortion coefficient threshold of the support structure, and the displacement vector angle is greater than 60°. It means that when there is a node where the local curvature distortion coefficient of the support structure and the displacement vector angle simultaneously satisfy and , that is, if the local curvature distortion of the support structure exceeds the limit and the displacement direction is greatly different (for example, slope slippage and support structure fracture), it is determined that the prefabricated support structure of the loess roadbed slope needs deformation warning.

[0048] In this way, by comparing the local curvature distortion coefficient of the support structure with the threshold of the local curvature distortion coefficient of the support structure, it is possible to determine whether the node has entered the plastic stage. Combined with the angle between the displacement vectors of adjacent nodes, invalid warnings can be reduced, the false alarm rate can be further reduced, and the accuracy and comprehensiveness of the deformation warning conditions can be improved.

[0049] According to an embodiment of the present invention, a method for monitoring deformation of a prefabricated support structure for a loess roadbed slope employs a three-dimensional displacement sensor array arranged in a rectangular grid across multiple support panels of the prefabricated support structure. This allows for comprehensive monitoring of the displacement of the support panels from three directions and multiple locations. The spacing between adjacent sensors is rationally set based on the panel specifications and the physical properties of the loess roadbed, allowing for more accurate capture of the support structure's deformation characteristics, improving monitoring efficiency and reducing monitoring costs. The local curvature distortion coefficient reflects the degree of local bending deformation of the support structure, identifying potential slip surfaces. This, combined with the displacement vector angle, determines deformation warning conditions, reducing the limitations of single-parameter judgment, improving the reliability and accuracy of deformation warnings, and facilitating the timely detection of abnormal deformation of the support structure. The spacing between adjacent sensors in the three-dimensional displacement sensor array is determined based on the slope inclination, panel specifications, and loess density. By setting a reasonable spacing between adjacent sensors, effective monitoring of support structure deformation can be achieved, while also reducing the over-configuration of sensors and lowering monitoring costs. When determining the local curvature distortion coefficient of the support structure, the node displacement vector modulus and the spacing between adjacent sensors can be used to determine the local curvature distortion coefficient of the support structure. The horizontal and vertical displacement differences between the target node and the adjacent nodes can more comprehensively reflect the local bending and twisting deformation of the support structure. Normalization can eliminate the influence of the rectangular grid size, making the data of different monitoring areas comparable. Dynamic spacing and normalization can amplify the local curvature distortion coefficient of the support structure in the high-risk area of ​​the slope top, which is in line with the characteristics of the slope top being more prone to instability in actual projects and reducing misjudgments. When determining the displacement vector angle, the node displacement vector angle can be determined by the node displacement vectors of three adjacent nodes. By analyzing the direction of the adjacent node displacement vectors, the dangerous phenomenon of overall slip and the normal phenomenon of local deformation can be more accurately distinguished. By analyzing the spatial distribution of the displacement vector angle, the area of ​​inharmonious deformation can be quickly located, providing a target for reinforcement design. When determining the deformation warning conditions, the local curvature distortion coefficient of the support structure can be compared with the local curvature distortion coefficient threshold of the support structure to determine whether the node has entered the plastic stage. Combined with the angle between the displacement vectors of adjacent nodes, invalid warnings can be reduced, the false alarm rate can be further reduced, and the accuracy and comprehensiveness of the deformation warning conditions can be improved.

[0050] Figure 5A block diagram of a deformation monitoring system for a prefabricated support structure for a loess roadbed slope according to an embodiment of the present invention is exemplarily shown. The system includes: a three-dimensional displacement sensor array module, which is used to deploy a three-dimensional displacement sensor array at multiple support panels of the prefabricated support structure, wherein the three-dimensional displacement sensor array is distributed in a rectangular grid; a panel specification parameter module, which is used to obtain panel specification parameters of the support panels, wherein the panel specification parameters include yield strength, panel elastic modulus, and panel thickness; a loess density module, which is used to obtain loess samples by drilling holes in the loess roadbed slope to obtain loess density; an adjacent sensor spacing module, which is used to determine the adjacent sensor spacing of the three-dimensional displacement sensor array based on the panel specification parameters and the loess density; a node displacement vector module, which is used to obtain node displacement vectors of multiple nodes of multiple support panels based on the three-dimensional displacement sensor array; a support structure local curvature distortion coefficient module, which is used to determine the local curvature distortion coefficient of the support structure based on the node displacement vector and the adjacent sensor spacing; and a deformation warning condition module, which is used to determine the deformation warning condition based on the local curvature distortion coefficient of the support structure, the panel specification parameters, and the node displacement vector.

[0051] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0052] Those skilled in the art will appreciate that the embodiments of the present invention described above and shown in the accompanying drawings are intended to be illustrative only and are not intended to limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Any variations or modifications may be made to the embodiments of the present invention without departing from the principles described.

Claims

1. A method for monitoring deformation of an assembled support structure for a loess roadbed slope, characterized in that: include: A three-dimensional displacement sensor array is arranged at multiple support panels of the prefabricated support structure, wherein the three-dimensional displacement sensor array is distributed in the form of a rectangular grid; panel specification parameters of the support panel are obtained, wherein the panel specification parameters include yield strength, panel elastic modulus and panel thickness; loess samples are obtained by drilling holes in the loess roadbed slope to obtain loess density; the adjacent sensor spacing of the three-dimensional displacement sensor array is determined based on the panel specification parameters and the loess density; based on the three-dimensional displacement sensor array, the node displacement vectors of multiple nodes of multiple support panels are obtained; based on the node displacement vectors and the adjacent sensor spacing, the local curvature distortion coefficient of the support structure is determined; based on the local curvature distortion coefficient of the support structure, the panel specification parameters and the node displacement vectors, the deformation warning conditions are determined.

2. The deformation monitoring method of the assembled support structure of loess roadbed slope according to claim 1 is characterized in that: Determining the spacing between adjacent sensors of the three-dimensional displacement sensor array based on the panel specification parameters and the loess density includes: obtaining the slope inclination angle of the loess roadbed slope where multiple support panels are located; determining the spacing between adjacent sensors of the three-dimensional displacement sensor array based on the slope inclination angle, the panel specification parameters and the loess density.

3. The deformation monitoring method of the assembled support structure of loess roadbed slope according to claim 2 is characterized in that: Determine the distance between adjacent sensors of the three-dimensional displacement sensor array according to the slope inclination, the panel specification parameters and the loess density, including: according to the formula Determine the adjacent sensor spacing of the three-dimensional displacement sensor array of the nth support panel , where E is the elastic modulus of the panel, H is the thickness of the panel, is the density of loess, is the acceleration due to gravity, is the slope inclination angle of the loess roadbed slope where the nth support panel is located.

4. The deformation monitoring method of the loess roadbed slope prefabricated support structure according to claim 1 is characterized in that: The local curvature distortion coefficient of the support structure is determined according to the node displacement vector and the adjacent sensor spacing, including: determining the node displacement vector modulus of multiple nodes of multiple support panels according to the node displacement vector; and determining the local curvature distortion coefficient of the support structure according to the node displacement vector modulus and the adjacent sensor spacing.

5. The deformation monitoring method of the assembled support structure of loess roadbed slope according to claim 4 is characterized in that: Determine the local curvature distortion coefficient of the support structure according to the node displacement vector modulus and the adjacent sensor spacing, including: according to the formula Determine the local curvature distortion coefficient of the support structure at the node of the i-th row and j-th column of the n-th support panel ,in, is the displacement vector modulus of the node at the i-th row and j-th column of the n-th support panel, is the displacement vector modulus of the node at the i+1th row and jth column of the nth support panel, is the displacement vector modulus of the node at the i-th row and j+1-th column of the n-th support panel, is the adjacent sensor spacing of the three-dimensional displacement sensor array of the nth support panel, and n, i and j are all positive integers.

6. The deformation monitoring method of the assembled support structure of loess roadbed slope according to claim 1 is characterized in that: Deformation warning conditions are determined based on the local curvature distortion coefficient of the support structure, the panel specification parameters and the node displacement vector, including: determining the local curvature distortion coefficient threshold of the support structure based on the panel specification parameters; determining the displacement vector angle based on the node displacement vector; and determining the deformation warning conditions based on the local curvature distortion coefficient of the support structure, the local curvature distortion coefficient threshold of the support structure and the displacement vector angle.

7. The deformation monitoring method of the assembled support structure of loess roadbed slope according to claim 6 is characterized in that: According to the node displacement vector, determining the displacement vector angle includes: according to the formula Determine the displacement vector angle of the node in the i-th row and j-th column of the n-th support panel ,in, is the displacement vector of the node at the i-th row and j-th column of the n-th support panel, is the displacement vector of the node at the i+1th row and jth column of the nth support panel, is the displacement vector of the node in the i-th row and j+1-th column of the n-th support panel, and n, i and j are all positive integers.

8. The deformation monitoring method of the assembled support structure of loess roadbed slope according to claim 6 is characterized in that: Determine the deformation warning condition according to the local curvature distortion coefficient of the support structure, the local curvature distortion coefficient threshold of the support structure and the displacement vector angle, including: according to the formula Determine the deformation warning condition C, where: is the local curvature distortion coefficient of the support structure at the node of the i-th row and j-th column of the n-th support panel, is the threshold value of the local curvature distortion coefficient of the support structure, is the displacement vector angle of the node in the i-th row and j-th column of the n-th support panel, N is the number of support panels, I is the number of rows of the three-dimensional displacement sensor array, J is the number of columns of the three-dimensional displacement sensor array, n≤N, i≤I, j≤J, and n, i, j, N, I and J are all positive integers.

9. A deformation monitoring system for a loess roadbed slope prefabricated support structure, used to implement the deformation monitoring method for a loess roadbed slope prefabricated support structure according to any one of claims 1 to 8, characterized in that: include: a three-dimensional displacement sensor array module, for arranging a three-dimensional displacement sensor array at multiple support panels of an assembled support structure, wherein the three-dimensional displacement sensor array is distributed in a rectangular grid; a panel specification parameter module, for obtaining the panel specification parameters of the support panel, wherein the panel specification parameters include yield strength, panel elastic modulus and panel thickness; a loess density module, for drilling holes in the loess roadbed slope to obtain loess samples and obtain loess density; an adjacent sensor spacing module, for determining the adjacent sensor spacing of the three-dimensional displacement sensor array based on the panel specification parameters and the loess density; a node displacement vector module, for obtaining the node displacement vectors of multiple nodes of multiple support panels based on the three-dimensional displacement sensor array; a support structure local curvature distortion coefficient module, for determining the local curvature distortion coefficient of the support structure based on the node displacement vector and the adjacent sensor spacing; a deformation warning condition module, for determining the deformation warning condition based on the local curvature distortion coefficient of the support structure, the panel specification parameters and the node displacement vector.

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