CN112925959B - Pile bearing capacity calculation system based on database - Google Patents
Pile bearing capacity calculation system based on database Download PDFInfo
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- CN112925959B CN112925959B CN202110334382.1A CN202110334382A CN112925959B CN 112925959 B CN112925959 B CN 112925959B CN 202110334382 A CN202110334382 A CN 202110334382A CN 112925959 B CN112925959 B CN 112925959B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/90335—Query processing
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F30/20—Design optimisation, verification or simulation
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- G06F2111/04—Constraint-based CAD
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Abstract
The invention provides a pile bearing capacity calculation system based on a database, which comprises a data storage module, a data acquisition module, a bearing capacity calculation module and a data management module, wherein the data storage module is used for storing data; the data storage module is used for storing a plurality of preset pile-soil models, a plurality of preset first constraint conditions and a first target curve corresponding to the first constraint conditions; the data acquisition module is used for acquiring the actual measurement data of the stress of the section of the pile body of the pile with the bearing capacity to be calculated; a second target curve of the measured data packet; the bearing capacity calculation module is used for selecting a first target curve from the first target curve, wherein the similarity between the first target curve and the second target curve is greater than a preset similarity threshold value, and the bearing capacity calculation module is further used for calculating a pile-soil model corresponding to the first target curve to obtain the bearing capacity of the pile with the bearing capacity to be calculated. The invention effectively reduces the time required when the bearing capacity of the pile needs to be calculated.
Description
Technical Field
The invention relates to the field of bearing capacity calculation, in particular to a pile bearing capacity calculation system based on a database.
Background
The high strain detection is a detection method for judging the vertical compression bearing capacity and the integrity of a pile body of a single pile, a heavy hammer is used for impacting the pile top in an experiment, the speed and force time-course curve of the pile top are measured in an actual mode, and the speed and force time-course curve is analyzed through a fluctuation theory. The waveform fitting method belongs to one of various implementation methods of high-strain detection, and is widely used for calculating pile bearing capacity due to high accuracy and reliability. However, in the existing waveform fitting method, different values of preset parameter types are generally input into the pile-soil model in a traversal manner to obtain a plurality of calculated time-course curves, then an optimal time-course curve is selected from the calculated time-course curves, and the corresponding parameter types and parameter values at the moment are used as the pile-soil model under the actual condition. However, since different values of all parameter types are traversed and calculated to obtain different calculated time-course curves only when calculating the bearing capacity, the time required for obtaining the best matching curve when measuring the pile bearing capacity in the field is too long, and the calculation speed of the pile bearing capacity is slow and the efficiency is low.
Disclosure of Invention
In view of the above, it is an object of the present invention to provide a pile bearing capacity calculation system based on a database.
The invention provides a pile bearing capacity calculation system based on a database, which comprises a data storage module, a data acquisition module, a bearing capacity calculation module and a data management module, wherein the data storage module is used for storing data;
the data storage module is used for storing a plurality of preset pile-soil models, a plurality of preset first constraint conditions and a first target curve corresponding to the first constraint conditions;
the data acquisition module is used for acquiring the actual measurement data of the stress of the section of the pile body of the pile with the bearing capacity to be calculated; a second target curve of the measured data packet;
the bearing capacity calculating module is used for selecting a first target curve with the similarity between the first target curve and the second target curve being larger than a preset similarity threshold value from the first target curve,
the bearing capacity calculation module is further used for calculating the pile-soil model corresponding to the first target curve to obtain the bearing capacity of the pile with the bearing capacity to be calculated;
the data management module is used for managing the data stored in the data storage module.
In one embodiment, the pile-soil model comprises a pile model and a soil model; the pile model comprises a continuous rod piece model, a joint relaxation model and a pile body damping model; the soil model comprises a pile side soil model and a pile end soil model.
In one embodiment, the first constraint includes a preset speed time course curve, and the first target curve includes a force time course curve calculated based on the first constraint and the pile soil model.
In one embodiment, the number of the pile soil models is recorded as ztmNum,
and (3) calculating the same preset speed time-course curve and the ztm pile soil models respectively to obtain a force time-course curve corresponding to the speed time-course curve.
In one embodiment, the pile-soil model includes a parameter type and a value of the parameter type, and the parameter type includes: pile body material damping, static resistance and dynamic resistance.
In one embodiment, the data storage module includes a database disposed in a cloud, and the database is configured to store the plurality of preset storage pile-soil models, the plurality of preset first constraint conditions, and a first target curve corresponding to the first constraint condition.
In one embodiment, the measured data includes a measured speed time course curve of the stress of the pile body section of the pile with the bearing capacity to be calculated and a second target curve; and the second target curve comprises an actually measured force time-course curve of the stress of the pile body section of the pile with the bearing capacity to be calculated.
Compared with the prior art, the invention has the advantages that:
in the prior art, when the bearing capacity of a pile needs to be calculated, a time-course curve of actually measured force or a time-course curve of speed is used as a constraint condition, then the numerical value of the parameter type in a pile-soil model is changed to obtain a calculated curve, the calculated curve is fitted with the actually measured curve to obtain an optimal pile-soil model, and then the bearing capacity of the pile is obtained based on the pile-soil model. In the processing mode, the calculation curve needs to be acquired during calculation, so the speed is slow and the efficiency is low when the bearing capacity of the pile is calculated.
Different pile-soil models and different constraint conditions are preset, a plurality of first target curves are obtained through pre-calculation based on the different pile-soil models and the different constraint conditions, and the plurality of first target curves are stored.
When the bearing capacity of the pile needs to be calculated, a second target curve of the pile is obtained, then a first target curve with the similarity between the first target curve and the second target curve being larger than a preset similarity threshold value is obtained from the plurality of first target curves, a pile-soil model corresponding to the first target curve is used as a pile-soil model under a real condition, and the pile-soil model is analyzed to obtain the bearing capacity of the pile.
According to the method, when the bearing capacity of the pile is calculated, the similarity between the curves is calculated, and the calculated time-course curve is obtained without adopting different values of parameter types as a pile soil model like the prior art, so that the time required when the bearing capacity of the pile needs to be calculated is effectively reduced, and the working efficiency when the bearing capacity of the pile needs to be calculated is improved.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an exemplary embodiment of a pile bearing capacity calculation system based on a database according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, the present invention provides a pile bearing capacity calculation system based on a database, which includes a data storage module, a data acquisition module, a bearing capacity calculation module, and a data management module;
the data storage module is used for storing a plurality of preset pile-soil models, a plurality of preset first constraint conditions and a first target curve corresponding to the first constraint conditions;
the data acquisition module is used for acquiring the actual measurement data of the stress of the section of the pile body of the pile with the bearing capacity to be calculated; a second target curve of the measured data packet;
the bearing capacity calculating module is used for selecting a first target curve with the similarity between the first target curve and the second target curve being larger than a preset similarity threshold value from the first target curve,
the bearing capacity calculation module is further used for calculating the pile-soil model corresponding to the first target curve to obtain the bearing capacity of the pile with the bearing capacity to be calculated;
the data management module is used for managing the data stored in the data storage module.
The measured data of the pile body section is collected by a sensor when the pile is hammered. The setting of the similarity threshold can avoid calculating the similarity between the second target curve and all the first target curves, and is beneficial to reducing the time for finding the first curves meeting the target requirements.
In one embodiment, the data stored in the data storage module includes a plurality of preset pile-soil models, a plurality of preset first constraints, and a first target curve corresponding to the first constraints.
In one embodiment, before calculating the similarity, the first target curve and the second target curve are segmented in the same way, and then a simple similarity calculation is performed based on a certain segment, if the similarity result of the segment is greatly smaller than the preset similarity threshold, it indicates that the difference between the two curves is too large, and then the next first target curve is switched to. The speed of finding the required first curve can thus be greatly saved.
The simple similarity calculation includes:
for a first target curve and a second target curve with the same abscissa interval, randomly selecting t sampling points in the abscissa interval from the first target curve and the second target curve respectively, wherein the set of the abscissas of the sampling points is { x1,x2,…,xNStoring the vertical coordinates of the t sampling points obtained from the first target curve into the set { ay }1,ay2,…,ayNStoring the vertical coordinates of the t sampling points acquired from the second target curve into a set { by }1,by2,…,byN}, calculating { ay1,ay2,…,ayNAnd { by }and1,by2,…,byNAnd taking the similarity as a similarity result.
The first curve and the second curve are placed in the same coordinate system, and similarity calculation is convenient to carry out.
In one embodiment, the pile-soil model comprises a pile model and a soil model; the pile model comprises a continuous rod piece model, a joint relaxation model and a pile body damping model; the soil model comprises a pile side soil model and a pile end soil model.
In one embodiment, the first constraint includes a preset speed time course curve, and the first target curve includes a force time course curve calculated based on the first constraint and the pile soil model.
In one embodiment, the number of the pile soil models is recorded as ztmNum,
and (3) calculating the same preset speed time-course curve and the ztm pile soil models respectively to obtain a force time-course curve corresponding to the speed time-course curve. The calculation is accomplished by solving a one-dimensional equation.
And taking the preset speed time-course curve as a constraint condition, and respectively calculating by using pile-soil models with different parameter type values to obtain force time-course curves of different pile-soil models under the constraint condition. Therefore, for the same preset speed time course curve, the corresponding force time course curves have ztmNum strips.
And recording the number of the preset speed time-course curves as spNum, wherein the total number of all the force time-course curves is spNum multiplied by ztmNum.
The computation of the spNum × ztmNum force time-course curves is completed on the cloud server, the force time-course curves are obtained before the bearing capacity of the pile is computed, the force time-course curves are not computed when the bearing capacity needs to be computed as in the prior art, and therefore the computation speed is obviously slowed down. Therefore, the embodiment of the application is beneficial to obviously improving the efficiency of the pile in the calculation of the bearing capacity.
In one embodiment, the pile-soil model includes a parameter type and a value of the parameter type, and the parameter type includes: pile body material damping, static resistance and dynamic resistance.
In one embodiment, the data storage module includes a database disposed in a cloud, and the database is configured to store the plurality of preset storage pile-soil models, the plurality of preset first constraint conditions, and a first target curve corresponding to the first constraint condition.
In one embodiment, the measured data includes a measured speed time course curve of the stress of the pile body section of the pile with the bearing capacity to be calculated and a second target curve; and the second target curve comprises an actually measured force time-course curve of the stress of the pile body section of the pile with the bearing capacity to be calculated.
In one embodiment, the similarity between the first target curve and the second target curve is calculated by:
in the formula, a1And a2Respectively represent a first target curve and a second target curve, M represents the total number of points included in the first target curve, the total number of points included in the second target curve is the same as the total number of points included in the first target curve, b represents a presetB is equal to [1,2 ]]，
And
values representing m-th points in the first target curve and the second target curve, respectively, sn represents a preset control coefficient for avoiding denominator of 0, sn is greater than 0, sidx (a)1,a2) Representing a degree of similarity between the first target curve and the second target curve,
represents the average of the values of all points in the second target curve.
In another embodiment, the similarity between the first target curve and the second target curve is obtained by calculating a degree of fit between the first target curve and the second target curve.
In one embodiment, the data management module comprises an identity verification submodule and a management submodule, wherein the identity verification submodule is used for performing authentication management on the identity of a person using the management submodule; the management submodule is used for managing the data stored in the data storage module.
In one embodiment, the performing authentication management on the identity of the person using the management submodule includes:
if the personnel fail to pass the identity authentication, prohibiting the personnel from operating the management submodule;
and if the person passes the identity authentication, allowing the person to operate the management submodule.
In one embodiment, the managing the data stored in the data storage module includes: and modifying the parameter types contained in the pile-soil model and updating the value ranges of the numerical values of the parameter types.
In one embodiment, the identity verification sub-module comprises an image acquisition unit, an image processing unit and an identity authentication unit;
the image acquisition unit is used for acquiring a face image of the person and transmitting the face image to the image processing unit;
the image processing unit is used for acquiring feature information contained in the face image and transmitting the feature information to the identity authentication unit;
the identity authentication unit is used for judging whether the personnel passes identity authentication or not based on the characteristic information.
In one embodiment, the determining whether the person passes the identity authentication based on the feature information includes:
recording the characteristic information acquired by the image processing unit as spt1Recording the characteristic information of the face image of the person with the use management submodule prestored by the identity authentication unit as sptiI belongs to [1, numI ]]numI denotes the total number of persons having the use management submodule,
computing sptiAnd spt1Similarity xsidx therebetweeni；
If xsidx is presentiIf the similarity of the person using the management submodule is larger than xsidxt, judging that the person using the management submodule passes identity authentication, otherwise, judging that the person using the management submodule does not pass identity authentication, wherein xsidxt represents a preset similarity threshold value.
In one embodiment, the acquiring the face image of the person includes:
before the face image is sent to the image processing unit, the face image is scored to obtain the score of the face image;
judging whether the score is larger than a preset score threshold value, if so, transmitting the face image to the identity authentication unit, and if not, re-acquiring the face image of the person;
the way in which the facial image is scored is as follows:
in the formula, scr represents the score of the face image, fNum represents the total number of pixels belonging to face skin pixels in the face image, totalNum represents the total number of pixels contained in the face image, zsf represents the standard deviation obtained by noise estimation of the face image, and lsf represents the difference value of all face skin pixels in the face image in the L component in Lab color space;
wherein, fU represents the set of all face skin pixel points in the face image, f (h) represents the value of the L component of the pixel point h in fU in Lab color space, and numfU represents the total number of pixel points contained in fU.
According to the embodiment of the invention, whether the face image meets the quality requirement is judged by obtaining the score of the face image, so that the low-quality image is prevented from entering the subsequent steps of feature extraction and the like, the useless calculation of the image processing unit and the identity verification unit is avoided, the processing speed of the identity authentication of the identity verification submodule is improved, and the user experience of the computing system is improved. When the score is calculated, the total number of the skin pixel points, the total number of the pixel points of the face image, noise estimation, the value of the L component and the like are considered, so that the score can comprehensively reflect the condition of the face image, and the face image with high quality, low noise, high occupation ratio of the face skin pixel points, low difference among the face skin pixel points and balanced illumination can be selected.
In one embodiment, the face skin pixel points are obtained by:
and (3) a coarse detection stage:
carrying out skin color detection on the face image by using a skin color detection model, and storing obtained skin pixel points into a set fU;
a fine detection stage:
for an element in the fU, if there are pixels in the 8 neighborhood of the element that do not belong to the fU, the element is stored in the set afU,
for element fU in afU, marking the pixel points in the 8 neighborhood of fU, which do not belong to fU, as nfu;
for nfu, the discriminating parameter dfidx (nfu, fu) between nfu and fu is calculated:
dfidx(nfu,fu)＝s1|L(nfu)-L(fu)+s2|a(nfu)-a(fu)|+s3|b(nfu)-b(fu)|
wherein, L (nfu), L (fu) respectively represent the values of L components of fu and nfu in Lab color space, a (nfu), a (fu) respectively represent the values of a components of fu and nfu in Lab color space, b (nfu), b (fu) respectively represent the values of b components of fu and nfu in Lab color space; s1、s2、s3Is a preset weight coefficient;
if dfidx (nfu, fU) is smaller than the preset distinguishing parameter threshold, nfu is stored in fU.
In the embodiment of the invention, after the skin color detection model is used for detecting the skin color of the face image, a part of skin pixel points are obtained, but due to the defects of the existing skin color detection model, part of the pixel points which originally belong to the skin pixel points cannot be correctly detected by a skin color detection algorithm, so that the missing skin pixel points are screened out through the fine detection stage and then stored in the skin pixel point set fU, so that a complete skin pixel point set is obtained, and the quality of the obtained face image is greatly improved. And when the distinguishing parameters are calculated, the distinguishing parameters are mainly calculated in a Lab color model, and compared with the prior art, accurate face skin pixel points can be obtained.
In one embodiment, the acquiring feature information included in the face image includes:
carrying out noise reduction processing on the face image to obtain a first processed image;
carrying out illumination adjustment processing on the first processed image to obtain a second processed image;
performing image segmentation processing on the second processed image to obtain a third processed image;
and extracting the feature information of the third processed image by using a feature information extraction algorithm to obtain the feature information contained in the face image.
In the prior art, generally, an image is subjected to illumination adjustment processing and then subjected to noise reduction processing to obtain a noise-reduced image, but this easily causes that part of pixel points in the image are erroneously enhanced to become noise points, thereby causing that erroneous detail information is added in the noise-reduced image, affecting the accuracy of feature information extraction, and further affecting the safety of the computing system of the present invention. The noise reduction processing is firstly carried out, so that the problem is well avoided.
In one embodiment, the performing the illumination adjustment process on the first processed image to obtain a second processed image includes:
and carrying out illumination adjustment processing on the first processed image by using gamma correction to obtain a second processed image.
In one embodiment, the denoising the face image to obtain a first processed image includes:
for a pixel u in the first processed image, judging the relationship type between u and a pixel in a neighborhood of v × v size of u:
if f (u) -f (avenei) > thr, the relationship type between u and the pixel points in the v × v-sized neighborhood of u belongs to a first relationship type; thr represents a preset judgment threshold value; f (avenei) represents the mean of pixel values of pixel points in a v × v neighborhood of u;
if f (u) -f (avenei) is ≦ thr, the type of relationship between u and the pixel points in the v × v sized neighborhood of u belongs to the second relationship type;
if the first relation type exists, the following formula is adopted to carry out noise reduction processing on u:
f'(u)＝mid(U)
in the formula, U represents a set of pixel points in a neighborhood of v × v of U, and mid (U) represents taking an intermediate value of U; f' (u) represents a pixel value of u after the noise reduction processing is performed on u;
if the second relation type exists, the following formula is adopted to perform noise reduction processing on u:
in the formula, f' (u) represents a pixel value of u after the noise reduction processing is performed on u; usp is obtained as follows:
calculate the pixel value difference xcf (U, su) between the elements su and U in U: xcf (u, su) | f (u) -f (su) |, if xcf (u, su) is less than a preset difference threshold xcfthr, storing su in the set Usp;
f (i) represents the pixel value of element i in Usp in the first processed image, f (u) represents the pixel value of u in the first processed image, vafir represents the absolute value of the difference in pixel values between element in Usp and u | f (i) -f (u) |, vased represents the absolute value of the difference in gradient magnitude between element in Usp and u | ti (i) -ti (u) |, ti (i) and ti (u) represent the gradient magnitudes of i and u, respectively.
According to the embodiment of the invention, when noise reduction is carried out, the relation type between u and the pixel point of the neighborhood is judged firstly, different noise reduction modes are set for different relation types, and the self-adaptability of the noise reduction modes is improved, so that the noise reduction effect is improved. When the relationship type between u and the pixel points of the neighborhood belongs to the first relationship type, the difference of the pixel values between u and the pixel points of the neighborhood is larger, and the difference of the pixel values of the pixel points of the neighborhood is not larger, so that the noise reduction processing is carried out on the pixel points in the median noise reduction mode. When the relationship type between U and the pixel point of the neighborhood belongs to the second relationship type, elements in U are screened to form a set Usp, the Usp is used for carrying out noise reduction processing on U, and the difference between the pixel values and the gradient amplitudes of U and i is mainly considered during noise reduction, so that more accurate weight is given to the elements in the Usp, the consistency of the pixel values in the first processed image is favorably maintained, and more detailed information is reserved. For example, when U belongs to an edge pixel, and a pixel in U includes a pixel having a large difference from U and also includes a pixel having a small difference from U, if all elements in U are used to perform noise reduction processing on U, it is very easy to cause a pixel value of U to be reduced, thereby causing a serious loss of edge information of an image after noise reduction. The embodiment of the invention can well avoid the occurrence of the phenomenon, and is beneficial to improving the accuracy of the noise reduction effect, thereby improving the accuracy of the identity authentication submodule and further improving the safety of the invention.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (7)
1. A pile bearing capacity calculation system based on a database is characterized by comprising a data storage module, a data acquisition module, a bearing capacity calculation module and a data management module;
the data storage module is used for storing a plurality of preset pile-soil models, a plurality of preset first constraint conditions and a first target curve corresponding to the first constraint conditions;
the data acquisition module is used for acquiring the actual measurement data of the stress of the section of the pile body of the pile with the bearing capacity to be calculated; the measured data comprises a second target curve;
the bearing capacity calculating module is used for selecting a first target curve with the similarity between the first target curve and the second target curve being larger than a preset similarity threshold value from the first target curve,
the bearing capacity calculation module is further used for calculating the pile-soil model corresponding to the first target curve to obtain the bearing capacity of the pile with the bearing capacity to be calculated;
the data management module is used for managing the data stored in the data storage module.
2. The database-based pile bearing capacity calculation system of claim 1, wherein the pile-soil model comprises a pile model and a soil model; the pile model comprises a continuous rod piece model, a joint relaxation model and a pile body damping model; the soil model comprises a pile side soil model and a pile end soil model.
3. The database-based pile bearing capacity calculation system of claim 1, wherein the first constraint includes a preset velocity time course curve, and the first target curve includes a force time course curve calculated based on the first constraint and the pile-soil model.
4. The database-based pile bearing capacity calculation system according to claim 3, wherein the number of pile soil models is represented as ztmNum,
and (3) calculating the same preset speed time-course curve and the ztm pile soil models respectively to obtain a force time-course curve corresponding to the speed time-course curve.
5. The database-based pile bearing capacity calculation system of claim 2, wherein said pile soil model comprises a parameter type and a value of said parameter type,
the parameter types include: pile body material damping, static resistance and dynamic resistance.
6. The database-based pile bearing capacity calculation system according to claim 5, wherein the data storage module comprises a cloud-based database, and the database is used for storing the preset pile-soil models, the preset first constraints, and the first target curves corresponding to the first constraints.
7. The database-based pile bearing capacity calculation system of claim 3, wherein the measured data comprises a measured speed time course curve of the stress of the pile body section of the pile of which the bearing capacity is to be calculated and a second target curve;
and the second target curve comprises an actually measured force time-course curve of the stress of the pile body section of the pile with the bearing capacity to be calculated.
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| CN102268886B (en) * | 2010-06-04 | 2013-08-14 | 中国海洋石油总公司 | Method for detecting and evaluating design bearing capacity of pile foundation of offshore oil platform |
| CN111985029A (en) * | 2020-08-17 | 2020-11-24 | 武汉工程大学 | Method for estimating bearing capacity of single threaded pile |
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| EP0849405A1 (en) * | 1988-12-29 | 1998-06-24 | Takechi Engineering Co., Ltd. | Methods of determining capability and quality of foundation piles and of designing foundation piles, apparatus for measuring ground characteristics, method of making hole for foundation pile such as cast-in-situ pile and apparatus therefor |
| CN101059408A (en) * | 2007-05-15 | 2007-10-24 | 吉林省交通科学研究所 | Bridge super long large diameter pile foundation bearing capacity test method |
| CN112417549A (en) * | 2020-11-05 | 2021-02-26 | 天津华勘基础工程检测有限公司 | Method for estimating bearing capacity of single pile of post-grouting cast-in-place pile in soft soil area |
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