Concrete bridge pile foundation risk detection method and system

Abstract

The invention provides a concrete bridge pile foundation risk detection method and system, and relates to the field of bridge pile foundations, and the method comprises the steps: through finite element analysis, based on the structure information and service information of a to-be-detected concrete bridge pile foundation, calculating the risk of the to-be-detected concrete bridge pile foundation; determining a plurality of risk detection points of the to-be-detected concrete bridge pile foundation and risk correlation coefficients among the plurality of risk detection points; arranging a risk detection device at each risk detection point; determining a plurality of ultrasonic detection positions of the to-be-detected concrete bridge pile foundation based on detection point state data collected by risk detection devices of the plurality of risk detection points and risk correlation coefficients among the plurality of risk detection points, and generating an ultrasonic detection scheme; according to the ultrasonic detection scheme, ultrasonic detection is conducted on the to-be-detected concrete bridge pile foundation, ultrasonic detection data of the to-be-detected concrete bridge pile foundation are obtained, risk analysis is conducted on the concrete bridge pile foundation, and the method has the advantage that nondestructive detection of the risk of the concrete bridge pile foundation is achieved.

Description

Technical Field

[0001] The present invention relates to the field of bridge pile foundations, and in particular to a risk detection method and system for concrete bridge pile foundations. Background Art

[0002] The pile foundation of a bridge is the main load-bearing component of the bridge. During the use of the bridge, when the bridge is hit or carries too much weight, cracks will appear inside the pile foundation. The damage to the pile foundation is usually reflected inside the pile foundation. At this time, non-destructive testing of the pile foundation of the bridge is required.

[0003] At present, some pile foundation inspections usually use acoustic detection tubes pre-buried in the equipment, and place acoustic wave transducers in the acoustic detection tubes. The parameters generated by the sound waves passing through the pile foundation are used to detect whether there are cracks in the pile foundation. However, when the acoustic detection tube is damaged, the acoustic wave transducer cannot enter the acoustic detection tube to inspect the pile foundation, which makes the bridge inspection unable to proceed normally.

[0004] Therefore, it is necessary to provide a concrete bridge pile foundation risk detection method and system for realizing non-destructive detection of concrete bridge pile foundation risks. Summary of the invention

[0005] The present invention provides a risk detection method for concrete bridge pile foundations, comprising: obtaining structural information and service information of a concrete bridge pile foundation to be detected; determining multiple risk detection points of the concrete bridge pile foundation to be detected and risk correlation coefficients between the multiple risk detection points based on the structural information and service information of the concrete bridge pile foundation to be detected by finite element analysis; setting a risk detection device at each risk detection point; determining multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected based on detection point status data collected by the risk detection devices of the multiple risk detection points and the risk correlation coefficients between the multiple risk detection points; generating an ultrasonic detection scheme based on the multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected, wherein the ultrasonic detection scheme includes an ultrasonic detection path and ultrasonic detection parameters corresponding to each ultrasonic detection position; performing ultrasonic detection on the concrete bridge pile foundation to be detected according to the ultrasonic detection scheme to obtain ultrasonic detection data of the concrete bridge pile foundation to be detected; and performing risk analysis of the concrete bridge pile foundation based on the ultrasonic detection data of the concrete bridge pile foundation to be detected.

[0006] Further, through finite element analysis, based on the structural information and service information of the concrete bridge pile foundation to be detected, multiple risk detection points of the concrete bridge pile foundation to be detected and the risk correlation coefficients between the multiple risk detection points are determined, including: based on the structural information of the concrete bridge pile foundation to be detected, a geometric model of the concrete bridge pile foundation to be detected is established; the geometric model of the concrete bridge pile foundation to be detected is meshed to obtain multiple finite element analysis units; based on the structural information of the concrete bridge pile foundation to be detected, material properties are specified for each of the finite element analysis units; boundary conditions are determined; based on the structural information and service information of the concrete bridge pile foundation to be detected, multiple groups of detection points are determined. Trial use of external loads; for each set of test external loads, solving to obtain test data of the concrete bridge pile foundation to be tested under the test external loads, wherein the test data includes simulated vibration data of multiple finite element analysis units; based on the test data of the concrete bridge pile foundation to be tested under each set of the test external loads, determining multiple initial test points; based on the test data of the concrete bridge pile foundation to be tested under each set of the test external loads, determining point risk correlation coefficients of multiple initial test points; based on the point risk correlation coefficients of the multiple initial test points, screening multiple risk detection points from the multiple initial detection points, and determining the risk correlation coefficients between the multiple risk detection points.

[0007] Furthermore, the service information includes traffic flow information and environmental information; based on the structural information and service information of the concrete bridge pile foundation to be tested, multiple groups of external loads for testing are determined, including: acquiring the structural information of the concrete bridge, and determining the static load of the concrete bridge pile foundation to be tested; predicting the traffic load range of the concrete bridge pile foundation to be tested based on the traffic flow information through a first load prediction model; predicting the environmental load range of the concrete bridge pile foundation to be tested based on the environmental information through a second load prediction model; determining the multiple groups of external loads for testing based on the static load, traffic load range and environmental load range of the concrete bridge pile foundation to be tested.

[0008] Furthermore, based on the test data of the concrete bridge pile foundation to be tested under each group of external loads for testing, multiple initial detection points are determined, including: for each of the finite element analysis units, based on the test data of the finite element analysis unit under each group of external loads for testing, the unit risk parameter of the finite element analysis unit is determined, and the finite element analysis unit whose unit risk parameter is greater than the unit risk parameter threshold is used as a risky finite element analysis unit; multiple risky finite element analysis units are clustered to determine multiple unit clusters, and the risky finite element analysis unit corresponding to the cluster center of the unit cluster is used as an initial detection point.

[0009] Furthermore, based on the test data of the concrete bridge pile foundation to be tested under each group of the external test loads, the point risk correlation coefficients of multiple initial test points are determined, including: for each group of the external test loads, based on the test data of each of the initial test points under the external test loads, calculating the load risk correlation coefficients of any two initial test points under the external test loads; based on the load risk correlation coefficients of any two initial test points under each group of the external test loads, calculating the point risk correlation coefficients of any two initial test points.

[0010] Further, based on the point risk correlation coefficients of the multiple initial detection points, multiple risk detection points are screened from the multiple initial detection points, and the risk correlation coefficients between the multiple risk detection points are determined, including: based on the point risk correlation coefficients of any two initial detection points, a first point risk correlation coefficient sequence corresponding to each of the initial detection points is determined; for each of the initial detection points, based on the first point risk correlation coefficient sequence corresponding to the initial detection point, a point risk correlation coefficient mean and a point risk correlation coefficient standard deviation are calculated, and an initial detection point whose point risk correlation coefficient mean is greater than a point risk correlation coefficient mean threshold and whose point risk correlation coefficient standard deviation is less than a point risk correlation coefficient standard deviation threshold is taken as a risk detection point; based on the point risk correlation coefficients of any two risk detection points, a second point risk correlation coefficient sequence corresponding to each of the risk detection points is determined; according to the second point risk correlation coefficient sequence corresponding to each of the risk detection points, multiple risk detection points are clustered to determine multiple point clusters; for each point cluster, according to the second point risk correlation coefficient sequence corresponding to each of the risk detection points included in the point cluster, the risk correlation coefficient between any two risk detection points included in the point cluster is determined.

[0011] Furthermore, based on the detection point status data collected by the risk detection device of the multiple risk detection points and the risk correlation coefficients between the multiple risk detection points, the multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected are determined, including: for each of the point clusters, based on the test data of the concrete bridge pile foundation to be detected under each group of the test external loads, a training sample corresponding to the point cluster is generated, an initial risk prediction model corresponding to the point cluster is established, the initial risk prediction model corresponding to the point cluster is trained using the training samples corresponding to the point cluster to obtain the risk prediction model corresponding to the point cluster, and the ultrasonic detection position corresponding to the point cluster and the risk information corresponding to the ultrasonic detection position are predicted by the risk prediction model corresponding to the point cluster based on the detection point status data collected by the risk detection device of the risk detection points included in the point cluster and the risk correlation coefficients between any two risk detection points included in the point cluster.

[0012] Furthermore, based on the multiple risk detection positions of the concrete bridge pile foundation to be detected, a risk detection scheme is generated, including: generating a risk detection path through a path generation model based on the structural information of the concrete bridge pile foundation to be detected and the multiple risk detection positions; generating ultrasonic detection parameters corresponding to the risk detection position based on the risk information corresponding to the ultrasonic detection position through a parameter generation model.

[0013] Furthermore, based on the ultrasonic detection data of the concrete bridge pile foundation to be detected, a risk analysis of the concrete bridge pile foundation is performed, including: based on the ultrasonic detection data of the concrete bridge pile foundation to be detected, a risk analysis of the concrete bridge pile foundation is performed through a risk analysis model.

[0014] The present invention provides a concrete bridge pile foundation risk detection system for executing the above-mentioned concrete bridge pile foundation risk detection method, comprising: an information acquisition module, for acquiring structural information and service information of the concrete bridge pile foundation to be detected; a risk monitoring module, for determining, through finite element analysis, a plurality of risk detection points of the concrete bridge pile foundation to be detected and risk correlation coefficients between the plurality of risk detection points based on the structural information and service information of the concrete bridge pile foundation to be detected, and for setting a risk detection device at each risk detection point; a risk prediction module, for predicting the risk of the detection point status data collected by the risk detection devices at the plurality of risk detection points and the risk correlation coefficients between the plurality of risk detection points A correlation coefficient is used to determine multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected; a scheme generation module is used to generate an ultrasonic detection scheme based on the multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected, wherein the ultrasonic detection scheme includes an ultrasonic detection path and ultrasonic detection parameters corresponding to each ultrasonic detection position; a risk monitoring module is used to perform ultrasonic detection on the concrete bridge pile foundation to be detected according to the ultrasonic detection scheme to obtain ultrasonic detection data of the concrete bridge pile foundation to be detected; a risk analysis module is used to perform risk analysis of the concrete bridge pile foundation based on the ultrasonic detection data of the concrete bridge pile foundation to be detected.

[0015] Compared with the prior art, the concrete bridge pile foundation risk detection method and system provided by the present invention have at least the following beneficial effects:

[0016] 1. By obtaining the structural information and service information of the concrete bridge pile foundation to be inspected, the actual situation of the pile foundation can be more comprehensively understood, providing a solid foundation for risk assessment. Using finite element analysis, based on the collected information, multiple risk detection points and the risk correlation coefficients between them can be accurately determined. This method can take into account the complex structure and multiple influencing factors of the pile foundation, and improve the scientificity and accuracy of risk assessment. A risk detection device is set at each risk detection point, which can realize the key monitoring of the key areas of the pile foundation and improve the pertinence and efficiency of the detection. Based on the status data and risk correlation coefficient of the risk detection point, multiple ultrasonic detection positions are determined to ensure that the detection work covers the main risk areas of the pile foundation and avoid unnecessary detection waste. The generated ultrasonic detection scheme includes a specific detection path and ultrasonic detection parameters corresponding to each detection position, which can ensure the accuracy and reliability of the detection results. The detection data obtained according to the ultrasonic detection scheme can fully reflect the actual situation of the pile foundation, including detailed information on defects such as cracks and deformations. Risk analysis based on detection data can accurately evaluate the safety performance of the pile foundation, discover potential risk points in a timely manner, and provide a scientific basis for subsequent maintenance and reinforcement work.

[0017] 2. By establishing geometric models, meshing, specifying material properties, and determining boundary conditions and external loads, the stress conditions of bridge pile foundations under different working conditions can be accurately simulated, thereby more accurately assessing their potential risks. Not only static loads are considered, but also traffic flow information and environmental information are combined. The range of traffic loads and environmental loads is determined through a prediction model, making risk assessment more comprehensive and closer to reality. Based on the simulated vibration data of the finite element analysis unit, risk finite element analysis units are screened out, and the initial detection points are determined by clustering, so that the detection points are more concentrated in the potential risk area. By calculating the point risk correlation coefficient, risk detection points are further screened out, and the risk correlation coefficient between them is determined, providing a scientific basis for subsequent ultrasonic testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0019] Figure 1 It is a schematic diagram of a process of a concrete bridge pile foundation risk detection method according to some embodiments of this specification;

[0020] Figure 2 It is a schematic diagram of a process for determining multiple risk detection points of a concrete bridge pile foundation to be detected and risk correlation coefficients between the multiple risk detection points according to some embodiments of this specification;

[0021] Figure 3 It is a module schematic diagram of a concrete bridge pile foundation risk detection system according to some embodiments of this specification. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0023] Figure 1 is a flow chart of a concrete bridge pile foundation risk detection method according to some embodiments of this specification, such as Figure 1 As shown, the risk detection method for concrete bridge pile foundation may include the following process.

[0024] Step 110, obtaining structural information and service information of the concrete bridge pile foundation to be inspected.

[0025] Specifically, the structural information of the concrete bridge pile foundation to be inspected may include the pile foundation type (for example, bored cast-in-place piles, immersed tube cast-in-place piles, artificially excavated piles, etc.) and size (for example, diameter, length, quantity and arrangement (for example, single row piles, multiple rows of piles, group piles, etc.)), material properties (for example, the strength grade and mix ratio of concrete, the specification, model, quantity and arrangement of steel bars, etc.), geometric structure (for example, the connection method between the pile top and the pedestal or pier body, whether the pile body is provided with special structures such as variable cross-section and enlarged head, etc.), design parameters (for example, design bearing capacity requirements (for example, vertical bearing capacity, horizontal bearing capacity and pull-out bearing capacity, etc.), design deformation limits (for example, settlement, inclination, etc.), etc.).

[0026] The service information of the concrete bridge pile foundation to be inspected includes traffic flow information (for example, daily traffic flow, vehicle type, proportion of heavy-loaded vehicles, pedestrian flow, etc.) and environmental information (for example, climatic conditions (for example, wind force, temperature, humidity, seismic activity, etc.), geological conditions (for example, soil type, groundwater level, etc.), hydrology, etc.).

[0027] Step 120, determining multiple risk detection points of the concrete bridge pile foundation to be detected and risk correlation coefficients between the multiple risk detection points based on the structural information and service information of the concrete bridge pile foundation to be detected by finite element analysis.

[0028] Figure 2is a flow chart of determining multiple risk detection points of a concrete bridge pile foundation to be detected and risk correlation coefficients between the multiple risk detection points according to some embodiments of this specification, such as Figure 2 As shown, step 120 specifically includes the following steps:

[0029] Based on the structural information of the concrete bridge pile foundation to be inspected, a geometric model of the concrete bridge pile foundation to be inspected is established;

[0030] Mesh the geometric model of the concrete bridge pile foundation to be inspected to obtain multiple finite element analysis units;

[0031] Based on the structural information of the concrete bridge pile foundation to be inspected, material properties (e.g., elastic modulus, Poisson's ratio, density, etc.) are specified for each finite element analysis unit;

[0032] Determine boundary conditions (e.g., interaction between the concrete bridge piles to be tested and the soil or foundation, etc.);

[0033] Based on the structural information and service information of the concrete bridge pile foundation to be tested, multiple groups of external loads for testing are determined;

[0034] For each set of test external loads, test data of the concrete bridge pile foundation to be tested under the test external loads are obtained, wherein the test data includes simulated vibration data of multiple finite element analysis units;

[0035] Determine multiple initial inspection points based on the test data of the concrete bridge pile foundation to be inspected under each set of test external loads;

[0036] Based on the test data of the concrete bridge pile foundation to be tested under each set of test external loads, the point risk correlation coefficients of multiple initial test points are determined;

[0037] Based on the point risk correlation coefficients of the plurality of initial detection points, a plurality of risk detection points are screened from the plurality of initial detection points, and the risk correlation coefficients between the plurality of risk detection points are determined.

[0038] Specifically, the geometric model of the concrete bridge pile foundation to be tested is divided into a series of interconnected finite element analysis units, and physical laws and boundary conditions are applied to each finite element analysis unit to simulate the response of the entire concrete bridge pile foundation to be tested. In finite element analysis, the displacement, stress, strain, vibration and other physical quantities inside each finite element analysis unit can be obtained by solving a set of linear or nonlinear equations.

[0039] Preferably, based on the structural information and service information of the concrete bridge pile foundation to be tested, multiple groups of external loads for testing are determined, including:

[0040] Acquire structural information of the concrete bridge and determine the static load of the pile foundation of the concrete bridge to be inspected (for example, the deadweight of the bridge, etc.);

[0041] Using a first load prediction model, based on traffic flow information, the traffic load range of the concrete bridge pile foundation to be inspected is predicted, wherein the first load prediction model may be a convolutional neural network (CNN) model;

[0042] The environmental load range of the concrete bridge pile foundation to be inspected is predicted based on the environmental information by using the second load prediction model, wherein the second load prediction model may be a convolutional neural network model;

[0043] Based on the static load, traffic load range and environmental load range of the concrete bridge pile foundation to be tested, multiple groups of external loads for testing are determined.

[0044] The traffic load range of the concrete bridge pile foundation to be tested can represent the value range of the loads imposed by vehicles and pedestrians on the concrete bridge pile foundation to be tested. The environmental load range of the concrete bridge pile foundation to be tested can represent the value range of the loads imposed by the environment on the concrete bridge pile foundation to be tested. For example, the environmental load range can include the river load range, wind load range, earthquake load range, temperature load range, etc.

[0045] The value can be taken from the traffic load range of the concrete bridge pile foundation to be tested as the traffic load, and the value can be taken from the environmental load range as the environmental load, and the application position and direction of the environmental load can be limited. The static load, traffic load and environmental load with determined position and direction constitute a group of external loads for testing.

[0046] Preferably, based on the test data of the concrete bridge pile foundation to be tested under each set of test external loads, multiple initial test points are determined, including:

[0047] For each finite element analysis unit, based on the test data of the finite element analysis unit under each set of test external loads, a unit risk parameter of the finite element analysis unit is determined, and a finite element analysis unit whose unit risk parameter is greater than a unit risk parameter threshold is regarded as a risky finite element analysis unit;

[0048] Clustering is performed on multiple risk finite element analysis units to determine multiple unit clusters, and the risk finite element analysis unit corresponding to the cluster center of the unit cluster is used as an initial detection point.

[0049] Specifically, for each set of test external loads, the test data of the finite element analysis unit under the set of test external loads can be subjected to variational modal decomposition to obtain a corresponding plurality of modal components, and component characteristics of each modal component can be extracted (for example, time domain characteristics (for example, amplitude, root mean square value, peak factor, waveform factor, etc.), frequency domain characteristics (for example, center frequency, bandwidth, spectral energy, spectral entropy, etc.), etc.).

[0050] For each finite element analysis unit, the unit risk parameter of the finite element analysis unit can be obtained based on the component characteristics of multiple modal components corresponding to the test data of the finite element analysis unit under each set of test external loads. As an example only, for each finite element analysis unit, the unit risk parameter of the finite element analysis unit can be obtained based on the component characteristics of multiple modal components corresponding to the test data of the finite element analysis unit under each set of test external loads through a risk parameter prediction model, wherein the risk parameter prediction model can be a convolutional neural network model.

[0051] A clustering algorithm (for example, K-means clustering algorithm, hierarchical clustering algorithm, etc.) can be used to cluster multiple risk finite element analysis units according to the unit risk parameters and spatial distances of the risk finite element analysis units to determine multiple unit clusters, wherein each unit cluster includes multiple risk finite element analysis units, and the spatial distance is used to characterize the distance between the spatial coordinates of two risk finite element analysis units.

[0052] As an example only, multiple risk finite element analysis units may be clustered according to the following process:

[0053] S11, initializing multiple unit clusters based on multiple risk finite element analysis units, wherein one risk finite element analysis unit corresponds to one initial unit cluster;

[0054] S12. Calculate the absolute difference between the unit risk parameters of any two risk finite element analysis units according to the unit risk parameter of each risk finite element analysis unit, wherein the absolute difference between the unit risk parameters is the absolute value of the difference between the unit risk parameters of the two risk finite element analysis units;

[0055] S13. For each risk finite element analysis unit, when the absolute difference between the unit risk parameters of the risk finite element analysis unit and a certain risk finite element analysis unit is less than the unit risk parameter absolute difference threshold and the spatial distance is less than the spatial distance threshold, the risk finite element analysis unit is used as a candidate merged risk finite element analysis unit, the merged parameters of the candidate merged risk finite element analysis units are calculated, and the risk finite element analysis unit is merged with the candidate merged risk finite element analysis unit with the largest merged parameters;

[0056] S14, determining whether the merged multiple unit clusters meet preset conditions, if so, completing clustering, if not, executing S15, wherein the preset conditions may include that the number of the merged multiple unit clusters is less than a number threshold or that the merge parameters of any two merged unit clusters are less than a merge parameter threshold;

[0057] S15. For each unit cluster, when the absolute difference in unit risk parameters between the risk finite element analysis unit corresponding to the cluster center of the unit cluster and the risk finite element analysis unit corresponding to the cluster center of a certain unit cluster is less than the absolute difference threshold of unit risk parameters and the spatial distance is less than the spatial distance threshold, the unit cluster is used as a candidate merged unit cluster, the merging parameters of the candidate merged unit cluster are calculated, and the unit cluster is merged with the candidate merged unit cluster with the largest merging parameters, and S14 is executed.

[0058] For example, the combined parameters of the candidate combined risk finite element analysis unit can be calculated according to the following formula:

[0059]

[0060] Among them, P (i,j) is the merging parameter of the risk finite element analysis unit of the i-th risk finite element analysis unit and its j-th candidate risk finite element analysis unit, a 1 and a 2 are weights, and a 1 and a 2 Both are greater than 0, a 1 +a 2 =1,η 1 and η 2 All are preset parameters, η 1 and η 2 Both are greater than 0, △R (i,j) is the absolute difference in unit risk parameters between the i-th risk finite element analysis unit and its j-th candidate merged risk finite element analysis unit, D (i,j) is the spatial distance between the i-th risk finite element analysis unit and its j-th candidate merged risk finite element analysis unit.

[0061] The calculation method of the merging parameters of the candidate merged unit cluster is similar to the calculation method of the merging parameters of the candidate merged risk finite element analysis unit. The absolute difference and spatial distance of the unit risk parameters between the risk finite element analysis units corresponding to the cluster centers of the two unit clusters are substituted into the above formula, which will not be repeated here.

[0062] Preferably, based on the test data of the concrete bridge pile foundation to be tested under each set of test external loads, the point risk correlation coefficients of the multiple initial test points are determined, including:

[0063] For each set of test external loads, based on the test data of each initial detection point under the test external load, calculate the load risk correlation coefficient between any two initial detection points under the test external load;

[0064] Based on the load risk correlation coefficient of any two initial detection points under each set of test external loads, the point risk correlation coefficient of any two initial detection points is calculated.

[0065] Specifically, the load risk correlation coefficient of any two initial detection points under the test external load can be calculated according to the following formula:

[0066]

[0067] Among them, r (i,j,m) A is the load risk correlation coefficient between the i-th initial detection point and the j-th initial detection point under the m-th group of test external loads, (i,t,m) A is the instantaneous amplitude of the i-th initial detection point at the t-th moment under the m-th set of test external loads, (j,t,m) is the instantaneous amplitude of the jth initial detection point at the tth moment under the mth set of test external loads, and T is the total number of sampling moments.

[0068] The point risk correlation coefficient of any two initial detection points can be calculated according to the following formula:

[0069]

[0070] Among them, r (i,j) is the point risk correlation coefficient between the ith initial detection point and the jth initial detection point, and M is the total number of groups of external loads used for testing.

[0071] Preferably, based on the point risk correlation coefficients of the multiple initial detection points, multiple risk detection points are screened from the multiple initial detection points, and the risk correlation coefficients between the multiple risk detection points are determined, including:

[0072] Based on the point risk correlation coefficients of any two initial detection points, determine the first point risk correlation coefficient sequence corresponding to each initial detection point, wherein an element in the first point risk correlation coefficient sequence corresponds to an initial detection point, for example, the first element in the first point risk correlation coefficient sequence corresponds to the initial detection point A, which is used to characterize the risk correlation coefficient between the current initial detection point and the initial detection point A, the second element in the first point risk correlation coefficient sequence corresponds to the initial detection point B, which is used to characterize the risk correlation coefficient between the current initial detection point and the initial detection point B, and the risk correlation coefficient of the initial detection point with itself is set to 1;

[0073] For each initial detection point, based on the first point risk correlation coefficient sequence corresponding to the initial detection point, the point risk correlation coefficient mean and the point risk correlation coefficient standard deviation are calculated, and the initial detection point whose point risk correlation coefficient mean is greater than the point risk correlation coefficient mean threshold and the point risk correlation coefficient standard deviation is less than the point risk correlation coefficient standard deviation threshold is taken as the risk detection point;

[0074] Based on the point risk correlation coefficients of any two risk detection points, determine the second point risk correlation coefficient sequence corresponding to each risk detection point, wherein one element in the second point risk correlation coefficient sequence corresponds to a risk detection point, the first element in the second point risk correlation coefficient sequence corresponds to risk detection point A, which is used to characterize the risk correlation coefficient between the current risk detection point and risk detection point A, the second element in the second point risk correlation coefficient sequence corresponds to risk detection point B, which is used to characterize the risk correlation coefficient between the current initial detection point and risk detection point B, and the risk correlation coefficient of the risk detection point with itself is set to 1;

[0075] Clustering the multiple risk detection points according to the second point risk correlation coefficient sequence corresponding to each risk detection point to determine multiple point clusters, wherein each point cluster includes multiple risk detection points;

[0076] For each point cluster, the risk correlation coefficient between any two risk detection points included in the point cluster is determined according to the second point risk correlation coefficient sequence corresponding to each risk detection point included in the point cluster.

[0077] Specifically, for any two risk detection points, the sequence similarity between the second point risk correlation coefficient sequences corresponding to the two risk detection points can be calculated. A clustering algorithm (e.g., K-means clustering algorithm, hierarchical clustering algorithm, etc.) can be used to cluster multiple risk detection points according to the sequence similarity between the second point risk correlation coefficient sequences corresponding to any two risk detection points to determine multiple point clusters.

[0078] For example, the sequence similarity can be calculated according to the following formula:

[0079]

[0080] Among them, S (i,j) is the sequence similarity between the second point risk correlation coefficient sequence corresponding to the i-th risk detection point and the second point risk correlation coefficient sequence corresponding to the j-th risk detection point, V (i,k) is the value of the kth element in the second risk correlation coefficient sequence corresponding to the i-th risk detection point, V (j,k) is the value of the kth element in the second point risk correlation coefficient sequence corresponding to the jth risk detection point, and K is the total number of risk detection points.

[0081] Step 130: Setting a risk detection device at each risk detection point.

[0082] Specifically, the risk detection device may include a sensor for detecting vibration, for example, a piezoelectric vibration sensor, an eddy current sensor, an inductive vibration sensor, a capacitive vibration sensor, and the like.

[0083] Step 140 , based on the detection point status data collected by the risk detection devices of the multiple risk detection points and the risk correlation coefficients between the multiple risk detection points, multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected are determined.

[0084] Preferably, step 140 specifically includes:

[0085] For each point cluster, based on the test data of the concrete bridge pile foundation to be tested under each set of external loads for testing, a training sample corresponding to the point cluster is generated, and an initial risk prediction model corresponding to the point cluster is established. The initial risk prediction model corresponding to the training point cluster is trained using the training samples corresponding to the point cluster to obtain the risk prediction model corresponding to the point cluster. The ultrasonic detection position corresponding to the point cluster and the risk information corresponding to the ultrasonic detection position are predicted by the risk prediction model corresponding to the point cluster based on the detection point status data collected by the risk detection device of the risk detection point included in the point cluster and the risk correlation coefficient between any two risk detection points included in the point cluster.

[0086] Specifically, the training samples corresponding to the point cluster may include a set of vibration information of multiple risk detection points included in the point cluster obtained by finite element analysis under a set of test external loads and the risk correlation coefficient between any two risk detection points included in the point cluster. The labels of the training samples of the point cluster may be the test external load and the risk information (e.g., crack information, dislocation information, deformation information, etc.) caused by the test external load to the concrete bridge pile foundation to be tested obtained by finite element analysis. The risk prediction model may be a long short-term memory network (LSTM) model.

[0087] Step 150: Generate an ultrasonic testing plan based on multiple ultrasonic testing positions of the concrete bridge pile foundation to be tested.

[0088] The ultrasonic detection scheme includes an ultrasonic detection path and ultrasonic detection parameters corresponding to each ultrasonic detection position.

[0089] Preferably, step 150 specifically includes:

[0090] Generate a risk detection path based on the structural information of the concrete bridge pile foundation to be detected and multiple risk detection positions through a path generation model, wherein the path generation model can be a Generative Adversarial Networks (GAN) model;

[0091] Based on the risk information corresponding to the ultrasonic detection position, the parameter generation model is used to generate ultrasonic detection parameters corresponding to the risk detection position (for example, frequency, gain, dynamic range, focal point, number of focuses, window size and depth, wave speed and medium sound speed, etc.), wherein the parameter generation model can be a generative adversarial network model.

[0092] Step 160 , performing ultrasonic testing on the concrete bridge pile foundation to be tested according to the ultrasonic testing scheme, and obtaining ultrasonic testing data of the concrete bridge pile foundation to be tested.

[0093] Specifically, the ultrasonic detection device can work according to the ultrasonic detection path and the ultrasonic detection parameters corresponding to each ultrasonic detection position to obtain the ultrasonic detection data of the concrete bridge pile foundation to be detected.

[0094] Step 170: Perform risk analysis of the concrete bridge pile foundation based on the ultrasonic detection data of the concrete bridge pile foundation to be detected.

[0095] Preferably, step 170 specifically includes:

[0096] The risk analysis of concrete bridge pile foundation is performed based on the ultrasonic detection data of the concrete bridge pile foundation to be detected through the risk analysis model, wherein the risk analysis model can be a convolutional neural network model, and the risk analysis model can output risk information of the concrete bridge pile foundation to be detected, such as crack location, crack size, dislocation location, dislocation size, deformation location, deformation size, etc.

[0097] Figure 3 is a schematic diagram of a module of a concrete bridge pile foundation risk detection system according to some embodiments of this specification, such as Figure 3 As shown, the concrete bridge pile foundation risk detection system may include an information acquisition module, a risk monitoring module, a risk prediction module, a solution generation module, a risk monitoring module and a risk analysis module.

[0098] The information acquisition module can be used to obtain the structural information and service information of the concrete bridge pile foundation to be tested;

[0099] The risk monitoring module can be used to determine multiple risk detection points of the concrete bridge pile foundation to be detected and the risk correlation coefficients between the multiple risk detection points based on the structural information and service information of the concrete bridge pile foundation to be detected through finite element analysis, and can also be used to set a risk detection device at each risk detection point;

[0100] The risk prediction module can be used to determine multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected based on the detection point status data collected by the risk detection devices of the multiple risk detection points and the risk correlation coefficients between the multiple risk detection points;

[0101] The scheme generation module can be used to generate an ultrasonic detection scheme based on multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected, wherein the ultrasonic detection scheme includes an ultrasonic detection path and ultrasonic detection parameters corresponding to each ultrasonic detection position;

[0102] The risk monitoring module can be used to perform ultrasonic testing on the concrete bridge pile foundation to be tested according to the ultrasonic testing scheme, and obtain ultrasonic testing data of the concrete bridge pile foundation to be tested;

[0103] The risk analysis module can be used to perform risk analysis of concrete bridge pile foundations based on ultrasonic testing data of the concrete bridge pile foundations to be tested.

[0104] The concrete bridge pile foundation risk detection system can be used to execute the concrete bridge pile foundation risk detection method. For more descriptions of the concrete bridge pile foundation risk detection system, please refer to the relevant descriptions of the concrete bridge pile foundation risk detection method, which will not be repeated here.

[0105] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for detecting risk of concrete bridge pile foundations, characterized in that: include: Obtain the structural information and service information of the concrete bridge pile foundation to be tested; Determine, by finite element analysis, a plurality of risk detection points of the concrete bridge pile foundation to be detected and risk correlation coefficients between the plurality of risk detection points based on the structural information and service information of the concrete bridge pile foundation to be detected; Risk detection devices are installed at each risk detection point; Determine multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected based on detection point status data collected by risk detection devices at multiple risk detection points and risk correlation coefficients between multiple risk detection points; Based on the multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected, an ultrasonic detection scheme is generated, wherein the ultrasonic detection scheme includes an ultrasonic detection path and ultrasonic detection parameters corresponding to each ultrasonic detection position; According to the ultrasonic detection scheme, ultrasonic detection is performed on the concrete bridge pile foundation to be detected to obtain ultrasonic detection data of the concrete bridge pile foundation to be detected; Based on the ultrasonic detection data of the concrete bridge pile foundation to be detected, a risk analysis of the concrete bridge pile foundation is performed.

2. The concrete bridge pile foundation risk detection method according to claim 1 is characterized in that: Through finite element analysis, based on the structural information and service information of the concrete bridge pile foundation to be detected, multiple risk detection points of the concrete bridge pile foundation to be detected and risk correlation coefficients between the multiple risk detection points are determined, including: Based on the structural information of the concrete bridge pile foundation to be detected, a geometric model of the concrete bridge pile foundation to be detected is established; Meshing the geometric model of the concrete bridge pile foundation to be inspected to obtain a plurality of finite element analysis units; Based on the structural information of the concrete bridge pile foundation to be tested, specifying material properties for each of the finite element analysis units; Determine boundary conditions; Determining multiple groups of external loads for testing based on the structural information and service information of the concrete bridge pile foundation to be tested; For each set of test external loads, solving and obtaining test data of the concrete bridge pile foundation to be tested under the test external loads, wherein the test data includes simulated vibration data of multiple finite element analysis units; Determining a plurality of initial detection points based on the test data of the concrete bridge pile foundation to be detected under each set of the test external loads; Based on the test data of the concrete bridge pile foundation to be tested under each group of the test external loads, determining the point risk correlation coefficients of the plurality of initial test points; Based on the point risk correlation coefficients of the multiple initial detection points, multiple risk detection points are screened from the multiple initial detection points, and risk correlation coefficients between the multiple risk detection points are determined.

3. The concrete bridge pile foundation risk detection method according to claim 2 is characterized in that: The service information includes traffic flow information and environmental information; Based on the structural information and service information of the concrete bridge pile foundation to be tested, multiple groups of external loads for testing are determined, including: Acquire structural information of the concrete bridge and determine the static load of the pile foundation of the concrete bridge to be tested; Predicting the traffic load range of the concrete bridge pile foundation to be inspected based on the traffic flow information by using a first load prediction model; Predicting the environmental load range of the concrete bridge pile foundation to be inspected based on the environmental information by a second load prediction model; The multiple groups of external loads for testing are determined based on the static load, traffic load range and environmental load range of the concrete bridge pile foundation to be tested.

4. The concrete bridge pile foundation risk detection method according to claim 2 or 3, characterized in that: Based on the test data of the concrete bridge pile foundation to be tested under each set of the test external loads, a plurality of initial test points are determined, including: For each of the finite element analysis units, based on the test data of the finite element analysis unit under each set of the test external loads, a unit risk parameter of the finite element analysis unit is determined, and a finite element analysis unit whose unit risk parameter is greater than a unit risk parameter threshold is taken as a risky finite element analysis unit; Clustering is performed on multiple risk finite element analysis units to determine multiple unit clusters, and the risk finite element analysis unit corresponding to the cluster center of the unit cluster is used as an initial detection point.

5. The concrete bridge pile foundation risk detection method according to claim 4 is characterized in that: Based on the test data of the concrete bridge pile foundation to be tested under each group of the test external loads, the point risk correlation coefficients of multiple initial test points are determined, including: For each set of the test external loads, based on the test data of each of the initial detection points under the test external loads, calculate the load risk correlation coefficient of any two initial detection points under the test external loads; Based on the load risk correlation coefficients of any two initial detection points under each set of the test external loads, the point risk correlation coefficients of any two initial detection points are calculated.

6. The concrete bridge pile foundation risk detection method according to claim 5 is characterized in that: Based on the point risk correlation coefficients of the multiple initial detection points, multiple risk detection points are screened from the multiple initial detection points, and risk correlation coefficients between the multiple risk detection points are determined, including: Based on the point risk correlation coefficients of any two initial detection points, determining a first point risk correlation coefficient sequence corresponding to each of the initial detection points; For each of the initial detection points, based on the first point risk correlation coefficient sequence corresponding to the initial detection point, the point risk correlation coefficient mean and the point risk correlation coefficient standard deviation are calculated, and the initial detection point whose point risk correlation coefficient mean is greater than the point risk correlation coefficient mean threshold and the point risk correlation coefficient standard deviation is less than the point risk correlation coefficient standard deviation threshold is taken as the risk detection point; Based on the point risk correlation coefficients of any two risk detection points, determining a second point risk correlation coefficient sequence corresponding to each of the risk detection points; Clustering a plurality of risk detection points according to a second point risk correlation coefficient sequence corresponding to each of the risk detection points to determine a plurality of point clusters; For each point cluster, the risk correlation coefficient between any two risk detection points included in the point cluster is determined according to the second point risk correlation coefficient sequence corresponding to each risk detection point included in the point cluster.

7. The concrete bridge pile foundation risk detection method according to claim 6, characterized in that: Based on the detection point status data collected by the risk detection devices of the multiple risk detection points and the risk correlation coefficients between the multiple risk detection points, multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected are determined, including: For each of the point clusters, based on the test data of the concrete bridge pile foundation to be tested under each group of external loads for testing, a training sample corresponding to the point cluster is generated, an initial risk prediction model corresponding to the point cluster is established, and the initial risk prediction model corresponding to the point cluster is trained using the training samples corresponding to the point cluster to obtain the risk prediction model corresponding to the point cluster; based on the detection point status data collected by the risk detection device of the risk detection point included in the point cluster and the risk correlation coefficient between any two risk detection points included in the point cluster, the ultrasonic detection position corresponding to the point cluster and the risk information corresponding to the ultrasonic detection position are predicted by the risk prediction model corresponding to the point cluster.

8. The concrete bridge pile foundation risk detection method according to claim 7, characterized in that: Based on the multiple risk detection positions of the concrete bridge pile foundation to be detected, a risk detection scheme is generated, including: Generate a risk detection path based on the structural information of the concrete bridge pile foundation to be detected and multiple risk detection positions through a path generation model; The ultrasonic detection parameters corresponding to the risk detection position are generated based on the risk information corresponding to the ultrasonic detection position through a parameter generation model.

9. The concrete bridge pile foundation risk detection method according to any one of claims 1 to 3, characterized in that: Based on the ultrasonic detection data of the concrete bridge pile foundation to be detected, a risk analysis of the concrete bridge pile foundation is performed, including: The concrete bridge pile foundation risk analysis is performed based on the ultrasonic detection data of the concrete bridge pile foundation to be detected by using a risk analysis model.

10. The concrete bridge pile foundation risk detection system is characterized by: The method for detecting risk of concrete bridge pile foundation according to any one of claims 1 to 9 comprises: An information acquisition module is used to obtain the structural information and service information of the concrete bridge pile foundation to be tested; A risk monitoring module, for determining, by finite element analysis, a plurality of risk detection points of the concrete bridge pile foundation to be detected and risk correlation coefficients between the plurality of risk detection points based on the structural information and service information of the concrete bridge pile foundation to be detected, and for setting a risk detection device at each risk detection point; A risk prediction module, used to determine multiple ultrasonic detection positions of the concrete bridge pile foundation to be detected based on the detection point status data collected by the risk detection devices of the multiple risk detection points and the risk correlation coefficients between the multiple risk detection points; A scheme generating module, used for generating an ultrasonic detection scheme based on a plurality of ultrasonic detection positions of the concrete bridge pile foundation to be detected, wherein the ultrasonic detection scheme includes an ultrasonic detection path and ultrasonic detection parameters corresponding to each ultrasonic detection position; A risk monitoring module, used to perform ultrasonic testing on the concrete bridge pile foundation to be tested according to the ultrasonic testing scheme, and obtain ultrasonic testing data of the concrete bridge pile foundation to be tested; The risk analysis module is used to perform risk analysis of the concrete bridge pile foundation based on the ultrasonic detection data of the concrete bridge pile foundation to be detected.

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