Electromagnetic detection device and method for monitoring external prestressing tendon slippage

Abstract

The invention discloses an electromagnetic detection device and method for monitoring slippage of an external prestressing tendon. The device comprises an exciting coil, a coil type sensing element, a multi-channel acquisition instrument and a signal control and demodulation instrument, the signal control and demodulation instrument drives the excitation coil to generate an electromagnetic field to magnetize the steel strand; the coil type sensing elements are arranged corresponding to the steel strands in a ring-dividing mode and capture magnetic characteristic signals of the steel strands in each ring layer. The multi-channel acquisition instrument independently acquires signals of each sensing element; the signal control and demodulation instrument adopts a full-connection neural network trained by calibration data to carry out intelligent analysis on the signals, and accurately judges specific circle layers and the number of slippage occurrence. According to the invention, non-contact, lossless and real-time online monitoring of the anchoring state of the external prestressing tendon is realized, the method has the advantages of high precision, good adaptability and strong anti-interference capability, and the technical problem that timely early warning, accurate positioning, quantitative evaluation and the like of a slippage accident cannot be realized by a traditional detection method is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of bridge engineering structure health monitoring and safety evaluation, and particularly relates to an electromagnetic detection device and method for monitoring external prestressed tendon slip. BACKGROUND

[0002] External prestressing technology is one of the key technologies for modern bridges, buildings and other large concrete structures. It applies load to the structure through prestressed tendons arranged outside the concrete section, which has the advantages of flexible tendon arrangement, small friction loss, easy detection and replacement, etc. However, the anchorage system of external prestressed tendon, as the core component for transmitting and maintaining large prestress, is in a long-term high stress state and directly exposed to the environment, which is prone to anchorage failure due to factors such as fatigue stress, stress corrosion, improper installation of clamps or vibration, i.e. external prestressed tendon slip. Such slip accidents are sudden and will cause a sudden loss of prestress, severely weakening the structural bearing capacity and stiffness, and even causing catastrophic consequences such as structural collapse, posing a great threat to public safety. Therefore, how to accurately and quickly monitor the anchorage state of external prestressed tendon has become a key technical problem to be solved in current engineering practice.

[0003] Currently, there is still a lack of efficient, accurate and easy-to-implement conventional means for detecting and monitoring external prestressed tendon slip or slip. Traditionally, marks can be made at the end of the steel strand during tensioning, and later the distance change between the mark point and the anchor end face is measured manually to determine whether it has slipped. This method is intuitive, but inefficient and difficult to implement large-scale and periodic rapid detection. At the same time, due to the sudden nature of external prestressed tendon slip accidents, manual detection methods are difficult to discover accidents in time and take effective measures.

[0004] Another method is to indirectly infer whether the external prestressed tendon has slipped by monitoring the change of cable force, and the conventional implementation methods mainly include ultrasonic guided wave method (such as Chinese patent application technology with publication number CN117109797A) and magnetic flux sensor method (such as Chinese patent technology with publication number CN203490007U). However, this kind of method has significant technical limitations: since external prestressed tendon is usually composed of multiple steel strands, if each steel strand is individually monitored, the cost will be too high; while using whole tendon prestress monitoring method cannot effectively distinguish whether the cause of cable force loss is single strand slip or whole tendon relaxation. SUMMARY

[0005] In view of the above, the present application provides an electromagnetic detection device and method for monitoring external prestressed tendon slip, which can solve the problems of traditional detection methods, such as interference with the structure, low detection accuracy, poor real-time performance, and inability to quantitatively locate the slip.

[0006] An electromagnetic detection device for monitoring the slippage of external prestressed tendons, comprising: An excitation coil arranged outside the anchor head of the external prestressed tendon for magnetizing the steel strands under the excitation of electric current to generate a magnetic field; A plurality of coil-type sensing elements installed outside the anchor head of the external prestressed tendon to generate corresponding magnetic characteristic electric signals through electromagnetic induction; A multi-channel acquisition instrument for independently collecting the magnetic characteristic electric signals generated by each coil-type sensing element; A signal control and demodulation instrument for providing current excitation for the excitation coil and monitoring the slippage state of the steel strands in the external prestressed tendon through diagnosis based on the collected magnetic characteristic electric signals.

[0007] The electromagnetic detection device realizes non-destructive testing of the slippage state of the steel strands in the external prestressed tendon through the cooperation of the electromagnetic excitation field and the sensing signal.

[0008] Further, the number of excitation coils is one, which is wound on the outer peripheral surface of a certain layer of steel strands outside the anchor head of the external prestressed tendon through the coil skeleton, and the number of steel strands inside the coil is approximately close to the number of steel strands outside the coil, so as to provide a uniformly distributed excitation magnetic field for the steel strands outside the anchor head.

[0009] Further, the coil-type sensing elements adopt a ring layout, and the number thereof is configured according to the distribution of the steel strands inside the anchor head, each coil-type sensing element is wound on the outer peripheral surface of the corresponding layer of steel strands through the coil skeleton, and the slippage state of each layer of steel strands is independently monitored.

[0010] Further, the signal control and demodulation instrument acquires the magnetic characteristic electric signals of each coil-type sensing element in real time, diagnoses whether the anchoring state of the external prestressed tendon is normal through an algorithm model, when an abnormal state is detected, the position and number of the steel strands that have slipped are determined, and diagnostic monitoring information is provided.

[0011] Further, the algorithm model adopts a pre-trained fully connected neural network, the input of which is the magnetic characteristic electric signals of each coil-type sensing element and the environmental temperature, and the output is the number and position of the slipped steel strands.

[0012] Further, in the pre-training process of the full connection neural network: first, the magnetic characteristic electric signals of each coil type sensing element, the environmental temperature, the number and position of the slipped steel strand under all damage conditions are obtained according to the finite element simulation model or laboratory calibration as data samples for training; then the data samples are normalized and divided into a training set, a validation set and a test set in proportion; the neural network parameters are initialized, including the bias vector and weight matrix of each layer, the learning rate and the optimizer; the training set samples are input into the full connection neural network one by one, and the corresponding prediction result, i.e. the number and position of the slipped steel strand, is obtained by forward propagation of the neural network; the loss function between the prediction result and the label is calculated, and the network parameters are iteratively updated by the optimizer through the gradient descent method according to the loss function until the loss function converges or the maximum iteration number is reached, and the training is completed; after the training is completed, the neural network is verified by using the validation set sample, and the neural network with the best performance on the validation set is taken as the final algorithm model.

[0013] Further, the loss function is the sum of the slipped number prediction error and the slipped position prediction error.

[0014] An in-vitro pre-stressed beam slip state diagnosis method based on the above electromagnetic detection device, comprising the following steps: (1) using a signal control and demodulation instrument to generate an excitation signal to drive the excitation coil to generate a magnetic field, and uniformly magnetizing the steel strand outside the anchor head of the in-vitro pre-stressed beam; (2) using a multi-channel acquisition instrument to collect the magnetic characteristic electric signals generated by each coil type sensing element, and amplifying and filtering these electric signals; (3) inputting the pre-processed electric signals and the environmental temperature into the algorithm model built in the signal control and demodulation instrument, and outputting the diagnosis result about the number and position of the slipped steel strand from the model.

[0015] The diagnosis method uses the output signals of the coil type sensing element and the environmental temperature as inputs, and realizes accurate judgment of the number and position of the in-vitro pre-stressed beam steel strand slip through the trained full connection neural network model.

[0016] The present application aims to solve the deficiencies of the existing in-vitro pre-stressed beam anchoring state detection technology, and the technical solution as an innovative means of non-contact and real-time monitoring has the technical advantages of not changing the original state and use performance of the in-vitro pre-stressed beam, and can realize accurate positioning and quantitative evaluation of the slip amount, and has important application value for safety monitoring and maintenance of the in-vitro pre-stressed beam.

[0017] The electromagnetic detection device realizes high-precision and non-destructive detection on the anchoring state of the external prestressed beam by the cooperation of the electromagnetic excitation field and the array coil type sensor element, the device generates a controllable electromagnetic field through the signal control subsystem, and obtains the detection signal in real time through the signal demodulation subsystem, and combines the full connection neural network for intelligent analysis, which can accurately judge the number and position of the slip. The electromagnetic detection method has the advantages of strong real-time, wide adaptability, high detection precision, effectively overcomes the problems of large interference, low precision and poor real-time of traditional detection technology, and can safely and reliably perform long-term monitoring in complex environment, and provides reliable data support for the safe operation of the external prestressed beam bridge. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of the electromagnetic detection device for monitoring the external prestressed beam slip of the application.

[0019] Figure 2 It is an installation schematic diagram of the excitation coil and the array coil type sensor element, wherein (a)-(d) correspond to the installation configuration in four typical anchorage devices.

[0020] Figure 3 It is a schematic diagram of the magnetic flux loop generated by the excitation coil.

[0021] Figure 4 It is a schematic diagram of the full connection neural network used in the diagnostic method of the application and its training process.

[0022] Figure 5 It is a schematic diagram of the external prestressed beam slip state diagnosis steps of the application.

[0023] Figure 6 It is a schematic diagram of the monitoring result of the external prestressed beam slip state of the application using the full connection neural network.

[0024] In the figure: 1 - signal control and demodulation instrument, 2 - signal connection line, 3 - display, 4 - excitation coil, 5 - coil type sensor element, 6 - multichannel acquisition instrument, 7 - external prestressed beam anchoring plate, 8 - external prestressed beam anti-loose plate, 9 - steel strand, 10 - inner layer steel strand, 11 - outer layer steel strand. DETAILED DESCRIPTION

[0025] In order to more specifically describe the application, the technical solutions of the application will be described in detail below in combination with the drawings and specific embodiments.

[0026] As Figure 1As shown, the electromagnetic detection device for monitoring the slippage of the external prestressed beam comprises a signal control and demodulation instrument 1, a display 3, an excitation coil 4, a coil type sensing element 5 and a multi-channel acquisition instrument 6, wherein the signal control and demodulation instrument 1 is connected to the excitation coil 4 to generate a controllable electromagnetic excitation field, the coil type sensing element 5 captures the magnetic characteristic electric signal, the multi-channel acquisition instrument 6 acquires the signals of multiple coil type sensing elements 5, and finally the signal control and demodulation instrument 1 realizes intelligent diagnosis.

[0027] The number of the excitation coil 4 is one, which is uniformly wound on the coil skeleton and arranged outside the external prestressed beam anchor head, so as to ensure that the number of the internal steel strands in the excitation coil 4 is closest to the number of the external steel strands and the steel strands outside the anchor head form a uniformly distributed excitation magnetic field.

[0028] The coil type sensing element 5 adopts a ring layout design, and the number thereof is strictly configured according to the circle arrangement characteristics of the steel strands 9 in the anchor head. Each sensing element 5 is arranged on the outer peripheral surface of the corresponding circle layer steel strand 9 through the coil skeleton, so as to realize the monitoring of the slippage state of each layer of steel strands 9. In this embodiment, the specific implementation structure of the coil type sensing element 5 is that each sensing element is composed of a ring-shaped coil wound by a copper core polyurethane enameled copper wire and is fixed on the coil skeleton. The coil skeleton is customized according to the anchor specifications (such as a 27-hole anchor).

[0029] The coil skeleton is used to provide mechanical support, and the number thereof is the sum of the total number of the required excitation coil 4 and the coil type sensing element 5, and the size of the coil skeleton is reasonably determined according to the specifications of the external prestressed beam anchor to adapt to the structures of anchors of different specifications.

[0030] The signal control and demodulation instrument 1 can generate pulse signals or alternating current signals and is connected to the excitation coil 4 for applying excitation signals.

[0031] The multi-channel acquisition instrument 6 is connected to each coil type sensing element 5 through a signal connection line 2 to realize the independent acquisition of the magnetic characteristic electric signals of each coil type sensing element 5.

[0032] The signal control and demodulation instrument 1 can acquire the magnetic characteristic electric signals of the coil type sensing element 5 in real time and determine whether the anchoring state of the external prestressed beam is normal through an intelligent algorithm. When an abnormal state is detected, the position and number of the steel strand slippage can be determined, and then the monitoring and diagnosis information is transmitted to the display 3 through the signal connection line 2 for display.

[0033] This embodiment takes the external prestressed beam with a 27-hole anchor specification as an example, which comprises an external prestressed beam anchoring plate 7, an external prestressed beam anti-loose plate 8 and 27 steel strands 9.

[0034] The excitation coil 4 and the coil type sensing element 5 are both mounted outside the prestressed beam anti-loose plate 8, the excitation coil 4 is mounted outside the second layer of steel strands, so that the number of steel strands inside the excitation coil 4 is 12, and the number of steel strands outside the excitation coil 4 is 15. The coil type sensing element 5 is mounted outside the periphery of each layer of steel strands 9, and a total of three layers of steel strands 9 are installed with three coil type sensing elements 5.

[0035] The shape and size of the excitation coil 4 and the coil type sensing element 5 are designed according to the type of the measured external prestressed beam anchor, Figure 2 The installation configuration examples of the excitation coil 4 and the coil type sensing element 5 in several typical anchor types are shown.

[0036] The magnetic flux loop generated in the steel strands under the action of the excitation signal of the excitation coil 4 is shown in Figure 3 The inner layer steel strands 10 generate upward magnetic flux, which enters the outer layer steel strands 11 through air or anti-corrosion grease, the magnetic flux direction of the outer layer steel strands 11 is downward, and finally returns to the inner layer steel strands 10 through air or anti-corrosion grease, thereby forming a closed magnetic flux loop.

[0037] Based on the structure of the above detection device, the embodiment further provides a full connection neural network external prestressed beam slip state diagnosis and monitoring algorithm, which is integrated in a signal control and demodulation instrument, and specifically: First, the magnetic characteristic electric signals, temperature, slip number and position distribution of the coil type sensing element under all damage conditions are obtained according to the finite element simulation model or actual measurement calibration, which are used as the data set for training the full connection neural network. The full connection neural network is trained using the data set, and the input of the full connection neural network is the output electric signal and temperature of each coil type sensing element, and the output is the slip number and position distribution.

[0038] The data set is input into the full connection neural network as shown in Figure 4 The data set is normalized to keep the data values of the training set, the validation set and the test set within [0, 1]; the data set is divided into the training set, the validation set and the test set in the ratio of 3:1:1. The training performance evaluation index of the full connection neural network is the sum of the slip position prediction error and the slip number prediction error; through continuous training and testing, until the slip position and slip number output by the neural network meet the accuracy requirements.

[0039] The trained full connection neural network is used for external prestressed beam slip state diagnosis and monitoring, and the specific process is shown in Figure 5 First step: magnetize the steel strands outside the anchoring end by generating an excitation magnetic field through the excitation coil.

[0040] ​Second step: Collect the magnetic characteristic electric signal through the coil type sensing element, and amplify and filter in the time domain.

[0041] Third step: Input the magnetic characteristic electric signal collected by the coil type sensing element and the environmental temperature into the trained full connection neural network, and intelligently diagnose the anchoring state of the external prestressed beam.

[0042] Figure 6 For the monitoring result of the external prestressed beam slip in the embodiment, the monitoring accuracy of the early slip of the external prestressed beam, that is, the total number of slipped steel strands is less than 6, reaches 100%, which shows that the electromagnetic detection device and method for the external prestressed beam slip have high reliability and can meet the requirements in the actual monitoring of the external prestressed beam slip.

[0043] The above description of the embodiments is for the convenience of the general technical personnel in the art to understand and apply the present application. Those skilled in the art can easily make various modifications to the above embodiments, and apply the general principles described herein to other embodiments without creative labor. Therefore, the present application is not limited to the above embodiments, and the improvements and modifications of the present application made by those skilled in the art according to the disclosure of the present application should be within the scope of protection of the present application.

Claims

1. An electromagnetic detection device for monitoring the slippage of an externally prestressed tendon, characterized in that Comprise: An excitation coil arranged outside the anchorage head of the external prestressed tendon bundle for generating a magnetic field to magnetize the steel strand under current excitation; A plurality of coil-type sensing elements installed outside the anchorage head of the external prestressed tendon bundle for generating corresponding magnetic characteristic electrical signals through electromagnetic induction; A multi-channel acquisition instrument for independently acquiring the magnetic characteristic electrical signals generated by each coil-type sensing element; A signal control and demodulation instrument for providing current excitation for the excitation coil and monitoring the steel strand slip state in the external prestressed tendon bundle through diagnosis based on the acquired magnetic characteristic electrical signals.

2. The electromagnetic detection apparatus of claim 1, wherein: The number of the excitation coil is one, which is wound on the outer peripheral surface of a certain layer of steel strand outside the anchorage head of the external prestressed tendon bundle through the coil framework, and the number of steel strands inside the coil is approximately close to the number of steel strands outside the coil, thereby providing a uniformly distributed excitation magnetic field for the steel strands outside the anchorage head.

3. The electromagnetic detection apparatus of claim 1, wherein: The coil-type sensing elements adopt a ring layout, and their number is configured according to the distribution of the steel strands inside the anchorage head, and each coil-type sensing element is wound on the outer peripheral surface of the corresponding layer of steel strand through the coil framework.

4. The electromagnetic detection apparatus of claim 1, wherein: The signal control and demodulation instrument acquires the magnetic characteristic electrical signals of each coil-type sensing element in real time, diagnoses whether the anchoring state of the external prestressed tendon bundle is normal through an algorithm model, and when an abnormal state is detected, it can determine the position and number of the slipped steel strands and provide diagnostic monitoring information.

5. The electromagnetic detection apparatus of claim 4, wherein: The algorithm model uses a pre-trained fully connected neural network, the input of which is the magnetic characteristic electrical signals of each coil-type sensing element and the environmental temperature, and the output is the number and position of the slipped steel strands.

6. The electromagnetic detection apparatus of claim 5, wherein: In the pre-training process of the fully connected neural network, first, the magnetic characteristic electrical signals of each coil-type sensing element, the environmental temperature, the number and position of the slipped steel strands under all damage conditions are obtained according to the finite element simulation model or laboratory calibration as data samples for training; then the data samples are normalized and divided into training set, validation set and test set in proportion; the neural network parameters are initialized, including the bias vector and weight matrix of each layer, learning rate and optimizer; the training set samples are input into the fully connected neural network one by one, and the corresponding prediction results, i.e. the number and position of the slipped steel strands, are obtained by forward propagation of the neural network, the loss function between the prediction results and the labels is calculated, and the network parameters are continuously updated by gradient descent method according to the loss function using the optimizer until the loss function converges or the maximum number of iterations is reached, and the training is completed; after the training is completed, the neural network is verified using the validation set sample, and the neural network with the best performance on the validation set is used as the final algorithm model.

7. The electromagnetic detection apparatus of claim 6, wherein: The loss function is the sum of the slip number prediction error and the slip position prediction error.

8. A method for diagnosing the slip state of an external prestressed tendon bundle based on the electromagnetic detection device according to any one of claims 1-7, comprising the following steps: (1) generating an excitation signal using the signal control and demodulation instrument to drive the excitation coil to generate a magnetic field, and uniformly magnetizing the steel strands outside the anchorage head of the external prestressed tendon bundle; (2) The magnetic characteristic electric signals generated by each coil type sensing element are collected by a multi-channel acquisition instrument, and the electric signals are amplified and filtered; (3) The preprocessed electric signals and the environmental temperature are input into the algorithm model built in the signal control and demodulation instrument, and the model outputs the diagnosis results about the number and position of the slipped steel strands.

9. The method of claim 8, wherein: The algorithm model adopts a pre-trained fully connected neural network, the input of which is the magnetic characteristic electric signals of each coil type sensing element and the environmental temperature, and the output is the number and position of the slipped steel strands; in the pre-training process of the fully connected neural network, firstly, the magnetic characteristic electric signals of each coil type sensing element, the environmental temperature, the number and position of the slipped steel strands under all damage conditions are obtained according to a finite element simulation model or laboratory calibration, as data samples for training; then the data samples are normalized and divided into a training set, a validation set and a test set in proportion; the neural network parameters are initialized, including the bias vector and weight matrix of each layer, the learning rate and the optimizer; the training set samples are input into the fully connected neural network one by one, the corresponding prediction results, i.e. the number and position of the slipped steel strands, are obtained by forward propagation of the neural network, the loss function between the prediction results and the labels is calculated, the network parameters are updated by gradient descent method through the optimizer according to the loss function, until the loss function converges or the maximum number of iterations is reached, and the training is completed; after the training is completed, the neural network is verified by using the validation set samples, and the neural network with the best performance on the validation set is taken as the final algorithm model.

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