CN111986238B - Concrete arch dam modal shape identification method based on unmanned aerial vehicle video shooting - Google Patents
Concrete arch dam modal shape identification method based on unmanned aerial vehicle video shooting Download PDFInfo
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Abstract
The invention discloses a concrete arch dam modal shape recognition method based on unmanned aerial vehicle video shooting, which comprises the following steps: arranging marking points on the top of the dam; acquiring a dam crest image through a cloud deck camera on an unmanned aerial vehicle, and acquiring a dam crest vibration video; acquiring a video amplified by the front four-order motion of the original dam vibration video according to the front four-order natural frequency of the arch dam; selecting a maximum frame of dam crest deformation in the original dam front four-order motion amplification video, identifying the centroid coordinates of each marking point in the image, acquiring the centroid coordinate change of each marking point before and after vibration, and normalizing the maximum value of the relative coordinate change of each marking point to obtain the front four-order modal shape of the dam crest of the arch dam. The method can realize approximate full-field measurement of the arch dam crest vibration mode by a non-contact method, has high spatial resolution, and can provide support for subsequent dam model updating and damage identification.
Description
Technical Field
The invention belongs to the technical field of dynamic characteristic analysis, finite element model updating and damage identification of concrete arch dams, and particularly relates to a method for identifying modal vibration modes of a concrete arch dam based on unmanned aerial vehicle video shooting.
Background
The arch dam is an important dam type in water conservancy and hydropower engineering, and makes great contribution to national economy and social development. However, during the service period, due to extreme environmental loads (such as earthquakes, floods and the like), construction defects, poor management and maintenance, aging after long-term use and the like, many dams have various diseases and hidden dangers, so that structural instability or strength damage can be caused, and the normal operation and benefit exertion of the project are seriously influenced. Therefore, regular or real-time health monitoring of the arch dam is of great significance to guarantee safe operation of the arch dam.
Dynamic parameters (frequency, damping and vibration mode) of the structure can be identified through dynamic testing, and then health monitoring and damage identification of the dam are achieved. At present, dam structure dynamic test mainly comprises contact and non-contact methods. In the contact method, a large number of sensors (e.g., accelerometers) are required to be attached to the dam structure for vibration measurement. Although these sensors are highly reliable, their installation is time consuming and laborious. In addition, the sensors only provide sparse discrete point measurement, and can only obtain low spatial resolution, which is far from sufficient for application scenarios such as model updating and damage identification. Compared with the traditional contact type vibration measurement method, the non-contact type camera measurement technology does not need to additionally install a sensor, is more convenient and efficient, has higher spatial resolution, and can realize vibration measurement similar to a full field.
The invention content is as follows:
in order to overcome the defects of the background art, the invention provides a concrete arch dam modal vibration mode identification method based on unmanned aerial vehicle video shooting, and aims to solve the problems that the installation of a sensor is time-consuming and labor-consuming, the spatial resolution is low and the like in the existing dam structure contact type dynamic test process.
In order to solve the technical problems, the invention adopts the technical scheme that:
a concrete arch dam modal shape recognition method based on unmanned aerial vehicle video shooting comprises the following steps:
step 1, arranging marking points on the top of a dam;
step 2, acquiring a dam crest image through a cloud deck camera on the unmanned aerial vehicle, and acquiring a dam crest vibration video;
step 4, performing singular value decomposition on the power spectral density of the motion signals of each sub-area to obtain four-order natural frequency in front of the arch dam;
step 5, acquiring a video amplified by the front four-order motion of the original dam vibration video according to the front four-order natural frequency of the arch dam;
and 6, selecting a maximum dam crest deformation frame in the original four-order motion amplification video in front of the dam, identifying the centroid coordinates of each mark point in the image, acquiring the centroid coordinate change of each mark point before and after vibration, and normalizing the maximum value of the relative coordinate change of each mark point to obtain the four-order modal shape of the front of the arch dam.
Preferably, the mark points distributed at the top of the dam are uniformly distributed at intervals, and the interval between every two mark points is at least meter.
Preferably, the number of sub-regions is 9, and the size of each sub-region is 20 × 20 pixels.
Preferably, the step 3 of extracting the local motion phase information of each sub-region is obtained by a steerable pyramid algorithm.
Preferably, in step 4, the front four-order natural frequency of the arch dam is: after the power spectrum density of the motion signal of each subarea is subjected to singular value decomposition, the first four peaks of the singular value of the first order are obtained.
Preferably, the method for acquiring the video amplified by the front four-order motion of the original dam vibration video according to the front four-order natural frequency of the arch dam in the step 5 comprises the following steps:
the method comprises the steps of respectively taking the front four-order natural frequency of the arch dam as the central frequency of four band-pass filters, setting the frequency bandwidth to be 0.5Hz, sequentially setting four amplification factors to be 10, 25, 50 and 100, decomposing a video signal into a local spatial amplitude and a phase signal through a steerable pyramid algorithm, and then sequentially filtering, amplifying and reconstructing the phase signal to obtain the video amplified by the front four-order motion of the original dam vibration video.
The invention has the beneficial effects that: the method can realize approximate full-field measurement of the arch dam crest vibration mode through a non-contact method, has high spatial resolution, and can provide support for subsequent dam model updating and damage identification. The method has the advantages that the identification process is rapid and convenient, a contact type acceleration or speed sensor is not required to be arranged, the modal shape of the dam can be obtained only through the shot dam crest vibration video, and therefore a large amount of manpower, material resources and financial resources are saved.
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FIG. 1 is a flow chart of a method of an embodiment of the present invention;
fig. 2 is a schematic diagram of a process of shooting the vibration of the top of the arch dam by the unmanned aerial vehicle in the embodiment of the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples.
The invention discloses a concrete arch dam modal shape recognition technology based on unmanned aerial vehicle video shooting, which comprises the steps of dam crest mark point arrangement, unmanned aerial vehicle flight control, dam crest vibration video shooting, video motion estimation, dam natural vibration frequency recognition, video motion amplification processing and dam modal shape recognition. The working principle of the invention is as follows: any motion process of the arch dam structure can be approximately linear superposition of the primary modal shape of the first orders, motion of a specific frequency band (including a certain order of natural frequency) of the arch dam structure is amplified through a phase-based video amplification technology, contribution of the corresponding order of modal shape is highlighted, and therefore the acquisition of the modal shape of the arch dam is achieved.
The dam crest mark point 3 adopts a red waterproof sticker with the radius of 0.3m, so that the mark point in the shot dam crest vibration video has enough definition.
The integrated holder camera 2 preassembled by the unmanned aerial vehicle 1 is a 10-bit color depth 4K resolution camera, and the sampling frequency of the camera is 60Hz, so that the precise capture of the micro vibration at the top of the dam is ensured.
The shot vibration video of the top of the concrete arch dam 4 is only cut for 20s, so that the subsequent video processing time is reduced.
In the step of identifying the natural frequency of the arch dam, a series of subregions need to be selected averagely along the mark points on the top of the dam, the phase information of the local motion of each subregion is extracted through a complex domain filter so as to estimate the local motion of each subregion, and finally the natural frequency of the arch dam is identified through a frequency domain decomposition method.
When the video motion is amplified, the bandwidth of an amplification frequency band is 0.5Hz, and the amplification factor is 10-100.
The phase-based video motion amplification technology mainly utilizes a controllable pyramid filter bank in a complex domain to decompose a video signal into a local spatial amplitude and a phase, and then filters, amplifies and reconstructs the phase signal, thereby obtaining a motion amplification video. The unmanned aerial vehicle control platform and the unmanned aerial vehicle flight line are shown in figure 2.
A concrete arch dam modal shape recognition method based on unmanned aerial vehicle video shooting comprises the following steps:
step 1, arranging marking points on the top of a dam; the mark points distributed at the top of the dam are uniformly distributed at intervals, and the interval between every two mark points is at least 3 meters.
Step 2, acquiring a dam crest image through a cloud deck camera on the unmanned aerial vehicle, and acquiring a dam crest vibration video;
The extraction of the local motion phase information of each sub-region is obtained by a steerable pyramid algorithm.
Step 4, performing singular value decomposition on the power spectral density of the motion signal of each subregion to obtain the front four-order natural frequency of the arch dam; the front four-order natural frequency of the arch dam is as follows: and after the power spectral density of the motion signal of each subregion is subjected to singular value decomposition, the first four peaks of the singular value of the first order are obtained.
Step 5, acquiring a video obtained by amplifying the front four-order motion of the original dam vibration video according to the front four-order natural frequency of the arch dam;
the method for acquiring the video amplified by the front four-order motion of the original dam vibration video according to the front four-order natural frequency of the arch dam comprises the following steps:
the method comprises the steps of respectively taking the front four-order natural frequency of the arch dam as the central frequency of four band-pass filters, setting the frequency bandwidth to be 0.5Hz, sequentially setting four amplification factors to be 10, 25, 50 and 100, decomposing a video signal into a local spatial amplitude and a phase signal through a steerable pyramid algorithm, and then sequentially filtering, amplifying and reconstructing the phase signal to obtain the video amplified by the front four-order motion of the original dam vibration video.
And 6, selecting a maximum dam crest deformation frame in the original four-order motion amplification video in front of the dam, identifying the centroid coordinates of each mark point in the image, acquiring the centroid coordinate change of each mark point before and after vibration, and normalizing the maximum value of the relative coordinate change of each mark point to obtain the four-order modal shape of the front of the arch dam.
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
The maximum dam height of the concrete arch dam 4 is 66.8m, the dam top length is 179m, the width is 6m, the dam bottom thickness is 21.4m, the maximum central angle is 90.383 degrees, and the minimum central angle is 6.059 degrees. The river valley of the dam site is V-shaped, and the dam bodies are approximately symmetrically arranged. In order to identify the modal shape, shooting the vibration process of the dam by adopting an Inspire 1 unmanned aerial vehicle 1 in the Xinjiang province:
step 1: dam crest mark point layout
In the embodiment, 66 red circular waterproof stickers are arranged as the mark points, the radius of the red circular waterproof stickers is 0.3m, the red circular waterproof stickers are uniformly pasted on the top of the arch dam along the axis of the dam, and the interval between every two mark points is 3 m.
And 2, step: unmanned plane flight control
Install propeller blade, power battery and integration cloud platform camera on the unmanned aerial vehicle organism, the camera is 10 bit colour depth 4K resolution ratio cameras, and its sampling frequency is 60 Hz. Planning the flight path of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to fly to the position right above the dam crest of the arch dam and hover by strictly trained technicians.
And step 3: dam crest vibration video shooting
The camera angle is adjusted to enable the camera to vertically and downwards align to the dam crest plane, then the whole dam crest area enters the camera view field by adjusting the height of the unmanned aerial vehicle and the camera focal length, and a vibration video of the dam crest under the action of a drainage load is shot under the condition that the definition of the mark points of the dam crest is guaranteed.
And 4, step 4: video motion estimation
Intercepting the dam crest vibration video with the time length of 20s, guiding the dam crest vibration video into a computer for processing, averagely selecting 9 sub-regions along the mark points of the dam crest, wherein the size of each sub-region is 20 multiplied by 20 pixels, and extracting local motion phase information of each sub-region through a steerable pyramid algorithm so as to estimate the local motion of each sub-region.
And 5: dam natural frequency identification
And (3) performing singular value decomposition on the power spectral density of the motion signal of each sub-region, wherein the first four peak points of the first-order singular value are the front four-order natural frequency of the arch dam.
And 6: video motion amplification process
And respectively taking the front four-order natural frequency of the dam as the central frequency of the four band-pass filters, wherein the frequency bandwidth is 0.5Hz, and the amplification factors are respectively 10, 25, 50 and 100. The video signal is decomposed into local spatial amplitude and phase by a steerable pyramid algorithm, and then the phase signal is filtered, amplified and reconstructed, so that the video of the original dam vibration video after the amplification of the front four-order motion is obtained.
And 7: dam modal shape recognition
Selecting a maximum frame of dam crest deformation in each-order motion amplification video, identifying the barycentric coordinates of each mark point in the image through an image processing technology, calculating the barycentric coordinate change situation before and after vibration of each mark point, and carrying out normalization processing on the maximum value of the relative coordinate change, thus obtaining the four-order modal shape in front of the dam crest of the arch dam.
It will be appreciated that modifications and variations are possible to those skilled in the art in light of the above teachings, and it is intended to cover all such modifications and variations as fall within the scope of the appended claims.
Claims (5)
1. The utility model provides a concrete arch dam modal shape recognition method based on unmanned aerial vehicle video shooting which characterized in that includes:
step 1, arranging marking points on the top of a dam;
step 2, acquiring a dam crest image through a cloud deck camera on the unmanned aerial vehicle, and acquiring a dam crest vibration video;
step 3, intercepting the dam crest vibration video with preset duration, selecting a plurality of sub-regions along the mark points of the dam crest, and extracting local motion phase information of each sub-region to further obtain local motion of each sub-region;
step 4, performing singular value decomposition on the power spectral density of the motion signals of each sub-area to obtain four-order natural frequency in front of the arch dam;
step 5, acquiring a video obtained by amplifying the front four-order motion of the original dam vibration video according to the front four-order natural frequency of the arch dam;
the method for acquiring the video amplified by the front four-order motion of the original dam vibration video according to the front four-order natural frequency of the arch dam in the step 5 comprises the following steps: respectively taking the front four-order natural frequency of the arch dam as the central frequency of four band-pass filters, setting the frequency bandwidth to be 0.5Hz, sequentially setting four amplification factors to be 10, 25, 50 and 100, decomposing a video signal into a local spatial amplitude and a phase signal through a steerable pyramid algorithm, and then sequentially filtering, amplifying and reconstructing the phase signal to obtain the video amplified by the front four-order motion of the original dam vibration video;
and 6, selecting a maximum frame of dam crest deformation in the original four-order motion amplification video in front of the dam, acquiring the barycentric coordinates of each mark point in the image and the variation of the barycentric coordinates of each mark point before and after vibration, and normalizing the maximum value of the variation of the relative coordinates of each mark point, namely the four-order modal shape of the front of the dam top of the arch dam.
2. The method for identifying the modal shape of the concrete arch dam based on unmanned aerial vehicle video shooting is characterized by comprising the following steps: the mark points distributed on the top of the dam are uniformly distributed at intervals, and the interval between every two mark points is at least 3 meters.
3. The method for identifying the modal shape of the concrete arch dam based on unmanned aerial vehicle video shooting is characterized by comprising the following steps: the number of the sub-regions is 9, and the size of each sub-region is 20 × 20 pixels.
4. The method for identifying the modal shape of the concrete arch dam based on unmanned aerial vehicle video shooting is characterized by comprising the following steps: the step 3 of extracting the local motion phase information of each sub-region is obtained by a steerable pyramid algorithm.
5. The method for identifying the modal shape of the concrete arch dam based on the video shooting of the unmanned aerial vehicle according to claim 1, wherein the four-order natural frequency before the arch dam in the step 4 is as follows: and after the power spectral density of the motion signal of each subregion is subjected to singular value decomposition, the first four peaks of the singular value of the first order are obtained.
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| CN112629549B (en) * | 2020-11-27 | 2024-02-23 | 三峡大学 | Method for planning running track line of concrete arch dam cable machine |
| WO2022141310A1 (en) * | 2020-12-30 | 2022-07-07 | 深圳市大疆创新科技有限公司 | Control method for movable platform, movable platform, and storage medium |
| CN113091622B (en) * | 2021-02-22 | 2022-12-06 | 长沙银汉空间科技有限公司 | Dam displacement and inclination angle measuring method and system |
| CN113343554B (en) * | 2021-04-15 | 2023-04-25 | 长江勘测规划设计研究有限责任公司 | Arch dam underwater damage identification method, terminal equipment and storage medium |
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