Dynamic updating and optimizing method for digital twinborn model in construction process of towering steel structure

Abstract

The invention discloses a dynamic updating and optimizing method for a digital twinborn model in the construction process of a towering steel structure, and relates to the technical field of towering steel structures. Comprising the following steps: S1, multi-source data acquisition: aiming at segmented assembly interfaces, temporary support nodes, main stress components and environment interaction interfaces of towering steel structure construction, S2, initial model construction; s3, dynamic updating; s4, optimizing regulation and control; s5, full-cycle data application; and determining correction logic and priority of different types of deviations, and combining exclusive data acquisition in a construction stage to ensure high synchronization of model updating and a real construction state. Through collaborative modeling of the temporary support and the main body structure, the model can accurately reflect the structural stress characteristics in the construction stage, and the technical problem that an existing model ignores the influence of the temporary support is solved.

Description

Technical Field

[0001] This invention relates to the field of tall steel structure technology, and in particular to a method for dynamic updating and optimization of digital twin models of tall steel structure construction processes. Background Technology

[0002] As the core structural form of iconic buildings such as TV towers, communication towers, and large factory columns, towering steel structures are characterized by their large height, high flexibility, and complex construction procedures. Their construction process involves multiple key stages such as component hoisting, node welding, and temporary support dismantling and assembly. The stress state and geometric shape of the structure change dynamically with the construction progress.

[0003] Currently, digital twin technology has been gradually applied in the field of structural engineering. Existing technologies mostly achieve synchronization between the model and the actual structure by constructing a digital model of the structure and combining it with data collected by sensors. For example, some solutions deploy sensors to collect data such as structural deformation and stress, and transmit this data to the digital twin model for status updates; other solutions use BIM technology to construct a structural geometric model to assist in construction simulation.

[0004] However, the application of existing technologies in the construction of tall steel structures still has many shortcomings: First, data collection lacks specificity for construction scenarios and does not fully cover construction-specific parameters such as temporary support stress and welding thermal deformation, resulting in insufficient adaptability between collected data and model update requirements; Second, digital twin models focus more on modeling the main structure, ignoring the collaborative stress relationship between temporary supports and the main structure, making it impossible for the model to accurately reflect the actual stress state of the structure during construction; Third, the model update mechanism lacks deviation tracing logic, achieving synchronization only through simple data replacement without establishing a correlation between deviation type, impact range, and parameter correction, resulting in insufficient update accuracy; Fourth, model optimization is not combined with construction process adjustments, failing to provide direct guidance for on-site construction, and there is a lack of effective connection between construction-stage data and operation and maintenance-stage data, making it difficult to support the full life cycle management of the structure; Fifth, the model's adaptability to different construction conditions is insufficient, lacking specific update rules for different conditions such as hoisting, welding, and support removal, limiting the model's dynamic response capability to the construction process.

[0005] In summary, existing technologies have failed to establish a dynamic updating and optimization system for digital twin models that is adapted to the construction process of tall steel structures. This results in a disconnect between the model and the actual construction, low update accuracy, and insufficient engineering practicality. There is an urgent need for a technical solution that is specific to the construction scenario, has a rigorous update logic, and a clear optimization direction. Summary of the Invention

[0006] This invention provides a method for dynamically updating and optimizing a digital twin model of the construction process of tall steel structures. The technical solution is as follows: A method for dynamic updating and optimization of digital twin models during the construction process of tall steel structures includes the following steps: S1 Multi-Source Data Acquisition: For the segmented assembly interfaces, temporary support nodes, main load-bearing components, and environmental interaction interfaces of tall steel structure construction, it deploys assembly deviation acquisition equipment, support stress sensing equipment, welding process monitoring equipment, and environmental load capture equipment to simultaneously collect component assembly alignment data, temporary support axial force data, welding heat-affected zone data, structural stress and strain data, and environmental and construction load coupling data. The data collection covers all construction stages, including component hoisting, node welding, temporary support disassembly and assembly, and structural forming, forming a multi-dimensional original dataset for the entire construction cycle. S2 Initial Model Construction: Integrating the design drawings of the tall steel structure, material mechanical parameters, construction organization plan and temporary support layout parameters, and based on the fusion reconstruction of point cloud scanning and BIM technology, a digital twin initial model is constructed, which includes the geometric shape of the main structure, the mechanical properties of temporary supports, construction sequence constraints, and dynamic parameters of material properties. The model has built-in contact constraint relationship between temporary supports and the main structure and mechanical equilibrium conditions during the construction stage. S3 Dynamic Update: Performs noise suppression, temporal alignment, and feature extraction operations on the original dataset, establishes a deviation tracing mechanism, and completes multi-dimensional data deviation analysis through a three-step process of deviation classification, impact range definition, correction, and priority ranking. Combined with the consistency verification results of virtual and real responses, it triggers hierarchical model updates. First, it corrects the mechanical parameters of temporary supports, then adjusts the assembly alignment deviation parameters, then updates the geometric and material performance parameters of the main structure, and finally optimizes the construction timing constraint parameters to achieve real-time synchronization between the model and the actual construction state. S4 Optimization and Control: Combining the modal analysis results of the construction stage with the collaborative stress analysis data of temporary supports and main structure, the construction process adaptation algorithm is adopted to generate model parameter correction schemes and construction process adjustment parameters for problems such as excessive assembly deviation, stress concentration, and support deformation. The adjustment directions are assembly alignment compensation, welding process parameters, temporary support layout parameters, and hoisting sequence parameters. S5 full-cycle data application: The updated and optimized digital twin model is output to the construction management and control platform to provide data support for construction risk management, process parameter correction and resource allocation adjustment. The model update trajectory and key construction data are stored synchronously. The construction stage data provides basic data for the iteration of the digital twin model in the structural operation and maintenance stage.

[0007] Beneficial effects Improve model update accuracy and construction adaptability: Clarify the correction logic and priority for different types of deviations, and combine them with data collection specific to the construction phase to ensure that model updates are highly synchronized with the actual construction status. Collaborative modeling of temporary supports and the main structure enables the model to accurately reflect the structural stress characteristics during the construction phase, solving the technical pain point of existing models neglecting the influence of temporary supports.

[0008] Enhanced guidance during construction: The construction process adaptation algorithm generates model parameter correction schemes and construction process adjustment parameters to address common construction problems such as excessive assembly deviations, stress concentration, and support deformation. These are directly linked to on-site construction operations, enabling the linkage between model optimization and construction process adjustment, reducing the risk of rework, and improving construction quality and efficiency.

[0009] Achieve dynamic adaptation to multiple working conditions: Establish exclusive model update rules for different construction conditions such as component hoisting, node welding, temporary support removal, and structural forming. Adjust the update frequency and parameters according to the structural stress characteristics and environmental load characteristics under each working condition to ensure that the model can accurately adapt to the construction state throughout the entire construction cycle.

[0010] Supporting full lifecycle management: Construction phase data is classified and stored in a dedicated database. The data is linked by component number to achieve effective connection between construction and operation and maintenance data. Data accumulated during the construction phase, such as assembly deviation, welding heat-affected zone, and temporary support stress, provide a foundation for model iteration during the operation and maintenance phase.

[0011] Enhancing the engineering feasibility of the technical solution: The adaptive adjustment of parameters throughout the entire process dynamically adjusts the acquisition frequency, algorithm parameters, and iteration step size based on the data preprocessing effect and model update consistency, ensuring that the technical solution can be stably implemented in different engineering scenarios. At the same time, the specific process and operation logic of the core algorithm are clearly defined, which facilitates its promotion and application in engineering practice. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A process flow diagram provided for an embodiment of this application. Detailed Implementation

[0014] The technical solution provided in this application will now be described in conjunction with the accompanying drawings.

[0015] To facilitate understanding of the embodiments of this application, the following points will be explained first: First, in this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the preceding and following related objects, but does not exclude the possibility of indicating an "and" relationship. The specific meaning can be understood in the context. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, "at least one of a, b, or c" can represent: a, b, c; a and b; a and c; b and c; or a and b and c. Here, a, b, and c can be single or multiple.

[0016] Second, in this application, the use of prefixes such as "first," "second," etc., is merely for the purpose of distinguishing and describing different things belonging to the same name category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no temporal, size, or priority relationship between them.

[0017] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] like Figure 1 As shown, the method for dynamic updating and optimization of the digital twin model during the construction process of tall steel structures includes the following steps: S1 Multi-Source Data Acquisition: For the segmented assembly interfaces, temporary support nodes, main load-bearing components, and environmental interaction interfaces of tall steel structure construction, it deploys assembly deviation acquisition equipment, support stress sensing equipment, welding process monitoring equipment, and environmental load capture equipment to simultaneously collect component assembly alignment data, temporary support axial force data, welding heat-affected zone data, structural stress and strain data, and environmental and construction load coupling data. The data collection covers all construction stages, including component hoisting, node welding, temporary support disassembly and assembly, and structural forming, forming a multi-dimensional original dataset for the entire construction cycle. S2 Initial Model Construction: Integrating the design drawings of the tall steel structure, material mechanical parameters, construction organization plan and temporary support layout parameters, and based on the fusion reconstruction of point cloud scanning and BIM technology, a digital twin initial model is constructed, which includes the geometric shape of the main structure, the mechanical properties of temporary supports, construction sequence constraints, and dynamic parameters of material properties. The model has built-in contact constraint relationship between temporary supports and the main structure and mechanical equilibrium conditions during the construction stage. S3 Dynamic Update: Performs noise suppression, temporal alignment, and feature extraction operations on the original dataset, establishes a deviation tracing mechanism, and completes multi-dimensional data deviation analysis through a three-step process of deviation classification, impact range definition, correction, and priority ranking. Combined with the consistency verification results of virtual and real responses, it triggers hierarchical model updates. First, it corrects the mechanical parameters of temporary supports, then adjusts the assembly alignment deviation parameters, then updates the geometric and material performance parameters of the main structure, and finally optimizes the construction timing constraint parameters to achieve real-time synchronization between the model and the actual construction state. S4 Optimization and Control: Combining the modal analysis results of the construction stage with the collaborative stress analysis data of temporary supports and main structure, the construction process adaptation algorithm is adopted to generate model parameter correction schemes and construction process adjustment parameters for problems such as excessive assembly deviation, stress concentration, and support deformation. The adjustment directions are assembly alignment compensation, welding process parameters, temporary support layout parameters, and hoisting sequence parameters. S5 full-cycle data application: The updated and optimized digital twin model is output to the construction management and control platform to provide data support for construction risk management, process parameter correction and resource allocation adjustment. The model update trajectory and key construction data are stored synchronously. The construction stage data provides basic data for the iteration of the digital twin model in the structural operation and maintenance stage.

[0020] As an optional embodiment, the multi-source data acquisition in step S1 includes a construction node synchronization mechanism: The assembly alignment data is collected by laser alignment equipment deployed on the component mating surface. The acquisition accuracy matches the model's geometric parameter correction requirements, and the data output format is consistent with the model assembly interface coordinate system. The axial force data of the temporary support is collected by stress sensing elements integrated into the support members. The collection points cover both ends of the support and the stress-sensitive area at the mid-span. The data sampling interval is dynamically adjusted according to the rate of change of the support force. Data on the heat-affected zone of welding is collected synchronously by temperature acquisition equipment and displacement monitoring equipment deployed in the welding area. Temperature data is used to correct the elastic modulus of the material in the welding area in the model, and displacement data is used to calibrate the thermal deformation parameters. Structural stress and strain data are collected through sensing devices at key sections of the main load-bearing components. Key sections include mid-span, node connections, and stress concentration areas. The data is directly correlated with the calibration of the model's mechanical properties. Environmental and construction load coupling data are collected by environmental load monitoring units deployed at different heights of the structure. The data includes wind load and temperature field distribution data, which are used to correct the boundary conditions for model load calculation. All data acquisition devices synchronize data through construction process node timestamps. The acquisition frequency of key construction processes is adjusted according to the structural dynamic response characteristics to ensure that the data can support the real-time update requirements of the model. Timing alignment achieves unified timestamps for data from multiple devices through synchronous triggering signals. Feature extraction extracts time-domain peak values ​​and frequency-domain dominant frequencies for vibration signals, extreme values ​​and rates of change for stress and strain data, and surface texture and geometric deformation features for visual images.

[0021] As an optional embodiment, the initial model construction in step S2 includes a collaborative modeling step of temporary supports and the main structure: The geometric model of the main structure is reconstructed by calibration of the deviation between the point cloud scan data and the BIM design model. The sampling density of the scan data is determined according to the accuracy requirements of the component dimensions. The deviation calibration is based on the design drawings and the least squares method is used to complete the registration of the point cloud data and the BIM model. The temporary support model is constructed based on the support layout scheme and material property parameters. It includes the geometric parameters of the support members, the constraint characteristics of the connection nodes, and the bearing capacity parameters. The mechanical relationship between the temporary support and the main structure is established through contact elements in the model. The stiffness parameters of the contact elements are determined according to the connection method between the support and the main structure. The construction sequence model associates the component hoisting sequence, welding operation process, and temporary support disassembly and assembly sequence, transforming the construction schedule into time constraint parameters that the model can recognize. The time constraint parameters are consistent with the component installation logic and structural stress forming law, forming a three-dimensional model architecture of geometric calibration, mechanical coupling, and time constraint. The dynamic parameters of material properties are determined based on structural design specifications and measured material data, including parameters affecting elastic modulus, yield strength, and welding residual stress.

[0022] As an optional embodiment, the dynamic update triggering mechanism of step S3 includes: The following thresholds are set: assembly deviation consistency threshold, support force balance threshold, stress response matching threshold, and thermal deformation allowable threshold. The assembly deviation consistency threshold is determined based on the assembly allowable deviation in the structural design code. The support force balance threshold is determined based on the rated bearing capacity of the temporary support and the load calculation results during the structural construction stage. The stress response matching threshold is determined by the ratio of the stress calculated by the model to the allowable stress of the material. The thermal deformation allowable threshold is set according to the thermal deformation control requirements in the welding process standard. When any threshold exceeds the set range, the model layer update process is initiated. The update priority is determined based on the core contradictions of structural safety during the construction phase. Temporary supports are the main load-bearing system during the structural construction phase, and their parameter deviations directly affect structural stability, so they are corrected first. Assembly deviations directly cause the structural geometry to deviate from the design, affecting subsequent processes and overall stress, so they are corrected second. Correction of main structural parameters involves overall model adjustment, so frequent changes should be avoided. Next, process sequence parameters have an indirect impact on structural safety, so they are corrected last. Deviations are classified by data type into geometric deviations, mechanical parameter deviations, and temporal deviations. The scope of influence is determined based on the number of components involved and the force transmission path. The priority of correction is determined based on the degree of impact of the deviation on structural safety and the connection requirements of construction procedures.

[0023] As an optional embodiment, the specific process of the construction process adaptation algorithm in step S4 is as follows: The first step involves combining the modal analysis results from the construction phase and extracting the coupling characteristics between the deformation of temporary supports and the vibration of the main structure using the modal decomposition response spectrum method. This process clarifies the frequency range and amplitude relationship corresponding to the coupling characteristics. The frequency range is determined based on the analysis of the structure's natural frequencies, while the amplitude relationship is obtained through modal parameter identification. The second step is to establish the mapping relationship between construction process parameters and structural response, the correlation between assembly alignment compensation and the measured value of assembly deviation and structural geometric stiffness distribution, the correlation between welding process parameters and stress distribution and thermal deformation in the welding heat-affected zone, the correlation between temporary support arrangement parameters and structural stress redistribution law, and the correlation between hoisting sequence parameters and cumulative structural deformation. The third step addresses the issue of excessive assembly deviations by adjusting the component hoisting alignment compensation and node splicing sequence based on the vibration mode contribution obtained from structural modal analysis, and optimizing the geometric parameters of the model assembly interface. The fourth step addresses the stress concentration problem caused by welding thermal deformation. Based on the stress redistribution law in the welding area, the welding current, welding speed, and interpass cooling time are adjusted to correct the material mechanical parameters of the welding area in the model. The fifth step addresses the deformation problem of temporary supports by adjusting the support spacing, increasing the cross-sectional size of the supports, or optimizing the support layout based on the excessive stress balance threshold of the supports, and updating the virtual mapping parameters of the temporary supports in the model. The optimization process is associated with constraints on the allocation of construction resources to ensure that the optimization plan is feasible within the allowable range of construction equipment capacity and operating space.

[0024] As an optional embodiment, the model dynamic update in step S3 incorporates the construction damage pre-identification results, and the specific steps are as follows: The first step is to construct a construction damage pre-identification model, which extracts the abrupt change characteristics of assembly deviation data, the cumulative characteristics of welding thermal deformation data, and the abnormal fluctuation characteristics of support stress data to form a damage early warning feature vector; The second step combines the structural mechanical properties and material fatigue performance parameters during the construction phase to determine the potential damage types and risk levels. Potential damage types include assembly misalignment damage, welding hot cracks, and support overload damage. The risk levels are divided into three levels according to the degree of impact of the damage on the structural bearing capacity. The third step is to establish the correspondence between risk level and parameter correction range. Level 1 risk corresponds to a higher parameter correction range than Level 2 risk, Level 2 risk corresponds to a higher parameter correction range than Level 3 risk, assembly misalignment damage corresponds to geometric parameter correction, welding hot crack corresponds to material mechanical parameter correction, and support overload damage corresponds to temporary support bearing parameter correction. The fourth step combines the damage pre-identification results with the parameter correction range and maps them to the digital twin model. This allows for targeted correction of the material mechanical parameters and structural constraints in areas with higher risk levels, enabling the model to accurately reflect the health status of the structure during the construction phase.

[0025] As an optional embodiment, the model optimization and control in step S4 includes construction resource linkage optimization: The first step is to simulate the structural construction response stress deformation and construction schedule under each scheme based on the updated digital twin model, by inputting different construction resource configuration schemes, including hoisting equipment models, number of workers and number of welding equipment. The second step is to establish a correlation calculation model between construction efficiency and structural safety factor and resource input cost. The objective function is to maximize the ratio of construction efficiency to resource input cost, with the premise that the structural safety factor meets the standard. The third step is to solve the objective function to select the optimal resource allocation scheme and output the resource scheduling adjustment parameters, including the number of equipment to be put into operation and the handover time of the construction process of the workers. The fourth step involves inputting the optimized resource allocation parameters back into the digital twin model to simulate and verify the synergistic improvement effect between structural construction response and schedule. Verification indicators include the structural stress compliance rate, the proportion of schedule reduction, and resource utilization rate, forming a complete closed loop of model optimization, resource scheduling, construction feedback, and model iteration.

[0026] As an optional embodiment, the model dynamic update in step S3 includes adaptive adaptation to construction conditions: For different construction conditions such as component hoisting, node welding, temporary support removal, and structural forming, based on the structural stress characteristics under the corresponding conditions and the stress redistribution and environmental load characteristics of the concentrated load in the hoisting condition, the thermal load in the welding condition, and the support removal condition, a case-specific model update rule is established. Under hoisting conditions, the update frequency of component attitude parameters and hoisting force parameters is increased, and the update cycle is consistent with the hoisting action cycle to ensure that the model reflects the position deviation and force changes of the component in real time during the hoisting process. Under welding conditions, the focus is on correcting the thermal deformation parameters and the stress parameters in the welding area. The update frequency is determined according to the welding thermal cycle, and the welding process parameters are adjusted synchronously. When temporary supports are removed, the support constraint release parameters and the stress redistribution parameters of the main structure are updated simultaneously. The update process is carried out in stages, and each time a set of supports is removed, a parameter update and stress verification are completed. The adaptation process references structural response mutation data during construction condition transitions. By comparing mutation data with model predictions, the model update rules are dynamically adjusted to ensure that the model's adaptability to different construction conditions is consistent with the actual construction structure.

[0027] As an optional embodiment, it also includes an adaptive adjustment step for parameters throughout the entire construction process: Construction status monitoring modules are set up in the data acquisition equipment, data processing unit and model computing platform to collect in real time the equipment working parameters, acquisition accuracy, transmission rate, construction progress parameters, process completion rate, schedule deviation, algorithm running parameters, filtering coefficients, feature extraction thresholds and model update accuracy, and the deviation between the data model predicted value and the measured value. Based on the data preprocessing effect, noise suppression signal-to-noise ratio, data alignment accuracy, model update and actual construction consistency, geometric parameter consistency, mechanical response consistency, construction feedback problem rectification effect, the sensor acquisition frequency is automatically adjusted, the acquisition frequency is increased when the signal-to-noise ratio is below the threshold, the data preprocessing algorithm parameters and filtering coefficients are dynamically adjusted according to the noise type and the model optimization iteration step size is reduced when the consistency is below the threshold. When the deviation between the construction deviation or stress state predicted by the model and the actual data exceeds the allowable range for construction, the incremental training process of the model is initiated, incorporating the latest construction stage data and structural response data, updating the feature mapping relationship of the model, and improving the accuracy of the model's dynamic updates and its adaptability to construction.

[0028] As an optional embodiment, the full-cycle data application in step S5 includes the integration of construction and operation and maintenance data: The update trajectory of the digital twin model during the construction phase, key construction parameters, structural state evolution data, and temporary support function data are classified and stored to build a dedicated database for the construction process. The database is indexed hierarchically according to the parameter types of structural parts in the construction process. The cumulative data of assembly deviations, the distribution data of welding heat-affected zones, the stress history data of temporary supports, and the data of material performance degradation trends during the construction phase are incorporated into the model according to the structural component numbers to form a complete data chain of the structural status during the construction phase. Structural health monitoring data during the operation and maintenance phase is linked to relevant parameters in the construction phase database through component numbers. Combined with changes in structural stress during the operation and maintenance phase, the model is continuously updated and optimized, providing data support for structural maintenance, reinforcement, life assessment, and renovation and upgrading throughout the entire construction cycle.

[0029] Specific implementation of each step 1. Specific implementation of multi-source data acquisition For the segmented assembly interfaces, temporary support nodes, main load-bearing components, and environmental interaction interfaces in the construction of tall steel structures, data acquisition equipment should be deployed and data acquisition should be performed in the following manner: Assembly alignment data: Laser alignment devices are evenly deployed around the component mating surface. The spacing between the devices is determined according to the cross-sectional dimensions of the component to ensure coverage of the entire mating surface. The collected data is directly output in a numerical format corresponding to the model assembly interface coordinate system, which is precisely matched with the model's geometric parameter correction requirements.

[0030] Temporary support axial force data: Stress sensing elements are integrated at both ends of the support members and in the mid-span stress-sensitive area. The data sampling interval is dynamically adjusted according to the rate of change of support force. When the force changes drastically, the sampling interval is shortened to ensure that the peak force and the trend of change of support force are captured.

[0031] Welding heat-affected zone data: Temperature acquisition equipment and displacement monitoring equipment are deployed around the welding area. The temperature acquisition equipment is placed close to the edge of the weld, and the displacement monitoring equipment is aimed at the center of the welding heat-affected zone. The two are synchronized through the same timestamp. The temperature data is used to correct the elastic modulus of the material in the welding area, and the displacement data is used to calibrate the thermal deformation parameters.

[0032] Structural stress and strain data: Sensing devices are placed at the mid-span, node connections, and stress concentration areas of the main load-bearing components. The installation direction of the sensing devices is consistent with the force direction of the components, and the data is directly correlated with the calibration of the mechanical properties of the model.

[0033] Environmental and construction load coupling data: Environmental load monitoring units are uniformly deployed at different heights of the structure, with at least 3 units deployed on each floor in a triangular distribution, to collect wind load and temperature field distribution data, which are used to correct the boundary conditions for model load calculation.

[0034] Data synchronization mechanism: All acquisition devices are synchronized through construction process node timestamps. The acquisition frequency of key construction processes is adjusted according to the structural dynamic response characteristics to ensure that the data can support real-time model updates. Time sequence alignment is achieved by unifying the timestamps of multiple devices through synchronization trigger signals. Feature extraction is performed to extract time domain peak values ​​and frequency domain dominant frequencies for vibration signals, extreme values ​​and rates of change for stress and strain data, and surface texture and geometric deformation features for visual images.

[0035] 2. Specific Implementation of Initial Model Construction Integrate the design drawings of the tall steel structure, material mechanical parameters, construction organization plan, and temporary support layout parameters, and construct the initial digital twin model according to the following steps: Construction of the geometric model of the main structure: The component is scanned in full size using a point cloud scanning device. The sampling density of the scanning data is determined according to the component size accuracy requirements. Based on the design drawings, the least squares method is used to complete the registration of the point cloud data and the BIM design model, correct the size deviation between the design model and the actual component, and accurately restore the component cross-sectional dimensions, node construction and segmented assembly interface form.

[0036] Temporary support model construction: Based on the support layout scheme and material property parameters, a temporary support model is constructed, which includes the geometric parameters of the support members, the constraint characteristics of the connection nodes, and the bearing capacity parameters. The mechanical relationship between the temporary support and the main structure is established through contact elements. The stiffness parameters of the contact elements are determined according to the connection method between the support and the main structure. Bolted connections and welded connections correspond to different stiffness parameter settings.

[0037] Construction sequence model construction: This involves linking the hoisting sequence of related components, welding procedures, and the dismantling and assembly sequence of temporary supports. The construction schedule is transformed into time-series constraint parameters that the model can recognize. These constraints are consistent with the component installation logic and structural stress-forming patterns, forming a three-dimensional model architecture of "geometric calibration – mechanical coupling – time-series constraints." Dynamic material performance parameters are determined based on structural design specifications and measured material data, including parameters influencing elastic modulus, yield strength, and welding residual stress.

[0038] 3. Specific implementation of dynamic updates After performing noise suppression, temporal alignment, and feature extraction operations on the original dataset, the model is dynamically updated according to the following process: Deviation source tracing and analysis: Deviations are classified into geometric deviations, mechanical parameter deviations, and temporal deviations according to data type. The scope of influence is defined based on the number of components involved in the deviation and the force transmission path. The priority of correction is determined based on the degree of impact of the deviation on structural safety and the connection requirements of construction procedures.

[0039] Update Triggering and Execution: The following thresholds are set: assembly deviation consistency threshold, support force balance threshold, stress response matching threshold, and allowable thermal deformation threshold. The assembly deviation consistency threshold is determined based on the allowable assembly deviation in the structural design code. The support force balance threshold is determined based on the rated bearing capacity of temporary supports and the load calculation results during the structural construction stage. The stress response matching threshold is determined by the ratio of the stress calculated by the model to the allowable stress of the material. The allowable thermal deformation threshold is set according to the thermal deformation control requirements in the welding process standard. When any threshold exceeds the set range, the model layered update process is initiated, and corrections are performed in the priority order of "temporary support parameters → assembly alignment deviation parameters → main structure geometric and material performance parameters → construction sequence constraint parameters".

[0040] Damage pre-identification results are integrated into a construction damage pre-identification model. This model extracts abrupt change characteristics from assembly deviation data, cumulative characteristics from welding thermal deformation data, and abnormal fluctuation characteristics from support stress data to form a damage warning feature vector. Combining the structural mechanical properties and material fatigue performance parameters during the construction phase, potential damage types and risk levels are determined. Potential damage types include assembly misalignment damage, welding hot cracking, and support overload damage. A correspondence between risk levels and parameter correction magnitudes is established: Level 1 risk corresponds to a higher parameter correction magnitude than Level 2 risk, and Level 2 risk corresponds to a higher parameter correction magnitude than Level 3 risk. Assembly misalignment damage corresponds to geometric parameter correction, welding hot cracking corresponds to material mechanical parameter correction, and support overload damage corresponds to temporary support bearing capacity parameter correction. The damage pre-identification results are combined with the parameter correction magnitudes and mapped to a digital twin model, allowing for targeted correction of material mechanical parameters and structural constraints in areas with higher risk levels.

[0041] Adaptive processing for different construction conditions: Dedicated update rules are implemented for different construction conditions. Under hoisting conditions, the update frequency of component attitude parameters and hoisting force parameters is increased, with the update cycle consistent with the hoisting action cycle. Under welding conditions, the focus is on correcting thermal deformation parameters and welding area stress parameters, with the update frequency determined based on the welding thermal cycle cycle. Under temporary support removal conditions, the support constraint release parameters and main structure stress redistribution parameters are updated in stages, with each set of supports removed completing a parameter update and stress verification. The adaptation process references structural response mutation data during construction condition transitions, dynamically adjusting the model update rules by comparing the mutation data with model predictions.

[0042] 4. Specific Implementation of Optimized Regulation Combining the modal analysis results from the construction phase with the stress analysis data of temporary supports and the main structure, the construction process adaptation algorithm is executed according to the following procedure: Coupling feature extraction: The coupling features between the deformation of temporary supports and the vibration of the main structure are extracted by modal decomposition response spectrum method. The frequency range and amplitude relationship corresponding to the coupling features are identified. The frequency range is determined based on the natural frequency analysis of the structure, and the amplitude relationship is obtained through modal parameter identification.

[0043] Mapping relationship establishment: Establish the mapping relationship between construction process parameters and structural response, link the assembly alignment compensation amount with the measured value of assembly deviation and the distribution of structural geometric stiffness, link the welding process parameters with the stress distribution and thermal deformation amount in the welding heat-affected zone, link the temporary support arrangement parameters with the stress redistribution law of the structure, and link the hoisting sequence parameters with the cumulative deformation amount of the structure.

[0044] Targeted optimizations: For the issue of excessive assembly deviations, based on the modal contribution obtained from structural modal analysis, the component hoisting alignment compensation and node splicing sequence were adjusted, and the geometric parameters of the model assembly interface were optimized. For the stress concentration issue caused by welding thermal deformation, according to the stress redistribution law in the welding area, the welding current, welding speed, and interlayer cooling time were adjusted, and the material mechanical parameters of the welding area in the model were corrected. For the deformation issue of temporary supports, combined with the excessive amplitude of the support stress balance threshold, the support spacing was adjusted, the support cross-sectional dimensions were increased, or the support layout was optimized, and the virtual mapping parameters of the temporary supports in the model were updated. The optimization process was linked to the constraints of construction resource allocation to ensure that the optimization scheme was feasible within the limits of construction equipment capacity and working space.

[0045] Construction resource linkage optimization: Based on the updated digital twin model, different construction resource configuration schemes (including hoisting equipment models, number of workers, and number of welding equipment) are input to simulate the structural construction response (stress, deformation) and schedule under each scheme. A correlation calculation model is established for construction efficiency, structural safety factor, and resource input cost. The objective function is to maximize the ratio of construction efficiency to resource input cost, assuming the structural safety factor meets the standard. The optimal resource configuration scheme is selected by solving the objective function, and resource scheduling adjustment parameters are output, including the number of equipment deployed, worker assignments, and construction process handover time. The optimized resource configuration parameters are then input back into the digital twin model. The collaborative improvement effect is verified through indicators such as structural stress compliance rate, schedule reduction ratio, and resource utilization rate, forming a complete closed loop of model optimization, resource scheduling, construction feedback, and model iteration.

[0046] 5. Specific Implementation of Full-Lifecycle Data Application Data storage and indexing: The update trajectory of the digital twin model during the construction phase, key construction parameters, structural state evolution data, and temporary support function data are stored in a classified manner according to construction procedures, structural parts, and parameter types. The database is dedicated to the civil construction process and adopts a hierarchical index structure to facilitate fast data retrieval and access.

[0047] Data integration between construction and operation and maintenance: Accumulated assembly deviation data, welding heat-affected zone distribution data, temporary support stress history data, and material performance degradation trend data during the construction phase are integrated into the model by structural component number, forming a complete data chain of structural status during the construction phase. Structural health monitoring data during the operation and maintenance phase is accessed through component number association with relevant parameters in the construction phase database, and the model is continuously updated and optimized by combining structural stress changes during the operation and maintenance phase.

[0048] Data application scenarios: The updated and optimized digital twin model is output to the construction management platform, providing data support for construction risk management, process parameter correction, and resource allocation adjustment. During the operation and maintenance phase, the model based on integrated construction data provides full-cycle data support for structural maintenance and reinforcement, life assessment, and renovation and upgrading.

[0049] 6. Specific implementation of adaptive control of parameters throughout the entire process Construction status monitoring modules are set up in the data acquisition equipment, data processing unit and model computing platform to collect equipment working parameters (acquisition accuracy, transmission rate), construction progress parameters (process completion rate, construction period deviation), algorithm running parameters (filter coefficient, feature extraction threshold) and model update accuracy data (deviation between model prediction value and measured value) in real time.

[0050] Based on the data preprocessing effect (signal-to-noise ratio after noise suppression, data alignment accuracy), the consistency between the model update and the actual construction (geometric parameter consistency, mechanical response consistency), and the rectification effect of construction feedback issues, the sensor acquisition frequency (increase the acquisition frequency when the signal-to-noise ratio is below the threshold), data preprocessing algorithm parameters (dynamically adjust the filter coefficient according to the noise type), and model optimization iteration step size (reduce the iteration step size when the consistency is below the threshold) are automatically adjusted.

[0051] When the deviation between the construction deviation or stress state predicted by the model and the actual data exceeds the allowable range for construction, the incremental training process of the model is initiated, incorporating the latest construction stage data and structural response data, updating the feature mapping relationship of the model, and improving the accuracy of the model's dynamic updates and its adaptability to construction.

[0052] (III) Inter-module collaboration mechanism Each step achieves collaboration through data transmission and logical connections: multi-source data acquisition provides basic data input for initial model construction; the initial model provides a benchmark framework for dynamic updates; the dynamically updated model provides an analytical object for optimization and control; and the optimization and control results guide the adjustment of data acquisition parameters and construction operations. Adaptive parameter control throughout the entire process ensures the stability and accuracy of data acquisition, model construction, updates, and optimization. Data storage during the construction phase is integrated with operation and maintenance data, enabling the extension of the technical solution throughout its entire lifecycle.

[0053] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. A method for dynamic updating and optimization of digital twin models of tall steel structure construction processes, characterized in that, Includes the following steps: S1 Multi-Source Data Acquisition: For the segmented assembly interfaces, temporary support nodes, main load-bearing components, and environmental interaction interfaces of tall steel structure construction, it deploys assembly deviation acquisition equipment, support stress sensing equipment, welding process monitoring equipment, and environmental load capture equipment to simultaneously collect component assembly alignment data, temporary support axial force data, welding heat-affected zone data, structural stress and strain data, and environmental and construction load coupling data. The data collection covers all construction stages, including component hoisting, node welding, temporary support disassembly and assembly, and structural forming, forming a multi-dimensional original dataset for the entire construction cycle. S2 Initial Model Construction: Integrating the design drawings of the tall steel structure, material mechanical parameters, construction organization plan and temporary support layout parameters, and based on the fusion reconstruction of point cloud scanning and BIM technology, a digital twin initial model is constructed, which includes the geometric shape of the main structure, the mechanical properties of temporary supports, construction sequence constraints, and dynamic parameters of material properties. The model has built-in contact constraint relationship between temporary supports and the main structure and mechanical equilibrium conditions during the construction stage. S3 Dynamic Update: Performs noise suppression, temporal alignment, and feature extraction operations on the original dataset, establishes a deviation tracing mechanism, and completes multi-dimensional data deviation analysis through a three-step process of deviation classification, impact range definition, correction, and priority ranking. Combined with the consistency verification results of virtual and real responses, it triggers hierarchical model updates. First, it corrects the mechanical parameters of temporary supports, then adjusts the assembly alignment deviation parameters, then updates the geometric and material performance parameters of the main structure, and finally optimizes the construction timing constraint parameters to achieve real-time synchronization between the model and the actual construction state. S4 Optimization and Control: Combining the modal analysis results of the construction stage with the collaborative stress analysis data of temporary supports and main structure, the construction process adaptation algorithm is adopted to generate model parameter correction schemes and construction process adjustment parameters for problems such as excessive assembly deviation, stress concentration, and support deformation. The adjustment directions are assembly alignment compensation, welding process parameters, temporary support layout parameters, and hoisting sequence parameters. S5 full-cycle data application: The updated and optimized digital twin model is output to the construction management and control platform to provide data support for construction risk management, process parameter correction and resource allocation adjustment. The model update trajectory and key construction data are stored synchronously. The construction stage data provides basic data for the iteration of the digital twin model in the structural operation and maintenance stage.

2. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, Step S1, multi-source data acquisition, includes a construction node synchronization mechanism: The assembly alignment data is collected by laser alignment equipment deployed on the component mating surface. The acquisition accuracy matches the model's geometric parameter correction requirements, and the data output format is consistent with the model assembly interface coordinate system. The axial force data of the temporary support is collected by stress sensing elements integrated into the support members. The collection points cover both ends of the support and the stress-sensitive area at the mid-span. The data sampling interval is dynamically adjusted according to the rate of change of the support force. Data on the heat-affected zone of welding is collected synchronously by temperature acquisition equipment and displacement monitoring equipment deployed in the welding area. Temperature data is used to correct the elastic modulus of the material in the welding area in the model, and displacement data is used to calibrate the thermal deformation parameters. Structural stress and strain data are collected through sensing devices at key sections of the main load-bearing components. Key sections include mid-span, node connections, and stress concentration areas. The data is directly correlated with the calibration of the model's mechanical properties. Environmental and construction load coupling data are collected by environmental load monitoring units deployed at different heights of the structure. The data includes wind load and temperature field distribution data, which are used to correct the boundary conditions for model load calculation. All data acquisition devices synchronize data through construction process node timestamps. The acquisition frequency of key construction processes is adjusted according to the structural dynamic response characteristics to ensure that the data can support the real-time update requirements of the model. Timing alignment achieves unified timestamps for data from multiple devices through synchronous triggering signals. Feature extraction extracts time-domain peak values ​​and frequency-domain dominant frequencies for vibration signals, extreme values ​​and rates of change for stress and strain data, and surface texture and geometric deformation features for visual images.

3. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, Step S2, the initial model construction, includes a collaborative modeling step between temporary supports and the main structure: The geometric model of the main structure is reconstructed by calibration of the deviation between the point cloud scan data and the BIM design model. The sampling density of the scan data is determined according to the accuracy requirements of the component dimensions. The deviation calibration is based on the design drawings and the least squares method is used to complete the registration of the point cloud data and the BIM model. The temporary support model is constructed based on the support layout scheme and material property parameters. It includes the geometric parameters of the support members, the constraint characteristics of the connection nodes, and the bearing capacity parameters. The mechanical relationship between the temporary support and the main structure is established through contact elements in the model. The stiffness parameters of the contact elements are determined according to the connection method between the support and the main structure. The construction sequence model associates the component hoisting sequence, welding operation process, and temporary support disassembly and assembly sequence, transforming the construction schedule into time constraint parameters that the model can recognize. The time constraint parameters are consistent with the component installation logic and structural stress forming law, forming a three-dimensional model architecture of geometric calibration, mechanical coupling, and time constraint. The dynamic parameters of material properties are determined based on structural design specifications and measured material data, including parameters affecting elastic modulus, yield strength, and welding residual stress.

4. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, The dynamic update triggering mechanism in step S3 includes: The following thresholds are set: assembly deviation consistency threshold, support force balance threshold, stress response matching threshold, and thermal deformation allowable threshold. The assembly deviation consistency threshold is determined based on the assembly allowable deviation in the structural design code. The support force balance threshold is determined based on the rated bearing capacity of the temporary support and the load calculation results during the structural construction stage. The stress response matching threshold is determined by the ratio of the stress calculated by the model to the allowable stress of the material. The thermal deformation allowable threshold is set according to the thermal deformation control requirements in the welding process standard. When any threshold exceeds the set range, the model layer update process is initiated. The update priority is determined based on the core contradictions of structural safety during the construction phase. Temporary supports are the main load-bearing system during the structural construction phase, and their parameter deviations directly affect structural stability, so they are corrected first. Assembly deviations directly cause the structural geometry to deviate from the design, affecting subsequent processes and overall stress, so they are corrected second. Correction of main structural parameters involves overall model adjustment, so frequent changes should be avoided. Next, process sequence parameters have an indirect impact on structural safety, so they are corrected last. Deviations are classified by data type into geometric deviations, mechanical parameter deviations, and temporal deviations. The scope of influence is determined based on the number of components involved and the force transmission path. The priority of correction is determined based on the degree of impact of the deviation on structural safety and the connection requirements of construction procedures.

5. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, The specific process of the construction technology adaptation algorithm in step S4 is as follows: The first step involves combining the modal analysis results from the construction phase and extracting the coupling characteristics between the deformation of temporary supports and the vibration of the main structure using the modal decomposition response spectrum method. This process clarifies the frequency range and amplitude relationship corresponding to the coupling characteristics. The frequency range is determined based on the analysis of the structure's natural frequencies, while the amplitude relationship is obtained through modal parameter identification. The second step is to establish the mapping relationship between construction process parameters and structural response, the correlation between assembly alignment compensation and the measured value of assembly deviation and structural geometric stiffness distribution, the correlation between welding process parameters and stress distribution and thermal deformation in the welding heat-affected zone, the correlation between temporary support arrangement parameters and structural stress redistribution law, and the correlation between hoisting sequence parameters and cumulative structural deformation. The third step addresses the issue of excessive assembly deviations by adjusting the component hoisting alignment compensation and node splicing sequence based on the vibration mode contribution obtained from structural modal analysis, and optimizing the geometric parameters of the model assembly interface. The fourth step addresses the stress concentration problem caused by welding thermal deformation. Based on the stress redistribution law in the welding area, the welding current, welding speed, and interpass cooling time are adjusted to correct the material mechanical parameters of the welding area in the model. The fifth step addresses the deformation problem of temporary supports by adjusting the support spacing, increasing the cross-sectional size of the supports, or optimizing the support layout based on the excessive stress balance threshold of the supports, and updating the virtual mapping parameters of the temporary supports in the model. The optimization process is linked to the constraints of construction resource allocation to ensure that the optimization plan is feasible within the limits of construction equipment capacity and working space.

6. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, Step S3 involves dynamically updating the model by incorporating the pre-identification results of construction damage. The specific steps are as follows: The first step is to construct a construction damage pre-identification model, which extracts the abrupt change characteristics of assembly deviation data, the cumulative characteristics of welding thermal deformation data, and the abnormal fluctuation characteristics of support stress data to form a damage early warning feature vector; The second step combines the structural mechanical properties and material fatigue performance parameters during the construction phase to determine the potential damage types and risk levels. Potential damage types include assembly misalignment damage, welding hot cracks, and support overload damage. The risk levels are divided into three levels according to the degree of impact of the damage on the structural bearing capacity. The third step is to establish the correspondence between risk level and parameter correction range. Level 1 risk corresponds to a higher parameter correction range than Level 2 risk, Level 2 risk corresponds to a higher parameter correction range than Level 3 risk, assembly misalignment damage corresponds to geometric parameter correction, welding hot crack corresponds to material mechanical parameter correction, and support overload damage corresponds to temporary support bearing parameter correction. The fourth step combines the damage pre-identification results with the parameter correction range and maps them to the digital twin model. This allows for targeted correction of the material mechanical parameters and structural constraints in areas with higher risk levels, enabling the model to accurately reflect the health status of the structure during the construction phase.

7. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, Step S4, model optimization and control, includes construction resource linkage optimization: The first step is to simulate the structural construction response stress deformation and construction schedule under each scheme based on the updated digital twin model, by inputting different construction resource configuration schemes, including hoisting equipment models, number of workers and number of welding equipment. The second step is to establish a correlation calculation model between construction efficiency and structural safety factor and resource input cost. The objective function is to maximize the ratio of construction efficiency to resource input cost, with the premise that the structural safety factor meets the standard. The third step is to solve the objective function to select the optimal resource allocation scheme and output the resource scheduling adjustment parameters, including the number of equipment to be put into operation and the handover time of the construction process of the workers. The fourth step involves inputting the optimized resource allocation parameters back into the digital twin model to simulate and verify the synergistic improvement effect between structural construction response and schedule. Verification indicators include the structural stress compliance rate, the proportion of schedule reduction, and resource utilization rate, forming a complete closed loop of model optimization, resource scheduling, construction feedback, and model iteration.

8. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, Step S3, the dynamic model update, includes adaptive adaptation to construction conditions: For different construction conditions such as component hoisting, node welding, temporary support removal, and structural forming, based on the structural stress characteristics under the corresponding conditions and the stress redistribution and environmental load characteristics of the concentrated load in the hoisting condition, the thermal load in the welding condition, and the support removal condition, a case-specific model update rule is established. Under hoisting conditions, the update frequency of component attitude parameters and hoisting force parameters is increased, and the update cycle is consistent with the hoisting action cycle to ensure that the model reflects the position deviation and force changes of the component in real time during the hoisting process. Under welding conditions, the focus is on correcting the thermal deformation parameters and the stress parameters in the welding area. The update frequency is determined according to the welding thermal cycle, and the welding process parameters are adjusted synchronously. When temporary supports are removed, the support constraint release parameters and the stress redistribution parameters of the main structure are updated simultaneously. The update process is carried out in stages, and each time a set of supports is removed, a parameter update and stress verification are completed. The adaptation process references structural response mutation data during construction condition transitions. By comparing mutation data with model predictions, the model update rules are dynamically adjusted to ensure that the model's adaptability to different construction conditions is consistent with the actual construction structure.

9. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, It also includes adaptive adjustment steps for parameters throughout the entire construction process: Construction status monitoring modules are set up in the data acquisition equipment, data processing unit and model computing platform to collect in real time the equipment working parameters, acquisition accuracy, transmission rate, construction progress parameters, process completion rate, schedule deviation, algorithm running parameters, filtering coefficients, feature extraction thresholds and model update accuracy, and the deviation between the data model predicted value and the measured value. Based on the data preprocessing effect, noise suppression signal-to-noise ratio, data alignment accuracy, model update and actual construction consistency, geometric parameter consistency, mechanical response consistency, construction feedback problem rectification effect, the sensor acquisition frequency is automatically adjusted, the acquisition frequency is increased when the signal-to-noise ratio is below the threshold, the data preprocessing algorithm parameters and filtering coefficients are dynamically adjusted according to the noise type and the model optimization iteration step size is reduced when the consistency is below the threshold. When the deviation between the construction deviation or stress state predicted by the model and the actual data exceeds the allowable range for construction, the incremental training process of the model is initiated, incorporating the latest construction stage data and structural response data, updating the feature mapping relationship of the model, and improving the accuracy of the model's dynamic updates and its adaptability to construction.

10. The method for dynamic updating and optimization of the digital twin model of the construction process of tall steel structures as described in claim 1, characterized in that, Step S5's full-cycle data application includes the integration of construction and operation and maintenance data: The update trajectory of the digital twin model during the construction phase, key construction parameters, structural state evolution data, and temporary support function data are classified and stored to build a dedicated database for the construction process. The database is indexed hierarchically according to the parameter types of structural parts in the construction process. The cumulative data of assembly deviations, the distribution data of welding heat-affected zones, the stress history data of temporary supports, and the data of material performance degradation trends during the construction phase are incorporated into the model according to the structural component numbers to form a complete data chain of the structural status during the construction phase. Structural health monitoring data during the operation and maintenance phase is linked to relevant parameters in the construction phase database through component numbers. Combined with changes in structural stress during the operation and maintenance phase, the model is continuously updated and optimized, providing data support for structural maintenance, reinforcement, life assessment, and renovation and upgrading throughout the entire construction cycle.

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