Assessment Method and Device for Deformation Risk of Existing Buildings

Abstract

The present invention provides a method and device for evaluating the deformation risk of existing buildings, which can be applied to the technical fields of tunnel engineering and construction engineering. The evaluation method includes: obtaining tunnel data, soil data corresponding to a newly built tunnel, and settlement range data; inputting the tunnel data, soil data, and settlement range data into a prediction model to obtain settlement results corresponding to multiple measuring points; and determining a risk assessment result of the building to be evaluated based on the settlement results, the building data to be evaluated, and the settlement range data. The prediction model is obtained by training through the following operations: constructing an initial prediction model using sample tunnel data, sample soil data, sample measuring point data, and multiple initial parameters; updating the multiple initial parameters based on multiple update strategies to obtain multiple target parameters; and updating the initial prediction model using the multiple target parameters to obtain the prediction model. This method can improve the accuracy of the building risk assessment result and reduce the consumption of system resources.

Description

Technical Field

[0001] The present invention relates to the technical fields of tunnel engineering and construction engineering, and particularly relates to a method and device for evaluating the deformation risk of existing buildings. Background Art

[0002] During the excavation of a newly built tunnel, formation disturbance will cause surface settlement of varying degrees. The surface settlement will cause structural deformation or damage to adjacent buildings. Especially under complex geological conditions, the impact on the safety of buildings is particularly significant. In related technologies, the prediction methods for the impact of newly built tunnel construction on building safety mainly include monitoring whether uneven settlement or inclination of the building is caused during the tunnel construction process through monitoring devices (such as settlement markers, crack meters, etc.); or using numerical models to calculate the potential risks of tunnel construction to surrounding buildings.

[0003] The inventor found that in related technologies, the use of monitoring devices requires continuous tracking and monitoring, and it is difficult to comprehensively evaluate building safety only relying on settlement data; traditional prediction models lack consideration of the own information of existing buildings, and the correlation between surface settlement and building deformation is relatively low, resulting in low accuracy of actual prediction results. There is an overfitting problem in the training process of traditional prediction models, and the calculation process is cumbersome and complex, resulting in large consumption of system computing resources. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method, device, equipment, medium and program product for evaluating the deformation risk of existing buildings.

[0005] According to a first aspect of the present invention, there is provided a method for evaluating the deformation risk of an existing building, including: obtaining tunnel data, soil data corresponding to the newly built tunnel, and settlement range data, wherein the tunnel data includes tunnel geometric data and deformation data; inputting the tunnel data, soil data and settlement range data into a prediction model to obtain settlement results corresponding to multiple measurement points; determining a risk assessment result of the building to be evaluated based on the settlement results, the building data to be evaluated and the settlement range data; wherein the prediction model is obtained by the following operations: constructing an initial prediction model using sample tunnel data, sample soil data, sample measurement point data and a plurality of initial parameters; updating the plurality of initial parameters based on a plurality of update strategies to obtain a plurality of target parameters, wherein the plurality of update strategies include a global update strategy and a local update strategy; updating the initial prediction model using the plurality of target parameters to obtain the prediction model.

[0006] The second aspect of the present invention provides an evaluation device for the deformation risk of existing buildings, including: a data acquisition module for acquiring tunnel data, soil data corresponding to a newly built tunnel, and settlement range data, wherein the tunnel data includes tunnel geometry data and deformation data; a data input module for inputting the tunnel data, soil data, and settlement range data into a prediction model to obtain settlement results corresponding to multiple measuring points; an evaluation result determination module for determining a risk evaluation result of the building to be evaluated based on the settlement results, the building data to be evaluated, and the settlement range data; wherein the prediction model is obtained by training through the following operations: constructing an initial prediction model using sample tunnel data, sample soil data, sample measuring point data, and a plurality of initial parameters; updating the plurality of initial parameters based on a plurality of update strategies to obtain a plurality of target parameters, wherein the plurality of update strategies include a global update strategy and a local update strategy; and updating the initial prediction model using the plurality of target parameters to obtain the prediction model.

[0007] The third aspect of the present invention provides an electronic device, including: one or more processors; a memory for storing one or more computer programs, wherein the above-mentioned one or more processors execute the above-mentioned one or more computer programs to implement the steps of the above-mentioned method.

[0008] The fourth aspect of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon, and the above-mentioned computer program or instruction, when executed by a processor, implements the steps of the above-mentioned method.

[0009] The fifth aspect of the present invention further provides a computer program product, including a computer program or instruction, and the above-mentioned computer program or instruction, when executed by a processor, implements the steps of the above-mentioned method.

[0010] According to the evaluation method, device, equipment, medium, and program product for the deformation risk of existing buildings provided by the embodiments of the present invention, through the fusion of the settlement results corresponding to multiple measuring points on the ground surface, the settlement range data, and the building data to be evaluated in a newly built shield tunnel, the correlation between the ground surface settlement and the prediction of building deformation risk is improved, making the accuracy of the building risk evaluation result higher. Since the target parameters are updated based on the global update strategy and the local update strategy, the overfitting problem of the traditional prediction model is avoided, the generalization ability of the model is improved, the consumption of system resources is reduced, and the complex engineering problems in practical applications are further satisfied. Description of the Drawings

[0011] Through the following description of the embodiments of the present invention with reference to the drawings, the above-mentioned content and other objects, features, and advantages of the present invention will become clearer. In the drawings:

[0012] Figure 1Shows an application scenario diagram of a method, device, equipment, medium, and program product for evaluating the deformation risk of existing buildings according to an embodiment of the present invention;

[0013] Figure 2 Shows a flowchart of a method for evaluating the deformation risk of existing buildings according to an embodiment of the present invention;

[0014] Figure 3 Shows a schematic diagram of the layout of measuring points for settlement monitoring corresponding to the excavation of a shield tunnel according to an embodiment of the present invention;

[0015] Figure 4 Shows a schematic diagram of prediction results based on a prediction model, Case 1, and Case 2 according to an embodiment of the present invention;

[0016] Figure 5 Shows a structural block diagram of an apparatus for evaluating the deformation risk of existing buildings according to an embodiment of the present invention;

[0017] Figure 6 Shows a block diagram of an electronic device suitable for implementing a method for evaluating the deformation risk of existing buildings according to an embodiment of the present invention. Detailed implementation manners

[0018] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present invention. However, it is obvious that one or more embodiments can be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts of the present invention.

[0019] The terms used herein are merely for describing specific embodiments and are not intended to limit the present invention. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0020] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0021] In the case of using expressions such as "at least one of A, B, and C", generally, it should be interpreted according to the meaning that those skilled in the art usually understand this expression (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0022] In the process of conceiving the present invention, the inventors found that in the related art, the equipment monitoring method needs to continuously use equipment to track and monitor buildings and tunnels, and it is difficult to comprehensively evaluate building safety only relying on settlement data; the traditional prediction model lacks consideration of the own data of existing buildings, and the correlation between surface settlement and building deformation is relatively low, resulting in low accuracy of actual prediction results. The traditional prediction model has an overfitting problem in the training process, and the calculation process is cumbersome and complex, resulting in large consumption of system computing resources.

[0023] In view of this, the present invention improves the correlation between surface settlement and building deformation risk prediction by fusing the settlement results, settlement range data corresponding to multiple measuring points on the ground, and the data of the building to be evaluated in a newly built shield tunnel, making the accuracy of the building risk assessment result higher. Since the target parameters are updated based on the global update strategy and the local update strategy, the overfitting problem of the traditional prediction model is avoided, the generalization ability of the model is improved, the consumption of system resources is reduced, and further complex engineering problems in practical applications are satisfied.

[0024] An embodiment of the present invention provides a method for evaluating the deformation risk of an existing building. The evaluation method includes: obtaining tunnel data, soil data corresponding to a newly built tunnel, and settlement range data, where the tunnel data includes tunnel geometric data and deformation data; inputting the tunnel data, soil data, and settlement range data into a prediction model to obtain settlement results corresponding to multiple measuring points; determining a risk assessment result of the building to be evaluated based on the settlement results, the data of the building to be evaluated, and the settlement range data; where the prediction model is obtained by training through the following operations: constructing an initial prediction model using sample tunnel data, sample soil data, sample measuring point data, and multiple initial parameters; updating the multiple initial parameters based on multiple update strategies to obtain multiple target parameters, where the multiple update strategies include a global update strategy and a local update strategy; and updating the initial prediction model using the multiple target parameters to obtain the prediction model.

[0025] Figure 1 The application scenario diagram of the method, device, equipment, medium, and program product for evaluating the deformation risk of an existing building according to the embodiment of the present invention is shown.

[0026] As Figure 1As shown, the application scenario according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, a server 105, and a data acquisition device 106. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0027] The data acquisition device 106 may include different types of settlement sensors, test equipment, and measurement equipment. For example, fiber optic sensors and lidar can be used to collect surface settlement and settlement change data. For example, a triaxial test device can be used to measure relevant data of the soil body. For example, a measurement device can be used to measure the range data or area data of the area to be monitored. The data acquisition device 106 and the server 105 can be communicatively connected through the network 104 to send the settlement data to the server 105 for corresponding processing. It can be understood that the type of the settlement sensor can be determined according to the actual situation and is not limited herein.

[0028] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for examples).

[0029] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop portable computers, and desktop computers, etc.

[0030] The server 105 can be a server that provides various services, such as a background management server that supports the websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (only for examples). The background management server can analyze and process data such as user requests received, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0031] It should be noted that the method for evaluating the deformation risk of existing buildings provided by the embodiments of the present invention can generally be executed by the server 105. Correspondingly, the device for evaluating the deformation risk of existing buildings provided by the embodiments of the present invention can generally be set in the server 105. The method for evaluating the deformation risk of existing buildings provided by the embodiments of the present invention can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Correspondingly, the device for evaluating the deformation risk of existing buildings provided by the embodiments of the present invention can also be set in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105.

[0032] It should be understood that Figure 1 the numbers of the terminal devices, networks, data acquisition devices, and servers in

[0033] Figure 2 shows a flowchart of the method for evaluating the deformation risk of existing buildings according to an embodiment of the present invention.

[0034] As Figure 2 shown, the method for evaluating the deformation risk of existing buildings in this embodiment may include operations S210 to S230.

[0035] In operation S210, tunnel data, soil data corresponding to the newly built tunnel, and settlement range data are acquired, where the tunnel data includes tunnel geometric data and deformation data.

[0036] In the embodiments of the present invention, different types of data acquisition devices can be used to acquire tunnel data, soil data, and settlement range data. The soil data may include the strength data of the soil within the settlement range. The deformation data may represent the data related to the volume deformation of the tunnel. The settlement range data may represent the settlement monitoring area corresponding to the construction area of the newly built shield tunnel where settlement risks may exist.

[0037] In operation S220, the tunnel data, soil data, and settlement range data are input into the prediction model to obtain settlement results corresponding to multiple measurement points.

[0038] In an embodiment of the present invention, the prediction model can be a model that meets a preset prediction accuracy obtained by training an initial prediction model, and can be used to predict the settlement values of measurement points at different horizontal distances from the axis of a newly built tunnel. The measurement points can be multiple monitoring points arranged on the ground within the settlement range, and the arrangement method of the monitoring points can be determined according to the data volume requirements and actual engineering conditions, which will not be limited herein. The settlement result can be the settlement value obtained by comparing the elevation of the measurement points before and after the tunnel excavation for different measurement points.

[0039] In operation S230, based on the settlement result, the building data to be evaluated, and the settlement range data, determine the risk assessment result of the building to be evaluated.

[0040] In an embodiment of the present invention, the building data to be evaluated can be building-related data indicating the existence of deformation risks in the area adjacent to the newly built tunnel. The risk assessment result can represent the degree of deformation caused to the building to be evaluated during or after the tunnel excavation, and can be represented by different deformation levels.

[0041] Preferably, the prediction model is obtained by the following training operations, and the training operations include operation S221 to operation S223.

[0042] In operation S221, construct an initial prediction model using sample tunnel data, sample soil data, sample measurement point data, and multiple initial parameters.

[0043] In operation S222, update the multiple initial parameters based on multiple update strategies to obtain multiple target parameters, where the multiple update strategies include a global update strategy and a local update strategy.

[0044] In operation S223, update the initial prediction model using the multiple target parameters to obtain the prediction model.

[0045] In an embodiment of the present invention, the sample data can include sample tunnel data, sample soil data, and sample measurement point data. The sample data can be sample data related to the newly built tunnel obtained by a data acquisition device during a historical time period, and can be used to construct the initial prediction model.

[0046] In an embodiment of the present invention, the global update strategy can represent a subtraction mean optimization algorithm that uses the subtraction mean of multiple agents to update the positions of population members in the search space. The local update strategy can represent a local reinforcement optimization algorithm. The global update strategy and the local update strategy can be combined, and the combined update strategy is used to update the multiple initial parameters in the initial prediction model to obtain multiple target parameters, and then the target parameters are substituted into the initial prediction model to obtain a prediction model that meets the prediction accuracy.

[0047] In a feasible embodiment, after obtaining tunnel data, soil data corresponding to a newly built tunnel, and settlement range data, considering the large amount of data and its impact on calculation efficiency, various data can be processed through data processing techniques, including extracting key data features, segmenting data, and reducing data dimensions.

[0048] For example, extract the features with the greatest contribution rate to prediction from various raw data (for example, extract the rate of change of soil pressure based on soil information) to reduce the data dimension and improve calculation efficiency. It can be understood that the data obtained by the data acquisition device is time series data. For time series data, time series analysis methods (such as autoregressive moving average model, autoregressive integrated moving average model) can be used for preprocessing to extract trend component data, thereby capturing the variation law of the data. For long time series data, it can be segmented for processing, and each segment of data is preprocessed and modeled separately to reduce the amount of data processed at one time and improve calculation efficiency. Through the above data processing methods, the complexity and calculation amount of the data can be effectively reduced, while the prediction accuracy and reliability are improved.

[0049] According to the embodiments of the present invention, by fusing the settlement results corresponding to multiple measuring points on the ground surface, the settlement range data, and the data of the building to be evaluated in a newly built shield tunnel, the correlation between the ground surface settlement and the prediction of building deformation risk is improved, making the accuracy of the building risk assessment result higher. Since the target parameters are updated based on the global update strategy and the local update strategy, the overfitting problem of the traditional prediction model is avoided, the generalization ability of the model is improved, the consumption of system resources is reduced, and the complex engineering problems in practical applications are further satisfied.

[0050] Figure 3 The schematic diagram of the measuring point layout for settlement monitoring corresponding to the excavation of the shield tunnel according to the embodiment of the present invention is shown.

[0051] As Figure 3 shown, the ground surface settlement occurring during the excavation of the newly built tunnel 31 poses a risk of settlement deformation to the existing building 32. By obtaining tunnel data corresponding to the newly built tunnel, such as the tunnel diameter D and the distance z0 from the tunnel center to the ground surface; settlement range data, such as the range and total width 2w of the settlement trough, and soil data corresponding to multiple measuring points 33, the above obtained data is input into the prediction model to obtain the settlement results corresponding to multiple measuring points 33, including the maximum settlement value S(x) max ; and then based on the settlement results, the length L of the building to be evaluated b and the settlement range data (settlement trough data), determine the risk assessment result of the building to be evaluated.

[0052] According to the embodiments of the present invention, the method further includes: determining the settlement range data based on the tunnel geometric data and the soil data.

[0053] In an embodiment of the present invention, the tunnel geometric data may include the diameter of the newly built tunnel and the distance value from the center line of the newly built tunnel to the ground surface. The settlement range can be regarded as the settlement trough range corresponding to the surface settlement risk existing for the newly built tunnel (shield tunnel), and the settlement trough can be calculated based on the diameter of the newly built tunnel in the tunnel geometric data and the strength data of the soil mass (such as the internal friction angle).

[0054] For example, denoting the internal friction angle of the soil mass as φ, the diameter of the newly built tunnel as D, and the distance value from the center line of the newly built tunnel to the ground surface as z0, the width w of the settlement trough is calculated as follows, as shown in formula (1):

[0055] (1);

[0056] Wherein, w can represent the width of the settlement trough. It can be understood that the total width of the settlement trough is 2w.

[0057] According to an embodiment of the present invention, the settlement result includes the maximum settlement value corresponding to the measuring point;

[0058] Based on the settlement result, the building data to be evaluated, and the settlement range data, determine the risk assessment result of the building to be evaluated, including: using the maximum settlement value, the building data to be evaluated, and the settlement range data to determine the deformation result of the building to be evaluated; mapping the deformation result to the risk level data set to determine the risk assessment result.

[0059] In an embodiment of the present invention, the settlement result includes the settlement values respectively corresponding to multiple measuring points, including the maximum settlement value and the minimum settlement value. The building to be evaluated includes the building length. The deformation result is proportional to the degree of deformation of the building. Taking the maximum settlement value as the central point position, the deformation result of the adjacent building can be calculated. The deformation result can represent the change amount of the relative angle between different parts of the building. The risk level data set can represent the corresponding relationship between the building deformation result and the degree of building deformation, and take the determined degree of building deformation as the risk assessment result of the building to be evaluated.

[0060] For example, denoting the deformation result as ψ, it can be calculated by the following formula (2):

[0061] (2);

[0062] Wherein, S(x) max can represent the maximum settlement value, x can represent the central point position corresponding to the maximum settlement value, L b can represent the length of the building to be evaluated, and w can represent the width of the settlement trough.

[0063] In an embodiment of the present invention, the deformation degree of a building can be divided into different levels, and the relationship between the deformation result of the building and the building deformation degree level can be as shown in Table 1 below.

[0064] Table 1

[0065]

[0066] According to an embodiment of the present invention, a plurality of initial parameters are updated based on a plurality of update strategies to obtain a plurality of target parameters, including: determining an initial value and an objective function corresponding to the initial parameter; updating the initial value based on a global update strategy and the objective function to obtain a global target value; and determining the target parameter based on the global target value and a local update strategy.

[0067] In an embodiment of the present invention, the initial parameters of the initial prediction model include a plurality. The initial values corresponding to the plurality of initial parameters can be determined according to experience or an initialization strategy. The objective function can be constructed based on the model prediction value corresponding to the initial parameter and the actual monitoring value. The global update strategy can be used to obtain the global target value corresponding to the initial parameter. On this basis, the local update strategy is used to update and optimize the global target value to obtain the target parameter that meets the convergence condition.

[0068] In an embodiment of the present invention, an initial prediction model can be constructed using sample tunnel data, sample soil data, sample measurement point data, and a plurality of initial parameters. The initial prediction model is denoted as S(x), and the initial prediction model is as shown in the following formula (3):

[0069] (3);

[0070] where x can represent the horizontal distance of the measurement point x from the central axis of the newly built tunnel, V L can represent the volume change rate of the tunnel, D can represent the diameter of the newly built tunnel, φ can represent the internal friction angle of the soil, and A and B can represent the initial parameters of the initial prediction model.

[0071] For example, take the global update strategy as the Subtraction-Average-Based Optimizer (SABO) and the local update strategy as the Partial Reinforcement Optimizer (PRO). The SABO algorithm can be combined with the PRO algorithm, and the advantages of the two algorithms can be combined using the hybrid algorithm (SABO-PRO algorithm) to obtain a strategy with both global and local dual optimizations. Through the SABO algorithm, a wide search is first carried out in the entire parameter space to prevent falling into local optimal solutions, and then the PRO algorithm is used to perform local fine-tuning on the candidate solutions based on the global search to better adapt to the specific optimization goal and obtain the target parameters.

[0072] According to an embodiment of the present invention, the SABO-PRO hybrid optimization algorithm combines the advantages of the Subtraction-Average-Based Optimizer (SABO) and the Partial Reinforcement Optimizer (PRO), and can effectively balance global search and local fine-grained optimization. SABO is responsible for global search to avoid falling into local optimal solutions, while PRO improves the quality of the solution through fine-tuning. When dealing with complex optimization problems, this hybrid algorithm saves the consumption of computing resources in the model training process and further improves the accuracy and optimization efficiency of the solution.

[0073] It can be understood that the update of the initial parameters based on the global update strategy and the local update strategy has been described above. Next, how to use the global update strategy to obtain the global target value will be further described.

[0074] According to an embodiment of the present invention, updating the initial value based on the global update strategy and the objective function to obtain the global target value includes: updating the initial value based on the global update strategy to obtain an intermediate value; determining the error between the intermediate value and the objective function; and when the error is less than or equal to a preset error, determining the intermediate value as the global target value.

[0075] In an embodiment of the present invention, the intermediate value can represent the intermediate parameter value in the process of updating the initial parameters using the global update strategy. The error between the intermediate prediction value obtained by the intermediate prediction model corresponding to the intermediate parameter value and the target value (actual monitoring value) is calculated based on the objective function. When the error (which can be represented by the fitness value) is less than or equal to the preset error, the global target value is obtained.

[0076] For example, the process of using the SABO algorithm to update the initial parameters to obtain the global target value may include operation S301 to operation S305.

[0077] In operation S301, the optimization objective is to update the initial parameters A and B to minimize the error between the predicted value of S(x) and the target value S. target The error between S(x) and the predicted value is minimized. The objective function f(A, B) can be defined, and the optimization objective is to minimize the error. The objective function f(A, B) is shown in the following formula (4):

[0078] (4);

[0079] where x i can represent the distance from different measuring points on the ground surface to the tunnel axis, and S target (x i ) is the settlement value obtained by settlement sensors at different distances from the tunnel axis. S(x i ; A, B) is the settlement value of measuring points at different distances from the tunnel axis calculated after updating the initial parameters A and B. N can represent the number of measuring points on the ground surface.

[0080] In operation S302, the population is initialized. n individuals are randomly generated as initial values within the parameter value ranges of the initial parameters A and B. The corresponding values of the initial parameters A and B within the parameter value ranges can be denoted as A i and B i . Specifically, they are shown in the following formulas (5)-(6):

[0081] (5);

[0082] (6);

[0083] where A i can represent the value of the i-th individual within the value range of A in the population, and the value range of A is [0.1, 10]; B i is the value of the i-th individual within the value range of B in the population, and the value range of B is [0.1, 10]; rand can represent a uniformly distributed random number [0, 1], A max can represent the maximum value of the initial parameter A within the value range, A min can represent the minimum value of the initial parameter A within the value range, B max can represent the maximum value of the initial parameter B within the value range, and B min can represent the minimum value of the initial parameter B within the value range.

[0084] In operation S303, the fitness is calculated. It can be determined by the square of the error between the intermediate predicted value predicted by the intermediate prediction model and the target value. Specifically, it is shown in the following formula (7):

[0085] (7);

[0086] Among them, f(A i , B i ) can represent the objective function value (the squared error between the intermediate prediction value and the target value).

[0087] In operation S304, population update (subtraction average). Randomly select two individuals (A1, B1) and (A2, B2) in the population, then calculate the average of the differences, and generate new individuals. Specifically, as shown in the following formulas (8)-(9):

[0088] (8);

[0089] (9);

[0090] Among them, A new can represent the value of the new individual A, and B new can represent the value of the new individual B. A1 and B1 represent the numerical values of the first individual randomly selected in the population, A2 and B2 represent the numerical values of the second individual randomly selected in the population, and α is a scaling factor, and its value can be (0, 1].

[0091] In operation S305, repeatedly execute operation S303 and operation S304 until the change in the fitness value is less than the set threshold (for example, the threshold is set to 10 -6 ), and obtain the global objective value (A * , B * ).

[0092] According to the embodiments of the present invention, by using the SABO algorithm through the strategy of subtraction average, the search range is gradually reduced during the search process, so that the initial parameters can be potentially optimized globally during the model training process. Compared with traditional local search methods, SABO has stronger global exploration ability. By initially generating multiple candidate solutions in the entire search space and gradually reducing the search range according to the average value, SABO can ensure exploring the solution space from multiple perspectives and finding a better optimization direction. It can avoid falling into local optimal solutions.

[0093] It can be understood that the above has illustrated the example of how to use the global update strategy to obtain the global objective value. Next, how to use the local update strategy to determine the target parameters will be further described.

[0094] According to an embodiment of the present invention, determining target parameters based on a global target value and a local update strategy includes: using the global target value as a local initial value and determining a local update range based on the local update strategy; determining candidate values based on the local initial value and the local update range, where the local update range is determined according to the global target value; comparing the difference between the candidate value and the global target value based on a preset reward strategy; and determining the target parameter when the difference is less than a first difference threshold.

[0095] In an embodiment of the present invention, the local update range can represent the range of local search starting from the global target value as the starting point of local search; thus, searching for candidate values corresponding to the local initial value within the local update range, and determining whether to update the current optimal solution by comparing the performance of the local initial value and the candidate value in model prediction. The preset reward update strategy can be a method of calculating the reward value by comparing the performance of the current solution and the candidate solution.

[0096] For example, the method of determining target parameters based on the global target value and the local update strategy may include operations S310 to S340.

[0097] In operation S310, use the global target value (A * , B * ) as the starting point of the local update strategy and define the range of local search. The local search range corresponding to the initial parameter A can be , and the local search range corresponding to the initial parameter B can be , where can take 10%A * , can take 10%B * .

[0098] In operation S320, generate candidate values. Candidate values can be generated within the local update range defined by A and B. Specifically, as shown in the following formulas (10)-(11):

[0099] (10);

[0100] (11);

[0101] where rand is a uniformly distributed random number [0, 1], can represent the candidate value corresponding to the initial parameter A, can represent the candidate value corresponding to the initial parameter B.

[0102] In operation S330, compare the performance of the candidate value and the current optimal solution based on the preset reward strategy, calculate the reward value, and decide whether to update the optimal solution. The reward value is calculated as shown in the following formula (12).

[0103] (12);

[0104] Where: R(A,B) is the reward value. A value greater than 0 can indicate that the candidate value is better than the current solution. A value equal to 0 can indicate that the candidate value is the same as the current solution. A value less than 0 can indicate that the candidate value is worse than the current solution. If the candidate value is better, then update the current optimal solution.

[0105] In operation S340, convergence judgment is performed. When the change in R(A,B) is less than a set threshold (e.g., 10 -6 ), it can indicate that the update has tended to be stable, and then the target parameters A optimal and B optimal .

[0106] According to an embodiment of the present invention, by using the global objective value as the initial value of the local update strategy (PRO), it is possible to avoid the local update strategy starting training from a random or less excellent initial point, significantly accelerate the convergence process of the model, reduce the initial exploration process, enable the local optimization to quickly focus on a better region, and improve the overall update and optimization efficiency.

[0107] According to an embodiment of the present invention, using multiple target parameters to update the initial prediction model to obtain a prediction model includes: substituting the target parameters into the initial prediction model to obtain an intermediate prediction model; determining a reference settlement result corresponding to the target measurement point based on the intermediate prediction model; and taking the intermediate prediction model as the prediction model when the settlement difference between the reference settlement result and the target settlement result is less than a second difference threshold.

[0108] In an embodiment of the present invention, after obtaining the target parameters A optimal and B optimal , the target parameters A optimal and B optimal can be substituted into the initial prediction model to obtain an intermediate prediction model, and calculate the maximum settlement of the ground surface at x = 0 of the target measurement point (the measurement point corresponding to the central axis of the newly built tunnel) predicted by the intermediate prediction model and the maximum settlement value measured by the settlement sensor at the actual target measurement point of the settlement difference, and judge whether it is necessary to iteratively update the model parameters according to the settlement difference. The calculation method of the maximum settlement is shown in the following formula (13):

[0109] (13);

[0110] The maximum settlement of the ground surface at the target measurement point x = 0 and the maximum settlement value measured by the settlement sensor at the actual target measurement point The error (settlement difference) can be denoted as , the error is calculated as shown in the following formula (14):

[0111] (14);

[0112] By determining the maximum settlement of the ground surface at the target measurement point x = 0 and the maximum settlement value measured by the settlement sensor at the actual target measurement point if the error is not less than the second difference threshold (e.g., 2%), otherwise, the parameters are updated again using the global update strategy and the local update strategy.

[0113] According to an embodiment of the present invention, the initial value is updated based on the global update strategy to obtain an intermediate value, including: selecting a first individual and a second individual from the initial values; determining the intermediate value based on a scaling factor and the individual difference between the first individual and the second individual.

[0114] In an embodiment of the present invention, the first individual and the second individual can respectively represent the individuals (A1, B1) and (A2, B2) corresponding to the initial parameters A and B respectively. The intermediate value can represent a new individual A new and B new , A new and B new The calculation methods of which can refer to formula (8) and formula (9).

[0115] According to an embodiment of the present invention, the initial value corresponding to the initial parameter is determined by the following operations: determining the parameter value range corresponding to the initial parameter and the valley value and peak value corresponding to the parameter value range; determining the initial value based on the parameter value range, the valley value, the peak value, and the distribution coefficient.

[0116] In an embodiment of the present invention, the initial value can represent randomly generating n individuals within the parameter value ranges of the initial parameters A and B as the initial value. The estimated value can be the maximum value within the parameter value range, and the valley value can represent the minimum value within the parameter value range. The distribution coefficient can represent a random number with uniform distribution within the value range. The calculation method of the initial value can refer to that shown in formula (5) and formula (6).

[0117] Figure 4 shows a schematic diagram of the prediction results based on the prediction model, Case 1, and Case 2 according to an embodiment of the present invention.

[0118] As Figure 4As shown in Figures (a) and (b), in order to verify the technical effects, relevant data from Case 1 and Case 2 were selected for verification, and the maximum ground settlement caused by the excavation of the shield tunnel was measured using settlement sensors. They were 74.87 mm and 29.71 mm respectively. The maximum ground settlement S(x; A optimal , B optimal ) max at the target measurement points was predicted using the prediction model to be 73.17 mm and 30.13 mm. The monitoring results at different measurement points were compared and verified with the predicted settlement results generated by the prediction model of the present invention. The errors between the maximum ground settlement values predicted by the prediction model and the actually monitored maximum settlement values are shown in Table 2 below. The errors do not exceed 5%, and the prediction accuracy of the prediction model can meet the prediction requirements of actual projects.

[0119] Table 2

[0120]

[0121] Based on the above method for evaluating the deformation risk of existing buildings, the present invention also provides an apparatus for evaluating the deformation risk of existing buildings. The following will describe this apparatus in detail in conjunction with Figure 5 this.

[0122] Figure 5 Fig. shows the structural block diagram of the apparatus for evaluating the deformation risk of existing buildings according to an embodiment of the present invention.

[0123] As Figure 5 shown, the apparatus for evaluating the deformation risk of existing buildings in this embodiment includes a data acquisition module 510, a data input module 520, and an evaluation result determination module 530.

[0124] The data acquisition module 510 is used to acquire tunnel data, soil data corresponding to the newly built tunnel, and settlement range data. Among them, the tunnel data includes tunnel geometric data and deformation data. In one embodiment, the data acquisition module 510 can be used to perform the operation S210 described above, which will not be elaborated here.

[0125] The data input module 520 is used to input the tunnel data, soil data, and settlement range data into the prediction model to obtain settlement results corresponding to multiple measurement points. In one embodiment, the data input module 520 can be used to perform the operation S220 described above, which will not be elaborated here.

[0126] The evaluation result determination module 530 is used to determine the risk evaluation result of the building to be evaluated based on the settlement results, the building data to be evaluated, and the settlement range data. In one embodiment, the evaluation result determination module 530 can be used to perform the operation S230 described above, which will not be elaborated here.

[0127] Preferably, the prediction model is trained by the following operations: constructing an initial prediction model using sample tunnel data, sample soil data, sample measurement point data and multiple initial parameters; updating the multiple initial parameters based on multiple update strategies to obtain multiple target parameters, wherein the multiple update strategies include global update strategies and local update strategies; and updating the initial prediction model using multiple target parameters to obtain a prediction model.

[0128] According to an embodiment of the present invention, the data acquisition module 510, the data input module 520 and the assessment result determination module 530 in the assessment device for the deformation risk of existing buildings improve the correlation between surface settlement and building deformation risk prediction by integrating the settlement results and settlement range data corresponding to multiple measuring points on the surface in the newly built shield tunnel with the building data to be assessed, so as to make the building risk assessment result more accurate. Since the target parameters are updated based on the global update strategy and the local update strategy, the overfitting problem of the traditional prediction model is avoided, the generalization ability of the model is improved, the consumption of system resources is reduced, and the complex engineering problems in practical applications are further met.

[0129] According to an embodiment of the present invention, the device further comprises: a settlement range data determination module, which is used to determine the settlement range data based on the tunnel geometry data and the soil data.

[0130] According to an embodiment of the present invention, the settlement result includes the maximum settlement value corresponding to the measuring point; the assessment result determination module 530 includes: a deformation result determination submodule and a risk assessment result determination submodule. The deformation result determination submodule is used to determine the deformation result of the building to be assessed by using the maximum settlement value, the building data to be assessed and the settlement range data; the risk assessment result determination submodule is used to map the deformation result to the risk level data set to determine the risk assessment result.

[0131] According to an embodiment of the present invention, multiple initial parameters are updated based on multiple update strategies to obtain multiple target parameters, including: determining initial values ​​and target functions corresponding to the initial parameters; updating the initial values ​​based on a global update strategy and the target function to obtain a global target value; and determining the target parameters based on the global target value and a local update strategy.

[0132] According to an embodiment of the present invention, the initial value is updated based on the global update strategy and the objective function to obtain the global objective value, including: updating the initial value based on the global update strategy to obtain an intermediate value; determining the error between the intermediate value and the objective function; when the error meets the preset error, determining the intermediate value as the global objective value.

[0133] According to an embodiment of the present invention, determining a target parameter based on a global target value and a local update strategy includes: using the global target value as a local initial value, and determining a local update range based on the local update strategy; determining a candidate value based on the local initial value and the local update range, where the local update range is determined according to the global target value; comparing a difference between the candidate value and the global target value based on a preset reward strategy; and determining the target parameter when the difference is less than a first difference threshold.

[0134] According to an embodiment of the present invention, updating an initial prediction model using a plurality of target parameters to obtain a prediction model includes: substituting the target parameter into the initial prediction model to obtain an intermediate prediction model; determining a reference settlement result corresponding to a target measurement point based on the intermediate prediction model; and using the intermediate prediction model as the prediction model when a settlement difference between the reference settlement result and the target settlement result is less than a second difference threshold.

[0135] According to an embodiment of the present invention, updating an initial value based on a global update strategy to obtain an intermediate value includes: selecting a first individual and a second individual from the initial value; and determining the intermediate value based on a scaling factor and an individual difference between the first individual and the second individual.

[0136] According to an embodiment of the present invention, an initial value corresponding to an initial parameter is determined by the following operations: determining a parameter value range corresponding to the initial parameter, and a valley value and a peak value corresponding to the parameter value range; and determining the initial value based on the parameter value range, the valley value, the peak value, and a distribution coefficient.

[0137] According to an embodiment of the present invention, any plurality of modules among the information acquisition module 510, the information input module 520, and the evaluation result determination module 530 may be combined and implemented in one module, or any one of the modules may be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules may be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the information acquisition module 510, the information input module 520, and the evaluation result determination module 530 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on a substrate, a system in a package, an application specific integrated circuit (ASIC), or may be implemented by any other reasonable manner of integrating or packaging circuits, etc., in hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the information acquisition module 510, the information input module 520, and the evaluation result determination module 530 may be at least partially implemented as a computer program module, and when the computer program module is run, it can execute the corresponding functions.

[0138] Figure 6 A block diagram of an electronic device suitable for implementing the method for evaluating the deformation risk of existing buildings according to an embodiment of the present invention is shown.

[0139] As Figure 6 shown, the electronic device according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 601 may also include on-board memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0140] In the RAM 603, various programs and data required for the operation of the electronic device are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to an embodiment of the present invention by executing the program in the ROM 602 and / or the RAM 603. It should be noted that the program may also be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to an embodiment of the present invention by executing the program stored in the one or more memories.

[0141] According to an embodiment of the present invention, the electronic device may further include an input / output (I / O) interface 605, and the input / output (I / O) interface 605 is also connected to the bus 604. The electronic device may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read from it can be installed into the storage portion 608 as needed.

[0142] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiments of the present invention is implemented.

[0143] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the above-described ROM 602 and / or RAM 603 and / or one or more memories other than ROM 602 and RAM 603.

[0144] An embodiment of the present invention further includes a computer program product, which includes a computer program that contains program code for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program code is used to enable the computer system to implement the method for evaluating the risk of deformation of existing buildings provided by the embodiments of the present invention.

[0145] When the computer program is executed by the processor 601, the above functions defined in the system / apparatus of the embodiments of the present invention are executed. According to an embodiment of the present invention, the above-described systems, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0146] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium and downloaded and installed through the communication part 609, and / or installed from the removable medium 611. The program code included in the computer program may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0147] In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiments of the present invention are performed. According to the embodiments of the present invention, the systems, devices, apparatuses, modules, units, etc. described above can be implemented by computer program modules.

[0148] According to the embodiments of the present invention, the program code for executing the computer program provided by the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include but are not limited to, such as Java, C++, python, the "C" language, or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by connecting through the Internet using an Internet service provider).

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0150] Those skilled in the art can understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present invention.

[0151] The embodiments of the present invention have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although the embodiments have been described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present invention.

Claims

1. An assessment method for the deformation risk of existing buildings, characterized in that, The method includes: Obtaining tunnel data, soil data corresponding to a newly built tunnel, and settlement range data, where the tunnel data includes tunnel geometric data and deformation data; Inputting the tunnel data, the soil data, and the settlement range data into a prediction model to obtain settlement results corresponding to multiple measurement points, where the settlement results include the maximum settlement value corresponding to the measurement point; Using the maximum settlement value, the building data to be evaluated, and the settlement range data to determine the deformation result of the building to be evaluated; Mapping the deformation result to a risk level dataset to determine the risk assessment result of the building to be evaluated; Among them, the prediction model is trained through the following operations: Constructing an initial prediction model using sample tunnel data, sample soil data, sample measurement point data, and multiple initial parameters; Updating the multiple initial parameters based on multiple update strategies to obtain multiple target parameters, where the multiple update strategies include a global update strategy and a local update strategy; Updating the initial prediction model using the multiple target parameters to obtain the prediction model.

2. The method according to claim 1, characterized in that, The method further includes: Determining the settlement range data based on the tunnel geometric data and the soil data.

3. The method according to claim 1, wherein Updating the multiple initial parameters based on multiple update strategies to obtain multiple target parameters, including: Determining an initial value and an objective function corresponding to the initial parameter; Updating the initial value based on the global update strategy and the objective function to obtain a global target value; Determining the target parameter based on the global target value and the local update strategy.

4. The method according to claim 3, characterized in that, Updating the initial value based on the global update strategy and the objective function to obtain a global target value, including: Updating the initial value based on the global update strategy to obtain an intermediate value; Determining the error between the intermediate value and the objective function; When the error is less than or equal to a preset error, determining the intermediate value as the global target value.

5. The method according to claim 3, wherein Determining the target parameter based on the global target value and the local update strategy, including: Taking the global target value as a local initial value and determining a local update range based on the local update strategy; Determining a candidate value based on the local initial value and the local update range, where the local update range is determined according to the global target value; Comparing the difference between the candidate value and the global target value based on a preset reward strategy; When the difference is less than a first difference threshold, determining the target parameter.

6. The method according to any one of claims 3 to 5, characterized in that Updating the initial prediction model using the multiple target parameters to obtain the prediction model, including: Substituting the target parameter into the initial prediction model to obtain an intermediate prediction model; Determining a reference settlement result corresponding to a target measurement point based on the intermediate prediction model; When the settlement difference between the reference settlement result and the target settlement result is less than a second difference threshold, taking the intermediate prediction model as the prediction model.

7. The method according to claim 4, characterized in that, Updating the initial value based on the global update strategy to obtain an intermediate value, including: Selecting a first individual and a second individual from the initial value; Determine the intermediate value based on the scaling factor and the individual difference between the first individual and the second individual.

8. The method according to claim 3, wherein The initial value corresponding to the initial parameter is determined by the following operations: Determine the parameter value range corresponding to the initial parameter, and the valley value and peak value corresponding to the parameter value range; Determine the initial value based on the parameter value range, the valley value, the peak value, and the distribution coefficient.

9. An evaluation device for the deformation risk of existing buildings, characterized in that, The device includes: A data acquisition module, configured to acquire tunnel data, soil body data corresponding to a newly built tunnel, and settlement range data, where the tunnel data includes tunnel geometric data and deformation data; A data input module, configured to input the tunnel data, the soil body data, and the settlement range data into a prediction model to obtain a settlement result corresponding to a plurality of measuring points, where the settlement result includes a maximum settlement value corresponding to the measuring point; A deformation result determination sub-module, configured to use the maximum settlement value, the building data to be evaluated, and the settlement range data to determine the deformation result of the building to be evaluated; A risk assessment result determination sub-module, configured to map the deformation result to a risk level data set to determine the risk assessment result of the building to be evaluated; Wherein, the prediction model is obtained by the following training operations: Construct an initial prediction model by using sample tunnel data, sample soil body data, sample measuring point data, and a plurality of initial parameters; Update the plurality of initial parameters based on a plurality of update strategies to obtain a plurality of target parameters, where the plurality of update strategies include a global update strategy and a local update strategy; Update the initial prediction model by using the plurality of target parameters to obtain the prediction model.

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