CN118350158B - Visual prestress tensioning method based on data optimization reminding - Google Patents
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Abstract
The invention provides a visual prestress tensioning method based on data optimization reminding, which relates to the technical field of electric pole production and processing, the system realizes repeated cyclic optimization of simulation and entity data by carrying out three-dimensional simulation experiments on prestress tendons and setting factors influencing tensioning as adjustment parameters, carrying out tension treatment on the prestress tendons by the cyclic optimization of simulation parameters and entity parameters and matching with the entity experiments to obtain optimal prestress tendon tensioning finished products and optimal parameters, the precision is higher, the parameter is laminated more, and the whole process only can carry out secondary stretch-draw to prestressing tendons at most, can automatic comparison emulation data and theoretical data, find out the difference of parameters such as tension size, node position and cross-section size fast to give optimal optimization interval and adjustment scheme, reduced manual adjustment's complexity and error, possess real-time feedback function, adopt this kind of mode, reduce the number of times of direct and prestressing tendons stretch-draw, reach accurate effect.
Description
Technical Field
The invention relates to the technical field of electric pole production and processing, in particular to a visual prestress tensioning method based on data optimization reminding.
Background
According to the self-adaptive prestress tensioning method and tensioning system disclosed by Chinese publication No. CN114319348B, the design tensioning control force of a single anchor rope/anchor rod is preset on an intelligent measurement and control system, four tensioning process schemes of hard rock, medium hard rock, soft rock and soil body are preset in the intelligent measurement and control system, the rock mass level is comprehensively defined, a set of tensioning method and tensioning system which are systematically suitable for rock-soil anchoring self-adaptive prestress is formed, a perfect tensioning process scheme is established according to the set of tensioning method system, the tensioning process scheme is reasonably selected according to different rock mass conditions, and an automatic flow thoroughly solves the problem of engineering quality caused by inadaptation of the existing tensioning method.
The above patent document and the prior art have the following technical problems when in use:
Firstly, directly processing the prestressed tendons, easily generating the problem that the surface parameters are inconsistent with the quality of finished products, greatly increasing the rejection rate in the production process of the prestressed tendons, and not being capable of carrying out precision adjustment on various parameters affecting the tensioning of the prestressed tendons, resulting in low quality of the finished products of the whole prestressed tendons and insufficient processing precision;
Secondly, aiming at the tensioning process, the display of various parameters of the system cannot be carried out, so that the tensioning process is comprehensively analyzed and mastered;
Thirdly, intelligent parameter analysis cannot be provided for the actual tensioning state of the surface of the actual prestressed tendon in the tensioning process, so that the tensioning processing parameters are difficult to adjust, and the processing efficiency is affected;
and fourthly, in the prestress tensioning process, the prestress rib in an undertensioned state is generally adjusted by repeated tensioning, so that the surface of the prestress rib is easy to damage and even break, and a tensioned finished product meeting the quality requirement cannot be achieved in a state with less tensioning times in the processing process.
Disclosure of Invention
The technical problems to be solved are as follows:
Aiming at the defects of the prior art, the invention provides a visual prestress tensioning method based on data optimization reminding, which solves the following problems:
1. the problems that the rejection rate of the physical processing is high and the parameter precision adjustment cannot be carried out in the processing process are directly carried out;
2. The problem that visual display cannot be realized in the tensioning process;
3. The intelligent degree is low during stretching processing, so that the difficulty in adjusting processing parameters is high;
4. the problem of high damage rate and poor quality of the prestressed tendons caused by excessive stretching times.
The technical scheme is as follows:
in order to achieve the above purpose, the invention is realized by the following technical scheme: the visual prestress tensioning method based on the data optimization reminding comprises the following steps:
Sp1: three-dimensional visual simulation, setting target parameters of the finished product, including setting a specified target diameter Length ofTensile strength of product prestressed tendons after tensioningBending strengthStrength of bearingAnd the number S of ribs is used for carrying out visual three-dimensional modeling according to set target parameters of the product, 20-30 groups of product data are selected in an arithmetic progression mode according to the target parameters of the prestressed product, the arithmetic progression mode is adopted as a basic database for tensioning the product, and the overall data of all set target parameters are recorded as B;
Sp2: simulation tensioning experiment; according to each target parameter preset by Sp1, selecting the model of the prestressed tendon with each parameter closest to the target parameter B by combining the parameters of each type of prestressed tendon in a basic database, controlling three parameters of a tensioning environment to correspond to the model, wherein the three parameters of the tensioning environment are respectively the adjustment target elongation, the position of a tensioning node and the temperature environment, performing a simulation tension experiment of the prestressed tendon, and recording the data after the simulation tensioning of the prestressed tendon as B1;
sp3: and (3) optimizing simulation parameters:
sp3.1: tensile Strength in B1 and B in Sp2 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
If K=B-B1 is more than 0, each parameter in B1 does not meet the requirement, the prestressed tendon of the model does not meet the requirement, the model of the prestressed tendon is selected again for simulation tensioning experiments, and Sp2 is entered;
if K=B-B1 is less than 0, indicating that each parameter in B1 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp3.2;
If k=b-b1=0, it indicates that each parameter in B1 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then the tendon enters into sp3.2;
Sp3.2: after a group of satisfied B1 data is obtained, 3-5 groups of optimization experiments are selected by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B1 with K=B-B1=0 is calculated, the model corresponding to the group B1 is used as a critical model number value capable of realizing target parameters, the model data of the prestress rib corresponding to the critical model B1 is used as a reference, 3-5 groups of data are selected by taking 5% of each of the positive and negative sides as gradient arithmetic progression, and the step Sp3.1 is repeated;
sp3.3: when the experimental data center value of Sp3.2 is the maximum value, the interval corresponding to the positive and negative 5% of the center value is the optimized interval;
Sp4: the entity tensioning experiment is carried out, the tensioning treatment of the entity prestressed tendons is carried out according to the model of the corresponding prestressed tendons in the optimization interval given by Sp3, three parameters of the tensioning environment are controlled to correspond to the model, and the tensioned data is recorded as B2;
Sp5: and (3) entity parameter optimization:
sp5.1: tensile strength in B2 and B in Sp4 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
if K=B-B2 > 0, each parameter in B2 does not meet the requirement, the prestressed tendon of the model does not meet the requirement, the model of the prestressed tendon is needed to be reselected for the entity tensioning experiment, and Sp4 is entered;
If K=B-B2 is less than 0, indicating that each parameter in B2 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp5.2;
If k=b-b2=0, it indicates that each parameter in B2 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then sp5.2 is entered;
Sp5.2: after a group of satisfied B2 data is obtained, 3-5 groups of data are selected for optimization experiments by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B2 with K=B-B2=0 is calculated, the model corresponding to the group B2 is used as a critical model value capable of realizing target parameters, the model data of the prestress rib corresponding to the B2 is used as a benchmark, 3-5 groups of data are selected for each of the positive and negative sides as gradient arithmetic progression, and the step Sp5.1 is repeated;
sp5.3: when the experimental data center value of Sp5.2 is the maximum value, the interval corresponding to the positive and negative 5% of the center value is the optimized interval, the optimized interval is fed back to Sp2 for simulation tensioning experiments, and step circulation is sequentially carried out;
Sp6: performing entity secondary tensioning, namely performing bit compensation tensioning of the prestressed tendons according to optimization interval data fed back by Sp5.3 in entity parameter optimization;
sp7 is an optimal result of the prestressed tendon, when the difference K of each parameter between each parameter B2 and the data B of the prestressed tendon after tensioning in a tensioning entity experiment is more than or equal to 0, the prestressed tendon after tensioning is indicated to meet the requirement, the prestressed tendon is directly output as the optimal tensioning parameter, when the difference is not met, the prestressed tendon is optimized through the entity parameter, and the secondary tensioning is carried out until the tensioning result meets the requirement, the optimal parameter is output, and the whole prestress tensioning treatment is completed.
Preferably, in the three-dimensional visual simulation, the content of the elongation for the prestress further includes the following content:
The elongation calculation formula of the prestressed tendon is as follows:
；
；
Wherein:
The unit is the theoretical elongation of the prestressed tendon ；
Is the average tension of the prestressed tendons, and the unit is；
The unit is the length of the prestressed tendon；
The unit is the cross-sectional area of the prestressed tendon；
The elastic modulus of the prestressed tendon is shown in the unit；
Is the stretching force of the stretching end of the prestressed tendon, and has the unit of；
In order to calculate the length of a section curve pore canal from the stretching end of the prestressed tendon, the unit is that；
Calculating the sum of included angles from the tensioning end of the prestressed tendon to the tangent line of the section curve pore canal part, wherein the unit is rad;
the influence coefficient of the local deviation of each meter of the pore canal on friction is given;
is the friction coefficient between the prestressed tendon and the pore canal wall.
Preferably, in the three-dimensional visual simulation, for the content of the measurement positioning node, the method further includes the following steps:
In the three-dimensional visual simulation, the starting point tensile force of the segmented tensioning of the prestressed tendon is equal to the end point tensile force of the last segment of steel beam, the end point tensile force of the prestressed tendon in the same segment is equal to the starting point tensile force minus the prestress loss of the segment caused by friction between the prestressed tendon and the pipeline wall, and therefore, the following can be obtained:
same section internal prestress rib the end point tensile force calculation formula is:
；
Wherein:
the unit is that the starting point tensile force of the segmented prestressed tendon ；
The unit is that the end point tensile force of the segmented prestressed tendon。
Preferably, in the three-dimensional visual simulation, the content of the influencing parameter for the temperature environment further includes the following content:
The calculation formula between the temperature and the expansion and contraction amount of the prestressed tendons is as follows:
；
；
Wherein:
T is the temperature variable of the prestressed tendon;
The effective prestress of the prestress steel strand;
is the elastic modulus of the prestress steel strand;
is the cross section area of the prestressed steel strand;
Linear expansion coefficient of the prestressed steel strand;
An effective tension of the prestressed steel strand.
Preferably, the physical simulation experiment aims at the retraction loss of the common anchor before and after the prestressing force in the first tensioning and the patch tensioningFriction lossLoss of temperatureStress relaxation loss of reinforcing steel barShrinkage and creep loss of concreteLoss of partial extrusionAnd carrying out calculation of a corresponding formula by six terms.
Preferably, in the simulation tensioning experiment and the entity tensioning experiment, the prestressing force of each model corresponds to a group of tensioning environment parameters, and according to the model of the selected prestressing tendons, the system automatically matches and adjusts the target elongation, the tensioning node position and the temperature environment by combining calculation formulas corresponding to the environment parameters.
The system comprises a general control system, a three-dimensional simulation system, webUI visual interaction systems, a simulation experiment system, a deep learning algorithm optimization system, an entity tensioning control system, an entity tensioning operation system and a tensioning data storage system, wherein the system further comprises the following contents:
and the general control system comprises: the system is used for controlling and adjusting all subsystems in the system;
three-dimensional simulation system: storing the model, length, section size and other parameter sizes of the prestressed tendons, carrying out three-dimensional modeling on the selected prestressed tendons, and carrying out theoretical initial setting on three indexes of the elongation of the prestressed tendons, the measuring and calculating positioning nodes and the temperature environment;
WebUI visual interaction system: displaying the three-dimensional modeling result, the simulation result, the entity result and the system data, and performing interactive control adjustment;
Simulation experiment system: performing simulation experiments after setting parameters of the prestressed tendons through a simulation algorithm arranged in the device;
Deep learning algorithm optimization system: optimizing simulation experiment parameters and entity parameters through a deep learning algorithm, performing data circulation processing in cooperation with a convolution network, giving out optimal simulation experiment parameters and entity experiment parameters, and performing autonomous learning by combining the stored data of a tension data algorithm storage system to continuously optimize;
Entity stretch-draw control system: the control and adjustment of data parameters are carried out on the entity stretching equipment, and the control of parameters such as position, clamping force, tension and measurement is included;
entity stretching operation system: the device used for stretching the prestressed tendon entity is integrated and comprises a force application device, a measuring device, a detecting device and a positioning device;
and the tension data algorithm storage system is used for storing each calculation formula and algorithm data in the system and storing experimental data in the system each time to form a classification database.
The beneficial effects are that:
The invention provides a visual prestress tensioning method based on data optimization reminding. The beneficial effects are as follows:
1. According to the system, the three-dimensional simulation experiment is carried out on the prestressed tendon, the factors influencing tensioning are set as the adjustment parameters, the tension treatment of the prestressed tendon is carried out by the cyclic optimization of the simulation parameters and the entity experiment, so that the optimal prestressed tendon tensioning finished product and the optimal parameters are obtained.
2. The system of the invention displays the processing process of the prestressed tendons by the WebUI visual interaction system, can intuitively and accurately display the tensioning process of the prestressed tendons by a three-dimensional visual simulation technology, is convenient for engineers and technicians to comprehensively understand and master various parameter changes in the tensioning process, and can simulate the tension performance of the prestressed tendons under different conditions by combining simulation tensioning experiments, thereby providing powerful data support for entity tensioning, and greatly improving the accuracy and reliability of tensioning operation.
3. The system is adopted, through a simulation parameter optimization step, the simulation data and the theoretical data can be automatically compared, the difference value of parameters such as tension magnitude, node position and section magnitude can be rapidly found, an optimal optimization interval and an optimal adjustment scheme are provided, the intelligent parameter optimization function not only reduces the complexity and error of manual adjustment, but also has a real-time feedback function, the data of an entity tensioning experiment can be rapidly fed back to the system and compared with the theoretical data, so that problems can be found and adjusted in time, the performance and efficiency of the whole prestress tensioning system can be improved, the optimal parameter combination can be found in a short time, and the tensioning efficiency and quality can be improved.
4. According to the application, the tensioning treatment of the prestressed tendons is carried out by a real-time tensioning control system and a real-time tensioning operation system in the system, the measurement, calculation and analysis of the first tensioning and the compensation tensioning are carried out when the prestressed tendons are tensioned, and the tensioning treatment of the prestressing is carried out according to the calculation formulas of the first tensioning and the compensation tensioning.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a system flow chart in a second embodiment of the invention;
FIG. 3 is a system flow chart in a third embodiment of the invention;
FIG. 4 is a system block diagram of the present invention;
FIG. 5 is a diagram showing the stress balance point of a tendon according to a sixth embodiment of the present invention;
FIG. 6 is a graph of tensile strength in an eighth embodiment of the invention;
FIG. 7 is a graph of bending strength in accordance with an eighth embodiment of the present invention;
Fig. 8 is a graph of the load bearing strength in an eighth embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
First embodiment:
As shown in fig. 1, the visual prestress tensioning method based on data optimization reminding comprises the following tensioning steps:
Sp1: three-dimensional visual simulation, setting target parameters of the finished product, including setting a specified target diameter Length ofTensile strength of product prestressed tendons after tensioningBending strengthStrength of bearingAnd the number S of ribs is used for carrying out visual three-dimensional modeling according to set target parameters of the product, 20-30 groups of product data are selected in an arithmetic progression mode according to the target parameters of the prestressed product, the arithmetic progression mode is adopted as a basic database for tensioning the product, and the overall data of all set target parameters are recorded as B;
Sp2: simulation tensioning experiment; according to each target parameter preset by Sp1, selecting the model of the prestressed tendon with each parameter closest to the target parameter B by combining the parameters of each type of prestressed tendon in a basic database, controlling three parameters of a tensioning environment to correspond to the model, wherein the three parameters of the tensioning environment are respectively the adjustment target elongation, the position of a tensioning node and the temperature environment, carrying out a simulation tension experiment of the prestressed tendon, and recording the data after the simulation tensioning of the prestressed tendon as B1;
sp3: and (3) optimizing simulation parameters:
sp3.1: tensile Strength in B1 and B in Sp2 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
If K=B-B1 is more than 0, each parameter in B1 does not meet the requirement, the prestressed tendon of the model does not meet the requirement, the model of the prestressed tendon is selected again for simulation tensioning experiments, and Sp2 is entered;
if K=B-B1 is less than 0, indicating that each parameter in B1 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp3.2;
If k=b-b1=0, it indicates that each parameter in B1 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then the tendon enters into sp3.2;
Sp3.2: after a group of satisfied B1 data is obtained, 3-5 groups of optimization experiments are selected by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B1 with K=B-B1=0 is calculated, the model corresponding to the group B1 is used as a critical model number value capable of realizing target parameters, the model data of the prestress rib corresponding to the critical model B1 is used as a reference, 3-5 groups of data are selected by taking 5% of each of the positive and negative sides as gradient arithmetic progression, and the step Sp3.1 is repeated;
sp3.3: when the experimental data center value of Sp3.2 is the maximum value, the interval corresponding to the positive and negative 5% of the center value is the optimized interval;
Sp4: the entity tensioning experiment is carried out, the tensioning treatment of the entity prestressed tendons is carried out according to the model of the corresponding prestressed tendons in the optimization interval given by Sp3, three parameters of the tensioning environment are controlled to correspond to the model, and the tensioned data is recorded as B2;
Sp5: and (3) entity parameter optimization:
sp5.1: tensile strength in B2 and B in Sp4 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
if K=B-B2 > 0, each parameter in B2 does not meet the requirement, the prestressed tendon of the model does not meet the requirement, the model of the prestressed tendon is needed to be reselected for the entity tensioning experiment, and Sp4 is entered;
If K=B-B2 is less than 0, indicating that each parameter in B2 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp5.2;
If k=b-b2=0, it indicates that each parameter in B2 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then sp5.2 is entered;
Sp5.2: after a group of satisfied B2 data is obtained, 3-5 groups of data are selected for optimization experiments by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B2 with K=B-B2=0 is calculated, the model corresponding to the group B2 is used as a critical model value capable of realizing target parameters, the model data of the prestress rib corresponding to the B2 is used as a benchmark, 3-5 groups of data are selected for each of the positive and negative sides as gradient arithmetic progression, and the step Sp5.1 is repeated;
sp5.3: when the experimental data center value of Sp5.2 is the maximum value, the interval corresponding to the positive and negative 5% of the center value is the optimized interval, the optimized interval is fed back to Sp2 for simulation tensioning experiments, and step circulation is sequentially carried out;
Sp6: performing entity secondary tensioning, namely performing bit compensation tensioning of the prestressed tendons according to optimization interval data fed back by Sp5.3 in entity parameter optimization;
sp7 is an optimal result of the prestressed tendon, when the difference K of each parameter between each parameter B2 and the data B of the prestressed tendon after tensioning in a tensioning entity experiment is more than or equal to 0, the prestressed tendon after tensioning is indicated to meet the requirement, the prestressed tendon is directly output as the optimal tensioning parameter, when the difference is not met, the prestressed tendon is optimized through the entity parameter, and the secondary tensioning is carried out until the tensioning result meets the requirement, the optimal parameter is output, and the whole prestress tensioning treatment is completed.
According to the method, the system is mainly used for continuously optimizing simulation parameters through a three-dimensional simulation experiment, then matching with entity tensioning to process, optimizing entity tensioning, further perfecting tensioning parameters, outputting optimal entity tensioning parameters, carrying out secondary circulation simulation on the entity parameters, giving secondary optimal parameters, carrying out entity position supplementing tensioning, obtaining optimal prestress tensioning finished products and optimal parameters, realizing repeated circulation optimization on simulation and entity data in a three-dimensional visual simulation process, realizing higher precision, and realizing the fact that the whole process can only stretch the prestress tensioning for the second time at most by adopting the steps, and directly achieving the effect of supplementing the tensioning for the first time.
Specific embodiment II:
As shown in fig. 2, in actual use, the method is realized through a pure simulation structure, when the method is used for processing the pole prestressed tendons, a single simulation system can be adopted for outputting tensioning parameters, and the specific steps are as follows:
Sp1: three-dimensional visual simulation, setting target parameters of the finished product, including setting a specified target diameter Length ofTensile strength of product prestressed tendons after tensioningBending strengthStrength of bearingAnd the number S of ribs is used for carrying out visual three-dimensional modeling according to set target parameters of the product, 20-30 groups of product data are selected in an arithmetic progression mode according to the target parameters of the prestressed product, the arithmetic progression mode is adopted as a basic database for tensioning the product, and the overall data of all set target parameters are recorded as B;
Sp2: simulation tensioning experiment; according to each target parameter preset by Sp1, selecting the model of the prestressed tendon with each parameter closest to the target parameter B by combining the parameters of each type of prestressed tendon in a basic database, controlling the three parameters and the model of the tensioning environment to correspond, respectively adjusting the target elongation, the position of the tensioning node and the temperature environment, carrying out a simulation tension experiment of the prestressed tendon, and recording the data after the simulation tensioning of the prestressed tendon as B1;
sp3: and (3) optimizing simulation parameters:
sp3.1: tensile Strength in B1 and B in Sp2 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
If K=B-B1 is more than 0, each parameter in B1 does not meet the requirement, the prestressed tendon of the model does not meet the requirement, the model of the prestressed tendon is selected again for simulation tensioning experiments, and Sp2 is entered;
if K=B-B1 is less than 0, indicating that each parameter in B1 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp3.2;
If k=b-b1=0, it indicates that each parameter in B1 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then the tendon enters into sp3.2;
Sp3.2: after a group of satisfied B1 data is obtained, 3-5 groups of optimization experiments are selected by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B1 with K=B-B1=0 is calculated, the model corresponding to the group B1 is used as a critical model number value capable of realizing target parameters, the model data of the prestress rib corresponding to the critical model B1 is used as a reference, 3-5 groups of data are selected by taking 5% of each of the positive and negative sides as gradient arithmetic progression, and the step Sp3.1 is repeated;
Sp3.3: when the experimental data center value of Sp3.2 is the maximum value, the interval corresponding to the center value of plus or minus 5% is the optimized interval.
By the steps, the pure simulation structure of the prestressed tendons is realized, the prestressed tendons of the subsequent entities are tensioned according to the simulation optimization scheme and data which are output most frequently, and compared with the simulation and entity circulation combination mode of the first embodiment, the simulation optimization parameter efficiency is higher.
Third embodiment:
As shown in fig. 3, in actual use, the system can perform inductive prestressing processing in a mode of simulating matching with a solid tensioning cycle, and the specific steps are as follows:
Sp1: three-dimensional visual simulation, setting target parameters of the finished product, including setting a specified target diameter Length ofTensile strength of product prestressed tendons after tensioningBending strengthStrength of bearingAnd the number S of ribs is used for carrying out visual three-dimensional modeling according to set target parameters of the product, 20-30 groups of product data are selected in an arithmetic progression mode according to the target parameters of the prestressed product, the arithmetic progression mode is adopted as a basic database for tensioning the product, and the overall data of all set target parameters are recorded as B;
Sp2: simulation tensioning experiment; according to each target parameter preset by Sp1, selecting the model of the prestressed tendon with each parameter closest to the target parameter B by combining the parameters of each type of prestressed tendon in a basic database, controlling the three parameters and the model of the tensioning environment to correspond, respectively adjusting the target elongation, the position of the tensioning node and the temperature environment, carrying out a simulation tension experiment of the prestressed tendon, and recording the data after the simulation tensioning of the prestressed tendon as B1;
sp3: and (3) optimizing simulation parameters:
sp3.1: tensile Strength in B1 and B in Sp2 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
If K=B-B1 is more than 0, each parameter in B1 does not meet the requirement, the prestressed tendon of the model does not meet the requirement, the model of the prestressed tendon is selected again for simulation tensioning experiments, and Sp2 is entered;
if K=B-B1 is less than 0, indicating that each parameter in B1 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp3.2;
If k=b-b1=0, it indicates that each parameter in B1 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then the tendon enters into sp3.2;
Sp3.2: after a group of satisfied B1 data is obtained, 3-5 groups of optimization experiments are selected by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B1 with K=B-B1=0 is calculated, the model corresponding to the group B1 is used as a critical model number value capable of realizing target parameters, the model data of the prestress rib corresponding to the critical model B1 is used as a reference, 3-5 groups of data are selected by taking 5% of each of the positive and negative sides as gradient arithmetic progression, and the step Sp3.1 is repeated;
sp3.3: when the experimental data center value of Sp3.2 is the maximum value, the interval corresponding to the positive and negative 5% of the center value is the optimized interval;
Sp4: the entity tensioning experiment is carried out, the tensioning treatment of the entity prestressed tendons is carried out according to the model of the corresponding prestressed tendons in the optimization interval given by Sp3, three parameters of the tensioning environment are controlled to correspond to the model, and the tensioned data is recorded as B2;
Sp5: sp5: and (3) entity parameter optimization:
sp5.1: tensile strength in B2 and B in Sp4 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
if K=B-B2 > 0, each parameter in B2 does not meet the requirement, the prestressed tendon of the model does not meet the requirement, the model of the prestressed tendon is needed to be reselected for the entity tensioning experiment, and Sp4 is entered;
If K=B-B2 is less than 0, indicating that each parameter in B2 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp5.2;
If k=b-b2=0, it indicates that each parameter in B2 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then sp5.2 is entered;
Sp5.2: after a group of satisfied B2 data is obtained, 3-5 groups of data are selected for optimization experiments by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B2 with K=B-B2=0 is calculated, the model corresponding to the group B2 is used as a critical model value capable of realizing target parameters, the model data of the prestress rib corresponding to the B2 is used as a benchmark, 3-5 groups of data are selected for each of the positive and negative sides as gradient arithmetic progression, and the step Sp5.1 is repeated;
Sp5.3: when the experimental data center value of Sp5.2 is the maximum value, the interval corresponding to the positive and negative 5% of the center value is the optimized interval, the optimized interval is fed back to Sp4 for entity tensioning experiments, and step circulation is sequentially carried out;
Sp6: performing entity secondary tensioning, namely performing bit compensation tensioning of the prestressed tendons according to optimization interval data fed back by Sp5.3 in entity parameter optimization;
sp7 is an optimal result of the prestressed tendon, when the difference K of each parameter between each parameter B2 and the data B of the prestressed tendon after tensioning in a tensioning entity experiment is more than or equal to 0, the prestressed tendon after tensioning is indicated to meet the requirement, the prestressed tendon is directly output as the optimal tensioning parameter, when the difference is not met, the prestressed tendon is optimized through the entity parameter, and the secondary tensioning is carried out until the tensioning result meets the requirement, the optimal parameter is output, and the whole prestress tensioning treatment is completed.
According to the steps, the entity tensioning can be carried out under the state of optimizing the simulation parameters, the position compensation tensioning treatment of the prestressed tendons is carried out by continuously optimizing the entity parameters, and compared with the pure simulation step and the mode of jointly circulating the simulation and the entity processing, the method is used for carrying out the cyclic tensioning treatment on the entity, can directly act on the prestressed tendon main body, and can monitor and analyze the surface change of the prestressed tendons through each tensioning, so that the tensioning effect of the prestressed tendons is clearer.
Fourth embodiment:
As shown in fig. 1 and 4, based on the technical solution of the first embodiment, the following is further disclosed: the system comprises a general control system, a three-dimensional simulation system, webUI visual interaction systems, a simulation experiment system, a deep learning algorithm optimization system, an entity tensioning control system, an entity tensioning operation system and a tensioning data storage system, wherein the system further comprises the following contents:
and the general control system comprises: the system is used for controlling and adjusting all subsystems in the system;
three-dimensional simulation system: storing the model, length, section size and other parameter sizes of the prestressed tendons, carrying out three-dimensional modeling on the selected prestressed tendons, and carrying out theoretical initial setting on three indexes of the elongation of the prestressed tendons, the measuring and calculating positioning nodes and the temperature environment;
WebUI visual interaction system: displaying the three-dimensional modeling result, the simulation result, the entity result and the system data, and performing interactive control adjustment;
Simulation experiment system: performing simulation experiments after setting parameters of the prestressed tendons through a simulation algorithm arranged in the device;
Deep learning algorithm optimization system: optimizing simulation experiment parameters and entity parameters through a deep learning algorithm, performing data circulation processing in cooperation with a convolution network, giving out optimal simulation experiment parameters and entity experiment parameters, and performing autonomous learning by combining the stored data of a tension data algorithm storage system to continuously optimize;
Entity stretch-draw control system: the control and adjustment of data parameters are carried out on the entity stretching equipment, and the control of parameters such as position, clamping force, tension and measurement is included;
entity stretching operation system: the device used for stretching the prestressed tendon entity is integrated and comprises a force application device, a measuring device, a detecting device and a positioning device;
and the tension data algorithm storage system is used for storing each calculation formula and algorithm data in the system and storing experimental data in the system each time to form a classification database.
According to the system structure, the specific contents are as follows:
The master control system is used as the core of the whole system and is responsible for coordinating and managing the operation of each subsystem, engineers or operators can control and regulate each subsystem in the system through the interface of the master control system, so that the high-efficiency and stable operation of the whole system is ensured, the overall operation efficiency of the system can be improved, the resource waste is reduced, the cooperative work among all subsystems is ensured, and the smooth progress of the prestressed tendon tensioning process is ensured.
The model, the length, the section size and other parameter dimensions of the prestressed tendons are stored in the three-dimensional simulation system, a user can select the parameters of the prestressed tendons according to requirements, the system can automatically conduct three-dimensional modeling, the elongation of the prestressed tendons, the measuring and calculating positioning nodes and the temperature environment are adjusted and set, the three-dimensional simulation system can intuitively display the model of the prestressed tendons, engineers and technicians are helped to understand the tensioning process better, basic data can be provided for subsequent simulation experiments and entity experiments through theoretical initial setting, and the accuracy and the reliability of the experiments are improved.
WebUI visual interaction system shows three-dimensional modeling result, simulation result, entity result and system data through the webpage interface, and the user can look over, analyze and compare experimental result through this system to can carry out interactive control and adjust, webUI visual interaction system makes the show of experimental result more directly perceived, convenient, and the user need not to install extra software and can visit the system, through interactive control and adjust, and the user can adjust system parameter in real time according to experimental result, optimizes experimental effect.
The simulation experiment system carries out simulation experiments after the prestressed tendon is set with parameters through a simulation algorithm arranged in the simulation experiment system, a user can set different experiment parameters according to needs, the system can carry out simulation calculation according to the parameters and give out experiment results, and the real experiment system can predict the tensioning effect of the prestressed tendon under the condition that tensioning experiments are not carried out actually, so that engineers and technicians can estimate and evaluate the tensioning process before the experiment, and the times and cost of actual experiments are reduced.
The deep learning algorithm optimizing system optimizes simulation experiment parameters and entity parameters through a deep learning algorithm, the system performs data circulation processing in cooperation with a convolution network, gives out optimal simulation experiment parameters and entity experiment parameters, performs autonomous learning by combining the stored data of the tension data algorithm storage system, continuously optimizes, and can automatically adjust and optimize the experiment parameters, improve the accuracy and reliability of the experiment, gradually improve the prediction and optimization capability through autonomous learning and continuous optimization, and provide more accurate guidance for tensioning of the prestressed tendons.
The entity tensioning control system controls and adjusts data parameters of the entity tensioning equipment, the entity tensioning control system and the entity tensioning operation system ensure accurate control of the entity tensioning process, and by monitoring and adjusting the equipment parameters in real time, the system can ensure that the prestressed tendons are tensioned according to preset requirements, and the tensioning quality and efficiency are improved.
The tension data algorithm storage system stores all calculation formulas and algorithm data in the system, sorts and stores experimental data each time to form a sort database, a user can access and inquire the data at any time, the tension data algorithm storage system provides data support for operation and experiments of the system, and the system can conveniently analyze and compare data by storing and sorting management experimental data, and provides reference basis for subsequent experiments and optimization.
Fifth embodiment:
as shown in fig. 1, based on the technical solution of the fourth embodiment, the following is further disclosed:
aiming at the technology of processing and optimizing data by a neural network and a deep learning algorithm, the prior neural network and the deep learning algorithm formula are adopted as the basis to set an input layer, and the training output of corresponding parameters is carried out by matching with the flow structure of a system, such as:
Input layer: for each input data point (a 1, a2, a 3), they are directly output as an input layer;
hidden layer: assuming a hidden layer, the output of each node can be expressed as:
；
Wherein:
Is the first The output of the hidden layer nodes;
is an activation function, and adopts one of ReLU, sigmoid or Tanh or carries out autonomous adjustment of the function;
Is the weight of the input layer to the hidden layer;
is a bias term;
Output layer: node output prediction value of output layer ；
；
Wherein:
Is a predicted value;
Is the weight of the hidden layer to the output layer;
is a bias term;
Is the first The output of the hidden layer nodes;
is an activation function, and adopts one of ReLU, sigmoid or Tanh or carries out autonomous adjustment of the function;
during training, weights are updated using loss functions such as MSE and optimizers Bias term。
In practice, the above formulas and steps are highly simplified and more complex model designs and parameter adjustments may be required in practice.
Specific embodiment six:
As shown in fig. 5, based on the technical solution of the first embodiment, the following is further disclosed:
In the three-dimensional visual simulation, the content of the elongation for the prestressing force further includes the following:
The elongation calculation formula of the prestressed tendons is as follows:
；
；
Wherein:
The unit is the theoretical elongation of the prestressed tendon ；
Is the average tension of the prestressed tendons, and the unit is；
The unit is the length of the prestressed tendon；
The unit is the cross-sectional area of the prestressed tendon；
The elastic modulus of the prestressed tendon is shown in the unit；
Is the stretching force of the stretching end of the prestressed tendon, and has the unit of；
In order to calculate the length of a section curve pore canal from the stretching end of the prestressed tendon, the unit is that；
Calculating the sum of included angles from the tensioning end of the prestressed tendon to the tangent line of the section curve pore canal part, wherein the unit is rad;
the influence coefficient of the local deviation of each meter of the pore canal on friction is given;
is the friction coefficient between the prestressed tendon and the pore canal wall.
In the three-dimensional visual simulation, for the content of the measurement positioning node, the method further comprises the following steps:
the starting point tensioning force of the segmented tensioning of the prestressed tendon is equal to the end point tensioning force of the last segment of steel beam, the end point tensioning force of the prestressed tendon in the same segment is equal to the starting point tensioning force of the same segment minus the prestress loss of the segment caused by friction between the prestressed tendon and the pipeline wall, and therefore the fact that:
same section internal prestress rib the end point tensile force calculation formula is:
；
Wherein:
the unit is that the starting point tensile force of the segmented prestressed tendon ；
The unit is that the end point tensile force of the segmented prestressed tendon。
And determining the position of a stress balance point of the prestressed tendon: assuming A, B to be the left and right stretching ends of the curve respectively, the stress balance points are located on the CD segments, and the starting and ending stretching forces of all segments of the prestressed tendon are calculated from A, B stretching ends respectively according to single-end stretching working conditions by the formula (3) until the stress balance point exists in a certain segment, as shown in fig. 5, the intersection point E of the folding lines in fig. 5 is the stress balance point, and the stretching forces on two sides of the stress balance point are equal, and the method can be obtained according to the formula (3):
；
Wherein:
The tensile force of the point C calculated for the tensioning end A is in kN;
the unit of the D point tension calculated for the B tension end is kN;
、 The unit is rad which is the sum of included angles of curve tangents of CE and ED sections;
、 The length of the curve duct is the CE and CD section, and the unit is m.
In the three-dimensional visual simulation, the content of the influence parameters for the temperature environment further comprises the following contents:
The calculation formula between the temperature and the expansion and contraction amount of the prestressed tendons is as follows:
；
；
Wherein:
T is the temperature variable of the prestressed tendon;
The effective prestress of the prestress steel strand;
is the elastic modulus of the prestress steel strand;
is the cross section area of the prestressed steel strand;
Linear expansion coefficient of the prestressed steel strand;
An effective tension of the prestressed steel strand.
Specific embodiment seven:
as shown in fig. 1, based on the technical solution of the first embodiment, the following is further disclosed:
in the entity simulation experiment, the retraction loss of the common anchor before and after the prestressing force is tensioned in the first tensioning and the patch tensioning Friction lossLoss of temperatureStress relaxation loss of reinforcing steel barShrinkage and creep loss of concreteLoss of partial extrusionAnd carrying out calculation of a corresponding formula by six terms.
Because the gap has no space for further compaction in the tensioning process, the anchor is retracted and lostOnly at the initial tension, the formula is:
；
according to the friction loss formula It can be seen that friction loss is generated in the process before and after the tensioning, and the friction loss in the front and rear stages is proportional to the tensioning stress before and after the tensioning, and the friction loss is respectively:
；
；
Wherein:
、 Friction losses at the initial tensioning stage and the tensioning compensation stage respectively;
、 the tensioning loads are respectively the initial tensioning stage and the complementary tensioning stage;
the friction coefficient of the prestressed tendons and the pore canal;
Is the sum of the included angles with the pore canal;
is the deviation influencing coefficient.
Because the concrete is cured and formed and has certain strength, no temperature loss exists in the initial tensioning process or the repair tensioning process。
According to the calculation formula of the stress relaxation loss of the steel bar: It is known that stress relaxation loss occurs in the process before and after the tensioning, expressed as:
；
；
Wherein:
、 the stress relaxation loss of the steel bars in the initial tensioning stage and the complementary tensioning stage respectively;
Is the standard value of the ultimate strength of the prestressed tendons.
According to the calculation formula in the specification, concrete shrinkage and creep loss can be generated in the process before and after the tensioning. The shrinkage and creep loss of the concrete in the initial tensioning and the additional tensioning processes are respectively as followsAnd (d) theThe concrete normal compressive stress at the resultant force points of the prestressed tendons in the two-stage tension zone is shown as the following formula respectivelyAnd (d) theThe following is shown:
；
；
；
；
Wherein:
、 the shrinkage and creep loss of the concrete in the initial tensioning stage and the complementary tensioning stage are respectively calculated;
、 The normal compressive stress of the concrete in the tension area in the initial tension stage and the tension compensating stage is respectively;
is the compressive strength of the concrete cube;
The area of the section of the concrete is the area of the section of the concrete where the prestressed tendons are located;
the method is characterized in that the method is a prestressed reinforcement pulling force in an initial tensioning stage, and in a prestressed concrete structure, the pulling force is applied when the prestressed reinforcement is initially tensioned;
In order to supplement the tension of the prestressed tendons in the tension stage, an extra tension is applied to the prestressed tendons in the tension stage;
the distance from the combined force point of the prestressed tendon to the neutral axis of the concrete section is the eccentricity of the prestressed tendon to the neutral axis of the concrete section;
The distance from the neutral axis of the concrete section to the edge of the tension zone (the area where the prestressed tendons are located) is the distance from the neutral axis of the concrete section to the edge of the tension zone;
the moment of inertia of the concrete section to the neutral axis is the moment of inertia of the concrete section relative to the neutral axis, and is used for calculating bending moment or stress;
the normal compressive stress of the concrete in the tension area;
And (5) the reinforcement ratio of the tension zone.
Since the prestress loss is already considered in the initial tensioning phase, the local compression loss is no longer considered in the tensioning phase.
Specific embodiment eight:
as shown in fig. 1-8, based on the technical solution of the first embodiment, the following is further disclosed:
experimental measurement is carried out on the prestress tension of the electric pole in the first embodiment, and the specified target diameter is measured And length ofTensile strength of tendon inside poleBending strengthStrength of bearingAnd the number S of tendons are subjected to an optimal data experiment, and the experimental contents are shown in the following table 1:
| object type | Design value | Simulation experiment one | One-time optimization | Simulation experiment II | Secondary optimization | Entity tensioning |
| Tensile strength of | 3.1MPa | 2.8 | 3.02 | 3.1 | 3.3 | 3.25 |
| Bending strength | 11.5MPa | 10.1 | 11.2 | 11 | 11.6 | 11.6 |
| Bearing strength | 1800.0KN | 1600 | 1675 | 1800 | 1910 | 1900 |
| Number of ribs | 16 | 16 | 16 | 16 | 16 | 16 |
TABLE 1
The model and parameters of the pre-stressed high strength steel wire selected for the experiment are shown in table 2 below:
| Diameter of steel wire (mm) | Tensile strength (MPa) | Yield strength (MPa) |
| 4 | 1500-1700 | 1200-1400 |
| 6 | 1600-1800 | 1300-1500 |
| 8 | 1700-1900 | 1400-1600 |
| 9 | 1800-2000 | 1500-1700 |
TABLE 2
The experimental results are shown in fig. 6-8, the length of the electric pole of the target is set to be 15m, the target diameter of the electric pole is set to be 240mm, in this state, after the prestressed tendons in the electric pole are required to be stretched, the tensile strength, the bending strength and the bearing strength reach the design values, according to the experimental results, a 4mm steel wire is selected for the simulation experiment I, a 6mm steel wire is selected for the second optimization and the entity stretching, the corresponding parameters reach the values close to the design values after the first optimization, the design values are reached after the simulation experiment I, the second optimization and the entity stretching, and therefore the output interval is the steel bar parameters selected for the second optimization and the second optimization, namely, the steel wire with the model of 6mm is selected for the stretching experiment, and the stretching requirement of the target electric pole can be met.
According to the selected 6mm steel wire, the optimal tensioning environment parameters which can reach the target parameter strength are given through the adjustment experiments of the elongation, the node position and the temperature during tensioning in the system, the whole tensioning process is completed, and the screening of the prestressed tendons and the adaptive adjustment of the tensioning parameters are realized.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
Although embodiments of the present 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 therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The visualized prestress tensioning method based on the data optimization reminding is characterized by comprising the following steps of: the tensioning method comprises the following steps:
Sp1: three-dimensional visual simulation, setting target parameters of the finished product, including setting a specified target diameter Length ofTensile strength of product prestressed tendons after tensioningBending strengthStrength of bearingAnd the number S of ribs is used for carrying out visual three-dimensional modeling according to set target parameters of the product, 20-30 groups of product data are selected in an arithmetic progression mode according to the target parameters of the prestressed product, the arithmetic progression mode is adopted as a basic database for tensioning the product, and the overall data of all set target parameters are recorded as B;
Sp2: simulation tensioning experiment; according to each target parameter preset by Sp1, selecting the model of the prestressed tendon with each parameter closest to the target parameter B by combining the parameters of each type of prestressed tendon in a basic database, controlling three parameters of a tensioning environment to correspond to the model, wherein the three parameters of the tensioning environment are respectively the adjustment target elongation, the position of a tensioning node and the temperature environment, performing a simulation tension experiment of the prestressed tendon, and recording the data after the simulation tensioning of the prestressed tendon as B1;
sp3: and (3) optimizing simulation parameters:
sp3.1: tensile Strength in B1 and B in Sp2 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
if K=B-B1 is more than 0, each parameter in B1 does not meet the requirement, and the model of the prestressed tendon does not meet the requirement, and Sp2 is required to be re-entered to select the model of the prestressed tendon for simulation tensioning experiment;
if K=B-B1 is less than 0, indicating that each parameter in B1 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp3.2;
If k=b-b1=0, it indicates that each parameter in B1 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then the tendon enters into sp3.2;
Sp3.2: after a group of satisfied B1 data is obtained, 3-5 groups of optimization experiments are selected by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B1 with K=B-B1=0 is calculated, the model corresponding to the group B1 is used as a critical model number value capable of realizing target parameters, the model data of the prestress rib corresponding to the critical model B1 is used as a reference, 3-5 groups of data are selected by taking 5% of each of the positive and negative sides as gradient arithmetic progression, and the step Sp3.1 is repeated;
sp3.3: when the experimental data center value of Sp3.2 is the maximum value, the interval corresponding to the positive and negative 5% of the center value is the optimized interval;
Sp4: the entity tensioning experiment is carried out, the tensioning treatment of the entity prestressed tendons is carried out according to the model of the corresponding prestressed tendons in the optimization interval given by Sp3, three parameters of the tensioning environment are controlled to correspond to the model, and the tensioned data is recorded as B2;
Sp5: and (3) entity parameter optimization:
sp5.1: tensile strength in B2 and B in Sp4 Bending strengthStrength of bearingAnd respectively comparing the number S of the ribs to obtain a difference K:
if K=B-B2 > 0, each parameter in B2 does not meet the requirement, the prestressed tendon of the model does not meet the requirement, the model of the prestressed tendon is needed to be reselected for the entity tensioning experiment, and Sp4 is entered;
If K=B-B2 is less than 0, indicating that each parameter in B2 exceeds a target parameter, and if the type of prestressed tendon meets the requirement, entering Sp5.2;
If k=b-b2=0, it indicates that each parameter in B2 is the same as the target parameter, the type of the tendon is the type of the nearest target parameter, and then sp5.2 is entered;
Sp5.2: after a group of satisfied B2 data is obtained, 3-5 groups of data are selected for optimization experiments by taking 5% of each of the positive and negative sides of the model of the prestress rib selected by the data as gradient arithmetic progression, the model corresponding to the group B2 with K=B-B2=0 is calculated, the model corresponding to the group B2 is used as a critical model value capable of realizing target parameters, the model data of the prestress rib corresponding to the B2 is used as a benchmark, 3-5 groups of data are selected for each of the positive and negative sides as gradient arithmetic progression, and the step Sp5.1 is repeated;
sp5.3: when the experimental data center value of Sp5.2 is the maximum value, the interval corresponding to the positive and negative 5% of the center value is the optimized interval, the optimized interval is fed back to Sp2 for simulation tensioning experiments, and step circulation is sequentially carried out;
Sp6: performing entity secondary tensioning, namely performing bit compensation tensioning of the prestressed tendons according to optimization interval data fed back by Sp5.3 in entity parameter optimization;
Sp7: and (3) the optimal result of the prestressed tendon, when the difference K of each parameter between each parameter B2 and the data B of the prestressed tendon after tensioning in a tensioning entity experiment is more than or equal to 0, indicating that each parameter of the prestressed tendon after tensioning meets the requirement, directly outputting the result as the optimal tensioning parameter, and when the difference is not met, optimizing through the entity parameter, and carrying out secondary tensioning, until the tensioning result meets the requirement, outputting the optimal parameter, and thus completing the whole process of prestressed tensioning.
2. The visual prestress tensioning method based on data optimization reminding according to claim 1, which is characterized by comprising the following steps: in the three-dimensional visual simulation, the content of the elongation for the prestress further includes the following:
The elongation calculation formula of the prestressed tendon is as follows:
；
；
Wherein:
The unit is the theoretical elongation of the prestressed tendon ；
Is the average tension of the prestressed tendons, and the unit is；
The unit is the length of the prestressed tendon；
The cross section area of the prestressed tendon is mm 2;
the elastic modulus of the prestressed tendon is shown in the unit ；
Is the stretching force of the stretching end of the prestressed tendon, and has the unit of；
In order to calculate the length of a section curve pore canal from the stretching end of the prestressed tendon, the unit is that；
Calculating the sum of included angles from the tensioning end of the prestressed tendon to the tangent line of the section curve pore canal part, wherein the unit is rad;
the influence coefficient of the local deviation of each meter of the pore canal on friction is given;
is the friction coefficient between the prestressed tendon and the pore canal wall.
3. The visual prestress tensioning method based on data optimization reminding according to claim 2, which is characterized by comprising the following steps: in the three-dimensional visual simulation, the content of the position of the stretching node further comprises the following contents:
In the three-dimensional visual simulation, the starting point tensile force of the segmented tensioning of the prestressed tendon is equal to the end point tensile force of the last segment of steel beam, the end point tensile force of the prestressed tendon in the same segment is equal to the starting point tensile force minus the prestress loss of the segment caused by friction between the prestressed tendon and the pipeline wall, and therefore, the following can be obtained:
same section internal prestress rib the end point tensile force calculation formula is:
；
Wherein:
the unit is that the starting point tensile force of the segmented prestressed tendon ；
The unit is that the end point tensile force of the segmented prestressed tendon。
4. The visual prestress tensioning method based on data optimization reminding according to claim 1, which is characterized by comprising the following steps: in the three-dimensional visual simulation, the content of the influence parameters aiming at the temperature environment further comprises the following contents:
The calculation formula between the temperature and the expansion and contraction amount of the prestressed tendons is as follows:
；
；
Wherein:
T is the temperature variable of the prestressed tendon;
The effective prestress of the prestress steel strand;
is the elastic modulus of the prestress steel strand;
is the cross section area of the prestressed steel strand;
Linear expansion coefficient of the prestressed steel strand;
An effective tension of the prestressed steel strand.
5. The visual prestress tensioning method based on data optimization reminding according to claim 1, which is characterized by comprising the following steps: the entity tensioning experiment aims at the retraction loss of the common anchor before and after the pre-stressing in the first tensioning and the patch tensioningFriction lossLoss of temperatureStress relaxation loss of reinforcing steel barShrinkage and creep loss of concreteLoss of partial extrusionAnd carrying out calculation of a corresponding formula by six terms.
6. The visual prestress tensioning method based on data optimization reminding according to claim 1, which is characterized by comprising the following steps: in the simulation tensioning experiment and the entity tensioning experiment, the prestressing force of each model corresponds to a group of tensioning environment parameters, and according to the model of the selected prestressing tendons, the system automatically matches and adjusts the target elongation, the tensioning node position and the temperature environment by combining calculation formulas corresponding to the environment parameters.
7. A system for implementing a data optimization reminder based visual prestress tensioning method according to claim 1, characterized by: the system comprises a master control system, a three-dimensional simulation system, webUI visual interaction system, a simulation experiment system, a deep learning algorithm optimization system, an entity tensioning control system, an entity tensioning operation system and a tensioning data storage system, and the system further comprises the following contents:
and the general control system comprises: the system is used for controlling and adjusting all subsystems in the system;
Three-dimensional simulation system: storing the model, length and section size parameter sizes of the prestressed tendons, performing three-dimensional modeling on the selected prestressed tendons, and performing theoretical initial setting on the elongation of the prestressed tendons, the measuring and calculating positioning nodes and the temperature environment as three indexes;
WebUI visual interaction system: displaying the three-dimensional modeling result, the simulation result, the entity result and the system data, and performing interactive control adjustment;
Simulation experiment system: performing simulation experiments after setting parameters of the prestressed tendons through a simulation algorithm arranged in the device;
deep learning algorithm optimization system: optimizing simulation experiment parameters and entity experiment parameters through a deep learning algorithm, performing data circulation processing in cooperation with a convolution network, giving out optimal simulation experiment parameters and entity experiment parameters, and performing autonomous learning by combining the stored data of a tensioning data storage system to continuously optimize;
entity stretch-draw control system: the control and adjustment of data parameters are carried out on the entity stretching equipment, including the control of position, clamping force, tension and measurement parameters;
entity stretching operation system: the device used for stretching the prestressed tendon entity is integrated and comprises a force application device, a measuring device, a detecting device and a positioning device;
and (3) stretching a data storage system: and storing each calculation formula and algorithm data in the system, and storing experimental data of each time in the system to form a classification database.
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| CN102182146A (en) * | 2011-03-29 | 2011-09-14 | 中国铁道科学研究院铁道建筑研究所 | Prestress rib tensioning equipment, system and method and concrete continuous box girder |
| CN102444290A (en) * | 2011-09-16 | 2012-05-09 | 柳州市建桥预应力机械厂 | Automatic tension control system and method thereof |
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| CN104314175B (en) * | 2014-10-17 | 2018-05-25 | 南京建工集团有限公司 | Big across steel truss arch structure Unloading Control technology based on prestressed stretch-draw monitoring |
| CN113487157B (en) * | 2021-06-30 | 2023-12-15 | 四川交达预应力工程检测科技有限公司 | Cloud technology-based prestress construction quality unmanned supervision platform and method |
| CN116021628A (en) * | 2022-12-30 | 2023-04-28 | 贵州长通集团智造有限公司 | Tensioning technology of prestressed concrete electric pole |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102182146A (en) * | 2011-03-29 | 2011-09-14 | 中国铁道科学研究院铁道建筑研究所 | Prestress rib tensioning equipment, system and method and concrete continuous box girder |
| CN102444290A (en) * | 2011-09-16 | 2012-05-09 | 柳州市建桥预应力机械厂 | Automatic tension control system and method thereof |
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