Intelligent identification method and system for defects of in-pipe concrete of CFST arch bridge based on MCMC model

Abstract

The application relates to a CFST arch bridge tube concrete defect intelligent identification method based on an MCMC model, which comprises the following steps: detecting a CFST arch bridge rib (1) section by using an elastic wave, acquiring detection data, and the detection data comprising an elastic wave velocity; inputting the detected elastic wave velocity into a probability model for quantitative identification; the probability model comprising: judging the compactness of the detection data according to the probability model; when the detection data is located in a 95% confidence interval range of the probability model, the compactness of the section detection data is good; and when the detection data is located outside the 95% confidence interval of the probability model, the compactness of the section detection data is poor; the method has simple steps, is convenient to operate, and has good effects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge health monitoring, and particularly relates to a CFST (Concrete-filled Steel Tubular) arch bridge pipe concrete defect intelligent identification method and system based on a MCMC (Markov Chain Monte Carlo) model. BACKGROUND

[0002] The CFST (Concrete-filled Steel Tubular) arch bridge is an arch bridge mainly using steel pipe concrete as the main material. At present, the CFST arch bridge has been widely applied in the country, and the span gradually develops to the super-long span, due to the excellent mechanical properties, construction performance and economic advantages. However, due to the influence of the construction pouring quality, temperature and pipe concrete shrinkage, the steel pipe concrete is prone to produce the pipe concrete non-dense phenomenon such as debonding and emptying. Therefore, accurately and comprehensively analyzing the dense state of the steel pipe concrete has important significance for the cost reduction and benefit increase strategy layout such as the steel pipe concrete pouring process improvement and the mix proportion optimization. However, the existing steel pipe concrete still has the following deficiencies in the quality detection.

[0003] The existing detection means still relies on manual operation, and the method has the problems of high operation difficulty, high risk and low detection efficiency in the high-altitude operation. In addition, since the construction basket exists only in the construction stage of the spliced segment of each arch rib, the detection personnel cannot carry out detection on the remaining positions of the bridge. At the same time, since the construction basket is removed during the bridge operation period, the dense detection of the steel pipe concrete arch bridge after the bridge is built for several years cannot be carried out. Finally, the existing steel pipe concrete arch bridge dense detection has the problems of poor integration of the monitoring data collection, analysis and storage, low intelligent degree and great dependence on technical personnel.

[0004] The existing steel pipe concrete arch bridge elastic wave quality detection evaluation mainly depends on artificial qualitative discrimination, field test comparison, and probability discrimination method proposed by the specification. It should be noted that the above methods have the following defects: (1) the artificial qualitative discrimination has great subjectivity, and has problems of poor discrimination accuracy and high experience requirement of the discrimination result on the technical personnel; (2) the field test comparison has a high discrimination accuracy to a certain extent, but the field test consumes time and materials, resulting in high construction cost; (3) the probability discrimination method proposed by the specification depends on a large number of field measuring points and measuring areas, resulting in a huge amount of work of the field staff, and the method depends on human experience judgment and lacks clear mechanism analysis evidence; (4) the method cannot reasonably consider the subjective and objective uncertainty of the material characteristic parameters of the steel pipe concrete, artificial detection error, and incomplete consideration factors. The existence of the above problems will restrict the monitoring of the compactness evaluation of the steel pipe concrete arch bridge, and weaken the significance of the bridge monitoring. SUMMARY

[0005] The purpose of the present application is to provide a CFST arch bridge in-pipe concrete defect intelligent identification method and system based on a MCMC model, in view of the problems of great subjectivity of artificial qualitative discrimination, poor discrimination accuracy, high experience requirement of the discrimination result on the technical personnel, time and material consumption of the field test, high construction cost, huge amount of work of the probability discrimination method proposed by the specification, dependence on human experience judgment, lack of clear mechanism analysis evidence, and inability to reasonably consider the subjective and objective uncertainty of the material characteristic parameters of the steel pipe concrete, artificial detection error, and incomplete consideration factors.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is:

[0007] A CFST arch bridge in-pipe concrete defect intelligent identification method based on a MCMC model, comprising the following steps:

[0008] Using elastic wave detection of the CFST arch bridge arch rib section, obtaining detection data, and the detection data comprising elastic wave wave speed;

[0009] Inputting the detected elastic wave wave speed into a probability model for quantitative identification, and the probability model comprising:

[0010]

[0011] In the formula, EWV is an elastic wave wave speed prediction value, f c fc is the compressive strength of the core concrete in the arch rib, t t is the detection time, is the coupling effect of the cementitious material type on the compressive strength, The coupling effect of cementitious material type on time-varying patterns. This represents the parameter vector of the probabilistic model that is updated based on different real bridge detection results. This is the amplification factor of elastic wave velocity when there is an internal steel structure within the arch rib. ,Right now It conforms to a standard normal distribution. Let be a standard normally distributed random variable. The systematic error of the model parameters;

[0012] The density of the detection data is determined based on the probability model. When the detection data is within the 95% confidence interval of the probability model, the density of the detection data in that section is good. When the detection data is outside the 95% confidence interval of the probability model, the density of the detection data in that section is poor.

[0013] Preferably, the test data of different well-dense parts of the arch ribs of the actual bridge are determined by elastic wave measurement, and the Markov chain Monte Carlo method is used to analyze the data. Update.

[0014] More preferably, the detection data at the arch foot of different real bridge arch ribs are determined by elastic wave measurement.

[0015] More preferably, for The update includes the following steps:

[0016] A. Given initial value This place makes Construct suggested distribution Given sample size Here, data on the density of the arch foot of the arch rib is taken;

[0017] B. Given the initial covariance matrix Assuming that the parameters of the probability model are independent of each other, and according to The initial covariance matrix is ​​constructed using a pre-defined initial standard deviation, and a non-adaptive interval iteration step is set. In the non-adaptive phase, there is no need to update the covariance matrix. However, once the number of iterations in the non-adaptive phase is exceeded... The covariance matrix is ​​updated using the following formula:

[0018]

[0019] In the formula, For small variables, s d This is the scaling factor. I d It is a d-dimensional identity matrix; The covariance matrix of the historical samples;

[0020] C. Follow the steps below until a series of Markov chains satisfying a stationary distribution are generated;

[0021] C1. Let the current parameter be... Then from the first-order proposed distribution Construct suggested values According to Find Where Z is a random number that follows a standard normal distribution. For the first-order proposed distribution standard deviation Represents the first-order proposal distribution Variables;

[0022] C2. Calculate the first-order acceptance probability. :

[0023]

[0024] In the formula, and This is the prior distribution function;

[0025] C3. Randomly select a number from 0 to 1. If satisfied If so, then accept the suggested values ​​for the model parameters. , that is to say Then proceed to step C6; otherwise proceed to step C4.

[0026] C4. Proceed to the second-order acceptance probability discrimination and adjust the covariance matrix. From the second-order proposed distribution Construct new suggested values According to Find ,in, For the second-order proposed distribution standard deviation Represents the second-order proposal distribution Variables;

[0027]

[0028] Similarly, a number is randomly selected from 0 to 1. If satisfied If so, then accept the suggested values ​​for the model parameters. , that is to say If yes, proceed to step C6; otherwise, proceed to step C5.

[0029] C5, and so on, into the first order acceptance probability discrimination, the corresponding adjustment covariance matrix , from the first order proposal distribution The new model parameter candidate value is constructed , that is, according to Solve , wherein The standard deviation of the first order proposal distribution ;

[0030]

[0031] Similarly, a number is randomly selected from 0~1 , if , the model parameter proposal value is accepted , that is, let , then enter step C6, otherwise let , repeat step C5;

[0032] C6, if Stop continuing iteration, otherwise increase the iteration number and return to step C1, wherein The current variable.

[0033] The application also provides a computer readable storage medium, and the program is executed by a processor to realize the MCMC model-based CFST arch bridge internal concrete defect intelligent identification method according to any one of the above.

[0034] The application also provides an electronic device, which comprises:

[0035] A memory, which stores a computer program;

[0036] A processor, which is used for executing the program in the memory to realize the MCMC model-based CFST arch bridge internal concrete defect intelligent identification method according to any one of the above.

[0037] The application further provides a CFST arch bridge in-pipe concrete defect intelligent identification system based on an MCMC model, which comprises an automatic arch climbing detection robot and a data processing and analysis module in communication connection with the automatic arch climbing detection robot.

[0038] Preferably, the automatic arch climbing detection robot further comprises a main controller, a driving device, a longitudinal movement device, a transverse movement device, a steel framework and a battery.

[0039] The steel framework comprises an arc-shaped first framework body and an arc-shaped second framework body, the main controller, the battery, the driving device and the longitudinal movement device are arranged on the first framework body, and the transverse movement device, the elastic wave receiving end and the elastic wave transmitting end are arranged on the second framework body.

[0040] The main controller is used for conveying instructions to the driving device, the longitudinal movement device, the transverse movement device, the elastic wave receiving end and the elastic wave transmitting end, the driving device is used for providing driving force to the longitudinal movement device and the transverse movement device, the longitudinal movement device is used for moving along the arch rib, the transverse movement device is used for driving the elastic wave receiving end and the elastic wave transmitting end to rotate along the arch rib section, and the battery is used for supplying power to the main controller, the driving device, the elastic wave receiving end and the elastic wave transmitting end.

[0041] Preferably, the CFST arch bridge in-pipe concrete defect intelligent identification system based on the MCMC model further comprises a wireless transmission module and a monitoring system module, the automatic arch climbing detection robot and the data processing and analysis module are in communication connection through the wireless transmission module, and the monitoring system module is in communication connection with the data processing and analysis module.

[0042] Further preferably, the monitoring system module comprises a defect position positioning module, a defect position early warning module, an image generation sub-module and a risk assessment sub-module, the defect position positioning module marks the defect position identified by the data processing and analysis module, the defect position early warning module sends early warning information according to the marked defect position, the image generation sub-module outputs the full-bridge defect position in the form of a real bridge image, and the risk assessment sub-module assesses the safety of the full bridge according to a pre-set defect rate.

[0043] As described above, due to the adoption of the above technical solutions, the application has the following beneficial effects:

[0044] 1. The MCMC model-based CFST arch bridge in-pipe concrete defect intelligent identification method, which combines the powerful data updating and nonlinear fitting capabilities of MCMC, establishes an MCMC probability model-based CFST arch bridge in-pipe concrete defect intelligent identification method, overcomes the objective uncertainty of traditional deterministic prediction models in considering the material characteristic parameters of steel pipe concrete, construction and construction pouring process and other factors, and the subjective uncertainty caused by artificial measurement errors, incomplete consideration of factors during model establishment or introduction of model assumptions, continuously updates model parameters based on detection data for different steel pipe concrete arch bridge design and construction parameters, improves the discrimination calculation and efficiency, is more suitable for practical engineering application, and has stronger guidance;

[0045] 2. The MCMC model-based CFST arch bridge in-pipe concrete defect intelligent identification method, which automatically optimizes through the Markov property method, calculates the optimal model parameters after setting the number of iterations, thereby reducing the difficulty and errors of model parameter adjustment during iteration updating, greatly improving the accuracy of model posterior parameter updating, and thus improving the model discrimination precision;

[0046] 3. The MCMC model-based CFST arch bridge in-pipe concrete defect intelligent identification method, which not only solves the problems of traditional data analysis and processing methods being subject to personnel experience, work efficiency and other aspects, but also has the outstanding advantages of being intuitive, fast and high-precision;

[0047] 4. The MCMC model-based CFST arch bridge in-pipe concrete defect intelligent identification system, which overcomes the problems of existing detection methods relying on manual operation, high-altitude operation difficulty, high risk and low detection efficiency, greatly reduces the labor cost, and has high detection efficiency;

[0048] 5. The MCMC model-based CFST arch bridge in-pipe concrete defect intelligent identification system, which can realize CFST arch bridge detection at any position without relying on hanging baskets, cranes and other means, overcomes the shortcomings of existing detection methods that can only detect arch rib splicing sections, improves the comprehensiveness of detection data, and provides convenient and effective detection means for bridge operation period detection;

[0049] 6, The CFST arch bridge in-pipe concrete defect intelligent identification system based on the MCMC model has the automatic data acquisition function of the automatic arch climbing detection robot, and obtains, analyzes and manages the monitoring data through the monitoring control platform, so that the standardization, automation and intelligent evaluation of the in-pipe concrete defect identification are realized, and the compaction change process in the steel pipe concrete pouring construction process can be ensured to be fed back in time; the identification system has simple structure, convenient use and good effect. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 It is a schematic diagram of the CFST arch bridge in-pipe concrete defect intelligent identification system;

[0051] Figure 2 It is a block diagram of the intelligent identification system;

[0052] Figure 3 It is a structural schematic diagram of the automatic arch climbing detection robot;

[0053] Figure 4 It is a detection section distribution schematic diagram of a single measurement area;

[0054] Figure 5 It is a measurement point distribution schematic diagram of a single section;

[0055] Figure 6 It is a Markov chain based on automatic updating;

[0056] Figure 7 It is a comparison diagram of model calculation value and field matching test value;

[0057] Figure 8 It is an in-pipe concrete defect identification schematic diagram based on 95% and 50% confidence intervals.

[0058] Markings in the figure: 1-arch rib, 11-arch foot, 2-automatic arch climbing detection robot, 21-main control unit, 22-driving device, 23-longitudinal movement device, 231-traveling wheel, 232-first magnetic attraction device, 24-lateral movement device, 241-rail, 25-elastic wave receiving end, 251-pressing device, 252-second magnetic attraction device, 26-steel skeleton, 27-elastic wave transmitting end, 28-battery, 3-wireless transmission module, 4-data processing and analysis module, 5-monitoring system module, 51-defect position positioning module, 52-defect position early warning module, 53-image generation submodule, 54-risk assessment submodule. DETAILED DESCRIPTION

[0059] The application will be described in detail below with reference to the drawings.

[0060] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0061] Embodiment 1

[0062] The intelligent identification method of the CFST arch bridge in-situ concrete defects based on the MCMC model comprises the following steps:

[0063] Step one, establishing a probability model for identifying in-situ concrete defects of the CFST arch bridge

[0064] The probability model comprises:

[0065]

[0066] In the formula, EWV is the predicted value of the elastic wave velocity;

[0067] is the compressive strength of the core concrete in the arch rib 1 of the CFST arch bridge, which is the 28d compressive strength value of the cube made of the same mixing ratio and materials, provided by the site laboratory;

[0068] t is the detection time;

[0069] and are the coupling influence factors of the types of cementitious materials on the compressive strength and the time-varying law, respectively. The values of the types of cementitious materials, such as ordinary concrete, fly ash concrete, slag concrete, and silica ash concrete, are shown in Table 1;

[0070] Table 1: Values of the coefficients of cementitious materials

[0071]

[0072] is the steel pipe concrete elastic wave velocity prediction model parameter vector considering the objective uncertainty of the arch rib 1 existing material characteristic parameters, construction and pouring process deviation and other factors, which can be automatically optimized and updated based on the measurement results of different actual bridges;

[0073] is the amplification coefficient of the elastic wave velocity when the stiffened plate, flange plate and other in-pipe steel structures exist in the arch rib 1, and the values when the stiffened plate, flange plate and inner lining pipe exist in the pipe are shown in Table 2;

[0074] Table 2: Values of the amplification coefficients of the in-pipe steel structures

[0075]

[0076] ;

[0077] is a standard normal distribution random variable;

[0078] is the system error of the model parameter, which is a model parameter added considering the objective uncertainty of artificial measurement error and other factors.

[0079] Step two, updating the probability model based on data sources

[0080] In this embodiment, in order to comprehensively consider the subjective and objective uncertainty of the material characteristic parameters of the steel pipe concrete, artificial measurement error, and other factors, the probability model parameters are corrected and updated based on the data of the dense arch rib 1 at the arch foot 11 position (the concrete in the arch foot 11 basically does not appear to be empty).

[0081] This embodiment determines the posterior distribution information of based on MCMC, and further determines the pipe concrete defect identification probability model based on the elastic wave velocity. The method comprises the following steps:

[0082] A, given the initial value of , here , the proposal distribution is constructed, and the sample size is given , here the data of the dense arch rib (1) at the arch foot (11) position is taken;

[0083] B, given the initial covariance matrix , it is assumed that the probability model parameters are independent of each other, and the initial covariance matrix is constructed according to the pre-set initial standard deviation of , and the non-adaptive interval iteration step is set, and the covariance matrix does not need to be updated in the non-adaptive stage, and when the iteration step number of the non-adaptive stage is exceeded, the covariance matrix is updated according to the following formula:

[0084]

[0085] In the formula, is a small variable, s d is a proportional scaling factor, I d is a d-dimensional unit matrix; is the covariance matrix of the historical sample;

[0086] ​​C. Follow the steps below until a series of Markov chains satisfying a stationary distribution are generated, such as... Figure 6 As shown;

[0087] C1. Let the current parameter be... Then from the first-order proposed distribution Construct suggested values According to Find Where Z is a random number that follows a standard normal distribution. First-order proposed distribution standard deviation Represents the first-order proposal distribution Variables;

[0088] C2. Calculate the first-order acceptance probability. :

[0089]

[0090] In the formula, and This is the prior distribution function;

[0091] C3. Randomly select a number from 0 to 1. If satisfied If so, then accept the suggested values ​​for the model parameters. , that is to say Then proceed to step C6; otherwise proceed to step C4.

[0092] C4. Proceed to the second-order acceptance probability discrimination and adjust the covariance matrix. From the second-order proposed distribution Construct new suggested values According to Find ,in, For the second-order proposed distribution standard deviation Represents the second-order proposal distribution Variables;

[0093]

[0094] Similarly, a number is randomly selected from 0 to 1. If satisfied If so, then accept the suggested values ​​for the model parameters. , that is to say If yes, proceed to step C6; otherwise, proceed to step C5.

[0095] C5, and so on, proceeding to the next step. Step C5, the acceptance probability discrimination, the corresponding adjustment covariance matrix , the first Step C4, the first Step C3, the first , that is, according to Step C2, the first , wherein is the standard deviation of the first Step C1, the first

[0096]

[0097] Similarly, a number is randomly selected from 0 to 1 , if it satisfies , the model parameter suggestion value is accepted , that is, let , then enter step C6, otherwise let , repeat step C5;

[0098] Step C6, if , stop the iteration, otherwise increase the iteration number and return to step C1, wherein is the current variable.

[0099] That is, a plurality of groups of data are measured at the position of the arch spring 11 by using elastic waves, and then the measured data are substituted into the EWV probability model to optimize the probability model parameters according to the data (posterior value), so as to obtain the optimized probability model.

[0100] Step three, the cross section of the full-bridge arch rib 1 is detected by using elastic waves to obtain detection data, and the detection data includes elastic wave velocity, wave amplitude, frequency and sound time.

[0101] Step four, the detected elastic wave velocity is input into the probability model for quantitative identification

[0102] According to the test test piece made of the same raw materials, mixing ratio and construction pouring process on site, the prediction accuracy of the model is verified, as shown in Figure 7 , the probability model calculation value is consistent with the test value of the test test piece, which shows that the above-mentioned probability model has high accuracy in identifying the defect of the CFST arch bridge pipe concrete.

[0103] As shown in Figure 8 :

[0104] When the detection data is located in the 50% confidence interval range of the probability model, the compactness of the cross section detection data is good;

[0105] ​When the detection data is within the 95% confidence interval of the probability model, the concrete in the section is slightly hollow, but acceptable, and the detection data of the section is dense;

[0106] When the detection data is outside the 95% confidence interval of the probability model, the concrete in the section is severely hollow, the detection data of the section is poor, and a warning process is performed.

[0107] Step five, the hollow area of the whole bridge is processed, which can be reinforced by drilling and grouting.

[0108] The MCMC model-based CFST arch bridge pipe concrete defect intelligent identification method described in the embodiment combines the powerful data updating and nonlinear fitting capability of MCMC, establishes an MCMC probability model-based CFST arch bridge pipe concrete defect intelligent identification method, overcomes the objective uncertainty of the traditional deterministic prediction model, which cannot reasonably consider the material characteristic parameters, construction and construction grouting process and other factors of the steel pipe concrete, and cannot consider the subjective uncertainty caused by the incomplete consideration of factors or the introduction of model assumptions in the model establishment process, and improves the discrimination calculation and efficiency based on the detection data to continuously update the model parameters, which is more suitable for practical engineering application and has stronger guidance. Through the Markov property method, the optimal model parameters are calculated after setting the number of iterations, thereby reducing the difficulty and error of model parameter adjustment in the iteration updating process, greatly improving the accuracy of model posterior parameter updating, and improving the model discrimination precision. Not only solves the problems of traditional data analysis and processing methods, such as being subject to personnel experience and work efficiency, but also has the advantages of being intuitive, fast and high-precision. The method has simple steps, convenient operation and good effect.

[0109] Embodiment 2

[0110] The computer readable storage medium described in the application has a computer program stored thereon, and the program is executed by a processor to realize the MCMC model-based CFST arch bridge pipe concrete defect intelligent identification method described in embodiment 1.

[0111] Embodiment 3

[0112] The electronic device described in the application comprises:

[0113] The memory has a computer program stored thereon;

[0114] The processor is configured to execute the program in the memory to realize the MCMC model-based CFST arch bridge pipe concrete defect intelligent identification method described in embodiment 1.

[0115] As one preferred implementation of the present embodiment, the electronic device can include a processor, a memory, and can further include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0116] The processor is configured to control overall operations of the electronic device to complete all or part of the steps of the above-mentioned method for intelligent identification of defects in in-pipe concrete of CFST arch bridges based on MCMC model.

[0117] The memory is configured to store various types of data to support operations of the electronic device, which can include, for example, instructions for operating any application or method on the electronic device, and application-related data; the memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0118] The multimedia component can include a screen, such as a touch screen, and an audio component for outputting and / or inputting audio signals; for example, the audio component can include a microphone for receiving external audio signals, and the received audio signals can be further stored in the memory or transmitted through the communication component; the audio component also includes at least one speaker for outputting audio signals.

[0119] The I / O interface provides an interface between the processor and other interface modules, which can be a keyboard, a mouse, a button, etc.; these buttons can be virtual buttons or physical buttons.

[0120] The communication component is configured to perform wired or wireless communication between the electronic device and other devices; wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, 4G or 5G, or a combination of one or more of them, so the corresponding communication component can include a Wi-Fi module, a Bluetooth module, an NFC module, and a mobile communication module.

[0121] As one preferred scheme of the embodiment, the electronic device can be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic elements for executing the above-mentioned intelligent identification method of CFST arch bridge in-situ concrete defects based on MCMC model.

[0122] In addition, the computer readable storage medium provided by the embodiment of the disclosure can be the above-mentioned memory including program instructions, and the above-mentioned program instructions can be executed by the processor of the electronic device to complete the above-mentioned intelligent identification method of CFST arch bridge in-situ concrete defects based on MCMC model.

[0123] Embodiment 4

[0124] As shown in Figure 1 , the intelligent identification system of CFST arch bridge in-situ concrete defects based on MCMC model comprises an automatic arch climbing detection robot 2, a wireless transmission module 3, a data processing and analysis module 4 and a monitoring system module 5.

[0125] The data processing and analysis module 4 uses the intelligent identification method of CFST arch bridge in-situ concrete defects based on MCMC model as described in embodiment 1.

[0126] As shown in Figure 2 and Figure 3 , the automatic arch climbing detection robot 2 comprises a main controller 21, a driving device 22, a longitudinal movement device 23, a transverse movement device 24, an elastic wave receiving end 25, a steel skeleton 26, an elastic wave transmitting end 27 and a battery 28; the steel skeleton 26 comprises an arc-shaped first frame body and an arc-shaped second frame body, the main controller 21, the battery 28, the driving device 22 and the longitudinal movement device 23 are arranged on the first frame body, and the transverse movement device 24, the elastic wave receiving end 25 and the elastic wave transmitting end 27 are arranged on the second frame body.

[0127] The main controller 21 transmits instructions to the driving device 22, the longitudinal moving device 23, the transverse moving device 24, the elastic wave receiving end 25 and the elastic wave transmitting end 27, the driving device 22 provides driving force to the longitudinal moving device 23 and the transverse moving device 24, and the battery 28 supplies power to the main controller 21, the driving device 22, the elastic wave receiving end 25 and the elastic wave transmitting end 27.

[0128] The longitudinal moving device 23 comprises a plurality of walking wheels 231, the walking wheels 231 are provided with first magnetic attraction devices 232, the driving device 22 drives the walking wheels 231 to walk along the arch rib 1, so that the automatic arch climbing detection robot 2 moves along the arch rib 1, the arc-shaped first frame body and the arc-shaped second frame body can make the automatic arch climbing detection robot 2 move away from the chord, the web or the connecting rod on the arch rib 1, and the first magnetic attraction devices 232 ensure that the walking wheels 231 are adsorbed on the surface of the arch rib 1, so as to prevent the automatic arch climbing detection robot 2 from falling.

[0129] The transverse moving device 24 comprises a plurality of running rails 241, the running rails 241 are connected to the second frame body, the driving device 22 drives the running rails 241 to rotate along the first frame body, so that the elastic wave receiving end 25 and the elastic wave transmitting end 27 rotate along the first frame body, the elastic wave receiving end 25 is arranged on the surface of the arch rib 1, and the transverse moving device 24 drives the elastic wave receiving end 25 and the elastic wave transmitting end 27 to rotate along the cross section of the arch rib 1, so that the elastic wave receiving end 25 and the elastic wave transmitting end 27 are arranged at a certain angle, which is usually 180°, that is, the elastic wave receiving end 25 and the elastic wave transmitting end 27 are located on the two sides of the radial cross section of the arch rib 1; if it is required to visualize and invert the image of the concrete in the pipe, the angles of the elastic wave receiving end 25 and the elastic wave transmitting end 27 are arranged in turn according to 30°, 60°, 90°, 120°, 150° and 180°, so as to ensure that the elastic wave receiving end 25 and the elastic wave transmitting end 27 are arranged in an array.

[0130] The elastic wave transmitting end 27 comprises a knocking hammer and a sensor arranged in the knocking hammer, the elastic wave receiving end 25 comprises a pressing device 251 and a second magnetic attraction device 252, the second magnetic attraction device 252 is arranged at the end of the elastic wave receiving end 25, and the pressing device 251 is arranged on the second frame body, the pressing device 251 can tightly press the elastic wave receiving end 25 on the surface of the arch rib 1, and the second magnetic attraction device 252 can adsorb the end of the elastic wave receiving end 25 on the surface of the arch rib 1, so as to ensure that the elastic wave receiving end 25 is closely attached to the surface of the arch rib 1 without gap, at the beginning of detection, the main controller 21 issues an instruction, the knocking hammer generates low-frequency elastic waves by knocking the surface of the steel pipe of the arch rib 1, the sensor receives the elastic waves and records the initial signal, the low-frequency waves pass through the steel pipe wall, the core concrete and the steel pipe wall on the other side of the arch rib 1 in a straight line, and then the elastic waves are received by the elastic wave receiving end 25 on the other side and the final signal is recorded, and the time difference between the initial signal and the final signal is the detection time.

[0131] As Figure 4 shown, after the automatic arch-climbing detection robot 2 moves a certain distance along the arch rib 1, it detects a cross-section. Generally, it detects a cross-section after moving at an equal interval L. In this embodiment, the value of L is 10 cm - 20 cm.

[0132] As Figure 5 shown, in this embodiment, the elastic wave receiving end 25 and the elastic wave transmitting end 27 are arranged at 180°. The elastic wave receiving end 25 and the elastic wave transmitting end 27 are respectively arranged with measuring points one, measuring points two, measuring points three, and measuring points four in a "meter" shape symmetry at each cross-section, totaling four pairs of measuring points.

[0133] The automatic arch-climbing detection robot 2 and the data processing and analysis module 4 are communicatively connected through the wireless transmission module 3. The wireless transmission module 3 includes a signal transmitter and a signal receiver. In some embodiments, the wireless transmission module 3 further includes a signal repeater for connecting the signal transmitter and the signal receiver. As Figure 3 shown, a signal transmitter is provided on the automatic arch-climbing detection robot 2, as Figure 1 shown, the signal receiver is communicatively connected to the data processing and analysis module 4, and the data processing and analysis module 4 is communicatively connected to the monitoring system module 5. Specifically, the wireless transmission module 3 adopts one or more of Wifi, infrared, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, and 5G.

[0134] The data processing and analysis module 4 analyzes the detection data transmitted by the wireless transmission module 3, introduces the confidence interval discrimination method, and establishes an uncertainty probability prediction model considering the 50% and 95% confidence intervals of model uncertainty. When the elastic wave velocity is within the 50% confidence interval, it is considered that the compactness of the measuring point is good. When the elastic wave velocity is within 95%, it is considered that the compactness of the measuring point is better. When the elastic wave velocity is outside the 95% confidence interval, it is considered that there is a defect in the measuring point.

[0135] The monitoring system module 5 is communicatively connected to the data processing and analysis module 4. The monitoring system module 5 is used for monitoring the compact state inside the concrete-filled steel tube arch bridge. The monitoring system module 5 includes a defect position positioning module 51, a defect position warning module 52, an image generation sub-module 53, and a risk assessment sub-module 54. The defect position positioning module 51 marks the defect position judged by the data processing and analysis module 4. The defect position warning module 52 issues a warning message according to the defect marked position, the image generation sub-module 53 outputs the defect positions of the whole bridge in the form of a real bridge BIM image, and the risk assessment sub-module 54 evaluates the safety of the whole bridge according to the preset defect rate.

[0136] The CFST arch bridge in-pipe concrete defect intelligent identification system based on the MCMC model overcomes the problems of high-altitude operation difficulty, great risk and low detection efficiency of the existing detection means which relies on manual operation, greatly reduces the labor cost, has high detection efficiency, can realize CFST arch bridge detection at any position without relying on hanging baskets, cranes and other means, overcomes the defects that the existing detection method can only detect the spliced section of the arch rib, improves the comprehensiveness of the detection data, provides a convenient and effective detection means for bridge operation period detection, the automatic arch climbing detection robot 2 has the automatic data acquisition function, and the monitoring and control platform is used to acquire, analyze and manage the monitoring data, so that the standardization, automation and intelligent evaluation of the in-pipe concrete defect identification are realized, and the tightness change process in the steel pipe concrete pouring construction process can be fed back in time, the identification system has the advantages of simple structure, convenient use and good effect.

[0137] The above merely describes the preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for intelligent identification of defects in in-pipe concrete of a CFST arch bridge based on an MCMC model, characterized in that, The method comprises the following steps: obtaining detection data by detecting the cross section of the CFST arch bridge arch rib (1) using elastic waves, wherein the detection data comprises elastic wave velocity; inputting the detected elastic wave velocity into a probability model for quantitative identification, wherein the probability model comprises: wherein EWV is the predicted value of elastic wave velocity, f c is the compressive strength of the core concrete in the arch rib (1), t is the detection time, is the coupling effect of the cementitious material type on the compressive strength, is the coupling effect of the cementitious material type on the time-varying law, is the parameter vector of the probabilistic model updated based on different real bridge detection results, is the amplification coefficient of the elastic wave velocity when there is a steel structure in the pipe in the arch rib (1), , is the standard normal distribution random variable, is the system error of the model parameters; The detection data of different real bridge arch ribs (1) good compactness parts are determined by elastic wave, and the detection data is updated based on Markov chain Monte Carlo method . The update to the includes the following steps:​ A, given the initial value , here let , construct the proposal distribution , given sample size , here take the data at the arch foot (11) of the arch rib (1) which is well dense; B, given initial covariance matrix , assuming that the parameters of the probabilistic model are independent of each other and according to the initial standard deviation set in advance to construct the initial covariance matrix, while setting the non-adaptive interval iteration step , there is no need to update the covariance matrix in the non-adaptive stage, and when the iteration step number exceeds the non-adaptive stage , the covariance matrix is updated according to the following formula: wherein is a small variable, s d is a scaling factor, I d is a d-dimensional identity matrix; is a covariance matrix of historical samples; C. According to the following steps, until a series of Markov chains satisfying the stationary distribution are generated; C1, let the current parameter be then the suggested value is constructed from the first-order suggested distribution i.e. according to solving where Z is a random number from a standard normal distribution, is the standard deviation of the first-order suggested distribution and is the variable of the first-order suggested distribution ; C2. Calculate the acceptance probability of the first order : wherein and is a prior distribution function; C3, randomly select a number from 0~1 , if , accept the model parameter proposal value , i.e. let , then go to step C6, otherwise go to step C4; C4, entering second order acceptance probability discrimination, adjusting covariance matrix from second order proposal distribution constructing new proposal value i.e. according to finding where is the second order proposal distribution standard deviation of the second order proposal distribution represents the variable of the second order proposal distribution ​ Similarly, a number is randomly drawn from 0~1 , if it satisfies , the model parameter suggestion value is accepted , that is, let , and then go to step C6, otherwise go to step C5; C5, and so on, into the order acceptance probability discrimination, the corresponding adjustment covariance matrix , from the order proposal distribution to construct a new model parameter candidate value , that is, according to , wherein is the standard deviation of the order proposal distribution ;​ Similarly, a number is randomly drawn from 0~1 , if it satisfies , the model parameter suggestion value is accepted , that is, let , and then enter step C6, otherwise let , repeat step C5; C6, if then stop continuing iteration, otherwise increase iteration number and return to step C1, wherein is the current variable; According to the probability model, the density of the detection data is judged, when the detection data is located in the 95% confidence interval of the probability model, the detection data of the cross section is dense, and when the detection data is located outside the 95% confidence interval of the probability model, the detection data of the cross section is poor.

2. The method according to claim 1, wherein, The detection data at the arch foot (11) of different actual bridge arch ribs (1) is determined by elastic waves.

3. A computer-readable storage medium, characterized in that, The program is executed by the processor to realize the intelligent identification method of the CFST arch bridge in-situ concrete defects based on the MCMC model according to any one of claims 1-2.

4. An electronic device, comprising: It comprises: a memory having a computer program stored thereon; a processor for executing the program in the memory to realize the intelligent identification method of the CFST arch bridge in-situ concrete defects based on the MCMC model according to any one of claims 1-2.

5. An intelligent identification system for defects in in-pipe concrete of a CFST arch bridge based on an MCMC model, characterized in that, The automatic arch climbing detection robot (2) and the data processing and analysis module (4) are connected in communication, the automatic arch climbing detection robot (2) can move along the arch rib (1), the automatic arch climbing detection robot (2) comprises an elastic wave receiving end (25) and an elastic wave transmitting end (27), the area between the elastic wave receiving end (25) and the elastic wave transmitting end (27) is the arch rib (1), the elastic wave transmitting end (27) comprises a knocking hammer and a sensor arranged in the knocking hammer, the knocking hammer is used for knocking the steel pipe of the arch rib (1) to generate low-frequency elastic waves, and the sensor receives the elastic waves and records the initial signal, the elastic wave receiving end (25) receives the elastic waves and records the final signal, and the data processing and analysis module (4) uses the intelligent identification method of the CFST arch bridge in-situ concrete defects based on the MCMC model according to any one of claims 1-2.

6. The CFST arch bridge internal concrete defect intelligent identification system based on the MCMC model according to claim 5, characterized in that, The automatic arch climbing detection robot (2) further comprises a main controller (21), a driving device (22), a longitudinal moving device (23), a transverse moving device (24), a steel skeleton (26) and a battery (28); The steel skeleton (26) comprises an arc-shaped first frame body and an arc-shaped second frame body, the main controller (21), the battery (28), the driving device (22) and the longitudinal moving device (23) are arranged on the first frame body, and the transverse moving device (24), the elastic wave receiving end (25) and the elastic wave transmitting end (27) are arranged on the second frame body; The steel skeleton (26) comprises an arc-shaped first frame body and an arc-shaped second frame body, the main controller (21), the battery (28), the driving device (22) and the longitudinal moving device (23) are arranged on the first frame body, and the transverse moving device (24), the elastic wave receiving end (25) and the elastic wave transmitting end (27) are arranged on the second frame body; The main controller (21) is used for communicating instructions to the driving device (22), the longitudinal moving device (23), the transverse moving device (24), the elastic wave receiving end (25) and the elastic wave transmitting end (27), the driving device (22) is used for providing driving force to the longitudinal moving device (23) and the transverse moving device (24), the longitudinal moving device (23) is used for moving along the arch rib (1), the transverse moving device (24) is used for driving the elastic wave receiving end (25) and the elastic wave transmitting end (27) to rotate along the section of the arch rib (1), and the battery (28) is used for supplying power to the main controller (21), the driving device (22), the elastic wave receiving end (25) and the elastic wave transmitting end (27).

7. The CFST arch bridge internal concrete defect intelligent identification system based on the MCMC model according to claim 5, characterized in that, The automatic arch climbing detection robot (2) and the data processing and analysis module (4) are connected in communication through the wireless transmission module (3), the monitoring system module (5) is connected in communication with the data processing and analysis module (4), and the monitoring system module (5) is used for monitoring the defects of the CFST arch bridge pipe concrete.

8. The CFST arch bridge internal concrete defect intelligent identification system based on the MCMC model according to claim 7, characterized in that, The monitoring system module (5) comprises a defect position positioning module (51), a defect position early warning module (52), an image generation sub-module (53) and a risk assessment sub-module (54), the defect position positioning module (51) marks the defect position identified by the data processing and analysis module (4), the defect position early warning module (52) sends early warning information according to the marked defect position, the image generation sub-module (53) outputs the full-bridge defect position in the form of a real bridge image, and the risk assessment sub-module (54) assesses the safety of the full bridge according to the pre-set defect rate.

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