Template support structure risk assessment method, device and equipment and storage medium

Abstract

The invention relates to the technical field of building construction, and discloses a formwork support structure risk assessment method, device and equipment and a storage medium. According to the method, a formwork support structure is divided into a plurality of formwork support unit structures; selecting a plurality of to-be-tested template support unit structures from the plurality of template support unit structures according to preset requirements; acquiring vertical rod axial force and horizontal displacement of the plurality of to-be-measured template support unit structures to form monitoring data; and based on the monitoring data of the plurality of to-be-tested template support unit structures, performing risk assessment by using the first risk model to obtain an overall risk assessment result of the template support structure. According to the method, the safety state of the failure template support structure and the whole structure can be quickly identified only according to limited monitoring data, and the efficiency and the accuracy of risk assessment are improved by utilizing an artificial neural network technology.

Description

Technical Field

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

[0002] The existing formwork support monitoring methods can accurately collect the stress and deformation conditions of the monitored components, but it is difficult to perceive the failure of components other than the monitored components, and it is also impossible to determine whether the failure of non-monitored components will cause the collapse of the remaining frame. The traditional formwork support safety assessment is mainly aimed at the mechanical analysis of local substructures, and lacks a digital-driven safety assessment method. For formwork support monitoring, when the frame is large and has many rods, traditional calculation methods (such as finite element simulation, etc.) alone cannot quickly use massive monitoring data to feedback the safety status of the frame during the monitoring process. Summary of the invention

[0003] The present invention provides a formwork support structure risk assessment method, device, equipment and storage medium, which can realize rapid identification of failed formwork support structures and the safety status of the overall structure based on limited monitoring data, and improve the efficiency and accuracy of risk assessment by using artificial neural network technology.

[0004] In order to solve the above technical problems, the present invention provides a template support structure risk assessment method, comprising:

[0005] Dividing the formwork support structure into a plurality of formwork support unit structures;

[0006] Selecting a plurality of template support unit structures to be tested from a plurality of the template support unit structures according to preset requirements;

[0007] 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;

[0008] 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.

[0009] Further, 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 specifically as follows:

[0010] Obtain the area and danger level of the construction area by analyzing the preset requirements;

[0011] 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.

[0012] Further, the acquisition of monitoring data of a plurality of the template support unit structures to be tested is specifically as follows:

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

[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 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 poles to be measured;

[0016] Determine 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;

[0017] The axial force and horizontal displacement of each of the uprights 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, the first risk model is used to perform risk assessment to obtain an overall risk assessment result of the template support structure, specifically:

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

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

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

[0031] Static analysis was performed 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 template support model with the multi-simulated pole combination removed respectively to obtain a number of multi-pole failure condition data; wherein the multi-simulated pole combination includes two or more simulated poles;

[0033] Determine the working condition data without pole failure, the working condition data of each single pole failure, and the working condition data of each multiple pole failure 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, it also 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 number 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, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the template support structure risk assessment method as described above is implemented.

[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 method, device, equipment and storage medium 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 pole 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 sense and predict the failure conditions of other components other than 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. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0056] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0057] Example 1

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

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

[0060] In the first embodiment of the present invention, the template support structure is divided into a plurality of template support unit structures according to a preset horizontal area threshold, wherein the horizontal area of ​​each divided template 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 schematic diagram of a structure 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 is selected in each formwork support unit structure as a monitoring object and numbered.

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

[0063] Further, 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 the 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 the test objects according to the size and danger level of the construction area of ​​the site. In order to achieve a better effect, the formwork support unit structures at the corners, edges and center of the formwork support structure can be selected as the formwork support unit structures to be tested.

[0067] Step 103: 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.

[0068] Further, 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] Determine the position data of the vertical poles to be tested in each of the template support unit structures to be tested;

[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 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 poles to be measured;

[0072] Determine 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;

[0073] The axial force and horizontal displacement of each of the uprights 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, the axial force and horizontal displacement of the pole are determined as monitoring parameters, and the horizontal displacement includes horizontal X-direction displacement and horizontal Y-direction displacement. During the monitoring process, the force sensor can be used as the pole axial force monitoring device, and the horizontal displacement meter can be used as the horizontal displacement monitoring device to obtain the monitoring data of the pole axial force and horizontal displacement of each pole to be tested. After the monitoring data is collected, it is transmitted in real time to the pre-trained first risk model for subsequent analysis.

[0075] See also Figure 3 , is a schematic diagram of a structure of an embodiment of a 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 displacement d scx and horizontal Y displacement d scy Among them, N sc The force sensor is used to collect 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] Further, 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 template support structure; wherein the dimension data includes: the longitudinal dimension of the template support structure, the transverse dimension of the template support structure and the height dimension of the template support structure;

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

[0080] Controlling the first risk model to perform risk assessment based on the input data, determining position information of a failed template support unit, and determining a risk level based on the position information of the failed template 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 invention, the input data of the first risk model is the monitoring data set of each selected template support unit structure to be tested, and the size data of the template support structure. Among them, the size data set of the template support structure includes the longitudinal size of the template support structure, the transverse 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 three data: the axial force of the vertical pole, 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 expressed as:

[0083]

[0084] Wherein, [X] is the input signal vector of the first risk model, including n×3+3 elements; The monitoring data set for each template support unit structure to be tested includes the axial force N of the vertical pole sc , horizontal X displacement d scx and horizontal Y displacement d scy ;X a is the longitudinal dimension of the formwork support; X b X is the horizontal dimension of the formwork support; h is the formwork support height.

[0085] In the first embodiment of the present invention, 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 location information and risk level of the failed template support unit, and the failed template support unit can be any template support unit structure in the template support structure. After obtaining the location information and 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 invention, the output of the first risk model can be expressed as: [N, A, B, L], wherein N represents the number of the failed formwork support unit; A and B represent the coordinate position numbers of the failed formwork support unit on the horizontal plane; and L represents the risk level of collapse of the formwork support structure, represented by the numbers 1, 2, 3, and 4.

[0087] Furthermore, in the first embodiment of the present invention, the first risk model is specifically:

[0088] Based on the template support model, a template support data set is obtained;

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

[0090] Using the template support data set to train the neural network model, and establish an input-output mapping relationship of the neural network model;

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

[0092] Further, in the first embodiment of the present invention, based on the template support model, the template support data set 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 the upright pole;

[0095] Static analysis was performed 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 template support model with the multi-simulated pole combination removed respectively to obtain a number of multi-pole failure condition data; wherein the multi-simulated pole combination includes two or more simulated poles;

[0097] Determine the working condition data without pole failure, the working condition data of each single pole failure, and the working condition data of each multiple pole failure 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, an ABAQUS finite element analysis platform is used to establish a formwork support model with 6 spans in both 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 properties can be taken according to Q235 steel.

[0100] In the first embodiment of the present invention, the failure of the component 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 pole has a more severe effect 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 pole, the formwork support data set under the failure of the pole can be used as the training data of the first risk model. The formwork support data set includes no pole failure condition data, single pole failure condition data and multiple pole failure condition data. Among them, the no pole failure condition data is the data obtained by static analysis of the formwork support model when all poles in the formwork support model have not failed. The single pole failure condition data is the data obtained by static analysis of the formwork support model with one pole removed each time when the failure of one pole is considered in the formwork support model. The multiple pole failure condition data is the data obtained by static analysis of the formwork support model with a group of poles removed each time when the failure of multiple poles is considered in the formwork support model. For example, the combination of multiple pole failures may include 4 poles failing at the same time, or 9 poles failing at the same time. After acquiring the data of the condition with no pole failure, the data of multiple single pole failures, and the data of multiple multiple pole failures, a template support data set is formed.

[0101] In the first embodiment of the present invention, in order to improve the accuracy of the formwork support data set, when obtaining the formwork support data set 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 value:

[0102]

[0103] In the first embodiment of the present invention, the training data of the first risk model also includes the overall simulated dimension data of the template support model, so the simulated longitudinal dimension, lateral dimension and height dimension of the template support model are obtained, determined as the simulated dimension data of the template support model, and the simulated dimension data is added to the template support data set, thereby using the template support data set as the training data for 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 the layers are randomly selected, and the input layer and the hidden layer are connected using the ReLU function. Figure 4 , is a schematic diagram of a structure of an embodiment of the first risk model provided by the present invention, wherein x 1 ~x n is the input layer parameter, y 1 ~y m is the output layer parameter, wij and w jk To connect the weights of each neuron, the network expresses the functional relationship from n independent variables to m dependent variables.

[0105] In the first embodiment of the present invention, after obtaining the training data of the first risk model and establishing the neural network model using the BP algorithm, the first risk model is trained using the training data. During the training process, the data in the template support data set is normalized so that the data of each element is in a similar numerical range to reduce the influence 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 training iteration number of the first risk model can be set to 12,000 steps, the optimizer uses Adam, and the learning rate is 0.01. As the training progresses, the accuracy of the model prediction will gradually increase, and the model is saved every 500 steps to verify the accuracy of the model prediction.

[0107] In the first embodiment of the present invention, the training process of the first risk model is to use a neural network to detect, diagnose and evaluate structural damage, that is, to establish a nonlinear mapping relationship between input (structural characteristics) and output (damage judgment, location and degree) to describe the process of changes in the structural physical system. The core of the BP neural network is to continuously correct the weights connecting each neuron so that the output value continuously approaches the expected value until a satisfactory error is reached. Therefore, a training error threshold of the first risk model is set. When the first risk model reaches the training error threshold during the training process, the model training is considered to be completed.

[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 number of monitoring elements can be optimized using principal component analysis. 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 elements with smaller contributions can be excluded to achieve dimensionality reduction of the data vector and reduce the arrangement of 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 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 a 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-direction displacement and horizontal Y-direction displacement of the corner measuring points 1, 3, 5, 7, the horizontal X-direction displacement of the center measuring point 9, and the axial force of the vertical poles of the edge measuring points 2, 4, 6, 8 can be determined as key elements. Therefore, each monitoring measuring point of the formwork support model can only retain the data of either axial force monitoring or horizontal displacement monitoring; for the measuring points where one of the vertical pole pressure sensors or horizontal displacement meters has been installed, the adjacent measuring points can consider installing another form of monitoring equipment, thereby reducing the monitoring workload. The present invention can greatly reduce the number of monitoring measuring point elements required for training the formwork support collapse risk model through the principal component analysis method.

[0109] The present invention utilizes BP neural network to establish a first risk model, and utilizes 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, which 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, it also 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 the failure site causes more than 15% of the total area of ​​the rods on the horizontal plane to yield, it is considered that the overall continuous collapse of the unit structure is about to occur, and construction needs to be stopped immediately, and all personnel on the working surface are evacuated urgently; when the risk level is level 2, it is dangerous: the failure site causes 3%ˉ15% of the total area of ​​the rods on the horizontal plane to yield, it is considered that partial collapse is about to occur, and construction needs to be stopped immediately, and personnel on the working surface where partial collapse may occur are evacuated urgently; when the risk level is level 3, it is warning: the failure site causes 1 to 3% of the total area of ​​the rods on the horizontal plane 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 is strengthened; when the risk level is level 4, it is safe: no component failure occurs or the failed component site does not cause other rods to yield, and it is only necessary to maintain normal monitoring and carry out construction 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 number of formwork support unit structures; 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 pole axial force and horizontal displacement of the number of formwork support unit structures to be tested to form monitoring data; based on the monitoring data of the number of formwork support unit structures to be tested, a first risk model is used 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.

[0117] Example 2

[0118] See also Figure 6 , is a schematic diagram of the structure 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 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;

[0122] The risk assessment module 204 is used to perform risk assessment based on the monitoring data of a plurality of the template support unit structures to be tested 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 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:

[0124] Obtain the area and danger level of the construction area by analyzing the preset requirements;

[0125] 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.

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

[0127] Determine the position data of the vertical poles to be tested in each of the template support unit structures to be tested;

[0128] 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;

[0129] Based on the position data of each of the 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 poles to be measured;

[0130] Determine 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;

[0131] The axial force and horizontal displacement of each of the uprights to be tested are determined as monitoring data of each of the formwork support unit structures to be tested.

[0132] Further, in the second 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:

[0133] Acquire the dimension data of the template support structure; wherein the dimension data includes: the longitudinal dimension of the template support structure, the transverse dimension of the template support structure and the height dimension of the template support structure;

[0134] Determine the monitoring data and the dimension data of each of the template support unit structures to be tested as input data of the first risk model;

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

[0136] 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.

[0137] Furthermore, in the second embodiment of the present invention, the first risk model is specifically:

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

[0139] Establish a neural network model based on BP algorithm;

[0140] Using the template support data set to train the neural network model, and 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 invention, based on the template support model, a template support data set is obtained, specifically:

[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 poles;

[0144] Performing static analysis on the formwork support model to obtain working condition data without failure of the upright pole;

[0145] Static analysis was performed on the formwork support model with a single simulated vertical pole removed to obtain several single vertical pole failure condition data;

[0146] Performing static analysis on the template support model with the multi-simulated pole combination removed respectively to obtain a number of multi-pole failure condition data; wherein the multi-simulated pole combination includes two or more simulated poles;

[0147] Determine the working condition data without pole failure, the working condition data of each single pole failure, and the working condition data of each multiple pole failure as sample data;

[0148] 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.

[0149] Furthermore, in the second embodiment of the present invention, after obtaining the overall risk assessment result of the template support structure, it also 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 by organically combining 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 pole 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 the first risk model to perform risk assessment to obtain the 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, and when the processor executes the computer program, the template support structure risk assessment method described in any of the above embodiments is implemented.

[0156] An embodiment of the present invention also provides a computer-readable storage medium, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and 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 purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for risk assessment of a formwork support structure, characterized in that: include: Dividing the formwork support structure into a plurality of formwork support unit structures; Selecting a plurality of template support unit structures to be tested from a plurality of the 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 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.

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 specifically as follows: Obtain the area and danger level of the construction area by analyzing the 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: Determine the position data of the vertical poles to be tested in each of the template support unit structures to be tested; 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 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 poles to be measured; Determine 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 uprights 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: Based on the monitoring data of the plurality of template support unit structures to be tested, the risk assessment is performed using the first risk model to obtain the overall risk assessment result of the template support structure, specifically: Acquire the dimension data of the template support structure; wherein the dimension data includes: the longitudinal dimension of the template support structure, the transverse dimension of the template support structure and the height dimension of the template support structure; Determine the monitoring data and the dimension data of each of the template support unit structures to be tested as input data of the first risk model; Controlling the first risk model to perform risk assessment based on the input data, determining position information of a failed template support unit, and determining a risk level based on the position information of the failed template 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.

5. The method for risk assessment of formwork support structure according to claim 4, characterized in that: 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; The trained neural network model is determined as the first risk model.

6. The method for risk assessment of formwork support structure according to claim 5, characterized in that: The step of obtaining a template support data set based on the template support model is as follows: 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 the upright pole; Static analysis was performed 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 template support model with the multi-simulated pole combination removed respectively to obtain a number of multi-pole failure condition data; wherein the multi-simulated pole combination includes two or more simulated poles; Determine the working condition data without pole failure, the working condition data of each single pole failure, and the working condition data of each multiple pole failure as sample data; 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.

7. The method for risk assessment of formwork support structure according to claim 4, 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.

8. A risk assessment device for a formwork support structure, characterized in that: include: Structural division module, structural selection module, data collection 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 number 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 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.

9. 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 as described in any one of claims 1 to 7 when executing the computer program.

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

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