CN113378455A - Intelligent optimization method for hoisting process of assembly type building component - Google Patents
Intelligent optimization method for hoisting process of assembly type building component Download PDFInfo
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Abstract
The invention discloses an intelligent optimization method for the hoisting process of an assembly type building component, which comprises the steps of building a Matlab-Python-Abaqus combined simulation interactive platform, taking a displacement value in the hoisting process of a frame and the frame speed at the end of hoisting as target functions, calling a genetic algorithm to carry out optimization solution on the target functions, and repeatedly iterating to obtain the optimal solution of the target functions, thereby obtaining the optimal hoisting speed and the optimal hoisting point arrangement scheme. The method flexibly utilizes the interaction of various software, realizes the integrated processes of modeling, data calculation, iterative recalculation, numerical value feedback and optimized solution, and provides great practicability for the setting of the hoisting speed of the assembly type building component. Compared with the traditional method, the method greatly reduces the unscientific property and insecurity of the traditional method when the speed is controlled by workers through experience, and better meets the requirement of the fine design and construction of the fabricated building and the major trend of the future development of the building industry.
Description
Technical Field
The invention belongs to the field of construction of fabricated buildings, and particularly relates to a hoisting process optimization method based on combination of a genetic algorithm and finite element software.
Background
As a novel building production mode, the assembly type building has the advantages of high construction production efficiency, less energy consumption, small environmental influence and small labor demand. However, in recent engineering practice, there are many hidden dangers in the hoisting process of the assembled building component, the requirement on the skill of workers is high, the operation discomfort can cause heavy objects to fall to hurt people, and the potential safety hazard is large. However, in the past, people have been more focused on the design of standard components, construction process of component joints, building earthquake resistance and the like, and few researches have been made on the hoisting process in the construction process of the fabricated building. At present, the hoisting speed of the assembly type building component is completely controlled by a crane driver through experience, and the mode has great potential safety hazard on one hand and cannot meet the large development trend of intellectualization, intensification and unmanned development of the assembly type building industry on the other hand. Therefore, an intelligent optimization method is urgently needed in the hoisting process of the fabricated building component.
Disclosure of Invention
The method aims to optimize the hoisting speed in the hoisting construction process of the assembly type building component so as to prevent the component from falling due to the fact that the hoisting speed is unreasonable and the component cannot be hoisted or is hoisted excessively. The method integrates a genetic algorithm and a Matlab-Python-Abaqus joint simulation interactive platform, so that the integration of complex operation, automatic data processing, software interaction and automatic optimization solution of data is realized, and the optimal hoisting speed setting scheme is obtained through continuous iterative optimization of the genetic algorithm.
In order to achieve the purpose, the invention adopts the following technical scheme, which specifically comprises the following steps:
s1: and determining the value range of the hoisting parameters based on the actual assembly type constructional engineering component.
S2: and (4) building a genetic algorithm framework in Matlab, and determining a target optimization function.
S3: and establishing an Abaqus finite element model inp file through an Abaqus interface of Matlab.
S4: matlab receives hoisting parameters generated by a genetic algorithm, modifies the inp file generated by S3, and submits the calculation through an Abaqus interface.
S5: and calling the states of the hoisting calculation of the member through Python, including the speed, stress state, position information and the like when the member is stopped, and feeding back the states to Matlab.
S6: matlab receives the relevant result parameters generated by the Abaqus calculation in S5, calculates the objective function of the genetic algorithm, and judges whether to terminate the iterative calculation.
Compared with the prior art, the invention has the technical effects that:
(1) compared with the existing method for controlling the hoisting speed of the assembly type building component, the method has the advantages of being large in technical advantages and application prospect, and more suitable for the trend of industry development. The traditional method for manually controlling the hoisting speed requires skilled hoisting experience of workers, the development trend of the building industry in the future is intellectualization, intensification and unmanned, and the traditional method cannot meet the requirements of industry development. According to the method, the hoisting construction process is simulated by means of finite element software, an optimal hoisting speed setting scheme of the assembly type building component is obtained, and accident potential in the hoisting construction process is greatly reduced.
(2) The method applies the extremely strong nonlinear optimization capability of the genetic algorithm and embeds the genetic algorithm into a Matlab-Python-Abaqus joint simulation interactive platform, thereby realizing the optimization solution of the objective function. The platform can realize complex operation and automatic processing of data and feedback interaction of three kinds of software, and has the characteristics of high calculation speed, accurate calculation, convenient iteration and the like. Therefore, the method has extremely high calculation efficiency and accuracy and extremely high practicability in the design of the hoisting speed scheme.
Drawings
FIG. 1 is a schematic view of the process interaction of the present invention.
Fig. 2 is a perspective view of (a) a frame and (b) a three-view and suspension point layout diagram in an example.
Fig. 3 is a diagram of the beginning and end positions of the frame and the hoisting process in the example.
FIG. 4 shows the results of the optimization calculations: (a) the fitness value (b) is the optimal hoisting speed.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
S1: and establishing a model of a hoisting process in the Abaqus, carrying out primary calculation to obtain an odb result file, and establishing an m file for iteratively generating a new inp file in an interactive calculation process by using the inp file of the model.
S2: and calling preset state parameters after the hoisting of the structure is finished through the matlab, wherein the state parameters comprise the speed, the stress state, the position information and the like when the calculation is terminated, and the information is contained in the odb file.
S3: and determining the value range of the hoisting parameters based on the preset structural state parameters. The value ranges are shown in the following table:
note: the three time additions in this example should be 60 s.
S4: and (4) building a genetic algorithm framework in Matlab, and determining a target optimization function. The optimization objective function is as follows:
f(x)＝|(x1-x2)-7600|×0.1+|(y1-y2)-7600|×0.1+|v1|
in the formula y1，y2Respectively, the vertical start and end positions, x, of the structural reference point1，x2As starting and ending positions in the horizontal direction of a structural reference point, v1The velocity of the reference point in the horizontal direction at the end of the hoist is 0.1, which is a weight coefficient, and f (x) is an objective function of the genetic algorithm.
S5: and establishing a time-speed function based on the setting in the S3 in Matlab, calculating the magnitude of the acceleration and the magnitude of the speed of the constant speed section according to the lengths of the acceleration section and the constant speed section, generating a section of speed amplitude parameter and inserting the section of speed amplitude parameter into inp.
S6: establishing an inp file of an Abaqus finite element model through Matlab, wherein the inp file is in a file format which can be identified in Abaqus and can be directly opened by CAE.
S7: receiving related parameters generated by Abaqus calculation in the previous step through Matlab, calling the inp file established in S6 to modify the hoisting speed parameters in the inp file, and calling Abaqus to submit calculation.
S8: and calling an ODB result file obtained by Abaqus calculation by using Python, and feeding the ODB result file back to Matlab to calculate a target function. If the judgment criterion of the termination of the genetic algorithm is met, the iteration is terminated, and the obtained hoisting speed setting scheme is the most reasonable scheme; if not, the process returns to S3 to continue the above steps. The specific interaction process is as follows: firstly, Matlab receives a hoisting speed parameter which is generated by genetic algorithm iteration and calculated by S5. Then automatically calling the inp file established by Python in S6 and realizing automatic modification of the hoisting speed in the inp file, and then automatically calling Abaqus to submit calculation. And finally, judging whether the Abaqus completes the calculation or not by monitoring whether the lck file exists or not, if the lck file does not exist, indicating that the Abaqus completes the calculation, automatically calling Python to automatically extract the Abaqus calculation result to feed back the Abaqub calculation result to Matlab, and calculating the target function. And continuously iterating and optimizing the genetic algorithm, if the result meets the termination judgment criterion of the genetic algorithm, terminating iteration, and setting the obtained hoisting speed as an optimal scheme.
After 321 times of iterative calculation, the optimal time period coefficient obtained by the calculation example is 19.4775-13.5938, the lifting speed of the constant speed section is calculated to be 279.5392 (unit: second, millimeter per second), and the optimal objective function value obtained is 54.0579.
As shown in the attached figure 4, a comparative diagram of the hoisting start and end positions obtained by the hoisting process optimization method based on the genetic algorithm is drawn in the diagram, and the final position can be intuitively seen to be vertical to the ground from the diagram, which also shows that the hoisting process optimization method based on the genetic algorithm provided by the invention has high calculation efficiency and precision, thereby greatly reducing the workload of operators of hoisting machinery, effectively reducing uncertain factors generated by artificial factors in the hoisting process, and having high practicability and good convenience.
The above description is only an example of the present invention, and it should not be understood that the scope of the present invention is limited by the above description, and those skilled in the art can combine, modify and modify the embodiments without departing from the principle and spirit of the present invention, and the changes of the environment and the like are all within the scope of the claims of the present invention.
Claims (3)
1. An intelligent optimization method for the hoisting process of an assembly type building component is characterized by comprising the following steps:
s1: determining the value range of hoisting parameters based on the actual assembly type constructional engineering component;
s2: building a genetic algorithm framework in Matlab, and determining a target optimization function;
s3: establishing an Abaqus finite element model inp file through an Abaqus interface of Matlab;
s4: matlab receives hoisting parameters generated by a genetic algorithm, modifies an inp file generated by S3, and submits the calculation through an Abaqus interface;
s5: the state of the hoisting calculation of the member is called through Python, including the speed, stress state and position information when the member is stopped, and the state is fed back to Matlab;
s6: matlab receives the relevant result parameters generated by the Abaqus calculation in S5, calculates the objective function of the genetic algorithm, and judges whether to terminate the iterative calculation.
2. The intelligent optimization method for the assembled building element hoisting process according to claim 1, wherein the genetic algorithm optimization parameter in the step S3 is the assembled building element hoisting speed, and the genetic algorithm optimization objective function is as follows:
f(x)＝|(x1-x2)-7600|×0.1+|(y1-y2)-7600|×0.1+|v1|
in the formula y1，y2Respectively, the vertical start and end positions, x, of the structural reference point1，x2As starting and ending positions in the horizontal direction of a structural reference point, v1The velocity of the reference point in the horizontal direction at the end of the hoist is 0.1, which is a weight coefficient, and f (x) is an objective function of the genetic algorithm.
3. The intelligent optimization method for the assembly type building element hoisting process according to claim 1, wherein the genetic algorithm iteration of the step S6 comprises the following specific steps:
calling an Abaqus calculation result by using Python, and feeding the Abaqus calculation result back to Matlab to calculate a target function; if the judgment criterion of the termination of the genetic algorithm is met, the iteration is terminated, and a hoisting speed setting scheme is obtained at the moment; if not, returning to S2 to continue the above steps; the specific interaction process is as follows: firstly, Matlab receives a hoisting speed parameter which is generated by genetic algorithm iteration and obtained by S4 calculation; then automatically establishing a new inp file, and then automatically calling Abaqus to submit calculation; finally, whether the Abaqus completes the calculation is judged by monitoring whether the lck file exists or not, if the lck file does not exist, the Abaqus completes the calculation, then, Python is automatically called to automatically extract the Abaqus calculation result to feed back the Abaqlab calculation result to the Matlab, and a target function is calculated; and (4) continuously iterating and optimizing the genetic algorithm, if the result meets the termination judgment criterion of the genetic algorithm, terminating iteration, and obtaining a hoisting speed setting scheme.
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| CN113761633A (en) * | 2021-09-18 | 2021-12-07 | 奥意建筑工程设计有限公司 | Shear wall structure optimization design method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109146118A (en) * | 2018-06-19 | 2019-01-04 | 浙江省建工集团有限责任公司 | A kind of prefabricated components stockyard optimization system and its optimization method based on optimization algorithm |
| CN110083880A (en) * | 2019-04-02 | 2019-08-02 | 江苏理工学院 | Combined optimization design method based on MATLAB and ABAQUS |
| CN110362872A (en) * | 2019-06-18 | 2019-10-22 | 中建一局集团建设发展有限公司 | A kind of three-dimensional positioning high-accuracy control method for hanging grandstand |
| CN112700228A (en) * | 2021-01-12 | 2021-04-23 | 中铁一局集团有限公司 | Assembly type building construction progress monitoring device |
-
2021
- 2021-05-21 CN CN202110557899.7A patent/CN113378455A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109146118A (en) * | 2018-06-19 | 2019-01-04 | 浙江省建工集团有限责任公司 | A kind of prefabricated components stockyard optimization system and its optimization method based on optimization algorithm |
| CN110083880A (en) * | 2019-04-02 | 2019-08-02 | 江苏理工学院 | Combined optimization design method based on MATLAB and ABAQUS |
| CN110362872A (en) * | 2019-06-18 | 2019-10-22 | 中建一局集团建设发展有限公司 | A kind of three-dimensional positioning high-accuracy control method for hanging grandstand |
| CN112700228A (en) * | 2021-01-12 | 2021-04-23 | 中铁一局集团有限公司 | Assembly type building construction progress monitoring device |
Non-Patent Citations (5)
| Title |
|---|
| YOONSEOK SHIN 等: "Simulation model incorporating genetic algorithms for optimal temporary hoist planning in high-rise building construction", 《AUTOMATION IN CONSTRUCTION》, vol. 20, pages 550 - 558, XP028216512, DOI: 10.1016/j.autcon.2010.11.021 * |
| 张建民, 郑皆连, 肖汝诚: "钢管混凝土拱桥吊装过程的最优化计算分析", 中国公路学报, no. 02 * |
| 王全华;汤婧;庄真源;宋林;: "船台吊装网络优化及仿真研究", 武汉理工大学学报(交通科学与工程版), no. 02 * |
| 陈伟;孙翔君;陈荣亮;李辉;: "基于QFD-DSM的装配式建筑预制构件设计管控优化", 土木工程与管理学报, no. 04 * |
| 马宏伟 等: "基于粗粒度并行遗传算法的阻尼器优化布置", 《华南理工大学学报(自然科学版)》, vol. 47, no. 11, pages 104 - 112 * |
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|---|---|---|---|---|
| CN113761633A (en) * | 2021-09-18 | 2021-12-07 | 奥意建筑工程设计有限公司 | Shear wall structure optimization design method |
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