Foundation pit deformation prediction method and system, intelligent terminal and storage medium

Abstract

The invention discloses a foundation pit deformation prediction method and system, an intelligent terminal and a storage medium. The foundation pit deformation prediction method comprises the steps that foundation pit measurement data and historical deformation monitoring data are acquired; constructing a foundation pit finite element model according to the foundation pit measurement data; obtaining a to-be-optimized soil layer parameter and a first prediction depth; according to the to-be-optimized soil layer parameters and the first prediction depth, first deformation prediction data are determined through the foundation pit finite element model, and deformation prediction errors corresponding to the to-be-optimized soil layer parameters are determined in combination with historical deformation monitoring data; determining a to-be-optimized soil layer parameter of a next iteration round according to the to-be-optimized soil layer parameter, and returning to execute the step of determining the first deformation prediction data through the foundation pit finite element model according to the to-be-optimized soil layer parameter and the first prediction depth until a parameter iteration termination condition is met, and determining an optimized target soil layer parameter; and determining second deformation prediction data through the foundation pit finite element model according to the target soil layer parameters and the second prediction depth. Therefore, the calculation amount of foundation pit deformation prediction can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of construction engineering prediction, and particularly relates to a foundation pit deformation prediction method, system, intelligent terminal and storage medium. Background Art

[0002] At present, construction safety has been paid more and more attention. Foundation pit deformation is one of the important factors affecting the safe construction of foundation pit projects. To effectively ensure construction safety, it is necessary to timely determine the deformation of the foundation pit during the construction process.

[0003] In the prior art, foundation pit deformation prediction is usually based on the Bayesian method. The problem with the prior art is that when predicting foundation pit deformation based on the Bayesian method, a relatively complex neural network model needs to be used, and the amount of calculation is large, which is not conducive to reducing the amount of calculation during foundation pit deformation prediction, and thus is not conducive to improving the processing efficiency of foundation pit deformation prediction.

[0004] Therefore, the related art still needs to be improved and developed. Summary of the Invention

[0005] The main purpose of the present application is to provide a foundation pit deformation prediction method, system, intelligent terminal and storage medium, aiming to solve the technical problem that when predicting foundation pit deformation based on the Bayesian method in the related art, a relatively complex neural network model needs to be used, the amount of calculation is large, which is not conducive to reducing the amount of calculation during foundation pit deformation prediction, and thus is not conducive to improving the processing efficiency of foundation pit deformation prediction.

[0006] To achieve the above object, in the first aspect of the present application, a foundation pit deformation prediction method is provided. The foundation pit deformation prediction method includes:

[0007] Obtain the foundation pit measurement data corresponding to the foundation pit, and the historical deformation monitoring data corresponding to the foundation pit;

[0008] Construct a foundation pit finite element model corresponding to the foundation pit according to the foundation pit measurement data;

[0009] Obtain the soil layer parameters to be optimized corresponding to the current iteration round, and the first prediction depth matching the historical deformation monitoring data;

[0010] According to the soil layer parameters to be optimized and the first prediction depth, through the foundation pit finite element model, determine the first deformation prediction data corresponding to the foundation pit at the first prediction depth;

[0011] Determine the deformation prediction error corresponding to the soil layer parameters to be optimized according to the first deformation prediction data and the historical deformation monitoring data;

[0012] According to the above soil layer parameters to be optimized, determine the soil layer parameters to be optimized corresponding to the next iteration round, and return to execute the step of determining the first deformation prediction data corresponding to the foundation pit at the above first predicted depth through the above foundation pit finite element model according to the above soil layer parameters to be optimized and the above first predicted depth, until the preset parameter iteration termination condition is met, and determine the target soil layer parameters after optimization according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round;

[0013] Obtain the second predicted depth, and determine the second deformation prediction data corresponding to the foundation pit at the second predicted depth through the above foundation pit finite element model according to the above target soil layer parameters and the second predicted depth.

[0014] Optionally, each iteration round corresponds to multiple groups of soil layer parameters to be optimized with different values;

[0015] The above step of determining the first deformation prediction data corresponding to the foundation pit at the above first predicted depth through the above foundation pit finite element model according to the above soil layer parameters to be optimized and the above first predicted depth includes:

[0016] For each group of soil layer parameters to be optimized, determine the first deformation prediction data corresponding to the group of soil layer parameters to be optimized through the above foundation pit finite element model according to the group of soil layer parameters to be optimized and the above first predicted depth.

[0017] Optionally, the above step of determining the deformation prediction error corresponding to the above soil layer parameters to be optimized according to the above first deformation prediction data and the above historical deformation monitoring data includes:

[0018] For the first deformation prediction data corresponding to each group of soil layer parameters to be optimized, determine the candidate prediction error corresponding to the group of soil layer parameters to be optimized according to the above first deformation prediction data and the above historical deformation monitoring data;

[0019] Take the minimum value among all the above candidate prediction errors as the deformation prediction error corresponding to the soil layer parameters to be optimized in the current iteration round.

[0020] Optionally, the above step of determining the candidate prediction error corresponding to each group of soil layer parameters to be optimized according to the above first deformation prediction data and the above historical deformation monitoring data includes:

[0021] For the first deformation prediction data corresponding to each group of soil layer parameters to be optimized, calculate the diaphragm wall deformation error and the ground surface settlement error according to the above first deformation prediction data and the above historical deformation monitoring data;

[0022] Perform a weighted sum of the above diaphragm wall deformation error and the above ground surface settlement error to obtain the candidate prediction error corresponding to the group of soil layer parameters to be optimized.

[0023] Optionally, determining the soil layer parameters to be optimized corresponding to the next iteration round according to the above soil layer parameters to be optimized includes:

[0024] Obtain the parameter value range and the parameter adjustment step size;

[0025] Generate the soil layer parameters to be optimized corresponding to the next iteration round according to the above soil layer parameters to be optimized, the above parameter value range, and the above parameter adjustment step size.

[0026] Optionally, determining the target soil layer parameters after optimization according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round includes:

[0027] Determine the target iteration round with the smallest deformation prediction error,

[0028] Determine multiple groups of target soil layer parameters according to multiple groups of soil layer parameters to be optimized corresponding to the target iteration round with the smallest deformation prediction error;

[0029] The above determining the second deformation prediction data corresponding to the foundation pit at the above second prediction depth through the above foundation pit finite element model according to the above target soil layer parameters and the above second prediction depth includes:

[0030] Sort the values of the deformation prediction errors corresponding to each group of target soil layer parameters, and determine at least one group of target calculation soil layer parameters from the above multiple groups of target soil layer parameters according to the sorting result of the deformation prediction errors;

[0031] Determine multiple candidate deformation prediction data respectively through the above foundation pit finite element model according to each group of target calculation soil layer parameters and the above second prediction depth;

[0032] Determine the above second deformation prediction data according to all the above candidate deformation prediction data.

[0033] Optionally, the above method further includes:

[0034] Generate a foundation pit deformation display diagram according to the above second deformation prediction data, and output the above foundation pit deformation display diagram;

[0035] Wherein, the above second deformation prediction data includes lateral displacement information and depth information corresponding to multiple data points.

[0036] The second aspect of the present application provides a foundation pit deformation prediction system, wherein the above foundation pit deformation prediction system includes:

[0037] The first data acquisition module is used to acquire the foundation pit measurement data corresponding to the foundation pit and the historical deformation monitoring data corresponding to the above foundation pit;

[0038] The finite element model construction module is used to construct the foundation pit finite element model corresponding to the above foundation pit according to the above foundation pit measurement data;

[0039] The second data acquisition module is used to acquire the soil layer parameters to be optimized corresponding to the current iteration round and the first predicted depth matched with the above historical deformation monitoring data;

[0040] The first prediction module is used to determine the first deformation prediction data corresponding to the above foundation pit at the above first predicted depth through the above foundation pit finite element model according to the above soil layer parameters to be optimized and the above first predicted depth;

[0041] The error calculation module is used to determine the deformation prediction error corresponding to the above soil layer parameters to be optimized according to the above first deformation prediction data and the above historical deformation monitoring data;

[0042] The iteration control module is used to determine the soil layer parameters to be optimized corresponding to the next iteration round according to the above soil layer parameters to be optimized, and return to execute the step of determining the first deformation prediction data corresponding to the above foundation pit at the above first predicted depth through the above foundation pit finite element model according to the above soil layer parameters to be optimized and the above first predicted depth, until a preset parameter iteration termination condition is satisfied, and determine the target soil layer parameters after optimization according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round;

[0043] The second prediction module is used to acquire the second predicted depth, and determine the second deformation prediction data corresponding to the above foundation pit at the above second predicted depth through the above foundation pit finite element model according to the above target soil layer parameters and the above second predicted depth.

[0044] A third aspect of the present application provides an intelligent terminal. The above intelligent terminal includes a memory, a processor, and a foundation pit deformation prediction program stored on the above memory and executable on the above processor. When the above foundation pit deformation prediction program is executed by the processor, the steps of any one of the above foundation pit deformation prediction methods are implemented.

[0045] A fourth aspect of the present application provides a computer-readable storage medium. The above computer-readable storage medium stores a foundation pit deformation prediction program. When the above foundation pit deformation prediction program is executed by a processor, the steps of any one of the above foundation pit deformation prediction methods are implemented.

[0046] As can be seen from the above, in the solution of the present application, the foundation pit measurement data corresponding to the foundation pit and the historical deformation monitoring data corresponding to the above foundation pit are obtained; a finite element model of the foundation pit corresponding to the above foundation pit is constructed according to the above foundation pit measurement data; the soil layer parameters to be optimized corresponding to the current iteration round and the first predicted depth matched with the above historical deformation monitoring data are obtained; according to the above soil layer parameters to be optimized and the above first predicted depth, through the above finite element model of the foundation pit, the first deformation prediction data corresponding to the above foundation pit at the above first predicted depth is determined; according to the above first deformation prediction data and the above historical deformation monitoring data, the deformation prediction error corresponding to the above soil layer parameters to be optimized is determined; according to the above soil layer parameters to be optimized, the soil layer parameters to be optimized corresponding to the next iteration round are determined, and the step of determining the first deformation prediction data corresponding to the above foundation pit at the above first predicted depth through the above finite element model according to the above soil layer parameters to be optimized and the above first predicted depth is returned until a preset parameter iteration termination condition is satisfied, and the target soil layer parameters after optimization are determined according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round; the second predicted depth is obtained, and according to the above target soil layer parameters and the above second predicted depth, through the above finite element model of the foundation pit, the second deformation prediction data corresponding to the above foundation pit at the above second predicted depth is determined.

[0047] Compared with the prior art, in the solution corresponding to the foundation pit deformation prediction method provided by the present application, there is no need to rely on a complex neural network model, but the foundation pit deformation prediction is carried out based on the finite element model of the foundation pit. Specifically, the soil layer parameters to be optimized are optimized based on the historical deformation monitoring data, and after obtaining the optimized target soil layer parameters, the foundation pit deformation prediction is carried out based on the finite element model of the foundation pit. In this way, it is beneficial to reduce the calculation amount during the foundation pit deformation prediction, and thus beneficial to improve the processing efficiency of the foundation pit deformation prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0049] Figure 1 is a schematic flowchart of a foundation pit deformation prediction method provided by an embodiment of the present application;

[0050] Figure 2 is a cross-sectional view of a foundation pit provided by an embodiment of the present application;

[0051] Figure 3 is a foundation pit deformation display diagram provided by an embodiment of the present application;

[0052] Figure 4 It is another foundation pit deformation display diagram provided by the embodiments of the present application;

[0053] Figure 5 It is another foundation pit deformation display diagram provided by the embodiments of the present application;

[0054] Figure 6 It is another foundation pit deformation display diagram provided by the embodiments of the present application;

[0055] Figure 7 It is another foundation pit deformation display diagram provided by the embodiments of the present application;

[0056] Figure 8 It is a specific process schematic diagram of a foundation pit deformation prediction method provided by the embodiments of the present application;

[0057] Figure 9 It is a schematic diagram of the composition modules of a foundation pit deformation prediction system provided by the embodiments of the present application;

[0058] Figure 10 It is a block diagram of the internal structure principle of an intelligent terminal provided by the embodiments of the present application. Detailed implementation manners

[0059] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0060] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0061] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0062] It should be further understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0063] As used in this specification and the appended claims, the term "if" can be interpreted, depending on the context, as "when" or "once" or "in response to determining" or "in response to classifying into". Similarly, the phrase "if determined" or "if classified into [the described condition or event]" can be interpreted, depending on the context, as meaning "once determined" or "in response to determining" or "once classified into [the described condition or event]" or "in response to classifying into [the described condition or event]".

[0064] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0065] Many specific details are set forth in the following description in order to provide a thorough understanding of the present application, but the present application may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0066] At present, construction safety has been paid more and more attention, and the prediction of foundation pit deformation has also been paid more and more attention. Braced excavation plays an important role in urban construction. However, this type of excavation will present complex problems in the interaction between soil and structure, involving the deflection of the side wall of the foundation pit and the ground settlement, thus causing damage to adjacent buildings and underground facilities. In order to ensure the safety of construction conditions, before the next stage of excavation, the deflection of the side wall and the ground settlement in the later excavation stage should be estimated. Overestimation will increase the construction cost, while underestimation will make the excavation unsafe and even cause accidents. Therefore, accurately and reliably predicting the side wall deflection and ground movement is crucial for good engineering practice. For example, it can help engineers make reliable decisions, thereby improving the safety, economy and risk control level of excavation operations.

[0067] In an application scenario, a method of stage update prediction can be used to achieve the back analysis of soil parameters, and the updated soil parameters are used to update the wall and / or ground response in subsequent excavation stages. In a specific application scenario, foundation pit deformation prediction can be carried out based on the Bayesian method. Specifically, an update procedure based on the Bayesian probability framework can be used for foundation pit deformation prediction. However, when carrying out foundation pit deformation prediction based on the Bayesian method, a relatively complex neural network model needs to be used, and the amount of calculation and the calculation complexity are relatively large, which is not conducive to reducing the amount of calculation and the calculation complexity when carrying out foundation pit deformation prediction, and thus is not conducive to improving the processing efficiency of foundation pit deformation prediction. At the same time, in the Bayesian update process, the updated soil parameters can predict the maximum lateral displacement and the maximum ground settlement, but the specific position corresponding to the maximum deformation and the accurate influence area related to the settlement caused by excavation cannot be predicted.

[0068] To solve at least one of the above-mentioned multiple technical problems, in the solution of this application, the foundation pit measurement data corresponding to the foundation pit and the historical deformation monitoring data corresponding to the foundation pit are obtained; a foundation pit finite element model corresponding to the foundation pit is constructed according to the foundation pit measurement data; the soil layer parameters to be optimized corresponding to the current iteration round and the first prediction depth matched with the historical deformation monitoring data are obtained; according to the soil layer parameters to be optimized and the first prediction depth, through the foundation pit finite element model, the first deformation prediction data corresponding to the foundation pit at the first prediction depth is determined; according to the first deformation prediction data and the historical deformation monitoring data, the deformation prediction error corresponding to the soil layer parameters to be optimized is determined; according to the soil layer parameters to be optimized, the soil layer parameters to be optimized corresponding to the next iteration round are determined, and the step of determining the first deformation prediction data corresponding to the foundation pit at the first prediction depth through the foundation pit finite element model according to the soil layer parameters to be optimized and the first prediction depth is returned and executed until a preset parameter iteration termination condition is met, and the target soil layer parameters after optimization are determined according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round; the second prediction depth is obtained, and according to the target soil layer parameters and the second prediction depth, through the foundation pit finite element model, the second deformation prediction data corresponding to the foundation pit at the second prediction depth is determined.

[0069] Compared with the prior art, in the solution corresponding to the foundation pit deformation prediction method provided by this application, there is no need to rely on a complex neural network model, but the foundation pit deformation prediction is carried out based on the foundation pit finite element model. Specifically, after optimizing the soil layer parameters to be optimized based on the historical deformation monitoring data and obtaining the target soil layer parameters after optimization, the foundation pit deformation prediction is carried out based on the foundation pit finite element model. In this way, it is beneficial to reduce the amount of calculation and the calculation complexity when carrying out foundation pit deformation prediction, and thus is beneficial to improving the processing efficiency of foundation pit deformation prediction.

[0070] As Figure 1 shown, an embodiment of the present application provides a method for predicting foundation pit deformation. Specifically, the above method includes the following steps:

[0071] Step S100: Obtain the foundation pit measurement data corresponding to the foundation pit and the historical deformation monitoring data corresponding to the above foundation pit.

[0072] Specifically, the foundation pit in the embodiment of the present application refers to the foundation pit for which foundation pit deformation prediction is required. The above foundation pit measurement data can characterize the geotechnical characteristics or geotechnical properties corresponding to the foundation pit, and the above historical deformation monitoring data is the foundation pit deformation monitoring data at each construction stage of the foundation pit.

[0073] During each stage of the construction of the foundation pit, the deformation of the foundation pit is monitored in real time to obtain the foundation pit deformation monitoring data. For example, at the current moment, the foundation pit has been excavated to 10 meters, and the above historical deformation monitoring data may include the foundation pit deformation monitoring data corresponding to 10 meters and each stage before 10 meters. The excavation depth corresponding to each stage can be determined according to the actual engineering excavation depth.

[0074] Step S200: Construct a finite element model of the foundation pit corresponding to the above foundation pit according to the above foundation pit measurement data.

[0075] Figure 2 is a cross-sectional view of a foundation pit provided by an embodiment of the present application. It should be noted that the specific data shown in the embodiment of the present application is obtained based on the on-site measurement of a super-deep foundation pit project.

[0076] It should be noted that the above foundation pit measurement data may include the foundation pit plan, the measurement data of the diaphragm wall deformation and the ground surface settlement, the parameters of the materials of the foundation pit supports, the materials and parameters of the diaphragm wall, and the relevant engineering geological data. These data are obtained through on-site monitoring, should cover the key areas of the foundation pit, and at the same time, the information of the construction working conditions can also be obtained as a reference.

[0077] In an application scenario, the above foundation pit measurement data includes geotechnical data and engineering measurement data. Specifically, the above engineering measurement data may include one or more of the following parameters: the plane size and cross-sectional size of the foundation pit; the depth, thickness, unit weight, and elastic modulus of the diaphragm wall; the position, length, cross-sectional area, spacing, and elastic modulus of the supports; the above geotechnical data includes one or more of the following parameters: the thickness of each soil layer and the basic physical and mechanical parameters of the soil layer (specifically including unit weight, internal friction angle, cohesion, water content, and permeability coefficient); the groundwater level.

[0078] Based on the above geotechnical data and relevant engineering data, a corresponding finite element model of the foundation pit is established in the finite element analysis software Plaxis 2D. And the soil layers for which the parameters need to be back-analyzed and optimized can be further selected. Among them, the above finite element model of the foundation pit can predict the deformation of the foundation pit when excavated to a specified depth based on the input soil parameters.

[0079] It should be noted that the specific foundation pit measurement data required can also be adjusted according to actual needs, and no specific limitation is made here. For the missing specific foundation pit measurement data parameters, the default values in the finite element analysis software can be used, and no specific limitation is made here either.

[0080] In an application scenario, the foundation pit deformation prediction is implemented based on the corresponding code or program. Specifically, the pre-processing and post-processing links of the finite element analysis software are connected to the parameter optimization process in the main program. Among them, the pre-processing process is the modeling and parameter setting links in the finite element software, and the post-processing link is to determine the foundation pit deformation prediction result according to the output of the finite element model of the foundation pit in the finite element analysis software. The main program is the main program for implementing the foundation pit deformation prediction function, which includes the previous data processing part (such as adjusting the format of parameters), the parameter optimization part, the final result output part, etc. And the deformation prediction part is implemented by the main program calling the finite element model of the foundation pit in the finite element software.

[0081] Before running the main program, the code controlling the pre-processing and post-processing of the finite element analysis software is integrated into the main program, so that only the main program needs to be run subsequently, and the update of the model parameters and the extraction of the post-processing data will be automatically completed by the main program. Specifically, when running the main program, the Python code controlling the pre-processing and post-processing of the finite element analysis software Plaxis 2D is connected to the main program. Subsequently, only the main program needs to be run, and the update of the model parameters and the extraction of the post-processing data are automatically performed by the main program each time. The code connected to the main program is also different when using different finite element software, and can be set and adjusted according to actual needs, and no specific limitation is made here.

[0082] Step S300, obtain the soil layer parameters to be optimized corresponding to the current iteration round, and the first predicted depth matching the above historical deformation monitoring data.

[0083] It should be noted that if the current iteration round is the initial iteration round, the soil layer parameters to be optimized corresponding to the current iteration round can be determined according to the preset values. The above preset values can be pre-input by the user, or the default initial values can be used, or a set of randomly generated initial values can be used, and no specific limitation is made here.

[0084] If the current iteration round is not the initial iteration round, the soil layer parameters to be optimized corresponding to the current iteration round are determined according to the soil layer parameters to be optimized corresponding to the previous iteration round.

[0085] The above first prediction depth is used to control the final depth when the foundation pit finite element model makes a prediction. Specifically, it is determined according to the deepest excavation depth corresponding in the historical deformation monitoring data. For example, if the historical deformation monitoring data includes the foundation pit deformation monitoring data at each stage when excavated to 10 meters, the first prediction depth can be set to 10 meters, or can be set to a value less than 10 meters. In this application, taking the setting of 10 meters as an example for illustration, it is to make full use of the historical deformation monitoring data, improve the accuracy of parameter optimization, and then improve the prediction accuracy.

[0086] Step S400, according to the above soil layer parameters to be optimized and the above first prediction depth, through the above foundation pit finite element model, determine the first deformation prediction data corresponding to the foundation pit at the above first prediction depth.

[0087] In an application scenario, the foundation pit finite element model calculates and outputs the first deformation prediction data matching multiple different excavation stages (the excavation depth corresponding to each excavation stage is different) including the first prediction depth based on the above soil layer parameters to be optimized and the above first prediction depth, that is, the first deformation prediction data only includes the deformation prediction data corresponding to when excavated to the first prediction depth and multiple excavation stages before excavated to the first prediction depth. At this time, the error calculation can be carried out based on the foundation pit deformation monitoring data corresponding to each stage in the historical deformation monitoring data. In this way, the accuracy of error calculation can be further improved, thereby improving the parameter optimization effect and then improving the final prediction effect.

[0088] Specifically, each iteration round corresponds to multiple groups of soil layer parameters to be optimized with different values;

[0089] The above determining the first deformation prediction data corresponding to the foundation pit at the above first prediction depth according to the above soil layer parameters to be optimized and the above first prediction depth through the above foundation pit finite element model includes:

[0090] For each group of soil layer parameters to be optimized, according to this group of soil layer parameters to be optimized and the above first prediction depth, through the above foundation pit finite element model, determine the first deformation prediction data corresponding to this group of soil layer parameters to be optimized.

[0091] Among them, the number of groups of soil layer parameters to be optimized corresponding to each iteration round can be set and adjusted according to actual needs. For example, it can be set that there are 50 groups of soil layer parameters to be optimized in one generation. Among them, each group of soil layer parameters to be optimized is a complete set of soil layer parameters to be optimized, that is, it includes all the soil layer parameters to be optimized required for the calculation of the foundation pit finite element model. The categories of the soil layer parameters to be optimized in different groups are the same, only the values are different.

[0092] Specifically, initially, the soil layer parameters to be optimized are input into the optimization program, and these parameters are configured (i.e., initialized) in the main program, and at the same time, an error calculation function is set. In the embodiment of the present application, the constitutive model selected for the soil body is the small strain soil hardening (HSS, HS-Small) model, and the selected parameters to be optimized include: μ and the small strain parameters and γ 0.7 , and the soil layers to be optimized include the silty clay layer and the clay layer. Therefore, there are a total of 12 parameters, that is, a group of soil layer parameters to be optimized includes 12 parameters. Among them, represents the tangent modulus; represents the secant modulus, represents the unloading and reloading modulus, μ represents the Poisson's ratio, represents the initial shear modulus, γ 0.7 represents the shear strain. Specifically, the ②, ③, and ④ layers with similar compression coefficients in Figure 2 can be combined into a silty clay layer, and the clay and silty clay layers in the ⑤ layer with similar compression coefficients in Figure 2 can be combined into a clay layer.

[0093] It should be noted that in the embodiment of the present application, the selected soil layers for which the back analysis parameters need to be optimized are the soil layers with large thickness, high compressibility, and are easily disturbed by the surrounding construction, resulting in a decrease in soil strength, including the soil layers above and below the foundation pit bottom. In the embodiment of the present application, the above two types of soil layers are taken as examples for specific description, but it is not a specific limitation.

[0094] Step S500, according to the above first deformation prediction data and the above historical deformation monitoring data, determine the deformation prediction error corresponding to the above soil layer parameters to be optimized.

[0095] Specifically, the above-mentioned determining the deformation prediction error corresponding to the above soil layer parameters to be optimized according to the above first deformation prediction data and the above historical deformation monitoring data includes:

[0096] For the first deformation prediction data corresponding to each group of soil layer parameters to be optimized, according to the above first deformation prediction data and the above historical deformation monitoring data, determine the candidate prediction error corresponding to this group of soil layer parameters to be optimized;

[0097] Take the minimum of all the above candidate prediction errors as the deformation prediction error corresponding to the soil layer parameters to be optimized in the current iteration round.

[0098] Furthermore, for the first deformation prediction data corresponding to each group of soil layer parameters to be optimized, according to the above first deformation prediction data and the above historical deformation monitoring data, determining the candidate prediction error corresponding to this group of soil layer parameters to be optimized includes:

[0099] For the first deformation prediction data corresponding to each group of soil layer parameters to be optimized, calculate the diaphragm wall deformation error and the ground surface settlement error according to the above first deformation prediction data and the above historical deformation monitoring data;

[0100] Perform weighted summation on the above diaphragm wall deformation error and the above ground surface settlement error to obtain the candidate prediction error corresponding to this group of soil layer parameters to be optimized.

[0101] Among them, the weight values corresponding to the above diaphragm wall deformation error and the above ground surface settlement error can be set and adjusted according to actual needs, and no specific limitation is made here.

[0102] It should be noted that when determining the error between the monitoring data and the prediction data, the difference between the corresponding deformation monitoring value and the deformation prediction value can be directly used as the error value, or the corresponding error value can also be determined based on a preset error calculation function, and no specific limitation is made here. Among them, the above error calculation function can be set and adjusted according to actual needs.

[0103] For braced excavation, the diaphragm wall deformation and the ground surface settlement are two extremely important indicators reflecting the influence of soil-structure interaction on excavation. After completing the finite element calculation, extract the required data, import the diaphragm wall deformation and the ground surface settlement data monitored in the previous stages, perform interpolation processing on the imported monitoring data so that the coordinate points of each data can coincide with the coordinate points of the data extracted from the calculation results, and calculate the error between the simulation results and the monitoring data at each stage. The error calculation formula between the simulation results and the monitoring data at each stage is shown in the following formula (1):

[0104]

[0105] Among them, x represents a group of soil layer parameters to be optimized in one iteration process, Error(x) represents the candidate prediction error corresponding to this group of soil layer parameters to be optimized, n is the number of observation points (i.e., data points), represents the value corresponding to the i-th observation point in the historical deformation monitoring data, represents the value corresponding to the i-th observation point in the first deformation prediction data, represents the maximum deformation value at the current stage in the historical deformation monitoring data. It should be noted that if the historical deformation monitoring data includes data corresponding to multiple excavation stages, then represents the maximum deformation value at the currently calculated stage. For example, if the first predicted depth is the depth when excavating to the third stage, then is the maximum deformation value when excavating to the third stage. It should be noted that the directly calculated error value may be small. For the convenience of subsequent calculation processes, in the embodiments of the present application, it is also multiplied by 100 times correspondingly to adjust the error value for facilitating subsequent calculations.

[0106] In an application scenario, a calculation formula for deformation prediction error is as shown in formula (2) below:

[0107] min[Error(x)]=min[Error wall (x)×w 1 +Error ground (x)×w 2 (2);

[0108] Among them, min[Error(x)] represents the minimum value among the candidate prediction errors during one round of iteration, that is, the deformation prediction error during this round of iteration. x represents a set of soil layer parameters to be optimized during this round of iteration. Error wall (x) represents the diaphragm wall deformation error, and w 1 represents its corresponding weight value; Error ground (x) represents the surface settlement error, and w 2 represents its corresponding weight value; The weight values of both can be allocated according to actual needs, and no specific limitation is made here.

[0109] It should be noted that the specific calculation methods of the diaphragm wall deformation error and the surface settlement error can refer to the above formula (1). For example, when calculating the diaphragm wall deformation error, the corresponding side wall deformation data is extracted from the first deformation prediction data and the historical deformation monitoring data, and the calculation is performed based on the above formula (1).

[0110] In an application scenario, after calculating the error between the simulation result and the monitoring data at each stage, further calculations can be performed based on the errors corresponding to each stage, as shown in formulas (3) and (4) below:

[0111] Error wall (x)=Error 1 (x)+Error 2 (x)+...+Error m (x) (3);

[0112] Errorground $(x) = Error$ 1 $(x$ 2 ) + Error 2 $(x$ 2 ) +... + Error m $(x$ 2 ) (4);

[0113] Wherein, Error wall $(x)$ is the sum of the diaphragm wall errors at each stage, Error 1 $(x)$ is the diaphragm wall error in the first stage, Error m $(x)$ is the diaphragm wall error in the $m$-th stage, and $m$ is the number of target excavation stages involved in the calculation of the error. Similarly, Error ground $(x)$ is the sum of the surface settlement errors at each stage, Error 1 $(x$ 2 ) is the surface settlement error in the first stage, Error m $(x$ 2 ) is the surface settlement error in the $m$-th stage.

[0114] Step S600: According to the above-mentioned soil layer parameters to be optimized, determine the soil layer parameters to be optimized corresponding to the next iteration round, and return to execute the step of determining the first deformation prediction data corresponding to the above-mentioned foundation pit at the above-mentioned first prediction depth through the above-mentioned foundation pit finite element model according to the above-mentioned soil layer parameters to be optimized and the above-mentioned first prediction depth, until the preset parameter iteration termination condition is met, and determine the target soil layer parameters after optimization according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round.

[0115] Specifically, the determination of the soil layer parameters to be optimized corresponding to the next iteration round according to the above-mentioned soil layer parameters to be optimized includes:

[0116] Obtain the parameter value range and the parameter adjustment step size;

[0117] Generate the soil layer parameters to be optimized corresponding to the next iteration round according to the above-mentioned soil layer parameters to be optimized, the above-mentioned parameter value range and the above-mentioned parameter adjustment step size.

[0118] Wherein, the above-mentioned parameter value range and the parameter adjustment step size can be set and adjusted according to actual needs. The above-mentioned parameter value range is greater than the parameter limit range determined in advance based on experiments, and the optimization program will optimize the parameters within this range and adjust them based on a reasonable parameter adjustment step size.

[0119] In an application scenario, based on the soil layer parameters to be optimized, within the above parameter value range, the above parameter adjustment step is used as the minimum adjustment step (or the maximum adjustment step), and the soil layer parameters to be optimized corresponding to the next iteration round are randomly generated, or adjusted equidistantly to generate the soil layer parameters to be optimized corresponding to the next iteration round. Other parameter generation methods can also be used, which are not specifically limited herein.

[0120] Specifically, the above preset parameter iteration termination conditions include that the number of iterations reaches the preset iteration threshold and / or the deformation prediction error is less than the preset error threshold. It should be noted that the above iteration threshold and error threshold can be set and adjusted according to actual needs. In the embodiments of the present application, to reduce the calculation cost, the iteration threshold is set to 50 - 70 times, but it is not specifically limited.

[0121] In a specific application scenario, the soil layer parameter optimization is carried out according to the following steps: Run the optimization main program, initialize the parameters, and update the soil layer parameters to be optimized of the foundation pit model. The program runs the finite element analysis software, automatically extracts the diaphragm wall deformation and ground surface settlement data in the calculation results after the finite element calculation is completed, and imports the foundation pit deformation monitoring data of each construction stage, including diaphragm wall deformation and ground surface settlement. Calculate the error between the calculation simulation results and the monitoring data of each constructed stage, optimize the parameters to minimize the error, and the program will continue to iterate to generate new parameters until the stop iteration standard is met. Specifically, within the set range, a generation of parameters is randomly generated. In each generation of parameters, after the calculation is completed, the parameters corresponding to the large error are eliminated, and multiple groups of parameters with small errors are taken to continue to iterate to generate a new generation of parameters to minimize the error.

[0122] Step S700, obtain the second predicted depth, and determine the second deformation prediction data corresponding to the foundation pit at the second predicted depth through the above foundation pit finite element model according to the above target soil layer parameters and the above second predicted depth.

[0123] Specifically, the above determining the target soil layer parameters after optimization according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round includes:

[0124] Determine the target iteration round with the smallest deformation prediction error,

[0125] Determine multiple groups of target soil layer parameters according to multiple groups of soil layer parameters to be optimized corresponding to the target iteration round with the smallest deformation prediction error;

[0126] The above determining the second deformation prediction data corresponding to the foundation pit at the second predicted depth through the above foundation pit finite element model according to the above target soil layer parameters and the above second predicted depth includes:

[0127] Sort the values of the deformation prediction errors corresponding to each set of target soil layer parameters, and determine at least one set of target calculated soil layer parameters from the above-mentioned multiple sets of target soil layer parameters according to the sorting results of the deformation prediction errors;

[0128] According to each set of target calculated soil layer parameters and the above-mentioned second prediction depth, respectively determine multiple candidate deformation prediction data through the above-mentioned foundation pit finite element model;

[0129] Determine the above-mentioned second deformation prediction data according to all the above-mentioned candidate deformation prediction data.

[0130] Among them, the second prediction depth is the excavation depth that actually needs to be predicted, and can be set and adjusted according to actual needs. That is, in the embodiments of the present application, for foundation pit deformation prediction, to predict the deformation situation of the foundation pit when it is excavated to the second prediction depth (for example, the next construction stage corresponds to the second prediction depth).

[0131] In the embodiments of the present application, the generation of soil layer parameters to be optimized with the smallest deformation prediction error (including multiple sets of soil layer parameters to be optimized) is used as the target soil layer parameters after optimization corresponding to the target iteration round.

[0132] In an application scenario, calculations can be performed based on all the target soil layer parameters of the target iteration round to determine the second deformation prediction data. In the embodiments of the present application, to further reduce the calculation amount, some target calculated soil layer parameters are selected from all the target soil layer parameters of the target iteration round for calculation. It should be noted that the number of selected target calculated soil layer parameters can be set and adjusted according to actual needs. For example, in the embodiments of the present application, from all the target soil layer parameters of the target iteration round, the top 30% of the parameters with the smallest deformation prediction error values are selected as the target calculated soil layer parameters, that is, the number of groups of target calculated soil layer parameters is 30% of the number of groups of target soil layer parameters of the target iteration round. And the mean value of the candidate deformation prediction data corresponding to each group of target calculated soil layer parameters is used as the final second deformation prediction data.

[0133] It should be noted that in the embodiments of the present application, multiple groups of target calculated soil layer parameters are used to calculate the second deformation prediction data. In addition to calculating the mean value of the candidate deformation prediction data corresponding to each group of target calculated soil layer parameters, the variance can also be calculated to better display the deformation situation. Specifically, the foundation pit deformation in the calculation results can also be extracted, and the mean value and variance of the deformation can be plotted for comprehensive evaluation.

[0134] In the embodiments of the present application, the above method further includes:

[0135] Generate a foundation pit deformation display diagram according to the above-mentioned second deformation prediction data, and output the above-mentioned foundation pit deformation display diagram;

[0136] Among them, the above-mentioned second deformation prediction data includes lateral displacement information and depth information corresponding to multiple data points.

[0137] Specifically, the above-mentioned foundation pit deformation display diagram can be drawn based on the specific deformation conditions in the second deformation prediction data. Figure 3 It is a foundation pit deformation display diagram provided by an embodiment of the present application. It should be noted that Figure 3 shows the lateral deformation curve of the diaphragm wall, and Figure 3 shows the lateral deformation curves of the diaphragm wall corresponding to multiple excavation stages. For example, excavation 2 represents the second stage of excavation, that is, the stage corresponding to two excavations. Similarly, excavation 3 represents the third stage of excavation, and so on. Figure 3 In it, C6 represents the name of the position of the sixth deformation monitoring point of the diaphragm wall of the foundation pit.

[0138] It should be noted that only the predicted deformation curves corresponding to the stages to be predicted can also be drawn in the foundation pit deformation display diagram. Figures 4 to 7 It is the foundation pit deformation display diagram provided by an embodiment of the present application. Specifically, Figures 4 to 7 draws the predicted lateral deformation curve of the diaphragm wall with error bars. In the embodiment of the present application, the deformation is predicted starting from the end of the third excavation until the final depth is excavated. For example, when the excavation reaches 10 meters for the first prediction, the deformation corresponding to 15 meters is predicted; then when the actual excavation reaches 15 meters, the deformation corresponding to 20 meters can be predicted until the excavation to the final depth is completed. It should be noted that in the embodiment of the present application, each excavation is 5 meters as an example for illustration. Figure 4 shows the comparison between the predicted data and the monitored data in the fourth excavation stage, Figure 5 shows the comparison between the predicted data and the monitored data in the fifth excavation stage, Figure 6 shows the comparison between the predicted data and the monitored data in the sixth excavation stage, Figure 7 shows the comparison between the predicted data and the monitored data in the seventh excavation stage. It should be noted that the simulation calculation is performed in the PLaxis 2D software; the first 30% of multiple groups of parameters in the generation with the smallest error are selected for calculation. After the calculation is completed, the deformation of the diaphragm wall is extracted, and their average value and variance are calculated.

[0139] It should be further noted that the specific form of the foundation pit deformation display diagram can be set and adjusted according to actual needs, and no specific limitation is made here.

[0140] As can be seen from the above, in the foundation pit deformation prediction method provided by the embodiments of the present application, foundation pit measurement data corresponding to the foundation pit and historical deformation monitoring data corresponding to the foundation pit are obtained; a foundation pit finite element model corresponding to the foundation pit is constructed according to the foundation pit measurement data; soil layer parameters to be optimized corresponding to the current iteration round and a first prediction depth matching the historical deformation monitoring data are obtained; according to the soil layer parameters to be optimized and the first prediction depth, through the foundation pit finite element model, first deformation prediction data corresponding to the foundation pit at the first prediction depth is determined; according to the first deformation prediction data and the historical deformation monitoring data, a deformation prediction error corresponding to the soil layer parameters to be optimized is determined; according to the soil layer parameters to be optimized, soil layer parameters to be optimized corresponding to the next iteration round are determined, and the step of determining, through the foundation pit finite element model, first deformation prediction data corresponding to the foundation pit at the first prediction depth according to the soil layer parameters to be optimized and the first prediction depth is returned and executed until a preset parameter iteration termination condition is satisfied, and target soil layer parameters after optimization are determined according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round; a second prediction depth is obtained, and according to the target soil layer parameters and the second prediction depth, second deformation prediction data corresponding to the foundation pit at the second prediction depth is determined through the foundation pit finite element model.

[0141] Compared with the prior art, in the solution corresponding to the foundation pit deformation prediction method provided by the embodiments of the present application, there is no need to rely on a complex neural network model, but the foundation pit deformation is predicted based on the foundation pit finite element model. Specifically, the soil layer parameters to be optimized are optimized based on the historical deformation monitoring data, and after obtaining the target soil layer parameters after optimization, the foundation pit deformation is predicted based on the foundation pit finite element model. In this way, it is beneficial to reduce the calculation amount and calculation complexity when predicting the foundation pit deformation, and further beneficial to improve the processing efficiency of the foundation pit deformation prediction.

[0142] In the embodiments of the present application, a specific application scenario is also used to specifically describe the above foundation pit deformation prediction method. Figure 8 It is a specific process schematic diagram of a foundation pit deformation prediction method provided by the embodiments of the present application. Specifically, as Figure 8As shown, in the embodiment of the present application, first obtain the foundation pit measurement data and historical deformation monitoring data, construct a finite element model of the foundation pit according to the foundation pit measurement data, and determine the soil layer parameters to be optimized. Initialize the soil layer parameters to be optimized, and based on the finite element model of the foundation pit, make a prediction for the first prediction depth according to the soil layer parameters to be optimized. Evaluate the error based on the prediction result and the historical deformation monitoring data, and determine whether the parameter iteration termination condition is met. If not, optimize and update the soil layer parameters to be optimized, and return to make a prediction at the first prediction depth again to achieve iterative update of the parameters. On the contrary, if the parameter iteration termination condition is met, determine the optimized target soil layer parameters, and make a corresponding prediction for the second prediction depth.

[0143] Among them, the process from parameter initialization to making a prediction at the second prediction depth can be executed by the main program. And during the operation of the main program, the finite element model of the foundation pit can be automatically called for corresponding prediction. And specifically when making a prediction and evaluating the error, perform simulation calculations, conduct finite element calculations based on the currently input soil layer parameters to be optimized, extract the required post-processing data (including the first deformation prediction data), and calculate the error according to the first deformation prediction data and the historical deformation monitoring data.

[0144] It should be noted that the above preset parameter iteration termination condition can include that the number of iterations reaches the preset iteration threshold and / or the deformation prediction error is less than the preset error threshold. Among them, the deformation prediction error can be determined according to each group of soil layer parameters to be optimized in one generation, or can be determined only according to the first thirty percent (the specific value can be set and adjusted according to actual needs) of the groups of soil layer parameters with relatively small corresponding candidate prediction error rankings in one generation.

[0145] In this way, in the embodiment of the present application, according to the on-site measurement data during the foundation pit excavation process, the parameters of the soil layer of the model are optimized, and the optimized parameters are used to predict the wall deflection and ground surface settlement in the subsequent unexcavated stage. Repeating the above steps can achieve the prediction of the foundation pit deformation during the entire excavation process. And this method can be applied to the actual staged excavation, and combined with the on-site data to predict the deformation in the subsequent stage.

[0146] Based on the foundation pit deformation prediction method provided by the embodiments of the present application, the maximum deformation value of the foundation pit and its occurrence location can be accurately predicted during the excavation process. Its core principle involves constructing a corresponding foundation pit model in finite element software and screening out key soil layer parameters, which will be optimized in subsequent back-analyses. By running the optimization program, the optimized back-analysis parameters can be regarded as the actual parameters corresponding to the soil body when the foundation pit is deformed during the actual construction process. Subsequently, these parameters are fed back into the finite element model, and the corresponding parameters are updated for simulation calculations. By analyzing the simulation results, the deformation data that the foundation pit may generate in the next construction stage can be extracted, and then, using the foundation pit deformation data monitored in the current construction stage, the foundation pit deformation situation in the next stage can be predicted. This method improves the prediction accuracy and can provide a more reliable scientific basis for construction safety.

[0147] As Figure 9 shown, corresponding to the above foundation pit deformation prediction method, the embodiments of the present application further provide a foundation pit deformation prediction system, and the above foundation pit deformation prediction system includes:

[0148] The first data acquisition module 910 is used to acquire the foundation pit measurement data corresponding to the foundation pit and the historical deformation monitoring data corresponding to the above foundation pit;

[0149] The finite element model construction module 920 is used to construct the foundation pit finite element model corresponding to the above foundation pit according to the above foundation pit measurement data;

[0150] The second data acquisition module 930 is used to acquire the soil layer parameters to be optimized corresponding to the current iteration round and the first prediction depth matched with the above historical deformation monitoring data;

[0151] The first prediction module 940 is used to determine the first deformation prediction data corresponding to the above foundation pit at the above first prediction depth through the above foundation pit finite element model according to the above soil layer parameters to be optimized and the above first prediction depth;

[0152] The error calculation module 950 is used to determine the deformation prediction error corresponding to the above soil layer parameters to be optimized according to the above first deformation prediction data and the above historical deformation monitoring data;

[0153] The iteration control module 960 is used to determine the soil layer parameters to be optimized corresponding to the next iteration round according to the above soil layer parameters to be optimized, and return to execute the step of determining the first deformation prediction data corresponding to the above foundation pit at the above first prediction depth through the above foundation pit finite element model according to the above soil layer parameters to be optimized and the above first prediction depth, until the preset parameter iteration termination condition is met, and determine the target soil layer parameters after optimization according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round;

[0154] The second prediction module 970 is configured to obtain a second prediction depth, and determine second deformation prediction data corresponding to the foundation pit at the second prediction depth through the finite element model of the foundation pit according to the target soil layer parameters and the second prediction depth.

[0155] Thus, in the solution provided by the embodiment of the present application, there is no need to rely on a complex neural network model, but the foundation pit deformation prediction is based on the finite element model of the foundation pit. Specifically, after optimizing the soil layer parameters to be optimized based on the historical deformation monitoring data to obtain the target soil layer parameters after optimization, the foundation pit deformation prediction is performed based on the finite element model of the foundation pit. Thus, it is beneficial to reduce the calculation amount and calculation complexity when performing the foundation pit deformation prediction, and further beneficial to improve the processing efficiency of the foundation pit deformation prediction.

[0156] It should be noted that the specific structure and implementation manner of the above foundation pit deformation prediction system and its various modules or units can refer to the corresponding descriptions in the above method embodiments, and will not be elaborated here.

[0157] It should be noted that the division method of each module of the above foundation pit deformation prediction system is not unique, and is not specifically limited here.

[0158] Based on the above embodiments, the present application further provides an intelligent terminal, and its principle block diagram can be as Figure 10 shown. The intelligent terminal includes a processor, a memory, a network interface, and a display screen connected through a system bus. Among them, the processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a foundation pit deformation prediction program. The internal memory provides an environment for the operation of the operating system and the foundation pit deformation prediction program in the non-volatile storage medium. The network interface of the intelligent terminal is used to communicate with an external terminal through a network connection. When the foundation pit deformation prediction program is executed by the processor, it implements the steps of any one of the above foundation pit deformation prediction methods. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.

[0159] Those skilled in the art can understand that Figure 10 the principle block diagram shown in

[0160] In one embodiment, an intelligent terminal is provided. The intelligent terminal includes a memory, a processor, and a foundation pit deformation prediction program stored on the memory and executable on the processor. When the foundation pit deformation prediction program is executed by the processor, the steps of any one of the foundation pit deformation prediction methods provided in the embodiments of the present application are implemented.

[0161] The embodiments of the present application further provide a computer-readable storage medium. A foundation pit deformation prediction program is stored on the computer-readable storage medium. When the foundation pit deformation prediction program is executed by a processor, the steps of any one of the foundation pit deformation prediction methods provided in the embodiments of the present application are implemented.

[0162] It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0163] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the above device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above device can refer to the corresponding process in the foregoing method embodiments and will not be described in detail here.

[0164] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0165] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0166] In the embodiments provided in the present application, it should be understood that the disclosed system / terminal device and method can be implemented in other ways. For example, the system / terminal device embodiments described above are merely illustrative. For example, the above-mentioned division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0167] If the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present application, it can also be completed by a computer program instructing relevant hardware. The above-mentioned computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The above-mentioned computer-readable medium can include: any entity or device that can carry the above-mentioned computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, and software distribution medium, etc. It should be noted that the content included in the above-mentioned computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0168] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for predicting foundation pit deformation, characterized in that: The method comprises: Acquire foundation pit measurement data corresponding to the foundation pit, and historical deformation monitoring data corresponding to the foundation pit; Constructing a foundation pit finite element model corresponding to the foundation pit according to the foundation pit measurement data; Obtaining the soil layer parameters to be optimized corresponding to the current iteration round and the first predicted depth matching the historical deformation monitoring data; According to the soil layer parameters to be optimized and the first predicted depth, determining first deformation prediction data corresponding to the foundation pit at the first predicted depth through the foundation pit finite element model; Determining a deformation prediction error corresponding to the soil layer parameter to be optimized according to the first deformation prediction data and the historical deformation monitoring data; According to the soil layer parameters to be optimized, the soil layer parameters to be optimized corresponding to the next iteration round are determined, and the step of determining the first deformation prediction data corresponding to the foundation pit at the first prediction depth through the foundation pit finite element model according to the soil layer parameters to be optimized and the first prediction depth is returned to execute until the preset parameter iteration termination condition is met, and according to the deformation prediction error corresponding to the soil layer parameters to be optimized in each iteration round, the target soil layer parameters after optimization are determined; A second predicted depth is obtained, and second deformation prediction data corresponding to the foundation pit at the second predicted depth is determined through the foundation pit finite element model according to the target soil layer parameters and the second predicted depth.

2. The method for predicting foundation pit deformation according to claim 1, characterized in that: Any iteration round corresponds to multiple sets of soil layer parameters to be optimized with different values; The step of determining first deformation prediction data corresponding to the foundation pit at the first prediction depth by using the foundation pit finite element model according to the soil layer parameters to be optimized and the first prediction depth includes: For each group of soil layer parameters to be optimized, first deformation prediction data corresponding to the group of soil layer parameters to be optimized is determined through the foundation pit finite element model according to the group of soil layer parameters to be optimized and the first predicted depth.

3. The method for predicting foundation pit deformation according to claim 2, characterized in that: The step of determining the deformation prediction error corresponding to the soil layer parameter to be optimized according to the first deformation prediction data and the historical deformation monitoring data includes: For each set of first deformation prediction data corresponding to the soil layer parameters to be optimized, determining candidate prediction errors corresponding to the set of soil layer parameters to be optimized according to the first deformation prediction data and the historical deformation monitoring data; The smallest one among all the candidate prediction errors is used as the deformation prediction error corresponding to the soil layer parameters to be optimized in the current iteration round.

4. The method for predicting foundation pit deformation according to claim 3, characterized in that: The first deformation prediction data corresponding to each set of soil layer parameters to be optimized, determining candidate prediction errors corresponding to the set of soil layer parameters to be optimized according to the first deformation prediction data and the historical deformation monitoring data, comprises: For each set of first deformation prediction data corresponding to the soil layer parameters to be optimized, the deformation error of the ground-connected wall and the surface settlement error are calculated according to the first deformation prediction data and the historical deformation monitoring data; A weighted summation is performed on the deformation error of the ground-anchored wall and the surface settlement error to obtain candidate prediction errors corresponding to the group of soil layer parameters to be optimized.

5. The method for predicting foundation pit deformation according to claim 1, characterized in that: The step of determining the soil layer parameters to be optimized corresponding to the next iteration round according to the soil layer parameters to be optimized includes: Get the parameter value range and parameter adjustment step; The soil layer parameters to be optimized corresponding to the next iteration round are generated according to the soil layer parameters to be optimized, the parameter value range and the parameter adjustment step size.

6. The method for predicting foundation pit deformation according to claim 3, characterized in that: Determining the optimized target soil layer parameters according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round includes: Determine a target iteration round with the smallest deformation prediction error, and determine multiple sets of target soil layer parameters according to multiple sets of soil layer parameters to be optimized corresponding to the target iteration round with the smallest deformation prediction error; The step of determining second deformation prediction data corresponding to the foundation pit at the second prediction depth by using the foundation pit finite element model according to the target soil layer parameters and the second prediction depth includes: Sorting the values ​​of deformation prediction errors corresponding to each group of target soil layer parameters, and determining at least one group of target calculation soil layer parameters from the multiple groups of target soil layer parameters according to the sorting result of the deformation prediction errors; Calculate soil layer parameters and the second predicted depth according to each group of targets, and determine a plurality of candidate deformation prediction data respectively through the foundation pit finite element model; The second deformation prediction data is determined according to all the candidate deformation prediction data.

7. The method for predicting foundation pit deformation according to claim 1, characterized in that: The method further comprises: generating a foundation pit deformation display diagram according to the second deformation prediction data, and outputting the foundation pit deformation display diagram; The second deformation prediction data includes lateral displacement information and depth information corresponding to a plurality of data points.

8. A foundation pit deformation prediction system, characterized in that: The system comprises: A first data acquisition module is used to acquire foundation pit measurement data corresponding to the foundation pit and historical deformation monitoring data corresponding to the foundation pit; A finite element model building module, used to build a foundation pit finite element model corresponding to the foundation pit according to the foundation pit measurement data; A second data acquisition module is used to obtain the soil layer parameters to be optimized corresponding to the current iteration round, and a first predicted depth matching the historical deformation monitoring data; A first prediction module, used for determining first deformation prediction data corresponding to the foundation pit at the first prediction depth through the foundation pit finite element model according to the soil layer parameters to be optimized and the first prediction depth; An error calculation module, used to determine the deformation prediction error corresponding to the soil layer parameter to be optimized according to the first deformation prediction data and the historical deformation monitoring data; an iterative control module, for determining the soil layer parameters to be optimized corresponding to the next iteration round according to the soil layer parameters to be optimized, and returning to execute the step of determining the first deformation prediction data corresponding to the foundation pit at the first prediction depth through the foundation pit finite element model according to the soil layer parameters to be optimized and the first prediction depth, until the preset parameter iteration termination condition is met, and determining the target soil layer parameters after optimization according to the deformation prediction errors corresponding to the soil layer parameters to be optimized in each iteration round; The second prediction module is used to obtain a second predicted depth, and determine second deformation prediction data corresponding to the foundation pit at the second predicted depth through the foundation pit finite element model according to the target soil layer parameters and the second predicted depth.

9. An intelligent terminal, characterized in that: The intelligent terminal includes a memory, a processor, and a foundation pit deformation prediction program stored in the memory and executable on the processor. When the foundation pit deformation prediction program is executed by the processor, the steps of the foundation pit deformation prediction method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a foundation pit deformation prediction program, and when the foundation pit deformation prediction program is executed by the processor, the steps of the foundation pit deformation prediction method according to any one of claims 1 to 7 are implemented.

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