CN-121706217-B - Intelligent pre-pressing control method and system for continuous rigid frame beam of high-speed railway

CN121706217BCN 121706217 BCN121706217 BCN 121706217BCN-121706217-B

Abstract

The invention provides an intelligent pre-pressing control method and system for a continuous rigid frame beam of a high-speed railway, and relates to the technical field of pre-pressing control, wherein the method comprises the steps of obtaining beam body structural design data of the continuous rigid frame beam of the high-speed railway, a three-dimensional live-action image of a rigid frame beam hanging basket and construction load parameters of a cast-in-situ section; determining a plurality of key structure response points of the rigid frame beam hanging basket and test pressure of each key structure response point based on beam body structure design data of the continuous rigid frame beam of the high-speed railway and a three-dimensional live-action image of the rigid frame beam hanging basket; based on the information of the plurality of full-field deformation points corresponding to each advanced test structure response point location and the information of the plurality of full-field deformation points corresponding to each key structure response point location, the construction admittance monitoring result of the continuous rigid frame beam cradle is determined.

Inventors

DING WEI
GAO HENG
Lu Picheng
Jiang Meishang
WANG KE
ZHANG SHIWEN
LU HAIYAN
LIU LINGJUN
ZHOU ZHONGYUN
TAO YU
LI ZHIHUA
TANG JUN
REN XIAOFENG
YANG XIONG
CHEN GANG
LUO QIDONG
KUANG WENYI
YANG LUN
LUO ZHENGXIN
WANG KAIGUO
LI NIANJUN
XU HONGGANG
PENG XUEYING
WANG YANGYANG

Assignees

中铁五局集团成都工程有限责任公司
中铁五局集团有限公司
长江沿岸铁路集团四川有限公司

Dates

Publication Date: 20260505
Application Date: 20260224

Claims (8)

1. An intelligent pre-pressing control method for a continuous rigid frame beam of a high-speed railway is characterized by comprising the following steps of: Acquiring beam body structural design data of a continuous rigid frame beam of a high-speed railway, and constructing load parameters of a cast-in-situ section by using a three-dimensional live-action image of a rigid frame beam hanging basket; Determining a plurality of key structure response points of the rigid frame beam hanging basket and test pressure of each key structure response point based on beam body structural design data of the continuous rigid frame beam of the high-speed railway and the three-dimensional live-action image of the rigid frame beam hanging basket; Loading the test pressure of each key structure response point position to the corresponding key structure response point position, and acquiring a plurality of full-field deformation point information corresponding to each key structure response point position; determining a plurality of advanced test structure response points and test pressures of each advanced test structure response point based on the plurality of full-field deformation point information corresponding to each key structure response point, wherein determining a plurality of advanced test structure response points and test pressures of each advanced test structure response point based on the plurality of full-field deformation point information corresponding to each key structure response point comprises: Clustering is carried out based on the full-field deformation point information corresponding to each key structure response point position, so as to determine a plurality of deformation feature clusters corresponding to each key structure response point position; determining a plurality of high-order deformation areas and a plurality of low-order deformation areas corresponding to each key structure response point based on the deformation feature clusters corresponding to each key structure response point; Determining a plurality of advanced test structure response points and test pressures of each advanced test structure response point by using an advanced structure response point determination model, wherein the advanced structure response point determination model is a depth neural network, the input of the advanced structure response point determination model is a plurality of high-order deformation areas corresponding to each key structure response point, the plurality of low-order deformation areas are test pressures of each advanced test structure response point, the output of the advanced structure response point determination model is a plurality of advanced test structure response points, the test pressures of each advanced test structure response point are test pressures of the corresponding low-order deformation areas, and the test pressures of the corresponding high-order deformation areas can be combined with each other by calculating correlation strength of the advanced structure response point determination model; loading the test pressure of each advanced test structure response point position to the corresponding advanced test structure response point position for pressure test, and obtaining a plurality of full-field deformation point information corresponding to each advanced test structure response point position; And determining construction admittance monitoring results of the continuous rigid frame beam hanging basket based on the full-field deformation point information corresponding to each advanced test structure response point and the full-field deformation point information corresponding to each key structure response point.
2. The intelligent pre-compaction control method for the continuous rigid frame beam of the high-speed railway according to claim 1, wherein the determining the construction admittance monitoring result of the continuous rigid frame beam hanging basket based on the plurality of full-field deformation point information corresponding to each advanced test structure response point and the plurality of full-field deformation point information corresponding to each key structure response point comprises: generating a plurality of full-field deformation point information corresponding to a plurality of simulation structure response points based on the plurality of full-field deformation point information corresponding to each advanced test structure response point and the plurality of full-field deformation point information corresponding to each key structure response point; Constructing a structural response map, wherein the structural response map comprises a plurality of key structural response point position nodes, a plurality of advanced test structural response point position nodes and a plurality of simulation structural response point position nodes, the node characteristics of each key structural response point position node are a plurality of full-field deformation point information corresponding to each key structural response point position, the node characteristics of each advanced test structural response point position node are a plurality of full-field deformation point information corresponding to each advanced test structural response point position, and the node characteristics of each simulation structural response point position node are a plurality of full-field deformation point information corresponding to each simulation structural response point position; processing the structural response map based on a graph neural network, and determining overall morphological evolution sequence information of the rigid frame beam cradle under a load gradient; determining a plurality of construction stress points of the rigid frame beam hanging basket and a load intensity change sequence corresponding to each construction stress point based on the cast-in-situ section construction load parameters and the rigid frame beam hanging basket three-dimensional live-action image; Generating a dynamic form simulation video of the continuous rigid frame beam hanging basket in the pouring process based on a plurality of construction stress points of the rigid frame beam hanging basket, a load intensity change sequence corresponding to each construction stress point, and integral form evolution sequence information of the rigid frame beam hanging basket under a load gradient; And determining a construction admittance monitoring result of the continuous rigid frame beam hanging basket based on the dynamic morphological simulation video of the continuous rigid frame beam hanging basket in the pouring process.
3. The intelligent pre-compaction control method for the continuous rigid frame beam of the high-speed railway according to claim 2, wherein the input of the graphic neural network is the structural response map, and the output of the graphic neural network is the integral morphological evolution sequence information of the rigid frame beam hanging basket under the load gradient.
4. An intelligent pre-compaction control system of continuous rigid frame roof beam of high-speed railway, characterized by comprising: The data acquisition module is used for acquiring beam body structural design data of the continuous rigid frame beam of the high-speed railway, a three-dimensional live-action image of the rigid frame beam hanging basket and construction load parameters of the cast-in-situ section; The key point position determining module is used for determining a plurality of key structure response points of the rigid frame beam hanging basket and test pressure of each key structure response point based on beam body structural design data of the continuous rigid frame beam of the high-speed railway and the three-dimensional live-action image of the rigid frame beam hanging basket; The key point position testing module is used for loading the testing pressure of each key structure response point position to the corresponding key structure response point position respectively and obtaining a plurality of full-field deformation point information corresponding to each key structure response point position; the advanced point location determining module is configured to determine a plurality of advanced test structure response points and test pressures of each advanced test structure response point based on the plurality of full-field deformation point information corresponding to each key structure response point, where the advanced point location determining module is further configured to: Clustering is carried out based on the full-field deformation point information corresponding to each key structure response point position, so as to determine a plurality of deformation feature clusters corresponding to each key structure response point position; determining a plurality of high-order deformation areas and a plurality of low-order deformation areas corresponding to each key structure response point based on the deformation feature clusters corresponding to each key structure response point; Determining a plurality of advanced test structure response points and test pressures of each advanced test structure response point by using an advanced structure response point determination model, wherein the advanced structure response point determination model is a depth neural network, the input of the advanced structure response point determination model is a plurality of high-order deformation areas corresponding to each key structure response point, the plurality of low-order deformation areas are test pressures of each advanced test structure response point, the output of the advanced structure response point determination model is a plurality of advanced test structure response points, the test pressures of each advanced test structure response point are test pressures of the corresponding low-order deformation areas, and the test pressures of the corresponding high-order deformation areas can be combined with each other by calculating correlation strength of the advanced structure response point determination model; the advanced point position testing module is used for loading the testing pressure of each advanced test structure response point position to the corresponding advanced test structure response point position for pressure testing, and acquiring a plurality of pieces of full-field deformation point information corresponding to each advanced test structure response point position; And the monitoring result determining module is used for determining the construction admittance monitoring result of the continuous rigid frame beam hanging basket based on the plurality of full-field deformation point information corresponding to each advanced test structure response point and the plurality of full-field deformation point information corresponding to each key structure response point.
5. The intelligent pre-compression control system of the continuous rigid frame beam of the high-speed railway, as set forth in claim 4, wherein the monitoring result determining module is further configured to: generating a plurality of full-field deformation point information corresponding to a plurality of simulation structure response points based on the plurality of full-field deformation point information corresponding to each advanced test structure response point and the plurality of full-field deformation point information corresponding to each key structure response point; Constructing a structural response map, wherein the structural response map comprises a plurality of key structural response point position nodes, a plurality of advanced test structural response point position nodes and a plurality of simulation structural response point position nodes, the node characteristics of each key structural response point position node are a plurality of full-field deformation point information corresponding to each key structural response point position, the node characteristics of each advanced test structural response point position node are a plurality of full-field deformation point information corresponding to each advanced test structural response point position, and the node characteristics of each simulation structural response point position node are a plurality of full-field deformation point information corresponding to each simulation structural response point position; processing the structural response map based on a graph neural network, and determining overall morphological evolution sequence information of the rigid frame beam cradle under a load gradient; determining a plurality of construction stress points of the rigid frame beam hanging basket and a load intensity change sequence corresponding to each construction stress point based on the cast-in-situ section construction load parameters and the rigid frame beam hanging basket three-dimensional live-action image; Generating a dynamic form simulation video of the continuous rigid frame beam hanging basket in the pouring process based on a plurality of construction stress points of the rigid frame beam hanging basket, a load intensity change sequence corresponding to each construction stress point, and integral form evolution sequence information of the rigid frame beam hanging basket under a load gradient; And determining a construction admittance monitoring result of the continuous rigid frame beam hanging basket based on the dynamic morphological simulation video of the continuous rigid frame beam hanging basket in the pouring process.
6. The intelligent pre-compaction control system of the continuous rigid frame beam of the high-speed railway according to claim 5, wherein the input of the graphic neural network is the structural response map, and the output of the graphic neural network is the integral morphological evolution sequence information of the rigid frame beam hanging basket under the load gradient.
7. An electronic device comprising a processor, a memory, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor to implement the intelligent pre-compaction control method of the continuous rigid frame beam of the high-speed railway as claimed in any one of claims 1 to 3.
8. A computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements the intelligent pre-compression control method of a continuous rigid frame beam for a high-speed railway as claimed in any one of claims 1 to 3.

Description

Intelligent pre-pressing control method and system for continuous rigid frame beam of high-speed railway Technical Field The invention relates to the technical field of prepressing control, in particular to an intelligent prepressing control method and system for a continuous rigid frame beam of a high-speed railway. Background The continuous rigid frame beam of the high-speed railway is a core structure of a high-speed railway large-span bridge, and the construction quality directly relates to the operation safety and durability of the line. The cantilever pouring of the cradle is a main stream construction process of the continuous rigid frame beam of the high-speed railway, and the pre-pressing of the counter-force frame is a key process before the construction of the process. However, the pre-compaction control of the continuous rigid frame Liang Fanli of the existing high-speed railway has obvious limitations, the traditional pre-compaction adopts a grading loading mode of fixed point positions, the pressure test is only carried out on a few preset key point positions such as main truss nodes of the hanging basket, the hanging basket anchoring points and the like, the global stress characteristics of the hanging basket structure are difficult to cover, the data acquisition is only focused on single-point deformation or stress values, the whole-field deformation association rule of the hanging basket under the action of load cannot be captured, the limitation not only results in structural response data one-sided of the pre-compaction test, but also weakens the characterization capability of the test result on the whole performance of the hanging basket, further the traditional pre-compaction test can only finish evaluation by means of few initial preset fixed point position data, hidden high-risk stress areas without distributed measuring points in the hanging basket structure are difficult to identify, and the optimization adjustment basis of the test scheme is also lacking. Meanwhile, the deformation trend of the hanging basket in the casting process of the pre-judging cast-in-situ section cannot be dynamically simulated in the traditional mode, so that the matching degree between the pre-pressing test and the actual construction working condition is insufficient, and the accurate construction admittance monitoring result is difficult to form. Therefore, how to accurately simulate the overall form evolution of the rigid frame beam hanging basket under complex load and determine the construction admission monitoring result is a current problem to be solved urgently. Disclosure of Invention The invention mainly solves the technical problem of accurately simulating the integral form evolution of the rigid frame beam hanging basket under complex load and determining the construction admittance monitoring result. According to a first aspect, the invention provides an intelligent pre-pressing control method of a continuous rigid frame beam of a high-speed railway, which comprises the steps of obtaining beam body structural design data of the continuous rigid frame beam of the high-speed railway, a rigid frame beam hanging basket three-dimensional live-action image and cast-in-place section construction load parameters, determining a plurality of key structure response points of the rigid frame beam hanging basket and test pressures of each key structure response point based on the beam body structural design data of the continuous rigid frame beam of the high-speed railway and the rigid frame beam hanging basket three-dimensional live-action image, loading the test pressures of each key structure response point to the corresponding key structure response point respectively, obtaining a plurality of full-field deformation point information corresponding to each key structure response point, determining a plurality of advanced test structure response point positions and test pressure of each advanced test structure response point based on the plurality of full-field deformation point information corresponding to each key structure response point, and determining the full-field deformation point information corresponding to each key structure response point based on the full-field deformation point information of each key structure response point. In one possible implementation manner, the determining of the plurality of advanced test structure response points based on the plurality of full-field deformation point information corresponding to each key structure response point location and the test pressure of each advanced test structure response point location comprise determining a plurality of deformation feature clusters corresponding to each key structure response point location based on the plurality of full-field deformation point information corresponding to each key structure response point location in a clustering mode, determining a plurality of high-order deformation areas and a plurality of low-o