Bridge modal parameter identification method in driving state based on SSA optimization VMD-HT algorithm

Abstract

The invention discloses a bridge modal parameter identification method in a driving state based on an SSA optimization VMD-HT algorithm, and the method specifically comprises the steps: S1, carrying out the preliminary exploration of a to-be-detected bridge, and obtaining the structural parameter data of the bridge; s2, a semi-closed road processing mode is adopted, and the safety of a test environment is ensured while traffic is not interrupted; s3, arranging acceleration sensors on the single side of the target bridge along the bridge direction, and acquiring acceleration response data {arr = 1, 2,..., n} of n measuring points on the bridge structure; wherein ar represents the acceleration response data of the rth measuring point; s4, recording and storing acceleration signals under the vehicle-mounted action by using a signal acquisition analyzer; s5, performing multi-objective optimization on the modal number k and the penalty function alpha in the VMD algorithm by using an SSA sparrow search algorithm so as to obtain an optimal parameter combination [k0, alpha0]; and S6, processing and analyzing all the collected data by adopting an SSA-based optimized VMD-HT algorithm, and identifying modal parameters of the bridge structure. According to the method, the modal parameters of the bridge can be effectively and accurately identified.

Description

Technical Field

[0001] The invention belongs to the field of digital signal processing and simulation technology, and specifically relates to a bridge modal parameter identification method under driving state based on SSA (Sparrow Search Algorithm) optimized VMD-HT (Variational Mode Decomposition-Hilbert-Huang Transform) algorithm. Background Art

[0002] With the rapid development of bridge engineering technology, larger and more complex bridge structures are also increasing. Therefore, how to efficiently and accurately judge the current reliability of bridge structures and to warn of possible damage in the future has become a hot research direction in the field of bridge engineering at home and abroad.

[0003] Among the many methods for detecting and warning the state of bridge structures, the bridge's operating state and overall stiffness can be evaluated through modal parameters such as the bridge's frequency, vibration mode, and damping ratio. This can comprehensively consider factors such as the bridge's boundary conditions, constitutive relations, and construction quality without damaging the bridge structure. It has high accuracy and good reliability, and has a high development prospect. Therefore, the bridge structure modal parameter identification technology has received extensive attention and development in the field of bridge engineering.

[0004] At present, most bridge modal detection methods measure the dynamic response signal of the bridge, and then use signal processing technology to analyze the response signal to obtain the dynamic characteristic information of the bridge. However, this method often requires the deployment of a large number of sensor devices on the bridge and interrupts traffic, causing economic losses. Therefore, it is of great significance to propose a practical bridge modal identification method based on the existing technical means to optimize the actual engineering efficiency and improve the level of bridge health detection and early warning. Summary of the invention

[0005] In order to solve the above technical problems existing in the existing bridge modal detection methods, the present invention provides a bridge modal parameter identification method under driving state based on SSA optimized VMD-HT algorithm. The present invention optimizes the existing bridge modal parameter identification method, increases the bridge detection efficiency, realizes the bridge modal parameter identification under vehicle load, and can effectively and accurately identify the bridge modal parameters without interrupting traffic.

[0006] The technical solution adopted by the present invention is:

[0007] A method for identifying modal parameters of a bridge under driving conditions based on an SSA optimized VMD-HT algorithm, characterized in that it specifically comprises the following steps:

[0008] S1: Conduct preliminary survey of the bridge to be tested and obtain the structural parameter data of the bridge;

[0009] S2: A semi-closed road approach is adopted to ensure the safety of the test environment without interrupting traffic;

[0010] S3: Arrange the acceleration sensor on one side of the target bridge along the bridge direction, and collect the acceleration response data of n measuring points on the bridge structure. r |r=1,2,···,n}; where a r Represents the acceleration response data of the rth measuring point;

[0011] S4: Use a signal acquisition analyzer to record and store the acceleration signal under the action of the vehicle;

[0012] S5: Use the SSA sparrow search algorithm to perform multi-objective optimization on the mode number k and penalty function α in the VMD algorithm to obtain the optimal parameter combination [k0, α0];

[0013] S6: The VMD-HT algorithm based on SSA optimization is used to process and analyze all collected data (including structural parameters, bridge vibration data, displacement and acceleration signal data) to identify the modal parameters of the bridge structure.

[0014] Furthermore, the step S1 specifically includes:

[0015] S11. Use measuring instruments to measure the geometric parameters of the bridge;

[0017] S12. Collect relevant information on the materials used in the bridge;

[0018] S13. Record the bridge restraint method, including the type and location of supports.

[0019] Furthermore, the step S2 specifically includes:

[0020] S21. During the construction period, a single lane is reserved for vehicle traffic, and the remaining lanes are set as detection areas and blocked;

[0021] S22. Temporary traffic signs, signboards and road cones are set up within the detection area for maintenance, thereby forming a closed detection area to ensure its safety.

[0022] Furthermore, the step S3 specifically includes:

[0023] The number of measuring points to be arranged is determined according to the span of the bridge to be measured, and the acceleration sensors are arranged along the direction of the bridge on one side of the target bridge.

[0024] Furthermore, the step S4 specifically includes:

[0025] Set the equipment parameters, calibrate the detection equipment, and then use the signal acquisition analyzer to record and store the acceleration signal under the action of the vehicle.

[0026] Furthermore, the step S5 specifically includes:

[0027] S51, defining algorithm-related parameters and randomly initializing the initial positions of the sparrows;

[0028] S52, calculating the fitness value of each sparrow in the initial state and sorting them to obtain the optimal fitness value;

[0029] S53, updating the discoverer, follower, and alarm to obtain the current optimal value;

[0030] S54, if the optimal value at this time is higher than the previous fitness value, it is updated, otherwise it is not updated until all iterations are completed and the optimal value is obtained.

[0031] Furthermore, the SSA sparrow search algorithm in step S5 specifically includes:

[0032] A population X consisting of n sparrows can be expressed as follows:

[0033]

[0034] Where d represents the dimension of the variable to be optimized; n represents the number of sparrows;

[0035] The fitness values ​​of all sparrows can be expressed as follows:

[0036]

[0037] Among them, f represents the fitness value;

[0038] During each iteration, the position update of the discoverer can be described by the following equation:

[0039]

[0040] Where t represents the current iteration number; j = 1, 2, 3, ..., d; iter m Indicates the maximum number of iterations, which is a constant; x ij Represents the position information state of the i-th sparrow individual in the j-th dimension; the range of α is (0,1], which is a random number.

[0041] Furthermore, the step S6 specifically includes:

[0042] S61. Perform VMD decomposition on the initial response signal ξ(t) to obtain K intrinsic mode components IMF, which are expressed as follows:

[0043]

[0044] S62, observe whether each IMF component can reflect the actual frequency characteristics of the structure, if not, return to step S61 to readjust the VMD parameter settings;

[0045] S63, perform Hilbert transform on each IMF component according to the following formula (5):

[0046]

[0047] The expression of the amplitude function is:

[0048]

[0049] The expression of the phase function is:

[0050] φ(t)=arctan(H[k i (t)] / k i (t)) (7)

[0051] The expression for instantaneous frequency is:

[0052]

[0053] The Hilbert spectrum expression of the original signal is:

[0054]

[0055] Compute the Hilbert marginal spectrum:

[0056]

[0057] Among them, ξ(t) is the initial signal; k i is the number of modes; H(k i (t)) Hilbert transform; a i (t) amplitude function; φ(t) phase function;

[0058] The frequency and vibration mode of the signal are solved according to the Hilbert marginal spectrum to realize the modal identification of the bridge structure.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] (1) The present invention can identify the modal parameters of a bridge without interrupting traffic, thus greatly reducing the detection cost.

[0062] (2) The present invention uses the SSA sparrow search algorithm to optimize the optimal combination of the number of eigenmodes k and the quadratic penalty function factor α in the VMD-HT modal decomposition, which is efficient and accurate, and can effectively solve the problem that the VMD algorithm cannot set parameters based solely on manual experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is the bridge modal identification process of VMD-HT based on SSA optimization in the present invention.

[0064] Figure 2 This is the flow chart of the SSA algorithm of the present invention.

[0065] Figure 3 This is the sensor measurement point layout diagram. DETAILED DESCRIPTION

[0066] The present invention is further described below in conjunction with examples and drawings. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0067] This embodiment selected a simply supported beam bridge with a span arrangement of 1×33.0m=33.0m as the test object. The upper structure of the bridge is a prestressed concrete box girder. The top width of the bridge is 14.3m, the bottom width is 12.11m, the height is 1.6m, and the main beam is made of C40 concrete.

[0068] refer to Figures 1 to 3 The present invention provides a bridge modal parameter identification method under driving state based on SSA optimized VMD-HT algorithm, which specifically includes the following steps:

[0069] S1: Conduct preliminary survey on the bridge to be tested, obtain the structural parameter data of the bridge, and calculate the modal information of the bridge through the finite element method;

[0070] Among them, the structural parameter data includes the structural dimensions, materials, constraints, etc. of the bridge.

[0071] Specifically, step S1 specifically includes:

[0072] S11. Use measuring instruments to measure the geometric parameters of the bridge;

[0073] Among them, the structural geometric parameters of the bridge include: Span: the horizontal distance across the structure of the bridge, which is an important indicator for evaluating the scale of the bridge.

[0075] Width: The width of the bridge, including the total width of the carriageway, sidewalks, guardrails, etc.

[0076] Clearance: The clear height and width of the lower part of the bridge to ensure navigation, drainage or other use requirements.

[0077] Arch rise: The vertical distance from the arch crown to the arch foot in an arch bridge, which reflects the degree of arch of the arch bridge.

[0078] 2. Bridge structure cross-section measurement

[0079] Small and medium span bridges: the number of measuring sections in a single span shall not be less than 3.

[0080] Large-span bridges: the number of measuring sections in a single span shall not be less than 5.

[0081] Bridge piers and bridge towers: The number of measuring sections should not be less than 3, and measuring sections should be arranged at the places where the cross-section changes suddenly.

[0082] Thickness of bridge deck pavement: It can be measured by radar combined with core drilling correction method, and the detection section should be arranged at the point where the span is divided into 4 equal parts.

[0083] S12. Collect relevant information on the materials used in the bridge;

[0084] Bridges are made of a variety of materials, each with its own unique properties and applicable scenarios. The following is a detailed description of the commonly used materials for bridges:

[0085] 1. Steel bars

[0086] Main uses: Steel bars are one of the core materials in bridge construction, mainly used to enhance the tensile strength of concrete and improve the overall bearing capacity and durability of bridges.

[0087] 2. Concrete

[0088] Main uses: Concrete is one of the most commonly used materials in bridge construction and is mainly used for the main structure of the bridge, such as beams, slabs, columns, etc.

[0089] 3. Steel

[0090] Main uses: Steel is mainly used in large and super-large bridges, such as suspension bridges, cable-stayed bridges, etc., as well as certain key parts of bridges, such as slings, anchors, etc.

[0091] 4. Other Materials

[0092] Anchor: A tensioning system used in bridges, such as the OVM anchoring system, to ensure the prestressed state of the bridge.

[0093] Plastic corrugated pipe: used as casing for prestressed steel tendons to protect them from corrosion.

[0094] Anti-corrosion coating: used to improve the corrosion resistance of bridges and extend their service life.

[0095] S13. Record the bridge restraint method, including the type and location of supports.

[0096] S2: A semi-closed road approach is adopted to ensure the safety of the test environment without interrupting traffic;

[0097] Specifically, step S2 specifically includes:

[0098] S21. During the construction period, a single lane is reserved for vehicle traffic, and the remaining lanes are set as detection areas and blocked;

[0099] S22. Temporary traffic signs, signboards and road cones are set up within the detection area for maintenance, thereby forming a closed detection area to ensure its safety.

[0100] S3: Select the acceleration sensor of Jiangsu Donghua Institute, place the acceleration sensor on one side of the target bridge along the bridge direction, and collect the acceleration response data of n measuring points on the bridge structure. r |r=1,2,···,n}; where a r Represents the acceleration response data of the rth measuring point;

[0101] Specifically, step S3 specifically includes:

[0102] The number of measuring points to be arranged is determined according to the span of the bridge to be measured, and the acceleration sensors are arranged along the direction of the bridge on one side of the target bridge.

[0103] S4: Use Donghua DHDAS signal acquisition and analysis instrument to record and store the acceleration signal under the action of the vehicle; Specifically, step S4 specifically includes:

[0105] Set the equipment parameters, calibrate the detection equipment, and then use the signal acquisition analyzer to record and store the acceleration signal under the action of the vehicle.

[0106] After completing a signal acquisition, the test is switched to another slow lane and steps 2 to 4 are repeated.

[0107] S5: Use the SSA sparrow search algorithm to perform multi-objective optimization on the mode number k and penalty function α in the VMD algorithm to obtain the optimal parameter combination [k0, α0];

[0108] Specifically, step S5 specifically includes:

[0109] S51, defining algorithm-related parameters and randomly initializing the initial positions of the sparrows;

[0110] Specific:

[0111] 1. Definition of VMD algorithm related parameters

[0112] The key parameters of the VMD algorithm mainly include the decomposition mode number K and the penalty factor alpha (α), which directly affect the decomposition effect and performance of VMD.

[0113] Decomposition mode number K: determines how many intrinsic mode functions (IMFs) the signal is decomposed into. K should be selected appropriately. Too large a value will lead to over-decomposition, while too small a value may not fully extract the signal features.

[0114] Penalty factor alpha (α): used to control the modal bandwidth and ensure that the decomposed IMF has a limited bandwidth. The choice of alpha is also important, as it directly affects the smoothness and resolution of the IMF.

[0115] 2. The process of optimizing VMD parameters by sparrow search algorithm

[0116] 1. Initialize the sparrow group.

[0117] 2. Calculate fitness.

[0118] 3. Update location.

[0119] 4. Determine the stopping condition.

[0120] S52, calculating the fitness value of each sparrow in the initial state and sorting them to obtain the optimal fitness value;

[0121] S53, updating the discoverer, follower, and alarm to obtain the current optimal value;

[0122] S54, if the optimal value at this time is higher than the previous fitness value, it is updated, otherwise it is not updated until all iterations are completed and the optimal value is obtained.

[0123] Specifically, the SSA sparrow search algorithm in step S5 specifically includes:

[0124] A population X consisting of n sparrows can be expressed as follows:

[0125]

[0126] Where d represents the dimension of the variable to be optimized; n represents the number of sparrows;

[0127] The fitness values ​​of all sparrows can be expressed as follows:

[0128]

[0129] Among them, f represents the fitness value;

[0130] During each iteration, the position update of the discoverer can be described by the following equation:

[0131]

[0132] Where t represents the current iteration number; j = 1, 2, 3, ..., d; iter m Indicates the maximum number of iterations, which is a constant; x ij Represents the position information state of the i-th sparrow individual in the j-th dimension; the range of α is (0,1], which is a random number.

[0133] S6: The VMD-HT algorithm based on SSA optimization is used to process and analyze all collected data (including structural parameters, bridge vibration data, displacement and acceleration signal data) to identify the modal parameters of the bridge structure.

[0134] Specifically, step S6 specifically includes:

[0135] S61. Perform VMD decomposition on the initial response signal ξ(t) to obtain K intrinsic mode components IMF, which are expressed as follows:

[0136]

[0137] S62, observe whether each IMF component can reflect the actual frequency characteristics of the structure, if not, return to step S61 to readjust the VMD parameter settings;

[0138] S63, perform Hilbert transform on each IMF component according to the following formula (5):

[0139]

[0140] The expression of the amplitude function is:

[0141]

[0142] The expression of the phase function is:

[0143] φ(t)=arctan(H[k i (t)] / k i (t)) (7)

[0144] The expression for instantaneous frequency is:

[0145]

[0146] The Hilbert spectrum expression of the original signal is:

[0147]

[0148] Compute the Hilbert marginal spectrum:

[0149]

[0150] Among them, ξ(t) is the initial signal; k i is the number of modes; H(k i (t)) Hilbert transform; a i (t) amplitude function; φ(t) phase function;

[0151] The frequency and vibration mode of the signal are solved according to the Hilbert marginal spectrum to realize the modal identification of the bridge structure.

[0152] In this embodiment, in step S2, a slow lane is occupied for testing, and the remaining lanes are left for vehicles to pass. Within the construction area, we will set up temporary traffic signs, signs and road cones for maintenance work, thereby forming a closed construction area. At the same time, we will be equipped with professional traffic controllers who will stand at the entrance of the closed area to direct traffic, guide vehicles and pedestrians to detour, and ensure traffic order and safety.

[0153] In step S3, the sensor equipment uses the DHDAS dynamic signal acquisition and analysis instrument of Jiangsu Donghua Institute. The measurement points are arranged in accordance with the requirements of the "Highway Bridge Load Test Code" (JTG / TJ21-01-2015). They are arranged at the nine points on both sides of the target bridge along the bridge direction, that is, the bridge is divided into eight sections for point arrangement; the transverse bridge direction is arranged on the motor vehicle lane 2.35m away from the guardrail according to the actual situation of the bridge, such as Figure 3 shown.

[0154] In step S5, the VMD-HT bridge modal identification algorithm based on SSA optimization is used to analyze the bridge vibration response signal collected under the state of vehicle driving. The analysis process mainly includes the following contents:

[0155] (1) Set the upper and lower limits of k and α in the VMD algorithm according to the actual working conditions when collecting data.

[0156] (2) Then, the size of the sparrow population and the number of iterative evolutions are determined based on the amount of collected data, the optimization efficiency and accuracy of the sparrow algorithm, and other factors.

[0157] (3) Parameter combination and VMD decomposition are performed within the upper and lower limits of k and α. The fitness function under different parameter combinations is calculated using sparrow search, and the comparison is performed within the number of evolutionary iterations. The optimal k and α combination is obtained by taking the lowest fitness function value as the search target.

[0158] (4) Use the sparrow search algorithm to find the best k and α to perform VMD decomposition, obtain the eigenmode components of each order of the initial signal, judge and eliminate the interference signal according to the dynamic characteristics of the bridge structure, reconstruct, and perform Hilbert transform to obtain the instantaneous frequency, amplitude function, and phase function of the signal. Finally, calculate the Hilbert marginal spectrum to obtain the bridge frequency parameters.

Claims

1. A bridge modal parameter identification method under driving state based on SSA optimized VMD-HT algorithm, characterized in that: The specific steps include: S1: Conduct preliminary survey of the bridge to be tested and obtain the structural parameter data of the bridge; S2: A semi-closed road approach is adopted to ensure the safety of the test environment without interrupting traffic; S3: Arrange the acceleration sensor on one side of the target bridge along the bridge direction, and collect the acceleration response data of n measuring points on the bridge structure. r |r=1,2,…,n}; Among them, a r Represents the acceleration response data of the rth measuring point; S4: Use a signal acquisition analyzer to record and store the acceleration signal under the action of the vehicle; S5: Use the SSA sparrow search algorithm to perform multi-objective optimization on the mode number k and penalty function α in the VMD algorithm to obtain the optimal parameter combination [k0, α0]; S6: All collected data are processed and analyzed using the VMD-HT algorithm based on SSA optimization to identify the modal parameters of the bridge structure.

2. According to claim 1, a bridge modal parameter identification method under driving state based on SSA optimized VMD-HT algorithm is characterized in that: The step S1 specifically includes: S11. Use measuring instruments to measure the geometric parameters of the bridge; S12. Collect relevant information on the materials used in the bridge; S13. Record the bridge restraint method, including the type and location of supports.

3. The method for identifying bridge modal parameters under driving state based on SSA optimized VMD-HT algorithm according to claim 2 is characterized in that: The step S2 specifically includes: S21. During the construction period, a single lane is reserved for vehicle traffic, and the remaining lanes are set as detection areas and blocked; S22. Temporary traffic signs, signboards and road cones are set up within the detection area for maintenance, thereby forming a closed detection area to ensure its safety.

4. The method for identifying bridge modal parameters under driving state based on SSA optimized VMD-HT algorithm according to claim 3 is characterized in that: The step S3 specifically includes: The number of measuring points to be arranged is determined according to the span of the bridge to be measured, and the acceleration sensors are arranged along the direction of the bridge on one side of the target bridge.

5. The method for identifying bridge modal parameters under driving state based on SSA optimized VMD-HT algorithm according to claim 4 is characterized in that: The step S4 specifically includes: Set the equipment parameters, calibrate the detection equipment, and then use the signal acquisition analyzer to record and store the acceleration signal under the action of the vehicle.

6. The method for identifying bridge modal parameters under driving state based on SSA optimized VMD-HT algorithm according to claim 5 is characterized in that: The step S5 specifically includes: S51, defining algorithm-related parameters and randomly initializing the initial positions of the sparrows; S52, calculating the fitness value of each sparrow in the initial state and sorting them to obtain the optimal fitness value; S53, updating the discoverer, follower, and alarm to obtain the current optimal value; S54, if the optimal value at this time is higher than the previous fitness value, it is updated, otherwise it is not updated until all iterations are completed and the optimal value is obtained.

7. The method for identifying bridge modal parameters under driving state based on SSA optimized VMD-HT algorithm according to claim 6 is characterized in that: The SSA sparrow search algorithm in step S5 specifically includes: A population X consisting of n sparrows can be expressed as follows: Where d represents the dimension of the variable to be optimized; n represents the number of sparrows; The fitness values ​​of all sparrows can be expressed as follows: Among them, f represents the fitness value; During each iteration, the position update of the discoverer can be described by the following equation: Where t represents the current iteration number; j = 1, 2, 3, ..., d; iter m Indicates the maximum number of iterations, which is a constant; x ij Represents the position information state of the i-th sparrow individual in the j-th dimension; the range of α is (0,1], which is a random number.

8. The method for identifying bridge modal parameters under driving state based on SSA optimized VMD-HT algorithm according to claim 1 is characterized in that: The step S6 specifically includes: S61. Perform VMD decomposition on the initial response signal ξ(t) to obtain K intrinsic mode components IMF, which are expressed as follows: S62, observe whether each IMF component can reflect the actual frequency characteristics of the structure, if not, return to step S61 to readjust the VMD parameter settings; S63, perform Hilbert transform on each IMF component according to the following formula (5): The expression of the amplitude function is: The expression of the phase function is: φ(t)=arctan(H[k i (t)] / k i (t))(7) The expression for instantaneous frequency is: The Hilbert spectrum expression of the original signal is: Compute the Hilbert marginal spectrum: Among them, ξ(t) is the initial signal; k i is the number of modes; H(k i (t)) Hilbert transform; a i (t) amplitude function; φ(t) phase function; The frequency and vibration mode of the signal are solved according to the Hilbert marginal spectrum to realize the modal identification of the bridge structure.

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