Steel box girder three-way walking type pushing system and deviation rectifying method

Abstract

The invention discloses a three-way walking type pushing system for a steel box girder and a deviation rectifying method, and relates to the technical field of bridge engineering construction.The three-way walking type pushing system comprises the steps that three-dimensional position parameters of the steel box girder, working state parameters of a pushing device and environmental influence parameters are collected in real time through a distributed sensing network; calculating a real-time deviation value and a deviation change rate by adopting a space-time coupling algorithm based on a theoretical trajectory constructed by the BIM model; inputting the data into a decision-making system based on a digital twinning technology, and generating a three-way collaborative correction scheme containing action time sequence, stroke amount and force value parameters of each pushing unit; based on the deviation rectification priority, deviation rectification action is executed, and the stress-strain state of the steel box girder is monitored synchronously; parameters of the decision-making system are continuously corrected through real-time feedback data; collecting the actual effect data of each correction, and optimizing the parameter weight of the decision model through a reinforcement learning algorithm; real-time accurate monitoring, scientific deviation analysis, intelligent safe deviation correction and efficient control of steel box girder pushing are achieved, and construction safety and precision are guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of bridge engineering construction technology, specifically a three-way walking-type jacking system for steel box girders and a method for correcting deviation. Background Technology

[0002] In the field of bridge construction, the steel box girder launching technology is widely used in bridge construction under complex conditions such as crossing rivers, railways, and highways due to its advantages, including the elimination of large hoisting equipment and minimal impact on surrounding traffic. Based on this engineering practice background, long-term technical research and project reviews have revealed that the technical shortcomings of traditional steel box girder launching systems and correction methods have become the core bottleneck restricting construction accuracy and safety. These deficiencies are not only reflected in the technical logic but have also led to numerous quality and safety accidents in actual projects.

[0003] Traditional jacking systems suffer from three major drawbacks in monitoring: heavy reliance on manual labor, limited sensor coverage, and weak data linkage. Firstly, manual measurement is constrained by environmental factors and personnel experience. In high-risk construction scenarios such as crossing railways or high piers, surveyors struggle to reach key monitoring points in real time, and manual readings can result in centimeter-level errors, far from meeting the millimeter-level accuracy requirements for jacking large-span steel box girders. Secondly, single-sensor monitoring can only collect localized data. For example, deploying only displacement sensors cannot simultaneously acquire core parameters such as the hydraulic pressure of the jacking jacks and the stress at key sections of the steel box girder, preventing the system from building a complete construction status assessment model.

[0004] As a large-span steel structure, the steel box girder undergoes significant deformation due to thermal expansion and contraction in environments with fluctuating temperatures. Traditional correction methods lack an effective temperature field compensation model, leading to systematic errors in deviation analysis. In high-temperature construction scenarios during summer, the viscosity of hydraulic oil decreases with increasing temperature, causing instability in the working pressure of the jacks. The deviation in the jacking force output can reach more than 10% of the design value, directly resulting in deviation of the girder's jacking trajectory. In mountainous or valley areas with large diurnal temperature differences, the deformation of the steel box girder can reach several centimeters, causing the girder's alignment to deviate significantly from the design value after jacking. This necessitates rework and adjustments in some projects, greatly extending the construction period.

[0005] The working conditions vary greatly in different construction scenarios. For example, the launching logic of variable cross-section steel box girders, narrow steel box girders, and small radius curved beams is fundamentally different. Each time a new working condition is entered, the parameters need to be readjusted, which not only prolongs the construction preparation cycle, but also easily causes deviations due to improper parameter adaptation.

[0006] To address the aforementioned deficiencies, this application provides a three-dimensional walking-type jacking system for steel box girders and a method for correcting deviations, thereby resolving the aforementioned technical problems. Summary of the Invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a three-way walking jacking system for steel box girders and a method for correcting deviation.

[0008] As the first aspect provided by this invention, this invention provides a three-dimensional step-by-step jacking correction method for steel box girders, comprising the following steps: Step S1: Collect the three-dimensional position parameters of the steel box girder, the working status parameters of the jacking device, and the environmental impact parameters in real time through a distributed sensor network; Step S2: Based on the theoretical trajectory constructed from the BIM model, the real-time deviation and deviation change rate of the steel box girder in the horizontal, longitudinal and vertical directions are calculated using a spatiotemporal coupling algorithm; Step S3: Input the data obtained in step S2 into the decision system based on digital twin technology to generate a three-dimensional coordinated correction scheme that includes the action sequence, stroke amount and force value parameters of each jacking unit; Step S4: Execute correction actions based on correction priorities, and simultaneously monitor the stress and strain state of the steel box girder; the priority order is "vertical leveling → lateral centering → longitudinal positioning"; Step S5: Continuously correct the decision system parameters through real-time feedback data to achieve dynamic improvement in correction accuracy; Step S6: Adaptive Learning: Collect actual effect data for each correction, and optimize the parameter weights of the decision model through reinforcement learning algorithms, so that the system's accuracy improves by no less than 30% after accumulating 100 corrections. This method is suitable for the jacking construction of various steel box girder structures such as long-span steel box girder bridges and curved steel box girder bridges, and can especially meet the requirements of high-precision and high-safety jacking operations.

[0009] Furthermore, the distributed sensing network adopts a multi-source fusion sensing network of fiber optic grating sensors, BeiDou positioning, and inertial measurement units, with a sampling frequency of not less than 100Hz and a position monitoring accuracy of ±2mm.

[0010] Furthermore, the multi-source fusion sensing network includes an abnormal data self-diagnosis mechanism, which is as follows: when a type of sensor data exceeds the confidence interval N times consecutively, it automatically switches to the redundant sensing channel and triggers a device maintenance warning, where N is a preset value.

[0011] Furthermore, the spatiotemporal coupling algorithm includes a temperature field compensation algorithm, which involves establishing a temperature deformation curve for the steel box girder and real-time correcting the theoretical trajectory parameters under different working conditions.

[0012] Furthermore, the decision system is used to simulate the attitude changes of the steel box girder under different correction schemes and automatically avoid schemes that may cause the structural stress to exceed the limit. The simulation time is no more than 10 seconds. The decision system has a built-in mechanical calculation module for the steel box girder jacking process. The mechanical calculation module is used to output the maximum stress value and position of the beam in real time during the correction process. When the stress value exceeds the set threshold, the correction rate is automatically adjusted.

[0013] Furthermore, the jacking unit adopts electro-hydraulic servo synchronous control, the action time difference between adjacent jacking units is controlled within 50ms, and the single-step correction amount is steplessly adjustable within the range of 0.1-10mm.

[0014] Furthermore, it also includes emergency intervention steps, which are as follows: The safety protection procedure will be activated immediately upon detection of any of the following: The instantaneous deviation change rate of the steel box girder exceeds 5 mm / s; The working pressure of the jacking device exceeds 110% of the rated value; The stress in the beam reaches 90% of the design yield strength; The safety protection procedures include unloading the thrust in stages, locking all actuators, generating an emergency response plan, and issuing audible and visual alarms.

[0015] Furthermore, three levels of precision control targets are set: initial stage control deviation ≤20mm, intermediate stage control deviation ≤10mm, and positioning stage control deviation ≤5mm, with differentiated PID control parameters used in each stage.

[0016] Furthermore, step S3 also includes a curved bridge jacking planning step, which is as follows: based on the curvature radius adaptation algorithm, the stroke difference between the jacking units on both sides is automatically adjusted according to the curve position of the beam, and the beam is smoothly advanced along the trajectory curve.

[0017] As a second aspect of the present invention, the present invention provides a three-dimensional walking-type jacking system for steel box girders, the system being used to implement the correction method described in any one of the first aspects, the system comprising: The distributed sensor network module is used to collect the three-dimensional position parameters of the steel box girder, the working status parameters of the jacking device, and the environmental impact parameters in real time. The deviation analysis module, based on the theoretical trajectory constructed from the BIM model and the spatiotemporal coupling algorithm, calculates the real-time deviation and deviation rate of the steel box girder in the transverse, longitudinal, and vertical directions. The intelligent decision-making module generates a three-dimensional collaborative correction scheme based on the decision-making system, which includes the action sequence, stroke amount and force value parameters of each jacking unit, and performs safety simulation. The graded execution module includes multiple electro-hydraulic servo-controlled push units, which are used to execute correction actions according to the set priority and correction scheme; The feedback optimization module is used to correct the parameters of the decision model in a closed loop based on real-time feedback data, thereby achieving dynamic improvement in accuracy. The adaptive learning module is used to optimize the decision model through reinforcement learning and federated learning algorithms.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention employs a multi-source fusion sensing scheme, combined with an abnormal data self-diagnosis mechanism, to achieve real-time and accurate monitoring of the three-dimensional position of the steel box girder, the working status of the jacking device, and environmental parameters, providing a reliable data foundation for deviation analysis. The decision-making system, which integrates digital twin technology, can quickly simulate the effect of the correction scheme with a pre-simulation time of no more than 10 seconds, automatically avoid stress exceeding the limit scheme, and the jacking unit adopts electro-hydraulic servo synchronous control with an action time difference of ≤50ms between adjacent units. The single-step correction amount can be steplessly adjusted, and combined with three-level precision closed-loop optimization, it achieves the precision control target at different stages. The adaptive learning architecture, which combines reinforcement learning and federated learning, improves the accuracy by no less than 30% after 100 corrections. It can also aggregate anonymized data from multiple projects to train the model, protecting data privacy. At the same time, it optimizes the model for different working conditions (high temperature, seismic zones, etc.) to improve system adaptability. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the correction process of the three-dimensional step-by-step jacking correction method for steel box girders of the present invention. Figure 2 This is a flowchart illustrating the emergency intervention and safety protection process for the three-dimensional step-by-step jacking correction method for steel box girders of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: As the first embodiment of this invention, this invention provides a three-dimensional walking-type jacking correction method for steel box girders to solve the problems of low accuracy, poor safety, and insufficient adaptability in existing steel box girder jacking construction. This invention integrates multi-source sensing, spatiotemporal coupling algorithms, digital twin decision-making, hierarchical servo control, and adaptive architecture, coupled with an anomaly emergency protection mechanism, to achieve real-time accurate monitoring, scientific deviation analysis, intelligent safety correction, and efficient control of steel box girder jacking, ensuring construction safety and accuracy. The method includes the following steps: Step S1: Collect the three-dimensional position parameters of the steel box girder, the working status parameters of the jacking device, and the environmental impact parameters in real time through a distributed sensor network; Step S2: Based on the theoretical trajectory constructed from the BIM model, the real-time deviation and deviation change rate of the steel box girder in the horizontal, longitudinal and vertical directions are calculated using a spatiotemporal coupling algorithm; Step S3: Input the data obtained in step S2 into the decision system based on digital twin technology to generate a three-dimensional coordinated correction scheme that includes the action sequence, stroke amount and force value parameters of each jacking unit; Step S4: Execute correction actions based on correction priority, and simultaneously monitor the stress and strain state of the steel box girder; Step S5: Continuously correct the decision system parameters through real-time feedback data to achieve dynamic improvement in correction accuracy; Step S6: Collect the actual effect data of each correction, and optimize the parameter weights of the decision model through reinforcement learning algorithm so that the system accuracy improves by no less than 30% after accumulating 100 corrections.

[0022] Example 2: like Figure 1 As shown, as the second embodiment provided by the present invention, the present invention is a three-dimensional step-by-step jacking correction method for steel box girders, comprising the following steps: Step S1: Dynamic monitoring: Real-time acquisition of the three-dimensional position parameters of the steel box girder, the working status parameters of the jacking device, and environmental impact parameters through a distributed sensor network; Step S2: Deviation Analysis: Based on the theoretical trajectory constructed from the BIM model, a spatiotemporal coupling algorithm is used to calculate the real-time deviation and deviation rate of the steel box girder in the transverse, longitudinal, and vertical directions; Step S3: Intelligent Planning: Input the deviation data into the decision system that integrates digital twin technology to generate a three-dimensional collaborative deviation correction scheme that includes the action sequence, stroke amount and force value parameters of each jacking unit; Step S4: Graded execution: Perform the correction actions in the priority order of "vertical leveling → horizontal centering → longitudinal positioning", and monitor the stress and strain status of the steel box girder simultaneously; Step S5: Closed-loop optimization: Continuously correct the decision model parameters through real-time feedback data to achieve dynamic improvement in correction accuracy; Step S6: Adaptive Learning: Collect actual effect data of each correction, and optimize the parameter weights of the decision model through reinforcement learning algorithm; adopt an adaptive learning architecture that combines reinforcement learning and federated learning, and improve the accuracy by no less than 30% after accumulating 100 corrections. It can also aggregate anonymized data from multiple projects to train the model, protect data privacy, and optimize the model for different working conditions (high temperature, earthquake zone, etc.) to improve the system's adaptability.

[0023] As an embodiment of the present invention, preferably, the distributed sensing network adopts a multi-source fusion sensing network of fiber optic grating sensors, BeiDou positioning, and inertial measurement units, with a sampling frequency of not less than 100Hz and a position monitoring accuracy of ±2mm. By employing a multi-source fusion sensing scheme of "fiber optic grating sensors + BeiDou positioning + inertial measurement units," combined with an abnormal data self-diagnosis mechanism, real-time and accurate monitoring of the three-dimensional position of the steel box girder, the working status of the jacking device, and environmental parameters is achieved. The sampling frequency is not less than 100Hz, and the position monitoring accuracy reaches ±2mm, providing a reliable data foundation for deviation analysis.

[0024] As an embodiment of the present invention, preferably, the multi-source fusion sensing network includes an abnormal data self-diagnosis mechanism. The abnormal data self-diagnosis mechanism is as follows: when a type of sensor data exceeds the confidence interval N times consecutively, it automatically switches to the redundant sensing channel and triggers the equipment maintenance warning. N is a preset value. For example, when a type of sensor data exceeds the confidence interval 5 times consecutively, it automatically switches to the redundant sensing channel and triggers the equipment maintenance warning.

[0025] As an embodiment of the present invention, preferably, the spatiotemporal coupling algorithm includes a temperature field compensation algorithm. The temperature field compensation algorithm is as follows: by establishing the temperature deformation curve of the steel box girder, the theoretical trajectory parameters under different working conditions are corrected in real time. The spatiotemporal coupling algorithm includes a temperature field compensation module, which can correct the influence of temperature deformation on the theoretical trajectory in real time. At the same time, it can make special corrections for different working conditions (such as curved bridges, high temperature, and seismic zones) to ensure accurate deviation calculation and avoid misjudgment.

[0026] As an embodiment of the present invention, preferably, the decision system is used to simulate the attitude changes of the steel box girder under different correction schemes and automatically avoid schemes that may cause structural stress to exceed limits. The pre-simulation time does not exceed 10 seconds. The decision system has a built-in mechanical calculation module for the steel box girder jacking process. The mechanical calculation module is used to output the maximum stress value and position of the beam during the correction process in real time. When the stress value exceeds a set threshold, the correction rate is automatically adjusted. The decision system integrating digital twin technology can quickly simulate the effect of the correction scheme, with a pre-simulation time of no more than 10 seconds. It automatically avoids schemes that exceed stress limits and has a built-in mechanical calculation module to monitor the stress of the beam in real time. When the stress exceeds the limit, the correction rate is automatically adjusted to ensure structural safety. A curvature radius adaptation algorithm is developed for curved bridges to ensure that the beam advances smoothly along the design curve.

[0027] As an embodiment of the present invention, preferably, the jacking unit adopts electro-hydraulic servo synchronous control, the action time difference between adjacent jacking units is controlled within 50ms, and the single-step correction amount is steplessly adjustable within the range of 0.1-10mm.

[0028] Example 3: like Figure 1-2As shown in Embodiment 2, as an embodiment provided by the present invention, preferably, it also includes an emergency intervention step, which is: when any of the following situations are detected, the safety protection procedure is immediately activated: The instantaneous deviation change rate of the steel box girder exceeds 5 mm / s; The working pressure of the jacking device exceeds 110% of the rated value; The stress in the beam reaches 90% of the design yield strength; The safety protection procedures include unloading the jacking force in stages, locking all actuators, generating an emergency response plan, and issuing audible and visual alarms. A comprehensive emergency intervention mechanism is in place. When situations such as the instantaneous deviation rate of the steel box girder exceeds 5 mm / s, the pressure of the jacking device exceeds the rated value by 110%, or the stress of the beam reaches 90% of the design yield strength occur, the safety protection procedures will be activated immediately to ensure construction safety.

[0029] As an embodiment of the present invention, preferably, in the closed-loop optimization step, the steps are executed in order of priority: "vertical leveling → horizontal centering → longitudinal positioning". The pushing unit adopts electro-hydraulic servo synchronous control, the time difference between adjacent unit actions is ≤50ms, and the single-step correction amount can be steplessly adjusted. Combined with three-level precision closed-loop optimization, the precision control targets at different stages are achieved. The three-level precision control targets are set as follows: initial stage control deviation ≤20mm, intermediate stage control deviation ≤10mm, and positioning stage control deviation ≤5mm. Differentiated PID control parameters are used for each stage.

[0030] As an embodiment of the present invention, preferably, step S3 further includes a curved bridge jacking planning step, which is as follows: based on the curvature radius adaptation algorithm, the stroke difference between the jacking units on both sides is automatically adjusted according to the curve position of the beam, and the beam is smoothly advanced along the trajectory curve.

[0031] Example 4: As a fourth embodiment of the present invention, the present invention provides a three-dimensional walking-type jacking system for steel box girders. The system is used to implement the correction method described in any one of embodiments one through three. The system includes: The distributed sensor network module is used to collect the three-dimensional position parameters of the steel box girder, the working status parameters of the jacking device, and the environmental impact parameters in real time. The deviation analysis module, based on the theoretical trajectory constructed from the BIM model and the spatiotemporal coupling algorithm, calculates the real-time deviation and deviation rate of the steel box girder in the transverse, longitudinal, and vertical directions. The intelligent decision-making module generates a three-dimensional collaborative correction scheme based on the decision-making system, which includes the action sequence, stroke amount and force value parameters of each jacking unit, and performs safety simulation. The graded execution module includes multiple electro-hydraulic servo-controlled push units, which are used to execute correction actions according to the set priority and correction scheme; The feedback optimization module is used to correct the parameters of the decision model in a closed loop based on real-time feedback data, thereby achieving dynamic improvement in accuracy. The adaptive learning module is used to optimize the decision model through reinforcement learning and federated learning algorithms.

[0032] Example 5: This application provides a three-dimensional step-by-step jacking correction method for steel box girders, such as for the jacking construction of long-span straight steel box girder bridges: This embodiment is applied to a long-span straight steel box girder bridge across a river. The bridge span is 150m, the self-weight of the steel box girder is 2000t, and the ambient temperature range during construction is -5℃ to 35℃. 1. Dynamic monitoring deployment Fiber grating sensors are deployed at key sections of the steel box girder to collect stress and strain data; a Beidou positioning terminal is deployed every 30m on the top surface of the steel box girder to obtain three-dimensional position parameters; and inertial measurement units are set at both ends of the steel box girder to collect attitude angle and acceleration data. The sampling frequency of the distributed sensor network is set to 120Hz, and the number of redundant sensor channels is 2 sets to ensure the continuity and reliability of data acquisition.

[0033] When the data from the BeiDou positioning terminal exceeds the ±2mm confidence interval five times consecutively during a construction operation, the system automatically switches to the backup BeiDou positioning terminal and sends equipment maintenance warning information to the management personnel. 2. Deviation Analysis and Temperature Compensation Based on the BIM model of the steel box girder, a theoretical launching trajectory was constructed.

[0034] Temperature sensors are used to collect real-time temperature data of the steel box girder's surface and interior, establishing a temperature deformation curve. A spatiotemporal coupling algorithm then corrects the theoretical trajectory parameters based on this curve. For example, during periods of high summer temperatures, the steel box girder elongates longitudinally due to temperature increases. The spatiotemporal coupling algorithm uses a temperature compensation module to correct the longitudinal parameters of the theoretical trajectory, preventing misjudgments caused by temperature deformation. 3. Intelligent Planning and Digital Twin Simulation The deviation data acquired through dynamic monitoring is input into the digital twin decision-making system. The system calls its built-in mechanical calculation module to simulate the attitude changes and stress distribution of the steel box girder under different correction schemes, with a simulation time of 8 seconds. For the case where the initial stage deviation is 18mm, the system generates a correction scheme: control each jacking unit to first perform vertical leveling, adjusting the stroke by 2mm, then perform lateral centering, adjusting the stroke by 1.5mm, and finally perform longitudinal positioning, adjusting the stroke by 3mm. Simultaneously, the simulation shows that the maximum stress value of the beam under this scheme is 75% of the design value, which meets the safety requirements. 4. Tiered Execution and Closed-Loop Optimization Each jacking unit employs electro-hydraulic servo synchronous control, executing correction actions in the sequence of "vertical leveling → lateral centering → longitudinal positioning." The time difference between adjacent unit actions is controlled within 40ms, and the single-step correction amount is adjusted within the range of 0.5-8mm based on the deviation. In the initial stage, PID control parameters are corrected using real-time feedback data to keep the deviation within 18mm. In the intermediate stage, PID parameters are adjusted to further reduce the deviation to 9mm. By the positioning stage, the deviation is controlled within 4mm, meeting the three-level precision control target. 5. Adaptive learning and emergency intervention During construction, data on the actual effects of each correction were collected, and the decision model parameters were optimized using reinforcement learning algorithms. After 100 corrections, the system accuracy improved by 35% compared to the initial state. In one jacking operation, the instantaneous deviation rate of the steel box girder was detected to reach 5.2 mm / s. The system immediately activated the safety protection program, gradually unloading the jacking force, locking all actuators, and issuing audible and visual alarms. At the same time, an emergency response plan was generated to guide the staff in handling the situation and prevent accidents. Example 6: Small-radius curved steel box girder bridge launching construction: This embodiment is applied to a small-radius curved steel box girder bridge in a city. The curve radius is 80m, the bridge span is 60m, the self-weight of the steel box girder is 800t, and the construction area is surrounded by dense buildings. 1. Dynamic monitoring and curve adaptation preparation In addition to arranging the distributed sensor network as described in Example 5, displacement sensors are additionally installed on both sides of the steel box girder to monitor lateral offset in real time. Considering the characteristics of curved bridges, bridge curve design parameters are imported into the digital twin system to provide basic data for the curvature radius adaptation algorithm. 2. Deviation analysis and curvature radius adaptation When calculating deviations, the spatiotemporal coupling algorithm considers the curvature characteristics of the curved bridge, focusing on the impact of lateral deviations on the beam's advancement along the curve. Simultaneously, the curvature radius adaptation algorithm calculates the stroke difference between the two jacking units in real time based on the steel box girder's position on the curve. For example, when the steel box girder is at the midpoint of the curve, the algorithm calculates that the inner jacking unit's stroke needs to be 5mm less than the outer jacking unit's to ensure the beam advances along the designed curve. 3. Intelligent planning and stress control When generating correction schemes, the digital twin system, in addition to simulating attitude changes, focuses on the stress concentration of the beam during curve advancement. When simulating a particular correction scheme, it was found that the stress value inside the curved section of the beam reached 82% of the design value. The system automatically adjusted the correction rate, reducing it from the planned 2 mm / s to 1.5 mm / s. After resimulating, the stress value decreased to 78% of the design value, meeting safety requirements, and the correction scheme was ultimately determined. 4. Tiered Implementation and Precise Control During the tiered execution process, due to the higher requirements for lateral alignment during the jacking of curved bridges, the precision control of the lateral alignment action was appropriately increased to keep the lateral deviation within 3mm. Each jacking unit strictly followed the stroke difference calculated by the curvature radius adaptation algorithm, and the time difference between adjacent unit actions was controlled within 35ms to ensure the smooth advancement of the beam along the curve. 5. Adaptive learning and data privacy protection A federated learning architecture was adopted to aggregate the anonymized and corrected data from this project with data from other similar small-radius curved bridge projects, thereby training and optimizing the decision-making model. While protecting the data privacy of each project, the system's adaptability to the small-radius curved bridge jacking scenario was significantly improved, with an accuracy improvement of 32% after 100 corrections. Example 7: Construction of steel box girder bridge by jacking under high temperature conditions: This embodiment is applied to a steel box girder bridge in a high-temperature environment in a desert region. The construction environment temperature can reach up to 50℃, the day-night temperature difference can reach 25℃, the bridge span is 120m, and the self-weight of the steel box girder is 1500t. 1. Dynamic monitoring and enhanced temperature data acquisition Multiple temperature sensors were deployed at different depths of the steel box girder, with the sampling frequency increased to 150Hz, to comprehensively acquire the temperature distribution of the steel box girder. The fiber optic grating sensors in the distributed sensor network were coated with a high-temperature adaptability layer to ensure normal operation in high-temperature environments, maintaining a sampling frequency above 100Hz and a stable position monitoring accuracy of ±2mm. 2. Deviation Analysis and Temperature Field Compensation Optimization Based on a large amount of temperature data, the temperature field compensation module is optimized to establish a more accurate temperature deformation curve for the steel box girder. The spatiotemporal coupling algorithm corrects the theoretical trajectory parameters in different regions according to the real-time temperature distribution. For example, during the midday high-temperature period, the temperature at the top of the steel box girder is higher than that at the bottom, causing the girder to arch upwards. The temperature field compensation module corrects the longitudinal and vertical theoretical trajectory parameters separately for the temperature difference between the top and bottom, avoiding misjudgments caused by temperature differences. 3. Intelligent planning and high-temperature operating condition simulation When simulating correction schemes, the digital twin system incorporates parameters related to material property changes under high-temperature conditions, such as the elastic modulus of the steel box girder as a function of temperature, making the mechanical calculations more realistic. When simulating a specific correction scheme, the system considers the impact of high temperatures on the strength of the steel box girder and adjusts the stress safety threshold from 80% to 75% of the design value to ensure the structural safety of the girder under high-temperature conditions. 4. Graded implementation and high-temperature protection of equipment During the tiered execution process, a cooling device is installed in the hydraulic system of the jacking device to prevent the hydraulic oil viscosity from decreasing due to high temperatures, which would affect control accuracy. Simultaneously, the continuous working time of the jacking unit is appropriately reduced to avoid overheating and damage to the equipment. The time difference between adjacent unit actions is controlled within 45ms, and the single-step correction amount is controlled within the range of 0.3-6mm based on the beam deformation characteristics under high temperatures, ensuring stable and reliable correction actions. 5. Adaptive learning and high-temperature model optimization By collecting correction data under high-temperature environments, the weights of temperature-related parameters in the decision-making model are optimized using reinforcement learning algorithms. After 100 corrections, the system's correction accuracy under high-temperature environments improves by 38%, enabling it to respond more quickly and accurately to deviations caused by high temperatures. Example 8: Incremental launching construction of steel box girder bridges in high-intensity earthquake zones This embodiment is applied to a steel box girder bridge in a high-intensity earthquake zone. The seismic fortification intensity is 8 degrees, the bridge span is 100m, and the self-weight of the steel box girder is 1200t. The impact of aftershocks needs to be considered during construction. 1. Supplement to dynamic monitoring and earthquake monitoring Based on the distributed sensor network, a seismic acceleration sensor is added to monitor the seismic motion parameters of the construction area in real time. When an aftershock is detected, the sampling frequency is increased to 200Hz to enhance the monitoring of the three-dimensional position and stress-strain of the steel box girder, ensuring timely capture of deviations caused by the earthquake. 2. Deviation Analysis and Earthquake Impact Correction The spatiotemporal coupling algorithm incorporates a seismic motion impact correction module. Based on data collected by seismic acceleration sensors, it analyzes the impact of earthquake aftershocks on the jacking deviation of the steel box girder, distinguishes between deviations caused by earthquakes and normal deviations during construction, and avoids including earthquake deviations in the routine correction range, which could lead to over-correction. 3. Intelligent planning and earthquake emergency simulation The digital twin system incorporates an earthquake emergency simulation module, capable of simulating the attitude changes and stress response of steel box girders under aftershocks of varying intensities. While generating conventional correction schemes, it also pre-determines emergency correction plans for aftershocks. For example, if seismic acceleration exceeds 0.2g, the pre-set emergency correction scheme is automatically activated to quickly adjust the steel box girder to a stable attitude. 4. Tiered Implementation and Earthquake Emergency Response During the tiered execution process, earthquake emergency response trigger conditions are set. When the earthquake acceleration sensor detects that the acceleration value exceeds the preset threshold, the routine correction actions are immediately suspended, and the emergency response procedure is initiated. If the steel box girder deviates significantly due to the earthquake, vertical leveling is prioritized according to the emergency correction plan to ensure the stability of the girder, followed by gradual lateral centering and longitudinal positioning. 5. Adaptive learning and seismic model training Employing a federated learning architecture, the system aggregates anonymized data from other steel box girder jacking projects in high-intensity earthquake zones to train and optimize the seismic-related parameters of the decision-making model. After 100 corrections, the system's correction accuracy under aftershock conditions improves by 34%, enabling it to more effectively address the impact of aftershocks on the jacking construction and ensure construction safety.

[0035] Examples 5 to 8, four examples, are compared and analyzed as follows: I. Application Scenarios: Covering diverse and complex working conditions, and specifically matching project requirements. The four examples each focus on different typical construction scenarios, forming a comprehensive coverage of technical applications. Example 5 targets long-span straight bridges (span 150m, self-weight 2000t), focusing on solving the problem of accuracy accumulation during long-distance jacking, and is suitable for construction in open areas such as across rivers; Example 6 targets small-radius curved bridges (radius 80m, span 60m), focusing on overcoming the lateral control problem of curved advancement, and adapting to the construction restrictions in densely built-up urban areas; Example 7 addresses extreme high-temperature environments (maximum 50℃, diurnal temperature range 25℃), solving the interference of temperature deformation on jacking accuracy, and can be used in projects in desert and tropical regions; Example 8 focuses on high-intensity earthquake zones (8-degree seismic fortification), strengthening earthquake emergency response capabilities, and meeting the construction safety needs of areas with frequent aftershocks.

[0036] In terms of scenario parameters, the steel box girders in the four embodiments cover a range of small to large spans in terms of self-weight (800t-2000t) and span (60m-150m), and the environmental conditions cover conventional, extreme temperatures and earthquake risks, which can be adapted to most steel box girder launching construction scenarios.

[0037] As one embodiment of the present invention, preferably, all four embodiments are based on the core technical framework of "dynamic monitoring-deviation analysis-intelligent planning", but targeted adjustments have been made according to the needs of the scenario, reflecting the flexibility of the technical system.

[0038] As an embodiment of the present invention, preferably, the present invention provides dynamic monitoring with unified basic configuration and supplementary configuration for special needs; In terms of commonalities, all adopt a multi-source fusion scheme of "fiber optic grating sensor + BeiDou positioning + inertial measurement unit" to ensure position monitoring accuracy of ±2mm. The differences are as follows: Example 6 adds displacement sensors on both sides of the curved bridge to enhance lateral offset monitoring; Example 7 adds multiple sets of depth temperature sensors (sampling frequency 150Hz) for high-temperature environments and adds a high-temperature coating to the fiber optic grating sensor; Example 8 adds a seismic acceleration sensor in the seismic zone, and increases the sampling frequency to 200Hz during aftershocks to achieve rapid risk perception.

[0039] As an embodiment of the present invention, preferably, the deviation analysis of the present invention features: unified core algorithm and supplemented scene module; The spatiotemporal coupling algorithm is the common basis of the four embodiments, with the difference being the addition of modules: Embodiments 5 and 7 focus on temperature field compensation (Embodiment 7 is further optimized to regional correction) to solve temperature deformation interference; Embodiment 6 adds a curvature radius adaptation algorithm to calculate the difference in pushing stroke on both sides in real time (e.g., the inner side of the curve midpoint is 5mm less than the outer side); Embodiment 8 adds a seismic motion influence correction module to avoid misjudging seismic deviation as construction deviation.

[0040] As an embodiment of the present invention, preferably, the intelligent planning of the present invention is based on digital twins and differentiated scene parameters; All four embodiments simulate the correction scheme through a digital twin system (pre-simulation time ≤ 10 seconds), but the simulation focus is different: Embodiment 5 is a conventional stress simulation (maximum stress ≤ 75% of design value); Embodiment 6 focuses on simulating stress concentration in the curve segment to prevent the inner stress from exceeding the limit; Embodiment 7 adds high-temperature performance parameters of materials (such as elastic modulus curve) and lowers the stress threshold to 75%; Embodiment 8 has a built-in earthquake emergency simulation module and presets an emergency plan when the acceleration is > 0.2g.

[0041] As an embodiment of the present invention, preferably, the key control indicators of the present invention are: unified accuracy standards and execution details adapted to different scenarios; (a) Synchronization control: Strict time difference control and adjustment to adapt to different scenarios. All four embodiments employ electro-hydraulic servo synchronous control, with the time difference between adjacent unit actions ≤50ms (35ms for Embodiment Six and 45ms for Embodiment Seven), ensuring smooth jacking. The single-step correction amount is adjusted according to the scenario: 0.5-8mm (normal range) for Embodiments Five and Six; reduced to 0.3-6mm for Embodiment Seven due to its sensitivity to high-temperature deformation; while Embodiment Eight, although within the normal range, emphasizes "prioritizing vertical leveling" to ensure beam stability under earthquake aftershocks.

[0042] (II) Accuracy Target: Unified three-level control, fine-tuning for scenario adaptation All adhere to the three-level accuracy target of "initial ≤20mm, intermediate ≤10mm, and positioning ≤5mm". In actual implementation, there are slight differences due to different scenarios: the positioning deviations of Examples 5 and 7 are 4mm and 4mm respectively, Example 8 is 4.5mm (reasonable control under the influence of earthquake), and Example 6 is further reinforced with lateral deviation ≤3mm (core requirement of curved bridge).

[0043] (III) Emergency Triggering: Common Standards + Scenario Supplements Common emergency conditions (deviation change rate > 5 mm / s, pressure exceeding 110%, stress reaching 90%) are all applicable. Example 8 adds an additional trigger condition of "earthquake acceleration > 0.2g". Example 7 actively prevents equipment failure through "hydraulic system plus cooling device", reflecting a scenario-based risk control approach.

[0044] As an embodiment of the present invention, preferably, the present invention features adaptive learning: common architecture + scenario-based optimization, achieving the target accuracy improvement; All four embodiments achieved adaptive optimization through "reinforcement learning + federated learning". After a total of 100 corrections, the accuracy improvement was ≥30% (35% for Embodiment 5, 32% for Embodiment 6, 38% for Embodiment 7, and 34% for Embodiment 8), which meets the technical requirements.

[0045] The differences lie in the focus of optimization: Example 5 focuses on optimizing conventional parameters, Example 6 aggregates "curved bridge data" to optimize lateral control, Example 7 focuses on adjusting the "weight of temperature-related parameters", and Example 8 focuses on training "seismic parameters" to achieve accuracy improvement through "data-driven + scenario adaptation", which ensures general performance while strengthening scenario specificity.

[0046] As one embodiment of the present invention, preferably, the technical system of the present invention is universal and flexible in adapting to different scenarios; Four embodiments verify the versatility of the technical system of the present invention. Whether it is a large span, a curve, a high temperature, or a seismic zone, high-precision and high-safety jacking can be achieved through the combination of "basic framework + scenario module". At the same time, the differentiated design of each embodiment (such as curvature adaptation, high temperature protection, and earthquake emergency response) also reflects the technology's flexible adaptability to complex working conditions, and can provide standardized and customized solutions for the jacking construction of different types of steel box girders.

[0047] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A three-dimensional step-by-step jacking method for correcting the deviation of a steel box girder, characterized in that, Includes the following steps: Step S1: Collect the three-dimensional position parameters of the steel box girder, the working status parameters of the jacking device, and the environmental impact parameters in real time through a distributed sensor network; Step S2: Based on the theoretical trajectory constructed from the BIM model, the real-time deviation and deviation change rate of the steel box girder in the horizontal, longitudinal and vertical directions are calculated using a spatiotemporal coupling algorithm; Step S3: Input the data obtained in step S2 into the decision system based on digital twin technology to generate a three-dimensional coordinated correction scheme that includes the action sequence, stroke amount and force value parameters of each jacking unit; Step S4: Execute correction actions based on correction priority, and simultaneously monitor the stress and strain state of the steel box girder; Step S5: Continuously adjust the decision system parameters using real-time feedback data; Step S6: Collect the actual effect data of each correction, and optimize the parameter weights of the decision model through reinforcement learning algorithm.

2. The method for correcting deviation of a steel box girder by three-way stepping jacking according to claim 1, characterized in that, The distributed sensing network adopts a multi-source fusion sensing network of fiber optic grating sensors, BeiDou positioning, and inertial measurement units, with a sampling frequency of not less than 100Hz and a position monitoring accuracy of ±2mm.

3. The method for correcting deviation of a steel box girder using a three-dimensional stepping jacking method according to claim 2, characterized in that, The multi-source fusion sensing network includes an abnormal data self-diagnosis mechanism. The abnormal data self-diagnosis mechanism is as follows: when a type of sensor data exceeds the confidence interval N times consecutively, it automatically switches to the redundant sensing channel and triggers a device maintenance warning, where N is a preset value.

4. The method for correcting deviation of a steel box girder by three-way stepping jacking according to claim 1, characterized in that, The spatiotemporal coupling algorithm includes a temperature field compensation algorithm, which involves establishing a temperature deformation curve for the steel box girder and real-time correcting the theoretical trajectory parameters under different working conditions.

5. The method for correcting deviation of a steel box girder by three-way stepping jacking according to claim 1, characterized in that, The decision system is used to simulate the attitude changes of steel box girders under different correction schemes and automatically avoid schemes that may cause structural stress to exceed limits. The simulation time is no more than 10 seconds. The decision system has a built-in mechanical calculation module for the steel box girder jacking process. The mechanical calculation module is used to output the maximum stress value and position of the beam in real time during the correction process. When the stress value exceeds the set threshold, the correction rate is automatically adjusted.

6. The method for correcting deviation of a steel box girder by three-way stepping jacking according to claim 5, characterized in that, The jacking unit adopts electro-hydraulic servo synchronous control, the action time difference between adjacent jacking units is controlled within 50ms, and the single-step correction amount is steplessly adjustable within the range of 0.1-10mm.

7. The method for correcting deviation of a steel box girder by three-way stepping jacking according to claim 1, characterized in that, It also includes emergency intervention steps, which are as follows: the safety protection procedure shall be activated immediately upon detection of any of the following: The instantaneous deviation change rate of the steel box girder exceeds 5 mm / s; The working pressure of the jacking device exceeds 110% of the rated value; The stress in the beam reaches 90% of the design yield strength; The safety protection procedures include unloading the thrust in stages, locking all actuators, generating an emergency response plan, and issuing audible and visual alarms.

8. The method for correcting deviation of a steel box girder by three-way stepping jacking according to claim 1, characterized in that, In step S5, when correcting the decision model parameters, three levels of precision control targets are set: initial stage control deviation ≤ 20mm, intermediate stage control deviation ≤ 10mm, and positioning stage control deviation ≤ 5mm.

9. The method for correcting deviation of a steel box girder by three-way stepping jacking according to claim 1, characterized in that, Step S3 also includes a curved bridge jacking planning step, which is as follows: based on the curvature radius adaptation algorithm, the stroke difference between the jacking units on both sides is automatically adjusted according to the curve position of the beam, and the beam is smoothly advanced along the trajectory curve.

10. A three-way walking-type jacking system for steel box girders, characterized in that, The system is used to implement the correction method according to any one of claims 1-9, and the system comprises: The distributed sensor network module is used to collect the three-dimensional position parameters of the steel box girder, the working status parameters of the jacking device, and the environmental impact parameters in real time. The deviation analysis module, based on the theoretical trajectory constructed from the BIM model and the spatiotemporal coupling algorithm, calculates the real-time deviation and deviation rate of the steel box girder in the transverse, longitudinal, and vertical directions. The intelligent decision-making module generates a three-dimensional collaborative correction scheme based on the decision-making system, which includes the action sequence, stroke amount and force value parameters of each jacking unit, and performs safety simulation. The graded execution module includes multiple electro-hydraulic servo-controlled push units, which are used to execute correction actions according to the set priority and correction scheme; The feedback optimization module is used to correct the parameters of the decision model in a closed loop based on real-time feedback data. The adaptive learning module is used to optimize the decision model through reinforcement learning and federated learning algorithms.

Images