Fabricated bridge intelligent construction management system and method based on digital twinning

Abstract

The invention relates to the technical field of bridge construction, in particular to an assembly type bridge intelligent construction management system and method based on digital twinning, and the method comprises the steps: constructing an assembly type bridge digital twinning body integrated with a plurality of physical models; performing construction simulation on the key process based on the digital twinborn body and outputting optimization parameters; collecting construction data in real time through a sensor and driving a physical model to calculate; comparing the calculation result with a preset threshold value to generate an adjustment instruction; executing the instruction and iteratively updating the model by using feedback data; by establishing a dynamic interaction mechanism between the digital twinborn body and the physical construction site, real-time monitoring, intelligent decision making and closed-loop control of the construction process are achieved, the problems that digital simulation and the physical process are isolated and dynamic feedback is lacked in traditional construction management are effectively solved, and the construction management efficiency is improved. And the construction quality control precision and the engineering safety management level are obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge construction, in particular to an intelligent construction management system and method for fabricated bridge based on digital twinning. BACKGROUND

[0002] In the field of fabricated bridge construction technology, the traditional construction management method is mainly based on static design drawings and predetermined construction organization scheme. This method relies on the pre-established finite element model for construction process simulation analysis, but such simulation analysis is often isolated from the actual construction process, and cannot realize dynamic interaction with the site construction state.

[0003] Due to the inability to obtain the actual pose state of the components in the construction process, the mechanical property changes of the connecting nodes, and the dynamic response characteristics of the structure system in real time, there is an information barrier between the construction management decision and the actual working condition on site. This static management mode is difficult to identify potential risks such as installation deviation, connection quality defects and structural dynamic abnormalities in the construction process in a timely manner, thereby affecting the accuracy of construction quality control and the effectiveness of engineering safety management. SUMMARY

[0004] The purpose of the present application is to provide an intelligent construction management system and method for fabricated bridge based on digital twinning, which solves the problem of being difficult to identify and respond to installation deviation, connection quality defects and structural dynamic abnormalities in the construction process in the traditional construction management method.

[0005] To achieve the above purpose, the present application provides an intelligent construction management method for fabricated bridge based on digital twinning, comprising the following steps: Constructing a digital twin of the fabricated bridge, the digital twin integrating a first physical model for simulating the mechanical properties of component nodes, a second physical model for simulating the time-varying effects of concrete structures, and a third physical model for simulating the dynamic response of the structure in the construction process; Based on the digital twin, at least one key process of pile foundation hole forming, component assembly and large component jacking is simulated in the construction process to predict construction disturbance, node stress and vibration response, and output optimized construction control parameters; Real-time acquisition of component pose data, node connection process data and structure response data by deployed sensors, and transmission of data to the digital twin to start the physical model for calculation; Comparing the calculation results of the physical model with the preset threshold, and generating adjustment instructions for the connection process, installation speed or hole forming parameters when the predicted performance indicators deviate; Executing the adjustment instructions to control the on-site equipment, and collecting the effect data after the execution of the instructions for iterative updating of the physical model parameters in the digital twin.

[0006] The specific step of performing construction process simulation on at least one key process of pile hole forming, component assembly and large component jacking based on the digital twin includes: Simulating stratum disturbance under different hole forming processes to optimize process schemes with hole forming accuracy and controlled settlement as targets; Simulating stress conduction and damage evolution of nodes in the assembly and stress process to verify the connection reliability; Simulate the dynamic behavior of large components in progressive installation, identify critical states and avoid resonance risk.

[0007] The specific step of comparing the calculation results of the physical model with the preset threshold and generating adjustment instructions for the connection process, installation speed or hole forming parameters when the predicted performance index deviates includes: Compare the node stress calculated by the first physical model with the allowable stress; Compare the vibration frequency and amplitude calculated by the third physical model with the safety threshold; Based on the comparison result, dynamically generate adjustment instructions for grouting pressure, jacking speed or hole forming process.

[0008] The specific step of executing the adjustment instructions to control the field equipment, and collecting the effect data after the instruction execution, for iterative updating of the physical model parameters in the digital twin includes: The generated adjustment instructions are sent to the corresponding grouting equipment, jacking equipment or pile construction equipment; Collect node stress, structure vibration and hole forming quality data after instruction execution as feedback; Use the feedback data to calibrate the constitutive relation or boundary condition parameters in the physical model.

[0009] The first physical model is a finite element model considering the bond-slip effect between steel bars and grouting material.

[0010] The analysis of the third physical model is based on the bending-torsional coupled vibration control equation derived by energy method.

[0011] It includes analysis of out-of-plane vibration modes of large components dominated by vertical displacement and dominated by torsional angle.

[0012] In another aspect, the present application also includes a digital-twin-based intelligent construction management system for fabricated bridges, comprising a modeling module, a simulation module, a monitoring module, an intelligent control module and an updating module, the modeling module being connected with the simulation module, the monitoring module and the updating module, the simulation module being connected with the intelligent control module, the monitoring module being connected with the intelligent control module and the updating module, and the intelligent control module being connected with field construction equipment. The modeling module is configured to construct and maintain a digital twin of the fabricated bridge integrated with a first physical model, a second physical model and a third physical model. The simulation module is configured to simulate processes and optimize parameters of pile hole forming, component assembly and large component jacking in the digital twin. The monitoring module is configured to collect component pose, node connection process and structural response data in real time through a sensor network and synchronize them to the digital twin. The intelligent control module is configured to generate and issue construction parameter adjustment instructions based on a comparison result of the physical model calculation result and a preset threshold value. The updating module is configured to calibrate and iterate the physical model parameters in the digital twin according to feedback data after instruction execution.

[0013] The digital-twin-based intelligent construction management system and method for fabricated bridges of the present application establish a deep fusion mechanism of digital space and physical site by constructing a digital twin integrated with multiple physical models, realize high-precision digital mapping of the entire construction process of fabricated bridges, and break through the limitations of traditional static simulation. Through dynamic comparison between real-time collected construction data and a preset threshold value, intelligent decision-making and closed-loop control of the construction process are realized, construction deviations can be found in time and optimization instructions can be automatically generated, effectively solving the information lag problem in traditional construction management. The physical model parameters in the digital twin are continuously iterated and updated through feedback data after instruction execution, so that the system has the ability of self-improvement and continuous optimization. Precise monitoring and control means are provided for key process links such as grouting sleeve connection, prestressed box girder stability and curve steel beam jacking vibration, significantly improving the reliability and consistency of construction quality. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced.

[0015] Figure 1 is a flowchart of the digital-twin-based intelligent construction management method for fabricated bridges of the present application.

[0016] Figure 2It is a structural schematic diagram of the digital-twin-based fabricated bridge intelligent construction management system of the application.

[0017] In the figure: 101 - modeling module, 102 - simulation module, 103 - monitoring module, 104 - intelligent control module, 105 - updating module. DETAILED DESCRIPTION

[0018] The embodiments of the application are described in detail below, examples of which are shown in the accompanying drawings, the embodiments described below by referring to the drawings are exemplary and are intended to explain the application, and cannot be understood as a limitation of the application.

[0019] First embodiment: Please refer to Figure 1 , wherein Figure 1 It is a flow chart of the digital-twin-based fabricated bridge intelligent construction management method.

[0020] The application provides a digital-twin-based fabricated bridge intelligent construction management method, comprising the following steps: S1: constructing a digital twin of a fabricated bridge, the digital twin integrating a first physical model for simulating mechanical properties of a component node, a second physical model for simulating time-varying effects of a concrete structure, and a third physical model for simulating structural dynamic responses in a construction process.

[0021] Specifically, the first physical model is a finite element model considering the bond-slip effect between the steel bar and the grouting material.

[0022] The analysis of the third physical model is based on a bending-torsional coupling vibration control equation derived by an energy method.

[0023] In the present embodiment, the construction of the digital twin is based on a unified BIM modeling platform, and a three-dimensional visual model containing full-factor information is formed by importing the component geometric information, material attribute parameters and construction process requirements of the fabricated bridge. The first physical model is realized by using the Midas-FEA finite element analysis module, and the nonlinear spring element between the steel bar surface and the grouting material is defined to accurately simulate the bond-slip behavior of the two in the stress process; the second physical model is based on the shrinkage and creep theory of concrete, and uses the age-adjusted effective modulus method to calculate the structural deformation and internal force redistribution under the time-varying effect by inputting parameters such as environmental temperature and humidity and concrete mix proportion; the third physical model is realized by programming in MATLAB, and the bending-torsional coupling vibration control equation derived by the energy method is converted into an eigenvalue solving problem, which can quickly calculate the vibration characteristics of the curved steel beam at different stages of pushing. The physical models interact with the BIM platform through the API interface, realizing the unified management of model parameters and the real-time visualization of calculation results.

[0024] S2: Based on the digital twin, at least one key process of pile hole forming, component assembly and large component jacking is simulated in the construction process, the construction disturbance, node stress and vibration response are predicted, and the optimized construction control parameters are output.

[0025] Specifically, S21: simulate the ground disturbance under different hole forming processes, and optimize the process scheme with hole forming accuracy and controlled settlement as the target; S22: simulate the stress conduction and damage evolution of nodes in the assembly and stress process, and verify the connection reliability; S23: simulate the dynamic behavior of large components in progressive installation, identify critical states and avoid resonance risk.

[0026] In simulating the dynamic behavior of large components in progressive installation: It includes analyzing the out-of-plane vibration modes of large components dominated by vertical displacement and dominated by torsional angle.

[0027] In this embodiment, for pile hole forming process simulation, a pile-soil interaction model is established by Plaxis 3D software to simulate the ground displacement field changes under different processes such as full casing static pressure, rotary drilling composite hole expansion, etc. Combined with the displacement monitoring data of adjacent structures, the key parameters such as casing pressure rate and mud specific gravity are dynamically optimized; for component assembly process simulation, a refined finite element model of grouting sleeve connection is established based on the ABAQUS platform, the interface behavior of steel and grouting material is simulated by setting contact properties, the stress distribution characteristics of the sleeve area under different load conditions are analyzed, and the optimal grouting pressure and pressure holding time are output; in large component jacking simulation, using the MATLAB calculation module developed independently, based on the real-time updated structure boundary conditions, the bending-torsional coupled vibration equation is solved, two typical out-of-plane vibration modes dominated by vertical displacement and dominated by torsional angle are identified, and the key factors affecting vibration characteristics are determined through parameter sensitivity analysis; all simulation results are transmitted to the digital twin platform in real time through the data interface, and compared with the preset safety threshold dynamically, when the predicted settlement, stress peak or vibration amplitude exceeds the limit value, the system automatically generates an optimization report containing process parameter adjustment suggestions.

[0028] S3: Real-time acquisition of component pose data, node connection process data and structure response data through deployed sensors, and transmission of data to the digital twin to start the physical model for calculation.

[0029] In the present embodiment, high-precision reflecting prisms are installed on the prefabricated components, and automatic tracking measurement is performed by a robotic total station instrument. The three-dimensional coordinates of the components during hoisting are collected at a frequency of 1 time per second to form a real-time pose data stream. Piezoelectric stress sensors and distributed optical fiber sensors are pre-embedded at the grouting sleeve connection parts to monitor the pressure changes during grouting and the stress development during the curing stage at a sampling frequency of 10 times per second. Three-axis acceleration sensors are arranged at key cross sections of the steel box girder to collect vibration signals during jacking at a sampling rate of 1000 Hz per second. In the pile foundation construction area, inclination sensors and pore water pressure gauges are arranged to monitor the verticality of the pile body and the stratum response in real time during hole forming. All sensors transmit real-time data to the cloud digital twin platform through the 5G industrial gateway deployed on site. After receiving the data, the platform first performs data preprocessing, including outlier rejection, noise filtering, and data standardization. Then, the processed data are input into the corresponding physical models for real-time calculation and analysis.

[0030] S4: Comparing the calculation results of the physical models with the preset threshold values, and generating adjustment instructions for the connection process, installation speed, or hole forming parameters when the predicted performance indicators deviate.

[0031] Specifically, S41: Comparing the node stress calculated by the first physical model with the allowable stress; S42: Comparing the vibration frequency and amplitude calculated by the third physical model with the safety threshold; S43: Based on the comparison results, dynamically generating adjustment instructions for the grouting pressure, jacking speed, or hole forming process.

[0032] In the present embodiment, the node stress calculated by the first physical model is compared with the allowable stress of the material in real time. When the maximum stress in the sleeve connection area exceeds 85% of the yield strength of the reinforcing steel, the grouting process adjustment program is triggered. At the same time, the system compares the vibration main frequency and amplitude calculated by the third physical model with the preset safety threshold, respectively. When the vibration main frequency is close to 0.8-1.2 times the natural frequency of the structure and the amplitude exceeds 80% of the limit value, the jacking speed optimization program is started. The system uses a fuzzy PID control algorithm to dynamically generate optimal adjustment instructions by considering multiple factors such as the current construction stage, environmental conditions, and equipment status. For the grouting process, the system automatically adjusts the grouting pressure in the range of 2.5-3.0 MPa based on the stress distribution characteristics, and the pressure stabilization time is controlled in the range of 30-45 minutes. For jacking construction, the jacking speed is dynamically adjusted in the range of 0.5-1.0 m / min based on the vibration characteristic analysis results. For pile foundation hole forming, the optimal hole forming process is automatically selected based on the stratum response data, and the drilling parameters are adjusted. All adjustment instructions are transmitted in real time to the control systems of the corresponding grouting equipment, jacking equipment, and pile foundation construction equipment through the industrial Internet of Things platform, and the instruction execution status is displayed synchronously on the digital twin platform.

[0033] S5: execute the adjustment instruction to control the field device, and collect the effect data after the instruction execution, which is used to iteratively update the physical model parameters in the digital twin.

[0034] In this embodiment, when the adjustment instruction is issued, the system adjusts the output pressure of the grouting device, the speed of the hydraulic system of the jacking device, and the drilling parameters of the pile foundation device in real time through the device controller. During the execution of the instruction, the system continuously collects the real-time pressure curve during grouting, the actual running speed curve of the jacking device, and the verticality change data of the pile foundation hole forming, forming a complete instruction execution effect data set; at the same time, the system collects the structural response data after the instruction execution through the deployed sensor network, including the stress relaxation data of the sleeve connection area, the vibration frequency spectrum change data of the steel box girder, and the stress redistribution data of the soil around the pile foundation. These data are used for parameter calibration of the physical model after preprocessing: for the first physical model, the bond-slip constitutive parameters between the reinforcement and the grouting material are inversed according to the measured stress-slip relationship curve; for the second physical model, the shrinkage and creep model parameters of the concrete are corrected by using the back analysis method based on the long-term monitored creep deformation data; for the third physical model, the stiffness coefficient and the damping coefficient in the bending-torsional coupled vibration control equation are optimized according to the difference between the measured vibration mode and the calculated mode; the model update uses an incremental learning algorithm, which only adjusts the parameters with significant deviations locally each time to ensure the continuity and stability of the model; the updated physical model will be used for prediction and control of the next round of construction process, forming a continuously optimized closed-loop management system.

[0035] The intelligent construction management method for fabricated bridge based on digital twinning can timely find quality deviations and safety hazards in the construction process through dynamic comparison based on real-time data acquisition and physical model calculation, automatically generate optimization instructions, and realize closed-loop control; it has continuous optimization learning ability, iteratively updates the physical model parameters by collecting feedback data after instruction execution and using an incremental learning algorithm, so that the system has self-improvement ability; it improves the accuracy of construction quality control, provides monitoring and control means for key process links such as grouting sleeve connection, prestressed box girder stability, and curved steel beam jacking vibration; it realizes whole-process traceability of the construction process, establishes a complete digital construction archive by correlatively storing the whole-process data from construction simulation to closed-loop control, and finally builds a complete intelligent construction management system, which realizes transparent management, intelligent decision-making, and dynamic control of the construction process through the deep integration of digital twinning technology and the construction site.

[0036] On the other hand, please refer to Figure 2 , Figure 2It is the structural schematic diagram of the prefabricated bridge intelligent construction management system based on digital twinning, and the prefabricated bridge intelligent construction management system based on digital twinning comprises a modeling module 101, a simulation module 102, a monitoring module 103, an intelligent control module 104 and an updating module 105, the modeling module 101 is connected with the simulation module 102, the monitoring module 103 and the updating module 105, the simulation module 102 is connected with the intelligent control module 104, the monitoring module 103 is connected with the intelligent control module 104 and the updating module 105, and the intelligent control module 104 is connected with field construction equipment; The modeling module 101 is used for constructing and maintaining the prefabricated bridge digital twin body integrated with the first physical model, the second physical model and the third physical model; The simulation module 102 is used for process simulation and parameter optimization of pile foundation hole forming, component assembly and large component jacking process in the digital twin body; The monitoring module 103 is used for collecting component pose, node connection process and structure response data in real time through a sensor network and synchronizing to the digital twin body; The intelligent control module 104 is used for generating and issuing construction parameter adjustment instructions based on the comparison result of the physical model calculation result and the preset threshold value; The updating module 105 is used for calibrating and iterating the physical model parameters in the digital twin body according to the feedback data after instruction execution.

[0037] In the embodiment, the modeling module 101 constructs the digital twin body containing multiple physical models, then the simulation module 102 performs process simulation on key construction processes based on the digital twin body, and outputs optimized construction control parameters to the intelligent control module 104; meanwhile, the monitoring module 103 collects field construction data in real time through a sensor network and transmits to the intelligent control module 104 and the updating module 105 respectively; the intelligent control module 104 compares and analyzes the received real-time data with the optimization parameters provided by the simulation module 102, generates construction adjustment instructions and issues to field construction equipment; the updating module 105 collects feedback data after instruction execution, calibrates and optimizes the physical model parameters in the digital twin body, and feeds back the updated model parameters to the modeling module 101; the modeling module 101 corrects the digital twin body according to the updated parameters, and starts a new round of construction process optimization cycle.

[0038] The above only discloses one or more preferred embodiments of the application, and cannot limit the scope of the application, and those skilled in the art can understand that all or part of the above-mentioned embodiments can be implemented, and equivalent changes made according to the claims of the application still belong to the scope covered by the application.

Claims

1. A digital-twin-based intelligent construction management method for a fabricated bridge, characterized in that, The method comprises the following steps: constructing a digital twin of the fabricated bridge, the digital twin integrating a first physical model for simulating mechanical properties of a component node, a second physical model for simulating time-varying effects of a concrete structure, and a third physical model for simulating structural dynamic responses in a construction process; based on the digital twin, simulating a construction process of at least one key technology among pile foundation hole forming, component assembly, and large component jacking, predicting construction disturbance, node stress, and vibration response, and outputting optimized construction control parameters; real-time collection of component pose data, node connection process data, and structural response data through deployed sensors, and transmission of the data to the digital twin to start the physical model for calculation; comparison of calculation results of the physical model with preset thresholds, generation of adjustment instructions for connection process, installation speed, or hole forming parameters when a predicted performance index deviates; execution of the adjustment instructions to control on-site equipment, and collection of effect data after execution of the instructions for iterative updating of physical model parameters in the digital twin.

2. The digital-twin-based intelligent construction management method for fabricated bridge according to claim 1, characterized in that, The specific steps of simulating a construction process of at least one key technology among pile foundation hole forming, component assembly, and large component jacking based on the digital twin include: simulation of stratum disturbance under different hole forming processes to optimize process schemes with hole forming accuracy and controlled settlement as targets; simulation of stress conduction and damage evolution of nodes in the assembly and stress process to verify the connection reliability; simulation of dynamic behavior of large components in progressive installation to identify critical states and avoid resonance risks. 3.The digital-twin-based intelligent construction management method for fabricated bridge according to claim 1, wherein, The specific steps of comparing calculation results of the physical model with preset thresholds, and generating adjustment instructions for connection process, installation speed, or hole forming parameters when a predicted performance index deviates include: comparison of node stress calculated by the first physical model with allowable stress; comparison of vibration frequency and amplitude calculated by the third physical model with safety thresholds; based on the comparison results, dynamically generating adjustment instructions for grouting pressure, jacking speed, or hole forming process. 4.The digital-twin-based intelligent construction management method for fabricated bridge according to claim 1, wherein, The specific steps of executing adjustment instructions to control on-site equipment, and collecting effect data after execution of the instructions for iterative updating of physical model parameters in the digital twin include: issuing the generated adjustment instructions to corresponding grouting equipment, jacking equipment, or pile foundation construction equipment; collecting node stress, structural vibration, and hole forming quality data after execution of the instructions as feedback; using the feedback data to calibrate constitutive relations or boundary condition parameters in the physical model.

5. The digital-twin-based intelligent construction management method for fabricated bridges according to claim 1, characterized in that: the first physical model is a finite element model considering the bond-slip effect between steel bars and grouting material.

6. The digital-twin-based intelligent construction management method for fabricated bridges according to claim 1, characterized in that: analysis of the third physical model is based on a bending-torsional coupled vibration control equation derived through an energy method.

7. The digital-twin-based intelligent construction management method for fabricated bridges according to claim 2, wherein, In simulating dynamic behavior of large components in progressive installation: The out-of-plane vibration modes dominated by vertical displacement and by torsional angle of the large component are included.

8. A digital-twin-based intelligent construction management system for fabricated bridges, configured to implement the digital-twin-based intelligent construction management method for fabricated bridges according to any one of claims 1-7, characterized in that, The modeling module, the simulation module, the monitoring module, the intelligent control module and the updating module are included, the modeling module is connected with the simulation module, the monitoring module and the updating module, the simulation module is connected with the intelligent control module, the monitoring module is connected with the intelligent control module and the updating module, and the intelligent control module is connected with the field construction equipment; The modeling module is used for constructing and maintaining the assembly type bridge digital twin integrated with the first physical model, the second physical model and the third physical model; The simulation module is used for process simulation and parameter optimization of pile hole forming, component assembly and large component jacking process in the digital twin; The monitoring module is used for real-time acquisition of component pose, node connection process and structure response data through a sensor network and synchronization to the digital twin; The intelligent control module is used for generating and issuing construction parameter adjustment instructions based on the comparison result of the physical model calculation result and the preset threshold value; The updating module is used for calibrating and iterating the physical model parameters in the digital twin according to the feedback data after instruction execution.

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