A formwork support structure risk assessment method, device, equipment and storage medium

Abstract

The present application relates to the technical field of building construction, and discloses a formwork support structure risk assessment method, device, equipment and storage medium. The method divides the formwork support structure into a plurality of formwork support unit structures; selects a plurality of to-be-tested formwork support unit structures from the plurality of formwork support unit structures according to preset requirements; obtains the vertical rod axial force and horizontal displacement of the plurality of to-be-tested formwork support unit structures to form monitoring data; and based on the monitoring data of the plurality of to-be-tested formwork support unit structures, uses a first risk model to perform risk assessment to obtain an overall risk assessment result of the formwork support structure. The present application realizes rapid identification of a failed formwork support structure and the safety state of the overall structure only according to limited monitoring data, and improves the efficiency and accuracy of risk assessment by using artificial neural network technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building construction, in particular to a formwork support structure risk assessment method, device, equipment and storage medium. BACKGROUND

[0002] The existing formwork support monitoring method can accurately collect the stress and deformation of the monitored component, but it is difficult to perceive the failure of other components outside the monitored component, and it is also impossible to determine whether the remaining frame will collapse after the failure of the non-monitored component. The traditional formwork support safety assessment is mainly a mechanical analysis of the local substructure, and lacks a safety assessment method based on digital driving. For formwork support monitoring, in the case of a large frame volume and numerous members, it is impossible to quickly use massive monitoring data to feedback the safety state of the frame during monitoring by relying only on traditional calculation methods such as finite element simulation. SUMMARY

[0003] The present application provides a formwork support structure risk assessment method, device, equipment and storage medium, which realizes rapid identification of failed formwork support structure and the safety state of the overall structure according to limited monitoring data, and improves the efficiency and accuracy of risk assessment by using artificial neural network technology.

[0004] To solve the above technical problems, the present application provides a formwork support structure risk assessment method, comprising:

[0005] The formwork support structure is divided into a plurality of formwork support unit structures;

[0006] According to the preset requirements, a plurality of to-be-tested formwork support unit structures are selected from the plurality of formwork support unit structures;

[0007] Obtain monitoring data of the plurality of to-be-tested formwork support unit structures; wherein the monitoring data includes vertical rod axial force and horizontal displacement;

[0008] Based on the monitoring data of the plurality of to-be-tested formwork support unit structures, a first risk model is used for risk assessment to obtain the overall risk assessment result of the formwork support structure.

[0009] Further, the plurality of to-be-tested formwork support unit structures are selected from the plurality of formwork support unit structures according to the preset requirements, specifically:

[0010] The area and danger level of the construction area are obtained by analyzing the preset requirements;

[0011] Based on the area and danger level of the construction area, a plurality of to-be-tested formwork support unit structures are selected from the plurality of formwork support unit structures.

[0012] Furthermore, the monitoring data of the plurality of template support unit structures to be tested are obtained, specifically:

[0013] Determining the position data of the vertical poles to be measured in each of the template support unit structures to be measured;

[0014] Based on the position data of each of the poles to be measured, using a force sensor to respectively monitor the pole axial force of each of the poles to be measured;

[0015] Based on the position data of each of the vertical poles to be measured, using a horizontal displacement meter to respectively monitor the horizontal X-direction displacement and the horizontal Y-direction displacement of each of the vertical poles to be measured;

[0016] Determining the horizontal displacement of each of the uprights to be measured based on the horizontal X-direction displacement and the horizontal Y-direction displacement of each of the uprights to be measured;

[0017] The axial force and horizontal displacement of each of the vertical poles to be tested are determined as monitoring data of each of the formwork support unit structures to be tested.

[0018] Furthermore, based on the monitoring data of the plurality of template support unit structures to be tested, a risk assessment is performed using a first risk model to obtain an overall risk assessment result of the template support structure, specifically:

[0019] Acquire the dimension data of the formwork support structure; wherein the dimension data includes: the longitudinal dimension of the formwork support structure, the transverse dimension of the formwork support structure and the height dimension of the formwork support structure;

[0020] Determining the monitoring data and the dimension data of each of the template support unit structures to be tested as input data of a first risk model;

[0021] Controlling the first risk model to perform risk assessment based on the input data, determining position information of a failed formwork support unit, and determining a risk level based on the position information of the failed formwork support unit;

[0022] The position information of the failed formwork support unit and the risk level are determined as the overall risk assessment result of the formwork support structure.

[0023] Furthermore, the first risk model is specifically:

[0024] Based on the template support model, a template support data set is obtained;

[0025] Establish a neural network model based on BP algorithm;

[0026] Using the template support data set to train the neural network model, and establish an input-output mapping relationship of the neural network model;

[0027] The trained neural network model is determined as the first risk model.

[0028] Furthermore, the template support data set is obtained based on the template support model, specifically:

[0029] A template support model is established on a finite element analysis platform based on preset template support structure data; wherein the template support model includes a plurality of simulated vertical poles;

[0030] Performing static analysis on the formwork support model to obtain working condition data without failure of upright poles;

[0031] Perform static analysis on the formwork support model with a single simulated vertical pole removed to obtain several single vertical pole failure condition data;

[0032] Performing static analysis on the formwork support model with the multi-simulated pole combination removed to obtain several multi-pole failure condition data; wherein the multi-simulated pole combination includes two or more simulated poles;

[0033] Determine the no-pole failure working condition data, the single-pole failure working condition data, and the multiple-pole failure working condition data as sample data;

[0034] The simulated size data of the template support model is acquired, and the simulated size data and the sample data are determined as a template support data set.

[0035] Furthermore, after obtaining the overall risk assessment result of the template support structure, the method further includes:

[0036] When the risk level in the overall risk assessment result is the first risk level, determining that the formwork support structure has an overall collapse risk;

[0037] When the risk level in the overall risk assessment result is the second risk level, determining that the formwork support structure has a risk of partial collapse;

[0038] When the risk level in the overall risk assessment result is the third risk level, it is determined that the formwork support structure has a potential local collapse risk;

[0039] When the risk level in the overall risk assessment result is the fourth risk level, it is determined that the formwork support structure is safe.

[0040] Accordingly, the present invention provides a template support structure risk assessment device, comprising: a structure division module, a structure selection module, a data acquisition module and a risk assessment module;

[0041] The structure division module is used to divide the template support structure into a plurality of template support unit structures;

[0042] The structure selection module is used to select a plurality of template support unit structures to be tested from a plurality of template support unit structures according to preset requirements;

[0043] The data acquisition module is used to obtain monitoring data of a plurality of the template support unit structures to be tested; wherein the monitoring data includes the axial force and horizontal displacement of the vertical pole;

[0044] The risk assessment module is used to perform risk assessment based on the monitoring data of several template support unit structures to be tested using a first risk model to obtain an overall risk assessment result of the template support structure.

[0045] The present invention also provides a template support structure risk assessment device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the template support structure risk assessment method as described above when executing the computer program.

[0046] The present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the template support structure risk assessment method as described above.

[0047] The present invention provides a formwork support structure risk assessment method, device, equipment, and storage medium. The method divides the formwork support structure into a plurality of formwork support unit structures; selects a plurality of formwork support unit structures to be tested from the plurality of formwork support unit structures according to preset requirements; obtains the vertical axial force and horizontal displacement of the plurality of formwork support unit structures to be tested to form monitoring data; and performs a risk assessment based on the monitoring data of the plurality of formwork support unit structures to be tested using a first risk model to obtain an overall risk assessment result for the formwork support structure. The present invention selects only a few monitoring components and can perceive and predict the failure conditions of other components outside the monitoring components through the high-speed calculation of the BP neural network model, thereby assessing the safety status of the entire structure, thereby improving the efficiency and accuracy of risk assessment and reducing assessment costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic flow chart of an embodiment of a method for risk assessment of a template support structure provided by the present invention;

[0049] Figure 2 A schematic structural diagram of an embodiment of the template support structure provided by the present invention;

[0050] Figure 3 A schematic structural diagram of an embodiment of a pole to be tested provided by the present invention;

[0051] Figure 4 A schematic structural diagram of an embodiment of the first risk model provided by the present invention;

[0052] Figure 5 A schematic diagram of the retention monitoring element provided by the present invention;

[0053] Figure 6 This is a structural schematic diagram of an embodiment of the template support structure risk assessment device provided by the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0056] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0057] Example 1

[0058] See also Figure 1 , is a flow chart of an embodiment of a method for risk assessment of a template support structure provided by the present invention, the method includes steps 101 to 104, each step is specifically as follows:

[0059] Step 101: Divide the formwork support structure into a plurality of formwork support unit structures.

[0060] In the first embodiment of the present invention, the formwork support structure is divided into a plurality of formwork support unit structures according to a preset horizontal area threshold, wherein the horizontal area of ​​each divided formwork support unit structure does not exceed the preset horizontal area threshold.

[0061] As an example of the first embodiment of the present invention, see Figure 2 , is a structural diagram of an embodiment of the template support structure provided by the present invention. The template support structure is divided into n template support unit structures, and each template support unit structure selects a component as a monitoring object. Figure 2For example, the formwork support structure can be divided into 9 formwork support unit structures, and a vertical pole in each formwork support unit structure is selected as a monitoring object and numbered.

[0062] Step 102: Selecting a plurality of template support unit structures to be tested from the plurality of template support unit structures according to preset requirements.

[0063] Furthermore, in the first embodiment of the present invention, a plurality of template support unit structures to be tested are selected from a plurality of template support unit structures according to preset requirements, specifically:

[0064] Obtain the area and danger level of the construction area by analyzing preset requirements;

[0065] Based on the area and danger level of the construction area, several formwork support unit structures to be tested are selected from the several formwork support unit structures.

[0066] In the first embodiment of the present invention, after the formwork support structure is divided into a plurality of formwork support unit structures, a plurality of formwork support unit structures to be tested can be selected from the plurality of formwork support unit structures as test objects based on the size and risk level of the construction area. For optimal results, formwork support unit structures located at the corners, edges, and center of the formwork support structure can be selected as test objects.

[0067] Step 103: Acquire monitoring data of a plurality of the formwork support unit structures to be tested; wherein the monitoring data includes the axial force and horizontal displacement of the vertical pole.

[0068] Furthermore, in the first embodiment of the present invention, the monitoring data of a plurality of the template support unit structures to be tested are obtained, specifically:

[0069] Determining the position data of the vertical poles to be measured in each of the template support unit structures to be measured;

[0070] Based on the position data of each of the poles to be measured, using a force sensor to respectively monitor the pole axial force of each of the poles to be measured;

[0071] Based on the position data of each of the vertical poles to be measured, using a horizontal displacement meter to respectively monitor the horizontal X-direction displacement and the horizontal Y-direction displacement of each of the vertical poles to be measured;

[0072] Determining the horizontal displacement of each of the uprights to be measured based on the horizontal X-direction displacement and the horizontal Y-direction displacement of each of the uprights to be measured;

[0073] The axial force and horizontal displacement of each of the vertical poles to be tested are determined as monitoring data of each of the formwork support unit structures to be tested.

[0074] In the first embodiment of the present invention, pole axial force and horizontal displacement are determined as monitoring parameters. Horizontal displacement includes horizontal X- and Y-axis displacement. During monitoring, a force sensor can be used as a pole axial force monitoring device, and a horizontal displacement meter can be used as a horizontal displacement monitoring device to obtain monitoring data on the pole axial force and horizontal displacement of each pole to be tested. Once the monitoring data is collected, it is transmitted in real time to a pre-trained first risk model for subsequent analysis.

[0075] See also Figure 3 , is a schematic diagram of the structure of an embodiment of the pole to be tested provided by the present invention. The monitoring parameters of the pole to be tested include the pole axial force N sc , horizontal X-direction displacement d scx and horizontal Y displacement d scy Among them, N sc Through the force sensor, d scx and d scy Collected by horizontal displacement meter.

[0076] Step 104: Based on the monitoring data of the plurality of template support unit structures to be tested, a risk assessment is performed using a first risk model to obtain an overall risk assessment result of the template support structure.

[0077] Furthermore, in the first embodiment of the present invention, based on the monitoring data of a plurality of the template support unit structures to be tested, a risk assessment is performed using a first risk model to obtain an overall risk assessment result of the template support structure, specifically:

[0078] Acquire the dimension data of the formwork support structure; wherein the dimension data includes: the longitudinal dimension of the formwork support structure, the transverse dimension of the formwork support structure and the height dimension of the formwork support structure;

[0079] Determining the monitoring data and the dimension data of each of the template support unit structures to be tested as input data of a first risk model;

[0080] Controlling the first risk model to perform risk assessment based on the input data, determining position information of a failed formwork support unit, and determining a risk level based on the position information of the failed formwork support unit;

[0081] The position information of the failed formwork support unit and the risk level are determined as the overall risk assessment result of the formwork support structure.

[0082] In the first embodiment of the present application, the input data of the first risk model is the monitoring data set of each template support unit structure to be tested and the size data of the template support structure. The size data set of the template support structure includes the longitudinal size of the template support structure, the lateral size of the template support structure and the height size of the template support structure. The monitoring data set of each template support unit structure to be tested includes the axial force of the vertical rod, the horizontal X-direction displacement and the horizontal Y-direction displacement. Assuming that the monitoring data of n template support unit structures to be tested are collected, the input data of the first risk model can be represented as:

[0083]

[0084] wherein [X] is the input signal vector of the first risk model, including n x 3 + 3 elements; is the monitoring data set of each template support unit structure to be tested, including the axial force N sc of the vertical rod, the horizontal X-direction displacement d scx and the horizontal Y-direction displacement d scy ; X a is the longitudinal size of the template support; X b is the lateral size of the template support; and X h is the height of the template support.

[0085] In the first embodiment of the present application, the first risk model performs high-speed calculation on the input data and outputs the final output result of the model. The final output result of the model includes the position information and the risk level of the failed template support unit, which can be any template support unit structure in the template support structure. After obtaining the position information and the risk level of the failed template support unit, the overall risk assessment result of the template support structure can be determined.

[0086] As an example of the first embodiment of the present application, the output of the first risk model can be represented as: [N, A, B, L]. Wherein N represents the number of the failed template support unit; A, B represents the coordinate position number of the failed template support unit in the horizontal plane; and L represents the collapse risk level of the template support structure, represented by the numbers 1, 2, 3 and 4.

[0087] Further, in the first embodiment of the present application, the first risk model specifically comprises:

[0088] obtaining a template support data set based on a template support model;

[0089] establishing a neural network model based on a BP algorithm;

[0090] training the neural network model using the template support data set to establish the input-output mapping relationship of the neural network model;

[0091] The trained neural network model is determined as the first risk model.

[0092] Furthermore, in the first embodiment of the present invention, based on the template support model, a template support dataset is obtained, specifically:

[0093] A template support model is established on a finite element analysis platform based on preset template support structure data; wherein the template support model includes a plurality of simulated vertical poles;

[0094] Performing static analysis on the formwork support model to obtain working condition data without failure of upright poles;

[0095] Perform static analysis on the formwork support model with a single simulated vertical pole removed to obtain several single vertical pole failure condition data;

[0096] Performing static analysis on the formwork support model with the multi-simulated pole combination removed to obtain several multi-pole failure condition data; wherein the multi-simulated pole combination includes two or more simulated poles;

[0097] Determine the no-pole failure working condition data, the single-pole failure working condition data, and the multiple-pole failure working condition data as sample data;

[0098] The simulated size data of the template support model is acquired, and the simulated size data and the sample data are determined as a template support data set.

[0099] In the first embodiment of the present invention, before training the first risk model, a formwork support model is first established to obtain a formwork support data set. The formwork support model can be established using a finite element analysis platform. Taking a typical formwork support unit structure model (6 spans and 4 steps) as an example, the ABAQUS finite element analysis platform is used to establish a formwork support model with 6 spans in both vertical and horizontal directions and 4 steps in the vertical direction (6×6×4). The vertical and horizontal distances of the vertical poles of the formwork support model are both 1.05m, the step distance is 1.5m, the height of the sweeping pole from the ground is 0.2m, the height of the horizontal pole cantilever is 0.5m, the height-to-width ratio of the frame is 1.59, and the formwork support is provided with vertical scissors braces and horizontal scissors braces. The outer diameter of the steel pipe is 48.0mm, the wall thickness is 3.0mm, and the steel material properties can be taken according to Q235 steel.

[0100] In the first embodiment of the present invention, component failure will cause the load path and geometric shape of the formwork support to change, thereby increasing the risk of collapse of the overall structure, wherein the failure of the vertical pole has a more severe impact on the degradation of the ultimate bearing performance of the frame than the failure of the fastener. Therefore, considering that the failure of the fastener will eventually lead to the buckling instability of the vertical pole, the formwork support data set under the condition of vertical pole failure can be used as the training data of the first risk model. The formwork support data set includes data on the condition of no vertical pole failure, data on the condition of single vertical pole failure, and data on the condition of multiple vertical pole failure. Among them, the data on the condition of no vertical pole failure is the data obtained by performing a static analysis on the formwork support model when all vertical poles in the formwork support model have not failed. The data on the condition of single vertical pole failure is the data obtained by performing a static analysis on the formwork support model when considering the failure of one vertical pole each time. The data on the condition of multiple vertical pole failure is the data obtained by performing a static analysis on the formwork support model when a group of vertical poles are removed each time when considering the failure of multiple vertical poles in the formwork support model. For example, a combination of multiple pole failures may include four poles failing simultaneously, or nine poles failing simultaneously. After acquiring data on a zero-pole failure condition, multiple single-pole failure condition data, and multiple multi-pole failure condition data, a formwork support dataset is generated.

[0101] In the first embodiment of the present invention, in order to improve the accuracy of the formwork support dataset, when obtaining the formwork support dataset corresponding to each working condition, a load is applied to the formwork support model according to the construction special plan. The following table is an example of load values:

[0102]

[0103] In the first embodiment of the present invention, the training data of the first risk model also includes the overall simulated size data of the template support model, so the simulated longitudinal size, lateral size and height size of the template support model are obtained, determined as the simulated size data of the template support model, and the simulated size data is added to the template support data set, thereby using the template support data set as the training data of the first risk model.

[0104] In the first embodiment of the present invention, the first risk model is established based on the BP algorithm. The neural network structure of the first risk model consists of an input layer, a hidden layer, and an output layer. The hidden layer of the first risk model may include 256 hidden neurons. The initial weights and initial thresholds between each layer are randomly selected, and the input layer and the hidden layer are connected using the ReLU function. Figure 4 , is a schematic diagram of the structure of an embodiment of the first risk model provided by the present invention, wherein x1~x n is the input layer parameter, y1~y m is the output layer parameter, w ij and wjk To connect the weights of each neuron, the network expresses the functional relationship from n independent variables to m dependent variables.

[0105] In a first embodiment of the present invention, after obtaining training data for a first risk model and establishing a neural network model using a BP algorithm, the first risk model is trained using the training data. During the training process, the data in the template support dataset is normalized so that each element data is within a similar numerical range, thereby reducing the impact of input signals of different dimensions on the BP neural network learning.

[0106] As an example of the first embodiment of the present invention, the number of training iterations for the first risk model can be set to 12,000, with the Adam optimizer and a learning rate of 0.01. As training progresses, the accuracy of the model's predictions gradually improves, and the model is saved every 500 steps to verify its accuracy.

[0107] In the first embodiment of the present invention, the training process of the first risk model utilizes a neural network for structural damage detection, diagnosis, and assessment. This involves establishing a nonlinear mapping relationship between input (structural characteristics) and output (damage determination, location, and extent) to describe the process of change in the structural physical system. The core of the BP neural network is to continuously modify the weights connecting each neuron so that the output value continuously approaches the expected value until a satisfactory error is achieved. Therefore, a training error threshold is set for the first risk model. When the first risk model reaches the training error threshold during training, model training is considered complete.

[0108] As an example of the first embodiment of the present invention, principal component analysis can be used to optimize the monitoring elements. Assume that 27 elements from 9 monitoring points are used to train the first risk model, but in order to reduce costs, the principal component analysis method can be used to optimize the number of monitoring elements. That is, principal component analysis is used to screen out several key elements, namely principal components, from the 27 monitoring elements that can integrate the original element information as much as possible. In principal component analysis, the contribution of each element can be judged according to the weight of the principal component, and the elements with smaller contributions can be excluded to achieve dimensionality reduction of the data vector and reduce the arrangement of the corresponding measuring points. The contribution of each element can be achieved by calculating the Pearson correlation coefficient with the original data. The correlation coefficient represents the strength and direction of the linear relationship between the two variables. Its value range is [-1,1]. The larger the absolute value, the stronger the linear relationship between the two variables. Therefore, in principal component analysis, elements with correlation coefficients greater than the preset correlation coefficient threshold can be screened as key elements. See Figure 5 , is a schematic diagram of the retention monitoring element provided by the present invention. As an example, Figure 5As shown, the horizontal X- and Y-direction displacements of corner measuring points 1, 3, 5, and 7, the horizontal X-direction displacement of center measuring point 9, and the axial force of the uprights at edge measuring points 2, 4, 6, and 8 can be identified as key elements. Therefore, each monitoring point in the formwork support model can retain data for either axial force monitoring or horizontal displacement monitoring. For measuring points already equipped with either an upright pressure sensor or a horizontal displacement meter, consideration can be given to installing the other type of monitoring equipment at adjacent measuring points, thereby reducing the monitoring workload. The present invention, through principal component analysis, can significantly reduce the number of monitoring point elements required to train a formwork support collapse risk model.

[0109] The present invention uses a BP neural network to establish a first risk model and uses a large amount of monitoring data and corresponding results to train the first risk model. This is based on the characteristics of artificial neural network technology that has autonomous learning and high-speed search for optimization solutions. It can enable the first risk model to have the ability to quickly identify failed components and predict the risk of frame collapse, thereby improving the efficiency and accuracy of risk assessment.

[0110] Furthermore, in the first embodiment of the present invention, after obtaining the overall risk assessment result of the template support structure, the method further includes:

[0111] When the risk level in the overall risk assessment result is the first risk level, determining that the formwork support structure has an overall collapse risk;

[0112] When the risk level in the overall risk assessment result is the second risk level, determining that the formwork support structure has a risk of partial collapse;

[0113] When the risk level in the overall risk assessment result is the third risk level, it is determined that the formwork support structure has a potential local collapse risk;

[0114] When the risk level in the overall risk assessment result is the fourth risk level, it is determined that the formwork support structure is safe.

[0115] In the first embodiment of the present invention, after the first risk model is used to identify the failure position of the pole and predict the collapse risk level, a corresponding prevention plan can be determined based on the risk level. As an example, four different risk levels can be set, and different prevention plans can be set based on different risk levels. Specifically, when the risk level is level 1, it is very dangerous, indicating that if the failure site causes more than 15% of the total area of ​​the members on the horizontal surface to yield, it is considered that the entire unit structure is about to collapse continuously, and construction needs to be stopped immediately, and all personnel on the work surface are urgently evacuated; when the risk level is level 2, it is dangerous: if the failure site causes 3% to 15% of the total area of ​​the members on the horizontal surface to yield, it is considered that partial collapse is about to occur, and construction needs to be stopped immediately, and personnel on the work surface where partial collapse may occur are urgently evacuated; when the risk level is level 3, it is warning: if the failure site causes 1 to 3% of the total area of ​​the members on the horizontal surface to yield, it is considered that there is a potential for partial collapse, and construction needs to be suspended to confirm the location of the failed component. Construction can only be continued after sufficient safety measures are taken, and subsequent monitoring should be strengthened; when the risk level is level 4, it is safe: no component failure has occurred or the failed component site has not caused other members to yield, and normal monitoring only needs to be maintained and construction should be carried out in accordance with the requirements of the specifications.

[0116] In summary, the first embodiment of the present invention provides a method for risk assessment of a formwork support structure, which divides the formwork support structure into a plurality of formwork support unit structures; selects a plurality of formwork support unit structures to be tested from the plurality of formwork support unit structures according to preset requirements; obtains the vertical axial force and horizontal displacement of the plurality of formwork support unit structures to be tested to form monitoring data; and performs risk assessment based on the monitoring data of the plurality of formwork support unit structures to be tested using a first risk model to obtain an overall risk assessment result of the formwork support structure. The present invention only selects a few monitoring components, and can perceive and predict the failure conditions of other components outside the monitoring components through the high-speed calculation of the BP neural network model, thereby evaluating the safety status of the overall structure, thereby improving the efficiency and accuracy of risk assessment and reducing the assessment cost.

[0117] Example 2

[0118] See also Figure 6 , is a schematic structural diagram of an embodiment of a template support structure risk assessment device provided by the present invention, the device includes a structure division module 201, a structure selection module 202, a data acquisition module 203 and a risk assessment module 204;

[0119] The structure division module 201 is used to divide the template support structure into a plurality of template support unit structures;

[0120] The structure selection module 202 is used to select a plurality of template support unit structures to be tested from a plurality of template support unit structures according to preset requirements;

[0121] The data acquisition module 203 is configured to acquire monitoring data of a plurality of template support unit structures to be measured; wherein the monitoring data comprises a vertical rod axial force and a horizontal displacement;

[0122] The risk assessment module 204 is configured to perform risk assessment based on the monitoring data of the plurality of template support unit structures to be measured by using a first risk model, to obtain an overall risk assessment result of the template support structure.

[0123] Further, in the second embodiment of the present application, a plurality of template support unit structures to be measured are selected from a plurality of template support unit structures according to preset requirements, specifically:

[0124] The area and the danger level of the construction area are acquired by analyzing the preset requirements;

[0125] Based on the area and the danger level of the construction area, a plurality of template support unit structures to be measured are selected from a plurality of template support unit structures.

[0126] Further, in the second embodiment of the present application, the monitoring data of a plurality of template support unit structures to be measured are acquired, specifically:

[0127] The position data of a to-be-measured vertical rod in each of the template support unit structures to be measured is determined;

[0128] Based on the position data of each to-be-measured vertical rod, a force sensor is used to monitor the vertical rod axial force of each to-be-measured vertical rod respectively;

[0129] Based on the position data of each to-be-measured vertical rod, a horizontal displacement meter is used to monitor the horizontal X-direction displacement and the horizontal Y-direction displacement of each to-be-measured vertical rod respectively;

[0130] Based on the horizontal X-direction displacement and the horizontal Y-direction displacement of each to-be-measured vertical rod, the horizontal displacement of each to-be-measured vertical rod is determined;

[0131] The vertical rod axial force and the horizontal displacement of each to-be-measured vertical rod are determined as the monitoring data of each template support unit structure to be measured.

[0132] Further, in the second embodiment of the present application, the monitoring data of a plurality of template support unit structures to be measured are acquired, specifically:

[0133] The size data of the template support structure are acquired; wherein the size data comprises a longitudinal size of the template support structure, a transverse size of the template support structure, and a height size of the template support structure;

[0134] The monitoring data and the size data of each template support unit structure to be tested are determined as input data of a first risk model;

[0135] The first risk model is controlled to perform risk assessment based on the input data, determine position information of a failed template support unit, and determine a risk level based on the position information of the failed template support unit;

[0136] The position information of the failed template support unit and the risk level are determined as an overall risk assessment result of the template support structure.

[0137] Further, in the second embodiment of the present application, the first risk model, in particular:

[0138] Based on a template support model, a template support data set is obtained;

[0139] A neural network model is established based on a BP algorithm;

[0140] The neural network model is trained using the template support data set to establish an input-output mapping relationship of the neural network model;

[0141] The trained neural network model is determined as the first risk model.

[0142] Further, in the second embodiment of the present application, based on a template support model, a template support data set is obtained, in particular:

[0143] A template support model is established on a finite element analysis platform based on preset template support structure data; wherein the template support model includes a plurality of simulated vertical rods;

[0144] Static analysis is performed on the template support model to obtain no-vertical-rod failure condition data;

[0145] Static analysis is performed on the template support model with a single simulated vertical rod removed to obtain a plurality of single-vertical-rod failure condition data;

[0146] Static analysis is performed on the template support model with a plurality of simulated vertical rod combinations removed to obtain a plurality of multi-vertical-rod failure condition data; wherein the plurality of simulated vertical rod combinations include two or more simulated vertical rods;

[0147] The no-vertical-rod failure condition data, the single-vertical-rod failure condition data, and the multi-vertical-rod failure condition data are determined as sample data;

[0148] Simulated size data of the template support model are obtained, and the simulated size data and the sample data are determined as a template support data set.

[0149] Furthermore, in the second embodiment of the present invention, after obtaining the overall risk assessment result of the template support structure, the method further includes:

[0150] When the risk level in the overall risk assessment result is the first risk level, determining that the formwork support structure has an overall collapse risk;

[0151] When the risk level in the overall risk assessment result is the second risk level, determining that the formwork support structure has a risk of partial collapse;

[0152] When the risk level in the overall risk assessment result is the third risk level, it is determined that the formwork support structure has a potential local collapse risk;

[0153] When the risk level in the overall risk assessment result is the fourth risk level, it is determined that the formwork support structure is safe.

[0154] In summary, the second embodiment of the present invention provides a risk assessment device for a formwork support structure, which divides the formwork support structure into a number of formwork support unit structures through the organic combination of modules; selects a number of formwork support unit structures to be tested from the number of formwork support unit structures according to preset requirements; obtains the vertical axial force and horizontal displacement of the number of formwork support unit structures to be tested to form monitoring data; and based on the monitoring data of the number of formwork support unit structures to be tested, uses a first risk model to perform risk assessment to obtain an overall risk assessment result of the formwork support structure. The present invention only selects a few monitoring components, and can perceive and predict the failure conditions of other components other than the monitoring components through the high-speed calculation of the BP neural network model, and then evaluates the safety status of the overall structure, thereby improving the efficiency and accuracy of risk assessment and reducing the assessment cost.

[0155] An embodiment of the present invention also provides a template support structure risk assessment device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the template support structure risk assessment method described in any of the above embodiments.

[0156] An embodiment of the present invention also provides a computer-readable storage medium, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the template support structure risk assessment method described in any of the above embodiments is implemented.

[0157] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for risk assessment of a formwork support structure, characterized in that: include: Divide the formwork support structure into a number of formwork support unit structures; Selecting a plurality of template support unit structures to be tested from the plurality of template support unit structures according to preset requirements; Acquire monitoring data of a plurality of the template support unit structures to be tested; wherein the monitoring data includes the axial force and horizontal displacement of the vertical pole; Based on the monitoring data of the plurality of template support unit structures to be tested, a risk assessment is performed using a first risk model to obtain an overall risk assessment result of the template support structure; The first risk model is specifically: Based on the template support model, a template support data set is obtained; Establish a neural network model based on BP algorithm; Using the template support data set to train the neural network model, and establish an input-output mapping relationship of the neural network model; Determine the trained neural network model as the first risk model; The template support data set is obtained based on the template support model, specifically: A template support model is established on a finite element analysis platform based on preset template support structure data; wherein the template support model includes a plurality of simulated vertical poles; Performing static analysis on the formwork support model to obtain working condition data without failure of upright poles; Perform static analysis on the formwork support model with a single simulated vertical pole removed to obtain several single vertical pole failure condition data; Performing static analysis on the formwork support model with the multi-simulated pole combination removed to obtain several multi-pole failure condition data; wherein the multi-simulated pole combination includes two or more simulated poles; Determine the no-pole failure working condition data, the single-pole failure working condition data, and the multiple-pole failure working condition data as sample data; Acquire simulated size data of the template support model, and determine the simulated size data and the sample data as a template support data set; The monitoring data of the plurality of template support unit structures to be tested are used to perform risk assessment using the first risk model to obtain an overall risk assessment result of the template support structure, specifically: Acquire the dimension data of the formwork support structure; wherein the dimension data includes: the longitudinal dimension of the formwork support structure, the transverse dimension of the formwork support structure and the height dimension of the formwork support structure; Determining the monitoring data and the dimension data of each of the template support unit structures to be tested as input data of a first risk model; Controlling the first risk model to perform risk assessment based on the input data, determining position information of a failed formwork support unit, and determining a risk level based on the position information of the failed formwork support unit; The position information of the failed formwork support unit and the risk level are determined as the overall risk assessment result of the formwork support structure.

2. The method for risk assessment of formwork support structure according to claim 1, characterized in that: The method of selecting a plurality of template support unit structures to be tested from a plurality of template support unit structures according to preset requirements is as follows: Obtain the area and danger level of the construction area by analyzing preset requirements; Based on the area and danger level of the construction area, several formwork support unit structures to be tested are selected from the several formwork support unit structures.

3. The method for risk assessment of formwork support structure according to claim 2, characterized in that: The acquisition of monitoring data of a plurality of the template support unit structures to be tested is specifically as follows: Determining the position data of the vertical poles to be measured in each of the template support unit structures to be measured; Based on the position data of each of the poles to be measured, using a force sensor to respectively monitor the pole axial force of each of the poles to be measured; Based on the position data of each of the vertical poles to be measured, using a horizontal displacement meter to respectively monitor the horizontal X-direction displacement and the horizontal Y-direction displacement of each of the vertical poles to be measured; Determining the horizontal displacement of each of the poles to be measured based on the horizontal X-direction displacement and the horizontal Y-direction displacement of each of the poles to be measured; The axial force and horizontal displacement of each of the vertical poles to be tested are determined as monitoring data of each of the formwork support unit structures to be tested.

4. The method for risk assessment of formwork support structure according to claim 3, characterized in that: After obtaining the overall risk assessment result of the template support structure, the method further includes: When the risk level in the overall risk assessment result is the first risk level, determining that the formwork support structure has an overall collapse risk; When the risk level in the overall risk assessment result is the second risk level, determining that the formwork support structure has a risk of partial collapse; When the risk level in the overall risk assessment result is the third risk level, it is determined that the formwork support structure has a potential local collapse risk; When the risk level in the overall risk assessment result is the fourth risk level, it is determined that the formwork support structure is safe.

5. A formwork support structure risk assessment device, characterized in that: include: Structure division module, structure selection module, data acquisition module and risk assessment module; The structure division module is used to divide the template support structure into a plurality of template support unit structures; The structure selection module is used to select a plurality of template support unit structures to be tested from a plurality of template support unit structures according to preset requirements; The data acquisition module is used to obtain monitoring data of a plurality of the template support unit structures to be tested; wherein the monitoring data includes the axial force and horizontal displacement of the vertical pole; The risk assessment module is used to perform risk assessment based on the monitoring data of the plurality of template support unit structures to be tested using a first risk model to obtain an overall risk assessment result of the template support structure; The first risk model is specifically: Based on the template support model, a template support data set is obtained; Establish a neural network model based on BP algorithm; Using the template support data set to train the neural network model, and establish an input-output mapping relationship of the neural network model; Determine the trained neural network model as the first risk model; The template support data set is obtained based on the template support model, specifically: A template support model is established on a finite element analysis platform based on preset template support structure data; wherein the template support model includes a plurality of simulated vertical poles; Performing static analysis on the formwork support model to obtain working condition data without failure of upright poles; Perform static analysis on the formwork support model with a single simulated vertical pole removed to obtain several single vertical pole failure condition data; Performing static analysis on the formwork support model with the multi-simulated pole combination removed to obtain several multi-pole failure condition data; wherein the multi-simulated pole combination includes two or more simulated poles; Determine the no-pole failure working condition data, the single-pole failure working condition data, and the multiple-pole failure working condition data as sample data; Acquire simulated size data of the template support model, and determine the simulated size data and the sample data as a template support data set; The monitoring data of the plurality of template support unit structures to be tested are used to perform risk assessment using the first risk model to obtain an overall risk assessment result of the template support structure, specifically: Acquire the dimension data of the formwork support structure; wherein the dimension data includes: the longitudinal dimension of the formwork support structure, the transverse dimension of the formwork support structure and the height dimension of the formwork support structure; Determining the monitoring data and the dimension data of each of the template support unit structures to be tested as input data of a first risk model; Controlling the first risk model to perform risk assessment based on the input data, determining position information of a failed formwork support unit, and determining a risk level based on the position information of the failed formwork support unit; The position information of the failed formwork support unit and the risk level are determined as the overall risk assessment result of the formwork support structure.

6. A formwork support structure risk assessment device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the template support structure risk assessment method according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the template support structure risk assessment method according to any one of claims 1 to 4.

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