CN117723175B - Device and method for detecting prestress structure based on piezoelectric fluctuation method - Google Patents
Device and method for detecting prestress structure based on piezoelectric fluctuation method Download PDFInfo
- Publication number
- CN117723175B CN117723175B CN202311650902.5A CN202311650902A CN117723175B CN 117723175 B CN117723175 B CN 117723175B CN 202311650902 A CN202311650902 A CN 202311650902A CN 117723175 B CN117723175 B CN 117723175B
- Authority
- CN
- China
- Prior art keywords
- prestress
- stress wave
- wavelet packet
- packet energy
- stress
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000001514 detection method Methods 0.000 claims abstract description 16
- 230000005284 excitation Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 239000011513 prestressed concrete Substances 0.000 description 9
- 239000000919 ceramic Substances 0.000 description 7
- 238000012360 testing method Methods 0.000 description 6
- 229910000831 Steel Inorganic materials 0.000 description 3
- 239000004567 concrete Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000003292 glue Substances 0.000 description 3
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 3
- 239000010959 steel Substances 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000003825 pressing Methods 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 210000002435 tendon Anatomy 0.000 description 2
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 238000004873 anchoring Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000003822 epoxy resin Substances 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- HFGPZNIAWCZYJU-UHFFFAOYSA-N lead zirconate titanate Chemical group [O-2].[O-2].[O-2].[O-2].[O-2].[Ti+4].[Zr+4].[Pb+2] HFGPZNIAWCZYJU-UHFFFAOYSA-N 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005498 polishing Methods 0.000 description 1
- 229920000647 polyepoxide Polymers 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000005507 spraying Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Landscapes
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
本发明属于预应力结构检测技术领域，具体涉及一种基于压电波动法的预应力结构检测装置及检测方法。本发明通过在预应力结构的多角螺母和垫板上设置对应的应力波驱动器和应力波传感器，应力波驱动器发送应力波，应力波在预应力结构内部传递，然后通过应力波传感器接收反馈信号，从而计算分析获得预应力结构内部的预应力损失情况。本发明公开的检测结构以及方法能够快速且准确地获得预应力结构内部的预应力损失情况，从而能够将预应力结构的内部预应力保持在合理范围内，避免预应力失效。
The present invention belongs to the technical field of prestressed structure detection, and specifically relates to a prestressed structure detection device and detection method based on a piezoelectric wave method. The present invention sets corresponding stress wave drivers and stress wave sensors on the polygonal nut and pad of the prestressed structure. The stress wave driver sends stress waves, which are transmitted inside the prestressed structure. Then, the stress wave sensor receives feedback signals, thereby calculating and analyzing the prestress loss inside the prestressed structure. The detection structure and method disclosed in the present invention can quickly and accurately obtain the prestress loss inside the prestressed structure, so that the internal prestress of the prestressed structure can be kept within a reasonable range to avoid prestress failure.
Description
Technical Field
The invention belongs to the technical field of prestress structure detection, and particularly relates to a prestress structure detection device and method based on a piezoelectric fluctuation method.
Background
The prestressed concrete structure is widely applied to the field of engineering construction, and the principle of prestressed concrete is that a stress with the opposite direction to the internal stress of a component is manually applied in advance before the component is used, so that the displacement and deformation resistance of the component in the using process is improved. The prestressed concrete structure can effectively relieve the defect of the inherent tensile resistance of the concrete, but because the finish rolling twisted steel is generally shorter, larger retraction loss can be generated during anchoring, and the loss and even failure of the vertical prestress are easily caused due to improper manual operation or shrinkage creep of the concrete itself, relaxation of the steel and other reasons during tensioning of the finish rolling twisted steel, and the development and application of the prestressed concrete structure are influenced by the cracking problem caused by the insufficient vertical prestress in practical application.
Therefore, the accurate detection of the vertical prestress has important significance for the reliability of the prestressed concrete structure, if the effective vertical prestress of the prestressed concrete structure is too high, the actual value is far greater than the theoretical value, so that potential safety hazards exist in the structure, and if the effective vertical prestress is too low, the performance of the vertical prestress rib is excessive, and the effect cannot be fully exerted. However, a rapid and accurate method for detecting vertical prestress of a prestressed concrete structure is lacking in the prior art.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defect of the prior art that a method for rapidly and accurately detecting the vertical prestress of the prestressed concrete structure is lacking, and the reliability of the prestressed concrete structure is limited, so that the device and the method for detecting the prestressed structure based on the piezoelectric fluctuation method are provided.
The prestress structure detection device based on the piezoelectric fluctuation method comprises a plurality of groups of stress wave drivers and stress wave sensors which are correspondingly arranged, prestress ribs are arranged in a component, the end parts of the prestress ribs extend out, polygonal nuts are arranged at the end parts of the prestress ribs in a threaded fit mode with the prestress ribs, a base plate is fixed on the surface of the component, one stress wave driver is fixedly arranged on each surface of the polygonal nuts, a plurality of stress wave sensors are fixedly arranged on the outer surface of the base plate in a one-to-one correspondence to the stress wave sensors, and the stress wave sensors are arranged in the direction facing the surface of the polygonal nut where the corresponding stress wave driver is located.
Further, the device also comprises a collector which is respectively connected with the stress wave driver and the stress wave sensor and is used for sending an excitation signal to the stress wave driver and acquiring a feedback signal from the stress wave sensor.
Further, the device also comprises a signal amplifier, wherein the signal amplifier is respectively connected with the acquisition instrument and the stress wave driver and is used for amplifying the excitation signal.
A prestress structure detection method based on a piezoelectric fluctuation method comprises the following steps:
step S1, the stress wave driver sends stress waves;
step S2, the stress wave sensor receives stress waves and outputs feedback signals;
and S3, analyzing the feedback signal to obtain the prestress loss degree of the prestress structure.
Further, the step S3 includes:
S3.1, carrying out wavelet packet energy analysis on the feedback signal to obtain an average wavelet packet energy value;
s3.2, calculating a damage index according to the average wavelet packet energy value;
And S3.3, substituting the damage index into a preset theoretical model to obtain the prestress loss rate.
Further, in the step S3.1, the average wavelet packet energy value is an average value of energy values obtained by performing wavelet packet energy analysis on feedback signals obtained by the stress wave driver and the stress wave sensor of each group.
Further, in the step S3.2, the method for calculating the damage index is as follows:
Wherein F DI is a damage index, the value range is (0-1), E l is the average wavelet packet energy value when the effective prestress is lost, and E f is the average wavelet packet energy value when the effective prestress is in a full load state.
Further, in the step S3.3, the theoretical model is:
wherein x is the damage index of wavelet packet energy collected by different prestressing force, y is the loss rate of prestressing force, a, b and c are undetermined coefficients, and the values of the coefficients are required to be determined according to historical experimental data;
The calculation formula of the prestress loss rate is as follows:
Wherein ρ is an effective prestress loss rate, the value range is (0-1), F max is an effective prestress (kN) at full load, and F i is an effective prestress (kN) after loss.
Further, the method also comprises the following steps:
s4, carrying out wavelet packet energy analysis according to feedback signals obtained by each group of stress wave drivers and stress wave sensors to obtain wavelet packet energy values of each group;
Setting a lower threshold and an upper threshold of the wavelet packet energy value according to the average wavelet packet energy value;
and judging the inclination condition of the nut according to the lower limit threshold value and the upper limit threshold value.
The prestress structure has the beneficial effects that the stress wave driver and the stress wave sensor are arranged on the polygonal nut and the backing plate of the prestress structure, the stress wave driver transmits stress waves, the stress waves are transmitted inside the prestress structure, and then feedback signals are received through the stress wave sensor, so that the prestress loss condition inside the prestress structure is calculated and analyzed. The detection structure and the method disclosed by the invention can rapidly and accurately obtain the prestress loss condition in the prestress structure, so that the internal prestress of the prestress structure can be kept in a reasonable range, and the prestress failure is avoided.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Reference numerals illustrate:
FIG. 1 is a schematic diagram of the overall structure of the present invention;
FIG. 2 is a schematic view of the overall structure of another angle of the present invention;
FIG. 3 is a schematic diagram of signal transmission according to the present invention;
FIG. 4 is a schematic diagram of the prestressing structure of the present invention.
11. The device comprises a stress wave driver, a stress wave sensor, a component 2, a prestress rib 3, a polygonal nut 4, a base plate 5, a base plate 6, a collector 7 and a signal amplifier.
Detailed Description
In order that the above objects, features and advantages of the application will be readily understood, a more particular description of the application will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the application, whereby the application is not limited to the specific embodiments disclosed below.
Embodiment one:
Referring to fig. 1 and 2, the embodiment discloses a prestress structure detection device based on a piezoelectric fluctuation method, which comprises a plurality of groups of stress wave drivers 11 and stress wave sensors 12 which are correspondingly arranged, wherein a prestress rib 3 is arranged in a member 2, the end part of the prestress rib 3 extends out, a polygonal nut 4 is in threaded fit with the prestress rib 3 and is arranged at the end part of the prestress rib 3, a backing plate 5 is fixed on the surface of the member 2, each surface of the polygonal nut 4 is fixedly provided with one stress wave driver 11, the outer surface of the backing plate 5 is fixedly provided with a plurality of stress wave sensors 12 corresponding to the stress wave sensors 12 one by one, and the stress wave sensors 12 are arranged in the direction facing the surface of the polygonal nut 4 where the corresponding stress wave drivers 11 are arranged.
Referring to fig. 3, the device further comprises a collector 6, wherein the collector 6 is respectively connected with the stress wave driver 11 and the stress wave sensor 12, and is used for sending an excitation signal to the stress wave driver 11 and acquiring a feedback signal from the stress wave sensor 12. And the device also comprises a signal amplifier 7, wherein the signal amplifier 7 is respectively connected with the acquisition instrument 6 and the stress wave driver 11 and is used for amplifying the excitation signal.
Specifically, in this embodiment, the member 2 is concrete, the tendon 3 is a prestressed reinforcement, and the pad 5 is a metal pad. In this embodiment, the stress wave driver 11 is a piezoelectric ceramic driver, the stress wave sensor 12 is a piezoelectric ceramic sensor, and the piezoelectric ceramic driver and the piezoelectric ceramic sensor are made by welding shield wires on the positive and negative electrodes of a piezoelectric ceramic sheet, respectively. As a preferable mode of the embodiment, the piezoelectric ceramic piece is PZT-5, the positive electrode and the negative electrode are positioned on the same side, the size is 15 multiplied by 5 multiplied by 2mm, the width of the negative electrode is 5mm, the width of the positive electrode is 8mm, the interval between the positive electrode and the negative electrode is 2mm, and the insulating shells at two ends of the shielding wire are stripped by wire stripper to expose a wire with the length of 1 cm.
Before the stress wave driver 11 and the stress wave sensor 12 are fixed, the polygonal nut 4 and the backing plate 5 are subjected to rust removal treatment, wherein the rust removal treatment comprises spraying a special rust remover on the surfaces of the polygonal nut 4 and the backing plate 5, wiping, polishing the surface to be measured, keeping the surface to be measured as flat as possible, and then cleaning with medical alcohol. When the stress wave driver 11 and the stress wave sensor 12 are fixed, a layer of epoxy resin glue is smeared on the surface of a point to be measured, the surface is smeared with 502 strong glue for insulation treatment, finally, the manufactured piezoelectric ceramic plate is stuck on the surface and pressed for 3 minutes to be loosened after the glue is solidified, and after the stress wave driver 11 and the stress wave sensor 12 are stuck, a universal meter is used for checking whether the path is available or not, so that whether the sticking is successful or not is checked.
In this embodiment, the polygonal nut 4 is a hexagonal nut, the hexagonal nut has an equilateral hexagon shape, the stress wave drivers 11 are provided with six stress wave drivers 11, the six stress wave drivers 11 are respectively fixed at the centers of six sides of the hexagonal nut, and the six stress wave sensors 12 are fixed at the positions, close to the corresponding stress wave drivers 11, of the upper side of the backing plate 5.
Preferably, the collector 6 is preferably a NIUSB-6361, and the signal amplifier 7 is preferably a high-frequency power amplifier of model number of core-of-natural E2022. The NIUSB-6361 collector 6 is provided with 16 channel ports, the acquisition program is compiled by combining with LabView in the embodiment to realize the visualization of signal acquisition, the excitation signal is a pulse signal excited by the LabView program, the amplitude range is-10V-10V, the signal amplifier 7 amplifies the excitation signal by 30 times and then applies the excitation signal to the prestress driver, finally the prestress sensor receives the signal, the NIUSB-6361 collector 6 is used as the equipment for signal acquisition, the NIUSB-6361 collector 6 and the notebook computer are connected through a USB data line, the detected data is stored in the computer in real time, and the computer realizes calculation and analysis.
Embodiment two:
The embodiment discloses a method for detecting a prestress structure based on a piezoelectric fluctuation method, which is provided by the embodiment 1, and comprises the following steps:
Step S1, the stress wave driver 11 transmits stress waves;
step S2, the stress wave sensor receives stress waves and outputs feedback signals;
and S3, analyzing the feedback signal to obtain the prestress loss degree of the prestress structure.
The step S3 includes:
S3.1, carrying out wavelet packet energy analysis on the feedback signal to obtain an average wavelet packet energy value;
s3.2, calculating a damage index according to the average wavelet packet energy value;
And S3.3, substituting the damage index into a preset theoretical model to obtain the prestress loss rate.
In this embodiment, the calculation method of wavelet packet energy analysis includes that the collected feedback signals are subjected to wavelet packet decomposition to form sub-signals of different frequency bands, and energy of sub-frequency bands of each sub-signal is calculated and summed to be a wavelet packet energy value.
In the step S3.1, the average wavelet packet energy value is an average value of energy values obtained by performing wavelet packet energy analysis on the feedback signals obtained by the stress wave driver 11 and the stress wave sensor 12 of each group.
The method also comprises the following steps:
s4, carrying out wavelet packet energy analysis according to feedback signals obtained by each group of stress wave drivers 11 and stress wave sensors 12 to obtain wavelet packet energy values of each group;
Setting a lower threshold and an upper threshold of the wavelet packet energy value according to the average wavelet packet energy value;
and judging the inclination condition of the nut according to the lower limit threshold value and the upper limit threshold value.
In the step S3.2, the method for calculating the damage index includes:
Wherein F DI is a damage index, the value range is (0-1), E l is the average wavelet packet energy value when the effective prestress is lost, and E f is the average wavelet packet energy value when the effective prestress is in a full load state. The damage index F DI of the wavelet packet energy can effectively characterize the damage degree of the prestress, preliminarily judge the loss degree of the effective prestress, and the full-load state of the prestress is set according to different detection conditions.
In this embodiment, the average wavelet packet energy value when the effective prestressing force of E f is in the full load state is a predicted calculation value, six groups of stress wave drivers 11 and stress wave sensors 12 are installed in advance in a system without prestressing loss, and the average value of the corresponding wavelet packet energy values is measured and is the average wavelet packet energy value in the full load state.
In the step S3.3, the theoretical model is:
The values of the coefficients are determined according to historical experimental data, and in the embodiment, the values a= -92.41, b=0.07 and c= 93.93 of the coefficients are measured and calculated according to the historical experimental data;
the theoretical model can be effectively fit to the relation between the damage index of wavelet packet energy and the loss rate of prestress, and in other embodiments of the invention, the theoretical model can be formed by fitting other expressions.
The calculation formula of the prestress loss rate is as follows:
Wherein ρ is an effective prestress loss rate, the value range is (0-1), F max is an effective prestress (kN) at full load, and F i is an effective prestress (kN) after loss. In this embodiment Fmax is the effective pre-stress when it is set to full load according to the design value of the pre-stress.
In this embodiment, the method for constructing the theoretical model includes:
Taking 600kN as the full-load state of the prestress, regarding the prestress under the other working conditions as the effective prestress after prestress loss, dividing the prestress into 7 groups of working conditions which are respectively 0kN, 25kN, 50kN, 100kN, 200kN, 400kN and 600kN, carrying out repeated tests for three times, carrying out complete replacement of the prestress rib 3, the polygonal nut 4 and the backing plate 5 before each test, obtaining the damage index of the wavelet energy and the damage index of the average value of the wavelet energy through historical experiments, and calculating the effective prestress loss rate under different working conditions.
By nonlinear fitting analysis of the damage index F DI and the prestress damage rate rho, a fitting curve of three tests and an average of the three tests is obtained:
Wherein x is the damage index of wavelet packet energy collected by different prestressing force, y is the loss rate of the prestressing force;
substituting the loss index obtained by the signals acquired by the test into a fitting curve, wherein the relative error is within +/-15%, so that the accuracy of the fitting result is feasible, the requirements of actual engineering are met, and finally, the fitting curve with average three tests is selected as a theoretical model of the final damage index-prestress loss rate.
In this embodiment, the method further includes the steps of:
s4, carrying out wavelet packet energy analysis according to feedback signals obtained by each group of stress wave drivers 11 and stress wave sensors 12 to obtain wavelet packet energy values of each group;
Setting a lower threshold and an upper threshold of the wavelet packet energy value according to the average wavelet packet energy value;
And judging the inclination condition of the polygonal nut 4 according to the lower limit threshold value and the upper limit threshold value.
Specifically, the feedback signals obtained by the stress wave driver 11 and the stress wave sensor 12 in six groups are set for wavelet packet energy analysis to obtain wavelet packet energy values of each group as X1, X2, X3, X4, X5 and X6 respectively, wherein the average wavelet packet energy value isAnd the lower limit threshold of X is set to be 95%The upper threshold is 105%The method can obtain:
Xn (n=1, 2, 3, 4, 5, 6) is 95% When the polygonal nut 4 and the backing plate 5 are not inclined at the position;
xn (n=1, 2, 3, 4, 5, 6) > upper threshold 105% When the inclination angle of the polygonal nut 4 at the position is deviated to the direction of the backing plate 5;
xn (n=1, 2, 3,4, 5, 6) < lower threshold 95% When the inclination angle of the polygonal nut 4 at the position is deviated to the direction away from the backing plate 5;
Thus, the inclination condition between the polygonal nut 4 and the pad plate 5 can be obtained through step S4, and the leveling process can be performed according to the inclination condition.
In the embodiment, the stress wave driver 11 and the stress wave sensor 12 are arranged on the polygonal nut 4 and the backing plate 5 of the prestress structure, the stress wave driver 11 transmits stress waves, the stress waves are transmitted inside the prestress structure, and then feedback signals are received through the stress wave sensor 12, so that the prestress loss condition inside the prestress structure is calculated and analyzed. The detection structure and the method disclosed by the invention can rapidly and accurately obtain the prestress loss condition in the prestress structure, so that the internal prestress of the prestress structure can be kept in a reasonable range, and the prestress failure is avoided.
Referring to fig. 4, the tensile force in the prestress structure is transmitted to the nut by the engagement of the member 2 with the tendon 3, and the nut presses the pad 5 to transmit the pressing force generated by the pressing to the member 2. In the present embodiment, the change of the prestress directly affects the contact pressure between the polygonal nut 4 and the backing plate 5, so that the actual contact area of the polygonal nut 4 and the backing plate 5 is changed according to the change of the prestress. When the prestress increases, the actual contact area between the polygonal nut 4 and the backing plate 5 increases, so that when the high-frequency stress wave generated by the piezoceramic actuator is transmitted between the nut and the backing plate 5, the propagation of the stress wave is easier along with the increase of the actual contact area, and therefore, the energy received by the piezoelectric sensor gradually increases along with the increase of the actual contact area. Along with the increase of the prestress, the actual contact area between the polygonal nut 4 and the backing plate 5 is increased, the signal received by the piezoelectric sensor is also enhanced, and the prestress can be detected by analyzing the difference of the received signals under different working conditions.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the claims. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (7)
1. The prestress structure detection device based on the piezoelectric fluctuation method is characterized by comprising a plurality of groups of stress wave drivers (11) and stress wave sensors (12) which are correspondingly arranged, wherein a prestress rib (3) is arranged in a component (2) and extends out of the end part, a polygonal nut (4) is arranged at the end part of the prestress rib (3) in a threaded fit manner with the prestress rib (3) and is used for fixing a backing plate (5) on the surface of the component (2), one stress wave driver (11) is fixedly arranged on each surface of the polygonal nut (4), a plurality of stress wave sensors (12) are fixedly arranged on the outer side surface of the backing plate (5) in a one-to-one correspondence manner with the stress wave sensors (12), and the stress wave sensors (12) are arranged in the direction facing the surface of the polygonal nut (4) where the corresponding stress wave drivers (11) are arranged;
The stress wave driver (11) is used for transmitting a stress wave, and the stress wave sensor (12) is used for receiving the stress wave and outputting a feedback signal;
The prestress structure detection device is used for obtaining an average wavelet packet energy value by carrying out wavelet packet energy analysis on the feedback signal, calculating a damage index according to the average wavelet packet energy value, and substituting the damage index into a preset theoretical model to obtain a prestress loss rate;
The prestress structure detection device is used for carrying out wavelet packet energy analysis according to feedback signals obtained by the stress wave driver (11) and the stress wave sensor (12) to obtain wavelet packet energy values, setting a lower limit threshold and an upper limit threshold of the wavelet packet energy values according to average wavelet packet energy values, and judging the inclination condition of the nut according to the lower limit threshold and the upper limit threshold.
2. The device for detecting the prestress structure based on the piezoelectric fluctuation method according to claim 1, further comprising a collector (6), wherein the collector (6) is respectively connected with the stress wave driver (11) and the stress wave sensor (12) and is used for sending an excitation signal to the stress wave driver (11) and acquiring a feedback signal from the stress wave sensor (12).
3. The device for detecting the prestress structure based on the piezoelectric fluctuation method according to claim 2, further comprising a signal amplifier (7), wherein the signal amplifier (7) is respectively connected with the acquisition instrument (6) and the stress wave driver (11) for amplifying the excitation signal.
4. A method for detecting a prestress structure based on a piezoelectric fluctuation method, characterized by adopting the device of any one of claims 1 to 3, comprising the steps of:
Step S1, the stress wave driver (11) sends stress waves;
step S2, the stress wave sensor (12) receives stress waves and outputs feedback signals;
s3, analyzing the feedback signal to obtain the prestress loss degree of the prestress structure;
The step S3 includes:
S3.1, carrying out wavelet packet energy analysis on the feedback signal to obtain an average wavelet packet energy value;
s3.2, calculating a damage index according to the average wavelet packet energy value;
s3.3, substituting the damage index into a preset theoretical model to obtain a prestress loss rate;
The method also comprises the following steps:
S4, carrying out wavelet packet energy analysis according to feedback signals obtained by each group of stress wave drivers (11) and stress wave sensors (12) to obtain wavelet packet energy values of each group;
Setting a lower threshold and an upper threshold of the wavelet packet energy value according to the average wavelet packet energy value;
and judging the inclination condition of the nut according to the lower limit threshold value and the upper limit threshold value.
5. The method for detecting a prestress structure based on a piezoelectric wave method according to claim 4, wherein,
In the step S3.1, the average wavelet packet energy value is an average value of energy values obtained by performing wavelet packet energy analysis on feedback signals obtained by each group of stress wave drivers (11) and stress wave sensors (12).
6. The method for detecting a prestress structure based on a piezoelectric wave method according to claim 5, wherein in the step S3.2, the method for calculating the damage index is as follows:
Wherein F DI is a damage index, the value range is (0-1), E l is the average wavelet packet energy value when the effective prestress is lost, and E f is the average wavelet packet energy value when the effective prestress is in a full load state.
7. The method according to claim 6, wherein in the step S3.3, the theoretical model is:
wherein x is the damage index of wavelet packet energy collected by different prestressing force, y is the loss rate of prestressing force, a, b and c are undetermined coefficients, and the values of the coefficients are required to be determined according to historical experimental data;
The calculation formula of the prestress loss rate is as follows:
Wherein ρ is an effective prestress loss rate, the value range is (0-1), F max is an effective prestress (kN) at full load, and F i is an effective prestress (kN) after loss.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311650902.5A CN117723175B (en) | 2023-12-05 | 2023-12-05 | Device and method for detecting prestress structure based on piezoelectric fluctuation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311650902.5A CN117723175B (en) | 2023-12-05 | 2023-12-05 | Device and method for detecting prestress structure based on piezoelectric fluctuation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117723175A CN117723175A (en) | 2024-03-19 |
| CN117723175B true CN117723175B (en) | 2025-01-03 |
Family
ID=90208051
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311650902.5A Active CN117723175B (en) | 2023-12-05 | 2023-12-05 | Device and method for detecting prestress structure based on piezoelectric fluctuation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117723175B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101419104A (en) * | 2008-11-28 | 2009-04-29 | 湖南科技大学 | Detecting based on concrete box beam bridge web vertical pre-stress rib stretching force |
| CN106840477A (en) * | 2017-01-16 | 2017-06-13 | 长沙理工大学 | Device and method for monitoring pre-stress loss of PSC structure for long time |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107084812B (en) * | 2017-06-14 | 2022-09-16 | 湖南科技大学 | Detection system and method of vertical prestressing tendon tension force of concrete box girder bridge web |
| CN114487109B (en) * | 2022-01-19 | 2022-08-23 | 哈尔滨工业大学 | Non-baseline data stress online monitoring method, system, equipment and medium based on single-mode multi-frequency signal fusion |
| CN219391183U (en) * | 2022-09-13 | 2023-07-21 | 南京林业大学 | Screw-on type intelligent sensor based on piezoelectric ceramic piece |
| CN115790924A (en) * | 2022-12-01 | 2023-03-14 | 上海应用技术大学 | A stress monitoring system and method for a prestressed expansion shell bolt |
-
2023
- 2023-12-05 CN CN202311650902.5A patent/CN117723175B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101419104A (en) * | 2008-11-28 | 2009-04-29 | 湖南科技大学 | Detecting based on concrete box beam bridge web vertical pre-stress rib stretching force |
| CN106840477A (en) * | 2017-01-16 | 2017-06-13 | 长沙理工大学 | Device and method for monitoring pre-stress loss of PSC structure for long time |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117723175A (en) | 2024-03-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102393407A (en) | Interfacial debonding monitoring method for steel tube concrete tube wall based on piezoelectric impedance measurement | |
| CN110455914B (en) | Grouting sleeve internal grouting state detection method based on built-in piezoelectric ceramic element | |
| CN106840477B (en) | Device and method for monitoring pre-stress loss of PSC structure for long time | |
| CN104406901B (en) | Concrete internal steel bar corrosion monitoring sensor based on Lamb wave principle | |
| CN103940893B (en) | Device and method for monitoring corrosion defects of anchorage section of stay rope | |
| CN103852492A (en) | Monitoring method for grouting compaction of pre-stressed pipe based on piezoelectric ceramic | |
| CN108896389A (en) | A kind of damages of concrete structures monitoring method based on piezoelectric intelligent aggregate | |
| Ai et al. | Damage localization on reinforced concrete slab structure using electromechanical impedance technique and probability-weighted imaging algorithm | |
| US10877003B2 (en) | Damage detection system and method for detecting damage in fastened structures | |
| CN113758996B (en) | Flange bolt loosening detection method and detection device based on mixing nonlinear ultrasound | |
| CN106932485A (en) | Prestressed tendon corrosion depth monitoring method based on piezoelectric intelligent aggregate | |
| CN105115652A (en) | Method for quantified monitoring bolt pre-tightening torque through active ultrasonic guided wave | |
| CN117704949A (en) | Structure crack identification monitoring system and method based on wide-range strain sensing element | |
| CN102331455A (en) | Engineering structure damage monitoring method based on active Lamb wave damage index | |
| CN117723175B (en) | Device and method for detecting prestress structure based on piezoelectric fluctuation method | |
| CN109142521A (en) | Steel Bridge Deck water-proof tack coat monitoring device and method based on piezoelectric transducer | |
| CN104198095B (en) | A kind of beams of concrete prestress value and pre-stress damage monitoring method | |
| CN110220629B (en) | Steel strand stress detection method based on guided wave singular value vector distance | |
| CN111006827A (en) | Prestressed concrete box girder health condition monitoring system and monitoring method | |
| CN107632069B (en) | Concrete beam reinforcement steel plate stripping detection system and detection method | |
| Zhang et al. | Effective pre-stress identification in steel strand based on ultrasonic guided wave and 1-dimensional convolutional neural network | |
| US5999004A (en) | Method of detecting a change in the property of a rubber member | |
| CN207992118U (en) | A kind of compacting end carrying pulling force non-destructive testing device | |
| CN115790949A (en) | Bolt connection state monitoring method based on wearable intelligent gasket | |
| CN113125558B (en) | Intelligent stock |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |