Lateral pressure applying device for concrete-filled steel tube structural body and lateral loading instability judging method

Abstract

The invention belongs to the technical field of stress monitoring of concrete structural bodies, and discloses a lateral pressure applying device for a concrete-filled steel tube structural body and a lateral loading instability judgment method. The cloud data control center adopts an ICP algorithm to perform registration on the real-time point cloud and a pre-stored initial three-dimensional model to realize deformation quantitative analysis and surface defect evaluation, and outputs a strain anomaly index; the optical signals are demodulated in real time to generate a stress distribution matrix, the stress distribution matrix is uploaded to the cloud data control center, differential operation is performed on the stress distribution matrix and a preset reference stress field matrix, a stress offset matrix is generated, and an abnormity judgment threshold value is dynamically corrected; and coupling the strain anomaly index and the stress offset matrix, constructing an anomaly probability model, outputting three-dimensional coordinates of an abnormal region, and completing positioning of the local buckling instability anomaly of the structure. The method can effectively prevent local instability damage of the concrete-filled steel tube structure body.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of stress monitoring of concrete structures, and particularly relates to a steel pipe concrete structure lateral pressure applying device and a lateral loading instability judging method. BACKGROUND

[0002] The coal pillarless mining technology has a crucial influence on the development of coal, and the automatic cutting and pressure releasing and roadway forming technology is widely applied and causes a large amount of research because it can optimize the stress structure of surrounding rock, reduce the gas gathering of the working face and improve the economic benefits. After the implementation of the technology, the rock stratum in the cutting area collapses rapidly to form a gangue bank. With the fracture and rotation of the rock mass not cut by the roof, the vertical force in the vertical direction promotes the further crushing, swelling and compaction of the gangue bank, which causes the continuous increase of the internal horizontal extrusion force. This force will inevitably make the gangue bank swell and deform towards the free space of the roadway, and continuously act on the support structure beside the gangue roadway, and the pressure often appears with the deformation and damage of the support structure beside the roadway. In order to ensure the safety, economy and efficiency of the self-forming roadway surrounding rock control, it is necessary to develop a support and roof and bank structure with high strength, high stiffness and convenient construction to simultaneously bear the vertical load of the roof and the lateral load of the collapsed rock mass in the goaf. The steel pipe concrete structure, as a composite component with the core concrete constrained by the steel pipe, can effectively solve this demand. Under the constraint of the steel pipe, the core concrete is in a three-way compression state, which not only significantly improves the compression bearing capacity and crack resistance of the concrete, but also suppresses the local buckling of the steel pipe. This synergistic effect enables the CFST to fully utilize the performance advantages of the two materials, and has the characteristics of high bearing capacity and high stiffness. When applied to the support beside the roadway, the steel pipe concrete structure can not only effectively support the roof, but also resist the lateral pressure from the collapsed rock mass in the goaf. Therefore, in-depth research on the deformation behavior and control method of the structure under the action of lateral pressure has important theoretical and practical significance for promoting the progress of the coal pillarless roadway support technology.

[0003] At present, the determination of the resistance of the steel pipe concrete structure to lateral pressure mainly adopts the three-point bending determination method and the four-point bending determination method. However, in the actual application, the lateral extrusion load of the gangue presents a non-uniform distribution of "small on the top and large on the bottom", and at the same time bears the vertical load of the roof surrounding rock, which is easy to cause the local instability and damage of the steel pipe concrete structure. Moreover, the lateral pressure may cause the hoop stress of the steel pipe, which further affects the constraint effect on the core concrete. Higher lateral pressure may reduce the constraint effect of the steel pipe, thereby easily affecting the ultimate bearing capacity of the structure. SUMMARY

[0004] To overcome the problems existing in the related art, the disclosed embodiments of the present invention provide a lateral pressure device for concrete-filled steel tube structures and a method for determining lateral loading instability. The present invention aims to provide a lateral loading device for concrete-filled steel tube structures and an intelligent monitoring and instability determination method. The device features simple assembly and disassembly operations and can be used in combination with various types of testing machines to complete vertical and lateral loading tests. Through intelligent monitoring data system analysis, the device provides real-time location of concrete-filled steel tube structure anomalies, assists in optimizing concrete-filled steel tube structure parameters, further strengthens areas prone to instability in concrete-filled steel tube structures, and prevents localized instability and damage to concrete-filled steel tube structures.

[0005] The technical solution is as follows: A method for determining instability of a concrete-filled steel tube structure under lateral loading, comprising the following steps: S1 uses a 3D laser scanning device to scan the steel tube concrete structure in real time, collects point cloud data on the surface of the steel tube concrete structure, and uploads the point cloud data to the cloud data control center in real time through a wireless signal receiver transmitter. The cloud data control center uses the ICP algorithm to align the real-time point cloud with the pre-stored initial 3D model to achieve deformation quantitative analysis and surface defect assessment, and output the strain anomaly index. ; S2, a distributed optical fiber stress sensor array arranged in a dot matrix to monitor the points, and demodulates the optical signal in real time to generate a stress distribution matrix , upload the cloud data control center and transfer the stress distribution matrix With the preset reference stress field matrix Perform differential operations to generate stress offset matrix , dynamically correct the abnormality judgment threshold ; S3, coupled strain anomaly index and the stress offset matrix , build an abnormal probability model, use the local buckling instability algorithm of convolutional neural network CNN to output the three-dimensional coordinates of the abnormal area, and complete the local buckling instability anomaly positioning of the structure.

[0006] In step S1, the cloud data control center adopts a hierarchical data processing architecture. The original point cloud is pre-processed and then enters the core analysis module for processing, and the strain anomaly index is output. Among them, the core analysis module includes the original point cloud preprocessing and data extraction module, the ICP registration algorithm module and the strain anomaly index modeling module.

[0007] Furthermore, the point cloud preprocessing and data extraction module includes an outlier filtering stage, a data downsampling stage, and a feature enhancement stage; The outlier filtering stage adopts statistical filtering algorithm to set the field radius , is the standard deviation of point cloud density, and more than Point of range; The data downsampling stage is based on octree voxel filtering, and the voxel size is adaptively adjusted according to the structural characteristics. The expression is: ; Where, is the voxel size, is the maximum component size, The value of is 6-8; The feature enhancement stage extracts the FPFH feature description factor and samples the FPFH feature description factor, which is expressed as: ; Where, is the normal angle, is the projection angle, is the distance ratio, for point Fast point feature histogram of , For each point in the point cloud, is the neighborhood point set, is the total neighborhood points, is a weight function used to measure the point Point The impact of these factors is usually related to the distance between them.

[0008] Furthermore, the ICP registration algorithm module adopts a hierarchical ICP registration strategy for the steel tube concrete structure to obtain the displacement field of the registered point cloud. The hierarchical ICP registration strategy includes a coarse registration stage, a fine registration stage, and a deformation analysis stage. In the coarse registration stage, the RANSAC algorithm is used to establish the initial correspondence relationship and solve the rigid body deformation matrix: ; Where, is the rotation matrix, is the translation vector, are the corresponding points of the real-time point cloud and the initial model, is the rigid body transformation matrix, which represents the transformation relationship from the real-time point cloud to the initial model. The number of RANSAC algorithm iterations. The more iterations, the higher the probability of finding the optimal transformation matrix, but the calculation time will also increase. The number of iterations needs to be adjusted according to the noise level and the proportion of outliers in the point cloud; The fine registration stage optimizes the objective function by introducing an adaptive weight factor: ; Where, The weight is determined by the point cloud curvature similarity, is the objective function, which represents the sum of the weighted distances of all corresponding point pairs. is a regularization parameter used to balance the registration error and weight factor; The deformation analysis stage applies the above relationship to the original point cloud data to calculate the displacement field of the registered point cloud: ; Where, is the first point in the original point cloud The position coordinates of the points, is the displacement field, representing each point in space The displacement vector, The first point in the target point cloud after registration and the first point in the original point cloud The position coordinates corresponding to the points; The strain anomaly index modeling module is derived from the displacement field calculation after registration to define the multi-dimensional strain anomaly index. : ; Where, is the maximum local deformation, is the displacement gradient modulus, is the curvature change, are weight coefficients, is the characteristic length or scale of the local area, is the mean displacement gradient modulus.

[0009] In step S2, fiber optic stress sensors are installed in a 54mm×54mm matrix along the circumferential and longitudinal directions of the pier on the concrete-filled steel tube structure. Data is collected, analyzed, and uploaded to the cloud data control center. This includes a data analysis system and dynamic threshold correction. The data analysis system uses Hooke's law to realize optical fiber strain and stress The stress values ​​of discrete points are reconstructed into a matrix according to the spatial position, and the cylindrical coordinate system is established with the bottom surface of the pier as the reference surface. , so the matrix elements Indicates the location The stress value at , generate the stress distribution matrix for: ; Where, For radial stratification, It is a circular partition, with one zone every 10°; It is a height layer, one layer every 54mm; Uploading the stress distribution matrix to the cloud data control center and the preset reference stress field matrix to generate a stress offset matrix: ; The dynamic threshold correction dynamically adjusts the threshold value of the anomaly determination based on the statistical characteristics : ; wherein is the mean value, is the mean value of the change amount of the stress offset matrix, is an empirical coefficient.

[0010] In step S3, the coupling strain anomaly index and the stress offset matrix , the anomaly probability model includes: The strain anomaly index and linearly related, the anomaly probability model is: ; ; wherein is the anomaly probability value, is a Sigmoid function, and the output probability value; is the Frobenius norm, which quantifies the overall offset strength; the Sigmoid function maps the output to the [0, 1] interval, is a negative exponential function with the natural constant e as the base, which is used to convert the input x into a probability value; determination as an anomaly, triggering a local buckling instability positioning algorithm based on a convolutional neural network (CNN), outputting the three-dimensional coordinates of the anomaly area, and completing the structure local buckling instability anomaly positioning.

[0011] Another object of the present application is to provide a steel pipe concrete structure lateral pressure device, which implements the steel pipe concrete structure lateral loading instability judgment method, and the device comprises: An external frame module is used to fix and adjust the steel pipe concrete structure lateral load applying device. A lateral loading module is used to intelligently and real-timely regulate and control the lateral pressure. A monitoring anomaly positioning module is used to real-timely monitor the deformation characteristics of the steel pipe concrete structure during the lateral pressure process of the steel pipe concrete structure, collect data to construct a multi-source data fusion criterion, dynamically correct the anomaly determination threshold value, realize the structure anomaly positioning, and predict the local instability prone area of the steel pipe concrete structure.

[0012] Furthermore, the external frame module is provided with an external frame, which is a truss structure assembled by transverse connecting beams and vertical steel support columns, wherein a slide groove is welded on the inner side of the vertical steel support column, threaded holes are reserved on both sides of the slide groove, and the bottom and the fixed support are anchored to the base plate by anchor bolts; The lateral loading module is arranged on the inner side of the external frame and consists of an upper steel plate clamp and a lower steel plate clamp. The upper steel plate clamp and the lower steel plate clamp are symmetrically arranged and fixed at the threaded holes by nuts. A positioning hole is provided in the middle of the upper steel plate clamp and the lower steel plate clamp; a telescopic hydraulic rod is placed in the positioning hole, and an arc-shaped steel plate is welded to the top of the telescopic hydraulic rod, which is in contact with the steel tube concrete structure. A high-pressure oil pipe is left at the bottom of the telescopic hydraulic rod. A pressure gauge and an intelligent gate valve are installed on the high-pressure oil pipe, which is connected to a small hydraulic pump. The pressure value of each node on the intelligent gate valve is controlled by programming to simulate the lateral force distribution of the goaf. When the preset pressure value is reached, the intelligent gate valve automatically closes.

[0013] Furthermore, the monitoring anomaly location module includes: a monitoring data collection module and an anomaly location module; The monitoring data collection module uses a three-dimensional laser scanning device to scan the three-dimensional topography data of each monitoring area of ​​the steel tube concrete structure in real time, and transmits the data to the cloud data control center through a wireless signal transmitter. At the same time, the stress distribution is obtained in real time through the optical fiber stress sensing network, and the data is transmitted to the cloud data control center through a wireless signal transmitter. The abnormality positioning module stores and analyzes the data of the cloud data control center for prediction of local buckling instability of the structure. At the same time, the cloud data is wirelessly transmitted to the data imaging display platform to monitor the deformation of the steel tube concrete structure.

[0014] Furthermore, the intelligent gate valve controls the pressure value of each node through programming to simulate the lateral force distribution of the goaf, including: According to the user input or program preset target pressure value of each node, start the small hydraulic pump and start supplying pressure to the system. Real-time monitoring is carried out through the pressure gauge to obtain the pressure value of the current node to determine whether the current node pressure has reached the preset value. If not, continue to supply pressure. If so, close the smart gate valve of the node and check whether all nodes have reached the preset pressure. If there are still nodes that have not reached it, continue to monitor and control. If all nodes have reached the preset pressure, stop the small hydraulic pump and end the application.

[0015] In combination with all the above technical solutions, the beneficial effects of the present invention are as follows: Firstly, the device comprises an external frame module, a lateral loading module, and an abnormal positioning module. The external frame module comprises a fixed support, a vertical support column, a transverse connecting beam, a sliding groove, and a foundation bolt. The lateral loading module comprises a steel plate, an extension hydraulic rod, a high-pressure oil pipe, a pressure gauge, an intelligent gate valve, and a small hydraulic pump. The abnormal positioning module comprises a three-dimensional laser scanning device, an optical fiber pressure sensor, a wireless signal receiver-transmitter, and a data imaging display platform.

[0016] The lateral load applying device is composed of the external frame module and the lateral loading module. Precise simulation of non-uniform lateral load is achieved through intelligent valve control, and the dynamic change process is adapted by intelligent control mechanism. The monitoring module transmits signals to the cloud master system through the three-dimensional laser scanning device and the pressure sensor through the wireless signal receiver-transmitter. The abnormal positioning module judges the stability of the steel pipe concrete pier column by multi-source data fusion processing of the uploaded cloud database, locates the position of the local unstable pier column, and takes measures to reinforce and support the unstable part in time. The device is easy to assemble and disassemble, and can be combined with multiple types of experimental machines for axial and lateral double-direction combined loading test. The invention simulates the real lateral load distribution of the gob side waste rock by adjustable non-uniform pressure, obtains the research on the steel pipe concrete structure of the gob-side entry retaining under the action of real lateral load, and can effectively prevent the local instability and damage of the steel pipe concrete structure.

[0017] Secondly, the invention uses intelligent loading and cloud early warning analysis technology to enable researchers to deeply study the response mechanism, failure mode and instability criterion of steel pipe concrete under complex real lateral load, accelerate the research and development and verification process of new, efficient and economic gob-side entry retaining support technology, and promote the improvement of related test standards. The stability of the steel pipe concrete structure of the gob-side entry retaining provides an effective solution to the structure prone to instability in the fields of tunnel engineering and underground space development. The technology can be widely applied in tunnel support, mine roadway protection and underground building design optimization, improving the safety and service life of the structure, reducing disaster control cost, and having significant market promotion value. Especially in the field of coal mining, using concrete-filled steel pipe support can effectively improve the stability and safety of the roadway and reduce the occurrence of roof collapse accidents, meeting the needs of coal mine safety production. Considering the huge market demand for mine roadway support, the application prospect of the technology is broad. In addition, the technology can also be applied to the stability monitoring and reinforcement of bridges, high-rise buildings and other structures, further expanding its commercial value.

[0018] Third, the application can accurately identify the "instability position" of the steel pipe concrete pier column under a specific load in the laboratory in advance, providing effective data support for further improving the structural stability design, so that targeted reinforcement support can be carried out on these key parts in actual engineering, changing passive rescue to active prevention, greatly reducing the later roadway maintenance cost and engineering failure risk. Through intelligent lateral loading and cloud early warning analysis technology, the application effectively simulates the complex load environment and accurately locates the structure instability area, provides reliable early warning for the predictability of steel pipe concrete structure instability, is closer to actual engineering application, and provides a more reliable basis for the optimization selection design and maintenance of the steel pipe concrete structure. BRIEF DESCRIPTION OF DRAWINGS

[0019] The drawings incorporated into the specification and forming a part thereof show embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure; Figure 1 is a schematic diagram of a steel pipe concrete structure lateral pressure device provided by the embodiments of the application; Figure 2 is a sectional view of the steel pipe concrete structure lateral pressure device provided by the embodiments of the application; Figure 3 is an intelligent gate valve programming control flowchart of the steel pipe concrete structure lateral pressure device provided by the embodiments of the application; Figure 4 is a schematic diagram of the connection of the optical fiber pressure sensor of the steel pipe concrete structure lateral pressure device provided by the embodiments of the application; Figure 5 is a steel pipe concrete structure abnormal positioning algorithm flowchart provided by the embodiments of the application; In the figure: 1, external frame; 101, transverse connecting beam; 102, vertical steel support column; 103, fixed support; 104, anchor bolt; 105, sliding groove; 106, threaded hole; 2, pressure testing machine; 3, steel pipe concrete structure; 4, upper steel plate clamp; 5, lower steel plate clamp; 6, nut; 7, positioning hole; 8, telescopic hydraulic rod; 9, arc steel plate; 10, high-pressure oil pipe; 11, pressure gauge; 12, intelligent gate valve; 13, small hydraulic pump; 14, three-dimensional laser scanning device; 15, wireless signal receiver transmitter; 16, optical fiber stress sensor; 17, data imaging display platform. DETAILED DESCRIPTION

[0020] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] The innovation of the present invention is that the lateral pressure device can simulate the actual working conditions more realistically, provide more reliable mechanical performance data that is closer to the actual working conditions, and directly guide the optimization of the support structure design. The intelligent monitoring system can monitor the structural status in real time and issue early warnings, and the instability judgment method can more accurately evaluate the stability of the structure, thereby improving the safety and reliability of steel tube concrete structures in tunnel support. Compared with traditional monitoring and pressure schemes, this method has significant improvements in accuracy, intelligence, and practicality, providing a reference for engineering design and construction, and providing more reliable technical guarantees for the application of steel tube concrete structures in coal mine tunnel support, effectively preventing and controlling tunnel instability and damage, and improving the safety production level of coal mines.

[0022] Example 1, as Figures 1-4 As shown, an embodiment of the present invention provides a lateral pressure device for a steel tube concrete structure, which specifically includes an external frame module, a lateral loading module, and an abnormality monitoring and positioning module; The external frame module is used to fix and adjust the lateral load application device of the steel tube concrete structure; The side loading module is used to intelligently regulate the side pressure in real time; The monitoring and abnormality positioning module is used to monitor the deformation characteristics of the steel tube concrete structure in real time during the lateral compression process of the steel tube concrete structure, collect data to construct a multi-source data fusion judgment criterion, dynamically correct the abnormality judgment threshold, realize structural abnormality positioning, and predict the local unstable area of ​​the steel tube concrete structure; Exemplarily, the external frame module is provided with an external frame 1, which is a truss structure assembled by a transverse connecting beam 101 and a vertical steel support column 102. A slide groove 105 is welded on the inner side of the vertical steel support column 102, and threaded holes 106 are reserved on both sides of the slide groove 105. The bottom and the fixed support 103 are anchored to the base plate through anchor bolts 104; The lateral loading module is arranged inside the outer frame 1 and is composed of an upper steel plate clamp 4 and a lower steel plate clamp 5, the upper steel plate clamp 4 and the lower steel plate clamp 5 are symmetrically arranged and are fixed at threaded holes 106 by nuts 6 to realize multi-interval and multi-stage positioning, and positioning holes 7 are arranged in the middle of the upper steel plate clamp 4 and the lower steel plate clamp 5; a telescopic hydraulic rod 8 is arranged in the positioning hole 7, an arc-shaped steel plate 9 is welded at the top end of the telescopic hydraulic rod 8 and is in contact with the steel pipe concrete structure, a high-pressure oil pipe 10 is arranged at the bottom of the telescopic hydraulic rod 8, a pressure gauge 11 and an intelligent gate valve 12 are arranged on the high-pressure oil pipe 10, a small-sized hydraulic pump 13 is connected, the intelligent gate valve 12 is programmed to control the pressure value of each node, the lateral stress distribution of the goaf can be accurately simulated, and the intelligent gate valve is automatically closed when the preset pressure value is reached.

[0023] As Figure 3 The intelligent gate valve programming control flowchart of the steel pipe concrete structure lateral pressure applying device is provided by the embodiment of the application, the intelligent gate valve is programmed to control the pressure value of each node, and is used for simulating the lateral stress distribution of the goaf, and comprises the following steps. According to the target pressure value of each node input by a user or preset by a program, the small-sized hydraulic pump is started to supply pressure to the system, the pressure value of the current node is monitored in real time through the pressure gauge, it is judged whether the pressure of the current node reaches the preset value, if not, the pressure supply is continued, if yes, the intelligent gate valve of the node is closed, it is checked whether all nodes reach the preset pressure, if not, the monitoring and control are continued, and if yes, the small-sized hydraulic pump is stopped to end the application.

[0024] According to the target pressure value of each node input by a user or preset by a program, the small-sized hydraulic pump 13 is started to supply pressure to the system, the pressure value of the current node is monitored in real time through the pressure gauge 11, it is judged whether the pressure of the current node reaches the preset value, if not, the pressure supply is continued (the intelligent gate valve 12 is kept open), if yes, the intelligent gate valve 12 of the node is closed, it is checked whether all nodes reach the preset pressure, if not, the monitoring and control are continued, and if yes, the small-sized hydraulic pump 13 is stopped to end the application.

[0025] The monitoring and abnormity positioning module comprises a monitoring data collection module and an abnormity positioning module. The monitoring data collection module scans the three-dimensional appearance data of each monitoring area of the steel pipe concrete structure 3 in real time through a three-dimensional laser scanning device 14, and uploads the data to a cloud data control center through a wireless signal receiver 15; meanwhile, the stress distribution is obtained in real time through a fiber stress sensor 16 network, and the data is transmitted to the cloud data control center through the wireless signal receiver 15; The abnormal positioning module analyzes the cloud data control center data storage, realizes the prediction of local buckling instability of the structure, and simultaneously transmits the data to the data imaging display platform 17 through wireless transmission to monitor the deformation of the concrete-filled steel tubular structure in the cloud data.

[0026] Illustratively, all the data monitored by the concrete-filled steel tubular structure 3 are uploaded to the cloud data control center for multi-source data fusion processing, the data abnormal instability region of the concrete-filled steel tubular pier is identified and positioned, and subsequent processing is performed, an abnormal positioning model of the concrete-filled steel tubular structure is constructed, and reinforcement processing is performed in advance for the local instability region.

[0027] In embodiment 2, the method for judging the lateral loading instability of the concrete-filled steel tubular structure (the abnormal positioning method for monitoring the lateral loading of the concrete-filled steel tubular structure) provided by the present application comprises the following steps: S1, using a three-dimensional laser scanning device 14 to scan in real time the key monitoring area arranged on the concrete-filled steel tubular structure 3, collecting point cloud data on the surface of the key monitoring area of the concrete-filled steel tubular structure, and uploading the point cloud data to the cloud data control center in real time through a wireless signal receiver transmitter 15, the cloud data control center uses an ICP algorithm to register the real-time point cloud with a pre-stored initial three-dimensional model, realizes quantitative analysis of deformation and evaluation of surface defects, and outputs a strain abnormality index ; Specifically, the three-dimensional laser scanning device is used to scan the key monitoring area (upper, middle, and lower) arranged on the concrete-filled steel tubular structure in real time, the surface point cloud data is collected at a frequency of ≥10 Hz, the point cloud data is uploaded to the cloud data control center in real time through a wireless signal receiver transmitter, the cloud uses an ICP algorithm to register the real-time point cloud with a pre-stored initial three-dimensional model, realizes quantitative analysis of deformation and evaluation of surface defects, and outputs a strain abnormality index I d ; S2, the distributed optical fiber stress sensor array in a dot matrix arrangement monitors the distribution of points, and generates a stress distribution matrix in real time , uploads the cloud data control center, and performs difference operation on the stress distribution matrix and a preset reference stress field matrix to generate a stress offset matrix , and dynamically corrects the abnormality judgment threshold ; S3, coupling the strain abnormality index and the stress offset matrix , constructing an abnormal probability model, using a local curve instability positioning algorithm of a convolutional neural network CNN, outputting three-dimensional coordinates of an abnormal region, and completing the abnormal positioning of local buckling instability of the structure.

[0028] Illustratively, assuming that the strain abnormality index and ΔS Linear correlation, the abnormal probability model is: ; ; Where, is the abnormal probability value, It is a Sigmoid function that outputs probability value; is the Frobenius norm, which quantifies the overall offset strength; the Sigmoid function maps the output to the [0,1] interval, It is a negative exponential function with the natural constant e as the base, which is used to convert the input x into a probability value; If it is determined to be abnormal, the local buckling instability positioning algorithm based on the convolutional neural network (CNN) is triggered, and the three-dimensional coordinates of the abnormal area are output to complete the positioning of the local buckling instability anomaly of the structure. The fusion of point cloud and stress data can improve the accuracy of anomaly detection.

[0029] For example, step S1 specifically includes: arranging a 3D laser scanning device 14 on the key parts of the steel tube concrete structure 3, collecting point cloud data on the surface of the key monitoring area of ​​the steel tube concrete structure, and uploading the point cloud data to the cloud data control center in real time through a wireless signal receiving transmitter. The cloud data control center adopts a layered data processing architecture, and the original point cloud enters the core analysis module after preprocessing for processing, and outputs the strain anomaly index. ; Mainly includes original point cloud preprocessing and data extraction module, ICP registration algorithm module and strain anomaly index modeling module; The point cloud preprocessing and data extraction module includes an outlier filtering stage, a data downsampling stage, and a feature enhancement stage; The outlier filtering stage adopts statistical filtering algorithm to set the field radius ( is the point cloud density standard deviation), and remove those exceeding The data downsampling stage is based on octree voxel filtering, and the voxel size is adaptively adjusted according to the structural characteristics: ; Where, is the voxel size, is the maximum component size, The value of is 6-8; The feature enhancement stage extracts the FPFH feature description factor and samples the FPFH feature description factor, which is expressed as: ; Where, is the normal angle, is the projection angle, is the distance ratio, for point Fast point feature histogram of , For each point in the point cloud, is the neighborhood point set, is the total neighborhood points, is a weight function used to measure the point Point The impact of these factors is usually related to the distance between them.

[0030] The ICP registration algorithm module adopts a hierarchical ICP registration strategy based on the characteristics of steel tube concrete structure, which mainly includes a coarse registration stage, a fine registration stage and a deformation analysis stage; In the coarse registration stage, the RANSAC algorithm is used to establish the initial correspondence relationship and solve the rigid body deformation matrix: ; Where, is the rotation matrix, is the translation vector, are the corresponding points of the real-time point cloud and the initial model, is the rigid body transformation matrix, which represents the transformation relationship from the real-time point cloud to the initial model. The number of RANSAC algorithm iterations. The more iterations, the higher the probability of finding the optimal transformation matrix, but the calculation time will also increase. The number of iterations needs to be adjusted according to the noise level and the proportion of outliers in the point cloud; The fine registration stage optimizes the objective function by introducing an adaptive weight factor: ; Where, The weight is determined by the point cloud curvature similarity, is the objective function, which represents the sum of the weighted distances of all corresponding point pairs. is a regularization parameter used to balance the registration error and weight factor; The deformation analysis stage applies the above relationship to the original point cloud data to calculate the displacement field of the registered point cloud: ; Where, is the first point in the original point cloud The position coordinates of the points, is the displacement field, representing each point in space The displacement vector, The first point in the target point cloud after registration and the first point in the original point cloud The position coordinates corresponding to the points; The strain anomaly index modeling module is derived from the displacement field after registration to calculate the multi-dimensional strain anomaly index : ; In the formula, is the maximum local deformation, is the displacement gradient modulus, is the curvature change, all are weight coefficients, which are calibrated according to experiments; is the characteristic length or the scale of the local area, is the average displacement gradient modulus.

[0031] Exemplarily, in step S2, the optical fiber stress sensors are installed on the steel pipe concrete structure along the ring direction and the longitudinal direction of the pier column with a 54mm*54mm lattice, data is collected, and the cloud data control center is uploaded. Mainly includes information collection system, data analysis system and dynamic threshold correction.

[0032] The data analysis system realizes the conversion of optical fiber strain and stress by Hooke's law, reconstructs the stress value of discrete points into a matrix according to the spatial position, takes the bottom surface of the pier column as the reference surface, establishes the column coordinate system Therefore, the matrix element represents the stress value at the position , and the stress distribution matrix is generated as follows: ; In the formula, is the radial layering, is the ring partition, one zone every 10°; is the height layer, one layer every 54mm; The cloud data control center uploads the stress distribution matrix and the preset reference stress field matrix to perform difference operation to generate the stress offset matrix: ; The dynamic threshold correction dynamically adjusts the threshold value of the abnormality determination based on the statistical characteristics : ; In the formula, is the mean value, is the mean value of the change of the stress offset matrix, is an empirical coefficient.

[0033] ​In an example, in step S3, the partial buckling instability anomaly positioning is realized, and the three-dimensional coordinates of the abnormal area are output. With Linear correlation, and the anomaly probability model is: ; ; In the formula, is the anomaly probability value, is a Sigmoid function, and the output probability value is output; is the Frobenius norm, which quantifies the overall offset strength; the Sigmoid function maps the output to the interval [0, 1], is a negative exponential function with a natural constant as the base, which is used to convert the input into a probability value; Determination of the abnormality triggers the local buckling instability positioning algorithm based on the convolutional neural network (CNN), and the three-dimensional coordinates of the abnormal area are output, thereby completing the local buckling instability anomaly positioning of the structure, and the fusion of the point cloud and the stress data can improve the accuracy of the anomaly detection.

[0034] In step S3, the vulnerable area is reinforced and supported in advance according to the anomaly positioning model. In step S3, the data is transmitted to the cloud data control center, the cloud service platform database is constructed through analysis and summarization, and the reinforcement scheme such as “local hoop reinforcement” or “grouting enhancement” is generated through wireless network transmission to the imaging display platform. The database is applied to the scene to provide reliable early warning for the abnormal instability of the on-site roadside steel pipe concrete structure body support, and to ensure the safety of the on-site construction and the stability of the support.

[0035] In another example, Figure 5 is the anomaly positioning algorithm flowchart of the steel pipe concrete structure provided by the embodiment of the application.

[0036] In embodiment 3, a test method of a steel pipe concrete structure lateral pressure device is provided, the method is operated by the steel pipe concrete structure lateral pressure device, and the method comprises the following steps: Step 1, assemble the lateral loading test device, which comprises an external frame module, a lateral loading module and a monitoring anomaly positioning module. According to the overall size of the existing pressure testing machine in the laboratory and the size of the test piece, the device is fixed and adjusted to realize vertical and lateral combined loading.

[0037] Step two, make the steel pipe concrete structure 3 sample, the sample size (diameter D x wall thickness t x height L) 108 mm x 5 mm x 1200 mm, first weld the steel plate to the bottom of the steel pipe, pay attention to ensure geometric alignment when welding, then pour the prepared core concrete into the opening of the steel pipe, use the concrete with the design strength C40, use the vibrating rod to vibrate the inside of the steel pipe when grouting, and shake the sample vigorously to ensure that the concrete in the steel pipe can be uniformly dense.After the sample is grouted, it is cured with the concrete block for 28 days, and water is constantly sprayed on the surface of the concrete to ensure a certain humidity during the curing process, so that the concrete reaches the preset strength.After curing, use high-strength mortar to fill the concrete surface below the steel pipe surface, and polish the concrete surface above the steel pipe surface to the same level as the steel pipe, so that both can be stressed simultaneously during loading.

[0038] Step three, place the sample into the pressure testing machine and fix it, install the lateral loading device on one side of the sample, and install the optical fiber stress sensor 16 along the ring and longitudinal direction of the pier column with a 54 mm x 54 mm dot matrix; Step four, divide the surface of the other side of the sample into three monitoring areas according to the upper, middle and lower, and deploy one three-dimensional laser scanning device 14 in each area, with the three-dimensional laser scanning device 14 being 0.5-1 m away from the surface of the pier column and the elevation angle being ≤30° to avoid point cloud distortion; Step five, start the pressure testing machine and the lateral loading device to load, record the load, displacement, strain and other conditions of the steel pipe concrete structure sample during loading, and upload the corresponding monitoring data to the cloud data control center.

[0039] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any modification, equivalent replacement and improvement made by those skilled in the art within the technical range disclosed by the present application, as long as it is within the spirit and principles of the present application, should be covered within the protection scope of the present application.

Claims

1. A method for judging the instability of a concrete-filled steel tube structure under lateral loading, characterized in that: The method comprises the following steps: S1 uses a 3D laser scanning device to scan the steel tube concrete structure in real time, collects point cloud data on the surface of the steel tube concrete structure, and uploads the point cloud data to the cloud data control center in real time through a wireless signal receiver transmitter. The cloud data control center uses the ICP algorithm to align the real-time point cloud with the pre-stored initial 3D model to achieve deformation quantitative analysis and surface defect assessment, and output the strain anomaly index. ; S2, a distributed optical fiber stress sensor array arranged in a dot matrix to monitor the points, and demodulates the optical signal in real time to generate a stress distribution matrix , upload the cloud data control center and transfer the stress distribution matrix With the preset reference stress field matrix Perform differential operations to generate stress offset matrix , dynamically correct the abnormality judgment threshold ; S3, coupled strain anomaly index and the stress offset matrix , build an abnormal probability model, use the local buckling instability algorithm of convolutional neural network CNN to output the three-dimensional coordinates of the abnormal area, and complete the local buckling instability anomaly positioning of the structure.

2. The method for determining instability of a concrete-filled steel tube structure under lateral loading according to claim 1, characterized in that: In step S1, the cloud data control center adopts a hierarchical data processing architecture. The original point cloud is pre-processed and then enters the core analysis module for processing, and the strain anomaly index is output. Among them, the core analysis module includes the original point cloud preprocessing and data extraction module, the ICP registration algorithm module and the strain anomaly index modeling module.

3. The method for determining instability of a concrete-filled steel tube structure under lateral loading according to claim 2, characterized in that: The point cloud preprocessing and data extraction module includes an outlier filtering stage, a data downsampling stage, and a feature enhancement stage; The outlier filtering stage adopts statistical filtering algorithm to set the field radius , is the standard deviation of point cloud density, and more than Point of range; The data downsampling stage is based on octree voxel filtering, and the voxel size is adaptively adjusted according to the structural characteristics. The expression is: ； Where, is the voxel size, is the maximum component size, The value of is 6-8; The feature enhancement stage extracts the FPFH feature description factor and samples the FPFH feature description factor, which is expressed as: ； Where, is the normal angle, is the projection angle, is the distance ratio, for point Fast point feature histogram of , For each point in the point cloud, is the neighborhood point set, is the total neighborhood points, is a weight function used to measure the point Point impact.

4. The method for determining instability of a concrete-filled steel tube structure under lateral loading according to claim 2, characterized in that: The ICP registration algorithm module adopts a hierarchical ICP registration strategy for the steel tube concrete structure to obtain the displacement field of the registered point cloud. The hierarchical ICP registration strategy includes a coarse registration stage, a fine registration stage and a deformation analysis stage. In the coarse registration stage, the RANSAC algorithm is used to establish the initial correspondence relationship and solve the rigid body deformation matrix: ； Where, is the rotation matrix, is the translation vector, are the corresponding points of the real-time point cloud and the initial model, is the rigid body transformation matrix, which represents the transformation relationship from the real-time point cloud to the initial model. The number of iterations of the RANSAC algorithm; In the fine registration stage, the objective function is optimized by introducing an adaptive weight factor: ； Where, The weight is determined by the point cloud curvature similarity, is the objective function, is the regularization parameter; In the deformation analysis stage, the above relationship is applied to the original point cloud data to calculate the displacement field of the registered point cloud: ； Where, is the first point in the original point cloud The position coordinates of the points, is the displacement field, representing each point in space The displacement vector, The first point in the target point cloud after registration and the first point in the original point cloud The position coordinates corresponding to the points; The strain anomaly index modeling module is derived from the displacement field after registration and calculates and defines the multi-dimensional strain anomaly index. : ； Where, is the maximum local deformation, is the displacement gradient modulus, is the curvature change, are weight coefficients, is the characteristic length or scale of the local area, is the mean displacement gradient modulus.

5. The method for determining instability of a concrete-filled steel tube structure under lateral loading according to claim 1, characterized in that: In step S2, fiber optic stress sensors are installed in a 54mm×54mm matrix along the circumferential and longitudinal directions of the pier on the steel tube concrete structure, and data is collected, analyzed, and uploaded to the cloud data control center; Including data analysis system and dynamic threshold correction; The data analysis system uses Hooke's law to realize optical fiber strain and stress The stress values ​​of discrete points are reconstructed into a matrix according to the spatial position, and the cylindrical coordinate system is established with the bottom surface of the pier as the reference surface. , so the matrix elements Indicates the location Stress value at , generate the stress distribution matrix for: ； Where, For radial stratification, It is a circular partition, with one zone every 10°; It is a height layer, one layer every 54mm; Upload the stress distribution matrix to the cloud data control center With the preset reference stress field matrix Perform a difference operation to generate the stress offset matrix: ； The dynamic threshold correction is based on Dynamically adjust the threshold value of abnormal judgment based on the statistical characteristics of : ； Where, is the mean, is the mean value of the variation of the stress offset matrix, is the empirical coefficient.

6. The method for determining instability of a concrete-filled steel tube structure under lateral loading according to claim 1, characterized in that: In step S3, the coupled strain anomaly index and the stress offset matrix , building an abnormal probability model includes: Strain anomaly index and Linear correlation, the abnormal probability model is: ； ； Where, is the abnormal probability value, It is a Sigmoid function that outputs probability value; is the Frobenius norm, which quantifies the overall offset strength; the Sigmoid function maps the output to the [0,1] interval, The natural constant A negative exponential function with base , which is used to convert the input Convert to probability value; If it is determined to be abnormal, the local buckling instability positioning algorithm based on the convolutional neural network (CNN) is triggered, and the three-dimensional coordinates of the abnormal area are output to complete the positioning of the local buckling instability anomaly of the structure.

7. A lateral pressure device for a steel tube concrete structure, characterized in that: The device implements the method for determining instability of a concrete-filled steel tube structure under lateral loading according to any one of claims 1 to 6, and comprises: External frame module, used to fix and adjust the lateral load application device of the steel tube concrete structure; Side loading module for intelligent real-time control of lateral pressure; The abnormality monitoring and positioning module is used to monitor the deformation characteristics of the steel tube concrete structure in real time during the lateral compression process of the steel tube concrete structure, collect data to construct multi-source data fusion judgment criteria, dynamically correct the abnormality judgment threshold, realize structural abnormality positioning, and predict the local unstable area of ​​the steel tube concrete structure.

8. The lateral pressure device for a concrete-filled steel tube structure according to claim 7, characterized in that: The external frame module is provided with an external frame (1), the external frame (1) is a truss structure assembled by a transverse connecting beam (101) and a vertical steel support column (102), a slide groove (105) is welded on the inner side of the vertical steel support column (102), threaded holes (106) are reserved on both sides of the slide groove (105), and the bottom and the fixed support (103) are anchored to the base plate through anchor bolts (104); The lateral loading module is arranged on the inner side of the external frame (1) and is composed of an upper steel plate clamp (4) and a lower steel plate clamp (5). The upper steel plate clamp (4) and the lower steel plate clamp (5) are symmetrically arranged and fixed to the threaded hole (106) by nuts (6). A positioning hole (7) is provided in the middle of the upper steel plate clamp (4) and the lower steel plate clamp (5); a telescopic hydraulic rod (8) is placed in the positioning hole (7), and an arc-shaped steel plate (9) is welded to the top of the telescopic hydraulic rod (8) to fit in contact with the steel tube concrete structure. A high-pressure oil pipe (10) is left at the bottom of the telescopic hydraulic rod (8), and a pressure gauge (11) and an intelligent gate valve (12) are installed on the high-pressure oil pipe (10), which is connected to a small hydraulic pump (13). The pressure value of each node on the intelligent gate valve (12) is controlled by programming to simulate the lateral force distribution of the goaf. When the preset pressure value is reached, the intelligent gate valve (12) automatically closes.

9. The lateral pressure device for a concrete-filled steel tube structure according to claim 7, characterized in that: The monitoring anomaly location module includes: a monitoring data collection module and an anomaly location module; The monitoring data collection module scans the three-dimensional topography data of each monitoring area of ​​the steel tube concrete structure (3) in real time through a three-dimensional laser scanning device (14), and uploads the data to the cloud data control center through a wireless signal receiving transmitter (15); at the same time, the stress distribution is obtained in real time through the optical fiber stress sensing (16) network, and the data is transmitted to the cloud data control center through the wireless signal receiving transmitter (15); The abnormality positioning module stores and analyzes the data of the cloud data control center for prediction of local buckling instability of the structure, and simultaneously transmits the data to the data imaging display platform (17) via wireless in the cloud data to monitor the deformation of the steel tube concrete structure.

10. The lateral pressure device for a concrete-filled steel tube structure according to claim 8, characterized in that: The intelligent gate valve (12) controls the pressure value of each node through programming to simulate the lateral force distribution of the goaf, including: According to the target pressure value of each node input by the user or preset by the program, the small hydraulic pump (13) is started to supply pressure to the system, and the pressure value of the current node is obtained through the pressure gauge (11) in real time monitoring to determine whether the pressure of the current node has reached the preset value. If not, the pressure supply is continued. If it is reached, the intelligent gate valve (12) of the node is closed to check whether all nodes have reached the preset pressure. If there are still nodes that have not reached the preset pressure, the monitoring and control are continued. If all nodes have reached the preset pressure, the small hydraulic pump (13) is stopped and the application is ended.

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