A mountainous high fill area bridge damage inducement inversion method based on multi-source fusion

Abstract

The present application relates to the technical field of bridge damage analysis, and discloses a mountainous high fill area bridge damage cause inversion method based on multi-source fusion, comprising the following steps: S1: a parameterized finite element model of the bridge is established, and the axial strain of each pile foundation under the action of different bridge damage causes is obtained; S2: the axial strain of the pile foundation under the same working condition as the physical model experiment is calculated through the parameterized model, and then the parameterized model is corrected using the physical model test data; S3: an inversion model based on a support vector machine is constructed, and the pile foundation strain sample data set is used as the input of the model; S4: the pile foundation strain data under multiple working conditions is obtained by using the corrected parameterized finite element model; S5: the support vector machine inversion model is loaded, and the inversion result is obtained, and the inversion parameters and model evaluation results under the action of multiple damage causes are analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge damage analysis, and more particularly to a method for inverting bridge damage causes in mountainous high-fill areas based on multi-source fusion. Background Art

[0002] Building bridges in mountainous areas with multiple sources requires backfilling to create a certain amount of storage area. However, overhead bridges in high-fill areas have many components and a complex working environment. It is crucial to clearly identify which components are used as measurement information carriers and accurately obtain information on structural mechanical behavior. Traditional methods for obtaining information carriers for bridge components mainly rely on manual inspections, regular testing, and structural monitoring. These methods are all local and intermittent. However, bridges are subject to various external loads, and the response information of the structure is continuous. Therefore, traditional information acquisition methods are incomplete.

[0003] At present, the method of structural damage inversion is mainly used to accurately obtain the information carrier of bridge components. The optimal model closest to the actual deformation law of the structure is identified mainly by observing the change law of information. That is, the response parameters of the structure before and after damage are compared, and the degree of damage of the structure is calculated according to the degree of change of the response parameters. Due to the difficulty in obtaining damage variables, the evolution law of structural damage is not very clear, and the constitutive equation based on damage needs further research. As a result, there are still many difficulties in the research of structural damage inversion. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the present invention provides a damage cause inversion method for bridges in mountainous high-fill areas based on multi-source fusion, which has the advantage of accurately judging and evaluating the causes of structural damage.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for inverting the damage causes of bridges in mountainous high-fill areas based on multi-source fusion, comprising the following steps;

[0006] S1: Establish a parametric finite element model of the bridge to determine the action form and inversion index of the damage inducing factors of the bridge structure. At the same time, conduct physical model tests to obtain the axial strain of each pile foundation under different damage inducing factors of the bridge;

[0007] S2: The axial strain of the pile foundation under the same working conditions as the physical model experiment is calculated through the parametric model. Then, the parametric model is modified using the physical model test data to provide support for the authenticity of the sample data set of the damage induction inversion model.

[0008] S3: Construct an inversion model based on support vector machines. The pile foundation strain sample dataset is used as the model input, and the type, intensity, and location of the damage cause are used as indicators for output. The specific construction steps are as follows:

[0009] (1) Obtain the strain characteristic data of each pile in the parameterized finite element model under different working conditions, then fuse and reconstruct these characteristic data, calculate the range and mean of the strain characteristics of each pile, and use these calculation results as a supplement to the sample characteristics. The sample data set Z constructed in this way contains the feature X and the corresponding label Y, where X∈Rm×n, y∈Rm×q, m is the number of samples, n is the number of features, and q=3 is the number of labels including category number, position number and strength value;

[0010] (2) The sample data set is processed using a normalization method to make the data have a uniform scale, while eliminating the adverse effects caused by singular sample data, improving the solution speed and accuracy. Then, the data set is randomly split into a training set A and a test set B in a ratio of 4:1 using the holdout method. At the same time, data samples are randomly extracted from the physical model experimental data to form a validation set C.

[0011] (3) Construct a support vector machine model. This model adopts a one-to-many multi-classification approach and constructs a classification hyperplane for different types of damage inducements and different action locations. When constructing the model, the penalty factor C of the inversion model and the bandwidth of the kernel function σ are selected;

[0012] (4) By training the training set A, the test set B is loaded into the model to further evaluate the generalization ability of the model, and the validation set C is used to verify the fit of the model to the real situation;

[0013] S4: Using the modified parametric finite element model, obtain the pile foundation strain data under multiple working conditions. Based on the requirements of the support vector machine, the data is normalized and dimensionally reduced to construct a sample data set.

[0014] S5: Finally, the support vector machine inversion model is loaded to obtain the inversion results. The inversion parameters and model evaluation results under various damage inducements are analyzed to evaluate the model prediction accuracy. At the same time, a certain number of strain data are randomly selected from the physical model test data as a validation set to verify the reliability of the inversion model and the effectiveness of the model correction technology.

[0015] As a preferred technical solution of the present invention, in S2, the parameterized model calculation formula of the pile foundation axial strain is:

[0016] [\eps il on=\frac{N}{A\cdotE}];

[0017] where \eps il on is the axial strain, N is the applied axial load, A is the cross-sectional area of ​​the pile, and E is the elastic modulus of the material.

[0018] As a preferred technical solution of the present invention, the reaction of the surrounding soil needs to be considered when calculating the axial strain of the pile foundation. The interaction between the pile foundation and the soil is accurately simulated by adopting the foundation reaction method, thereby obtaining an accurate axial strain of the pile foundation.

[0019] As a preferred technical solution of the present invention, the following formula is used when calculating the foundation elastic force using the foundation reaction method:

[0020] R = q\cdotA;

[0021] Where R is the foundation reaction, q is the load per unit area, and A is the area of ​​the foundation.

[0022] As a preferred technical solution of the present invention, in S3, the inversion model of the support vector machine adopts a nonlinear transformation method, that is, the sample data in the low-dimensional space is mapped to the high-dimensional space using a nonlinear transformation, and the linear model is used in the high-dimensional space to model the data.

[0023] As a preferred technical solution of the present invention, in S4, during the construction of the sample data set, the sample data set is linearly separable. The core of SVM is to find the optimal hyperplane in the high-dimensional space to perform classification or regression tasks. If the sample data set is not linearly separable, it is necessary to use a kernel function to map it to the high-dimensional space in order to find the optimal hyperplane.

[0024] As a preferred technical solution of the present invention, the function used when normalizing the data is:

[0025]

[0026] Among them, x i is the stress data, max is the maximum value of the stress data, and min is the minimum value of the stress data.

[0027] As a preferred technical solution of the present invention, the LDA method is used to perform dimensionality reduction processing on the data, and the directional projection is performed by maximizing the difference between classes and minimizing the difference within classes, so as to effectively separate samples of different classes.

[0028] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention calculates the axial strain of the pile foundation under the same working conditions as the physical model experiment through a parameterized model, and then constructs an inversion model of a support vector machine, and uses the pile foundation strain sample data set as the input of the model, and uses the type, intensity and location of the damage cause as indicators as output. In this way, theoretical and numerical models close to engineering practice can be established to achieve accurate judgment and evaluation of the causes of structural damage. DETAILED DESCRIPTION

[0029] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] The present invention provides a method for inverting the damage causes of bridges in mountainous high-fill areas based on multi-source fusion, comprising the following steps:

[0031] S1: Establish a parametric finite element model of the bridge to determine the action form and inversion index of the damage inducing factors of the bridge structure. At the same time, conduct physical model tests to obtain the axial strain of each pile foundation under different damage inducing factors of the bridge;

[0032] S2: The axial strain of the pile foundation under the same working conditions as the physical model experiment is calculated through the parametric model. Then, the physical model test data is used to modify the parametric model to provide support for the authenticity of the sample data set of the damage induction inversion model. The calculation formula of the parametric model of the axial strain of the pile foundation is:

[0033] [\eps il on=\frac{N}{A\cdotE}];

[0034] Where, \eps il on is the axial strain, N is the applied axial load, A is the cross-sectional area of ​​the pile, and E is the elastic modulus of the material. When calculating the axial strain of the pile foundation, the reaction of the surrounding soil must be considered. By accurately simulating the interaction between the pile foundation and the soil using the foundation reaction method, the accurate axial strain of the pile foundation can be obtained. Where,: When using the foundation reaction method to calculate the foundation elastic force, the following formula is used:

[0035] R = q\cdotA;

[0036] Where R is the foundation reaction, q is the load per unit area, and A is the area of ​​the foundation;

[0037] S3: Construct an inversion model based on a support vector machine. The inversion model of the support vector machine uses a nonlinear transformation method, that is, it uses nonlinear transformation to map sample data in a low-dimensional space to a high-dimensional space, and uses a linear model to model the data in the high-dimensional space. The pile foundation strain sample data set is used as the input of the model, and the type, intensity, and location of the damage inducer are used as indicators as output. The specific construction steps are as follows:

[0038] (1) Obtain the strain characteristic data of each pile in the parameterized finite element model under different working conditions, then fuse and reconstruct these characteristic data, calculate the range and mean of the strain characteristics of each pile, and use these calculation results as a supplement to the sample characteristics. The sample data set Z constructed in this way contains the feature X and the corresponding label Y, where X∈Rm×n, y∈Rm×q, m is the number of samples, n is the number of features, and q=3 is the number of labels including category number, position number and strength value;

[0039] (2) The sample data set is processed using a normalization method to make the data have a uniform scale, while eliminating the adverse effects caused by singular sample data, improving the solution speed and accuracy. Then, the data set is randomly split into a training set A and a test set B in a ratio of 4:1 using the holdout method. At the same time, data samples are randomly extracted from the physical model experimental data to form a validation set C.

[0040] (3) Construct a support vector machine model. This model adopts a one-to-many multi-classification approach and constructs a classification hyperplane for different types of damage inducements and different action locations. When constructing the model, the penalty factor C of the inversion model and the bandwidth of the kernel function σ are selected;

[0041] (4) By training the training set A, the test set B is loaded into the model to further evaluate the generalization ability of the model, and the validation set C is used to verify the fit of the model to the real situation;

[0042] S4: Using the modified parameterized finite element model, the strain data of the pile foundation under multiple working conditions are obtained. The data is normalized and dimensionally reduced to meet the needs of the support vector machine to construct a sample data set. During the construction of the sample data set, the sample data set is linearly separable. The core of the SVM is to find the optimal hyperplane in the high-dimensional space to perform classification or regression tasks. If the sample data set is not linearly separable, a kernel function is needed to map it to the high-dimensional space to facilitate the search for the optimal hyperplane. The function used in normalizing the data is:

[0043]

[0044] Among them, x iis stress data, max is the maximum value of stress data, and min is the minimum value of stress data. LDA method is used to reduce the dimension of data, and the directional projection is maximized and the intra-class difference is minimized to effectively separate samples of different classes.

[0045] S5: Finally, the support vector machine inversion model is loaded to obtain the inversion results. The inversion parameters and model evaluation results under various damage inducements are analyzed to evaluate the model prediction accuracy. At the same time, a certain number of strain data are randomly selected from the physical model test data as a validation set to verify the reliability of the inversion model and the effectiveness of the model correction technology.

[0046] The present invention calculates the axial strain of the pile foundation under the same working conditions as the physical model experiment through a parameterized model, and then constructs an inversion model of a support vector machine. The pile foundation strain sample data set is used as the input of the model, and the type, intensity and location of the damage inducement are used as indicators as output. In this way, theoretical and numerical models close to engineering practice can be established to achieve accurate judgment and evaluation of the causes of structural damage.

[0047] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0048] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-source fusion-based inversion method for bridge damage causes in mountainous high-fill areas, characterized by: The method includes the following steps: S1: Establish a parametric finite element model of the bridge to determine the action form and inversion index of the damage inducing factors of the bridge structure. At the same time, conduct physical model tests to obtain the axial strain of each pile foundation under different damage inducing factors of the bridge; S2: The axial strain of the pile foundation under the same working conditions as the physical model experiment is calculated through the parametric model. Then, the parametric model is modified using the physical model test data to provide support for the authenticity of the sample data set of the damage induction inversion model. S3: Construct an inversion model based on support vector machines. The pile foundation strain sample dataset is used as the model input, and the type, intensity, and location of the damage cause are used as indicators for output. The specific construction steps are as follows: (1) Obtain the strain characteristic data of each pile in the parameterized finite element model under different working conditions, then fuse and reconstruct these characteristic data, calculate the range and mean of the strain characteristics of each pile, and use these calculation results as a supplement to the sample characteristics. The sample data set Z constructed in this way contains the feature X and the corresponding label Y, where X∈Rm×n, y∈Rm×q, m is the number of samples, n is the number of features, and q=3 is the number of labels including category number, position number and strength value; (2) The sample data set is processed using a normalization method to make the data have a uniform scale, while eliminating the adverse effects caused by singular sample data, improving the solution speed and accuracy. Then, the data set is randomly split into a training set A and a test set B in a ratio of 4:1 using the holdout method. At the same time, data samples are randomly extracted from the physical model experimental data to form a validation set C. (3) Construct a support vector machine model. This model adopts a one-to-many multi-classification approach and constructs a classification hyperplane for different types of damage inducements and different action locations. When constructing the model, the penalty factor C of the inversion model and the bandwidth of the kernel function σ are selected; (4) By training the training set A, the test set B is loaded into the model to further evaluate the generalization ability of the model, and the validation set C is used to verify the fit of the model to the real situation; S4: Using the modified parametric finite element model, obtain the pile foundation strain data under multiple working conditions. Based on the requirements of the support vector machine, the data is normalized and dimensionally reduced to construct a sample data set. S5: Finally, the support vector machine inversion model is loaded to obtain the inversion results. The inversion parameters and model evaluation results under various damage inducements are analyzed to evaluate the model prediction accuracy. At the same time, a certain number of strain data are randomly selected from the physical model test data as a validation set to verify the reliability of the inversion model and the effectiveness of the model correction technology.

2. The method for inversion of bridge damage causes in mountainous high-fill areas based on multi-source fusion according to claim 1 is characterized by: In S2, the parameterized model calculation formula for pile foundation axial strain is: [\epsilon=\frac{N}{A\cdotE}]; where \epsilon is the axial strain, N is the applied axial load, A is the cross-sectional area of ​​the pile, and E is the elastic modulus of the material.

3. The method for inverting the causes of bridge damage in mountainous high-fill areas based on multi-source fusion according to claim 2 is characterized by: When calculating the axial strain of the pile foundation, the reaction of the surrounding soil needs to be considered. By using the foundation reaction method to accurately simulate the interaction between the pile foundation and the soil, the accurate axial strain of the pile foundation can be obtained.

4. The method for inversion of bridge damage causes in mountainous high-fill areas based on multi-source fusion according to claim 3 is characterized by: The following formula is used when calculating foundation elastic force using the foundation reaction method: R = q\cdotA; Where R is the foundation reaction, q is the load per unit area, and A is the area of ​​the foundation.

5. The method for inversion of bridge damage causes in mountainous high-fill areas based on multi-source fusion according to claim 1 is characterized by: In S3, the inversion model of the support vector machine adopts a nonlinear transformation method, that is, the sample data in the low-dimensional space is mapped to the high-dimensional space using a nonlinear transformation, and the linear model is used in the high-dimensional space to model the data.

6. The method for inversion of bridge damage causes in mountainous high-fill areas based on multi-source fusion according to claim 1 is characterized by: In S4, during the construction of the sample data set, the sample data set is linearly separable. The core of SVM is to find the optimal hyperplane in the high-dimensional space to perform classification or regression tasks. If the sample data set is not linearly separable, it is necessary to use a kernel function to map it to the high-dimensional space in order to find the optimal hyperplane.

7. The method for inversion of bridge damage causes in mountainous high-fill areas based on multi-source fusion according to claim 6 is characterized by: The function used to normalize the data is: Among them, x i is the stress data, max is the maximum value of the stress data, and min is the minimum value of the stress data.

8. The method for inversion of bridge damage causes in mountainous high-fill areas based on multi-source fusion according to claim 7 is characterized by: The LDA method is used to reduce the dimensionality of the data. By maximizing the difference between classes and minimizing the directional projection of the difference within classes, the samples of different classes can be effectively separated.

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