Targeted digital twinning construction method for large-span arch bridge construction control
By using a targeted digital twin construction method and taking stress-free state quantities as a benchmark, the twin granularity and sensor configuration are dynamically selected, which solves the problems of data silos and system redundancy in bridge digital twins and realizes precise control and efficient management of the construction of long-span arch bridges.
Patent Information
- Application Number
- CN202511738512.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-25
- Publication Date
- 2026-02-13
AI Technical Summary
Existing technologies for bridge digital twin applications suffer from data silos and heterogeneity issues, making it difficult to achieve complete implementation throughout the entire lifecycle. Furthermore, full-element simulation results in high data acquisition costs and high computing power consumption, failing to achieve a balance between accuracy and economy.
By employing a targeted digital twin construction method, a mapping relationship between physical and virtual space is established using stress-free state quantities as a benchmark. The twin granularity is dynamically selected, key elements are focused on for control, and the types, quantities, and deployment locations of sensors are configured to achieve precise construction control.
It has improved the accuracy of construction monitoring of long-span arch bridges, reduced system redundancy, and realized the transformation from passive adjustment to proactive pre-control, ensuring efficient and precise management and control of the construction process.
Smart Images
Figure CN121525316A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of digital twins and intelligent construction control for long-span arch bridges, specifically a targeted digital twin construction method for construction control of long-span arch bridges. Background Technology
[0002] Digital twin technology originated in industrial fields such as aerospace and high-end manufacturing, but its application and research in civil engineering, especially bridge engineering, has lagged behind. This technology aims to construct a virtual entity that is precisely mapped to the physical bridge and drive its evolution with the help of sensor data to achieve state synchronization, advanced prediction, and decision support. Ultimately, it has become a research hotspot in the industry due to its real-time performance, predictability, and full lifecycle management capabilities.
[0003] However, the in-depth application of bridge digital twins still faces fundamental challenges. First, the industry lacks a unified definition, understanding, and application paradigm, and related technical standards and supporting systems are still immature, hindering its evolution to a higher level of maturity. A prominent contradiction lies in the fact that the industry has excessively high expectations for it, urgently needing it to solve systemic problems, but the underlying data architecture suffers from "island" and "heterogeneous" issues, making it difficult to support systematic applications. Furthermore, the "full lifecycle" concept is difficult to fully implement in actual business operations, and is usually deconstructed into independent stages such as the "construction period" and the "operation and maintenance period"; this invention focuses on the construction control stage of long-span arch bridges.
[0004] In existing construction control research, the application of digital twins often falls into the misconception of "full-element, high-fidelity". Although extensive element coverage can improve the theoretical fidelity of the model, this approach seriously ignores the resulting data acquisition costs, computing power consumption, and engineering feasibility, failing to achieve a reasonable balance between accuracy and economy.
[0005] Therefore, a new solution is needed to address the above problems. Summary of the Invention
[0006] The purpose of this invention is to provide a targeted digital twin construction method for the construction control of long-span arch bridges. This invention explores a new paradigm for targeted twinning in the construction of long-span arch bridges, which can dynamically select the twin granularity based on actual control objectives and focus on key elements. This is of great significance for promoting the efficient application of this technology in practical engineering and solving the technical problems mentioned in the background.
[0007] To achieve the above objectives, the present invention provides the following technical solution: a targeted digital twin construction method for construction control of long-span arch bridges, comprising at least the following steps:
[0008] S1: Determine the targeted twin mapping based on the stress-free state benchmark, establish the mapping relationship Φ between the physical construction space (P-Space) and the virtual model space (V-Space), and establish the stress-free state quantities. As a consistency axiom of the mapping, it guarantees the stress-free state quantities of the physical space. stress-free state quantities in virtual space Always consistent;
[0009] S2: Establish a targeted element decision model based on the stress-free state control mechanism, targeting the construction control objectives τ of the current construction stage. Based on mechanical sensitivity analysis, dynamic decisions are made regarding the quantities required to achieve a stress-free state. The most critical set of target elements ;
[0010] S3: Stress-free state-aware resource allocation for target elements, based on the target element set. Importance weights of each element Calculate and configure the required sensor types, quantities, and deployment locations;
[0011] S4: Implement construction control and reliability evaluation based on stress-free state benchmark.
[0012] Furthermore, S1 includes at least the following steps:
[0013] S1.1: The physical construction space is P-Space, and the virtual model space is V-Space. First, define the targeted twin mapping A between the physical construction space and the virtual model space, and its mathematical expression is:
[0014] (1)
[0015] This mapping must adhere to the twin consistency axiom, which guarantees stress-free state quantities in both physical and virtual spaces. Always maintain consistency:
[0016] (2)
[0017] in, Represents the stress-free state quantity of physical space. Represents the stress-free state quantity in virtual space;
[0018] Stress-free state quantities Including the stress-free curvature and stress-free cable length of the component, stress-free state quantities. It is the inherent geometric shape of a component under stress-free conditions; stress-free state quantity. As an invariant independent of the construction path and load history, based on this axiom, we construct the full state vector X(t,τ):
[0019] (3)
[0020] Where t is time, a continuous variable describing the process; τ is the construction stage, defining the control targets for different construction stages; E(t) is the environmental field vector, defining the time-varying physical environmental factors acting on the bridge structure, which include at least temperature field, wind field, and humidity; L(t, τ) is the load field vector, defining the load combination applied to the structure during construction, mainly composed of permanent loads and construction loads; R(t, τ) is the structural response field vector, which is the direct basis for assessing the state and making control decisions, mainly composed of displacement, stress, and dynamic characteristics (frequency, mode shape), and this vector provides a unified mathematical representation for subsequent targeted decision-making and state analysis.
[0021] Furthermore, S2 includes at least the following steps:
[0022] S2.1: Establish a deterministic mapping from construction control objectives to target elements. This mapping relationship is based on a predefined mechanical transmission path and prior knowledge of construction technology, and the expression is as follows:
[0023] (4)
[0024] in, Let j be the construction control objectives (e.g., alignment elevation, cable tension); τ be the construction stage; the decision logic of M is based on: in stage τ, to achieve objective o j Which components or nodes have a mechanical state that is directly and must be controlled?
[0025] S2.2: Define the decision algebraic expression for the target feature set, where the target feature set C(τ) is determined by the following set operations:
[0026] (5)
[0027] S2.3: Define the importance weight of the targeted element, element c i Importance weight w in the target set i (τ) is uniquely determined by its mechanical sensitivity to the target controlled by stress-free state quantities, and the relative rationality of the weight allocation is ensured through normalization:
[0028] (6)
[0029] Where Z is the normalization factor, its value is the sum of the mechanical sensitivities of all elements in the target set C(τ); Sensitivityi(τ) is the mechanical sensitivity; Mechanical Elements( τ ) (Mechanical sensitive element set) is the set of structural elements that are most sensitive to the realization error of stress-free state quantity S0 during the construction stage τ.
[0030] The set of mechanically sensitive elements was determined through mechanical sensitivity analysis, whereby the mechanical sensitivity Sensitivityi(τ) quantitatively describes the target element. The effect of unit state change on the control objective The extent of the impact.
[0031] Furthermore, element c i The required number of sensors N Sensors( c i) Determined by the following formula:
[0032] (7)
[0033] Where R is the system's basic redundancy coefficient; For element c i The importance weight, which is directly determined by its mechanical sensitivity to achieving the stress-free state quantity S0; Complexity(c i ) is the target element c i Monitor complexity factors; Base Number(ci) This is the baseline quantity.
[0034] Furthermore, S4 includes at least the following steps:
[0035] Calculate target element c i Deviation control index DE i(t) ;
[0036] Deviation control index DE i(t) The core lies in comparing the monitored state quantity M i (t) and the expected state quantity P derived based on the stress-free state quantity S0. i The difference between (S0, t) is normalized:
[0037]
[0038] (7)
[0039] in, The allowable deviation value for the element.
[0040] Compared with the prior art, the beneficial effects of the present invention are:
[0041] 1. The targeted digital twin construction method proposed in this invention fundamentally improves the accuracy of construction monitoring of long-span arch bridges by introducing stress-free state quantities (stress-free curvature and stress-free cable length) as benchmark invariants for construction control. Based on the mechanical sensitivity analysis of the parametric perturbation method, this method can accurately identify key monitoring elements in the main arch alignment and cable force control during the design stage, and establish a three-dimensional optimization model of sensor type-quantity-spatial location, so that limited monitoring resources can be accurately focused on the mechanically sensitive areas under the cantilever construction state of the main arch, realizing a fundamental shift from "experience-based deployment" to "model-driven".
[0042] 2. The targeted digital twin construction method proposed in this invention achieves a deep integration of professional theory and digital twin technology by establishing a twin mapping relationship with stress-free state quantities as anchor points. The model focuses on control parameters directly related to stress-free state quantities, such as the main arch alignment elevation, key section stress, and cable tension. It constructs a lightweight twin that is highly realistically synchronized with the bridge entity, overcoming the system redundancy problem caused by the pursuit of "full bridge simulation" in traditional digital twins in the construction of large-span arch bridges. It forms a practical engineering intelligent entity specifically for the construction of large-span arch bridges.
[0043] 3. The targeted digital twin construction method proposed in this invention achieves precise control over the construction process of long-span arch bridges by constructing a closed-loop control system based on stress-free state quantities as a unified benchmark. This system compares monitoring data with expected state quantities derived from stress-free state quantities to accurately assess construction deviations and predict trends. This ensures that the control strategy always serves the goal of achieving stress-free state quantities, promoting a paradigm shift in construction control from "passive adjustment" to "active pre-control," and guaranteeing a high degree of consistency between the completed bridge state and design objectives with minimal control costs. Attached Figure Description
[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the 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.
[0045] Figure 1 This is a schematic diagram of the targeted digital twin construction method of the present invention. Detailed Implementation
[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0047] This invention abandons the traditional digital twin modeling approach that pursues uniform modeling of all elements and the entire life cycle. Instead, it innovatively proposes to take the stress-free state quantities in long-span arch bridges as the theoretical core and invariant benchmark. By establishing targeted twin mapping, it realizes the transformation from "comprehensive simulation" to "precise control" and dynamically focuses computing and sensing resources on the most critical elements in the construction process of long-span arch bridges.
[0048] Specifically as follows:
[0049] Example 1:
[0050] Please see Figure 1 A targeted digital twin construction method for construction control of long-span arch bridges includes at least the following steps:
[0051] S1: Determine the targeted twin mapping based on the stress-free state benchmark, establish the mapping relationship Φ between the physical construction space (P-Space) and the virtual model space (V-Space), and establish the stress-free state quantities. As a consistency axiom of mapping, it guarantees the stress-free state quantities of physical space. stress-free state quantities in virtual space Always consistent;
[0052] S2: Establish a targeted element decision model based on the stress-free state control mechanism, targeting the construction control objectives τ of the current construction stage. Based on mechanical sensitivity analysis, dynamic decisions are made regarding the quantities required to achieve a stress-free state. The most critical set of target elements ;
[0053] This step aims to address the core issue of "how to dynamically determine target elements." The theoretical foundation of this model lies in the stress-free state quantity control theory, and by introducing domain knowledge from the construction of long-span arch bridges, it transforms this knowledge into a series of objective and repeatable mechanical criteria to deterministically identify and lock in the key elements of each construction stage.
[0054] S3: Stress-free state-aware resource allocation for target elements, based on the target element set. Importance weights of each element Calculate and configure the required sensor types, quantities, and deployment locations;
[0055] The aim is to precisely allocate limited sensing resources to the most critical target elements for realizing stress-free state variables. Its allocation logic directly inherits from the decision-making result of targeting stress-free state variables in step two, ensuring that the physical sensing system directly serves the core control theory.
[0056] S4: Implement construction control and reliability evaluation based on stress-free state benchmark.
[0057] S1 includes at least the following steps:
[0058] S1.1: The physical construction space is P-Space, and the virtual model space is V-Space. First, define the targeted twin mapping A between the physical construction space and the virtual model space, and its mathematical expression is:
[0059] (1)
[0060] This mapping must adhere to the twin consistency axiom, which guarantees stress-free state quantities in both physical and virtual spaces. Always maintain consistency:
[0061] (2)
[0062] in, Represents the stress-free state quantity of physical space. Represents the stress-free state quantity in virtual space;
[0063] Stress-free state quantities Including the stress-free curvature and stress-free cable length of the component, stress-free state quantities. It is the inherent geometric shape of a component under stress-free conditions; stress-free state quantity. As an invariant independent of the construction path and load history, based on this axiom, we construct the full state vector X(t,τ):
[0064] (3)
[0065] Where t is time, a continuous variable describing the process; τ is the construction stage, defining the control targets for different construction stages; E(t) is the environmental field vector, defining the time-varying physical environmental factors acting on the bridge structure, which include at least temperature, wind, and humidity; L(t, τ) is the load field vector, defining the load combination applied to the structure during construction, mainly consisting of permanent loads and construction loads; R(t, τ) is the structural response field vector, which is the direct basis for assessing the state and making control decisions, mainly consisting of displacement, stress, and dynamic characteristics (frequency, mode shape), providing a unified mathematical representation for subsequent targeted decision-making and state analysis.
[0066] S2 includes at least the following steps:
[0067] S2.1: Establish a deterministic mapping from construction control objectives to target elements. This mapping relationship is based on a predefined mechanical transmission path and prior knowledge of construction technology, and the expression is as follows:
[0068] (4)
[0069] in, Let j be the construction control objectives (e.g., alignment elevation, cable tension); τ be the construction stage; the decision logic of M is based on: in stage τ, to achieve objective o j Which components or nodes have a mechanical state that is directly and must be controlled?
[0070] S2.2: Define the decision algebraic expression for the target feature set, where the target feature set C(τ) is determined by the following set operations:
[0071] (5)
[0072] S2.3: Define the importance weight of the targeted element, element c i Importance weight w in the target set i (τ) is uniquely determined by its mechanical sensitivity to the target controlled by stress-free state quantities, and the relative rationality of the weight allocation is ensured through normalization:
[0073] (6)
[0074] Where Z is the normalization factor, its value is the sum of the mechanical sensitivities of all elements in the target set C(τ); Sensitivityi(τ) is the mechanical sensitivity; Mechanical Elements( τ ) (Mechanical Sensitive Element Set) is the set of structural elements most sensitive to the realization error of the stress-free state quantity S0 during the construction stage τ; this set is based on deterministic structural mechanics methods such as parametric sensitivity analysis or influence matrix; Measurable Elements A set of physically monitorable elements determined by field sensor technology;
[0075] The set of mechanically sensitive elements was determined through mechanical sensitivity analysis, and the mechanical sensitivity Sensitivityi(τ) quantitatively describes the target elements. The effect of unit state change on the control objective The degree of influence is calculated using one of the following methods:
[0076] Based on the influence matrix method: The mechanical influence matrix J(τ) of the construction stage τ is constructed using the unit force method or the unit displacement method. For candidate elements c... i By applying a unit virtual change, the change in the control target Δo is calculated through positive mechanics analysis. j This change is element c. i Mechanical sensitivity.
[0077] Based on the parametric perturbation method: In the finite element model of stage τ, for element c iThe control parameters (preferably cable force or stress-free length) are subjected to a small perturbation (e.g., 1%), and the rate of change of the control target |Δo is calculated. j / △p i |As mechanical sensitivity.
[0078] Element c i The required number of sensors N Sensors( c i) Determined by the following formula:
[0079] (7)
[0080] Where R is the system's basic redundancy coefficient; For element c i The importance weight, which is directly determined by its mechanical sensitivity to achieving the stress-free state quantity S0; Complexity(c i ) is the target element c i Monitor complexity factors; Base Number(ci) This is the baseline quantity.
[0081] The selection and placement of sensors should prioritize the high-precision sensing of physical quantities related to the stress-free state quantity S0. For example, to control stress-free curvature, sensors (such as total stations) for monitoring installation alignment and coordinates should be prioritized. To control stress-free cable length, sensors (such as vibrating wire strain gauges) for monitoring cable force and anchor point displacement should be prioritized.
[0082] S4 includes at least the following steps:
[0083] Calculate target element c i Deviation control index DE i(t) ;
[0084] Deviation control index DE i(t) The core lies in comparing the monitored state quantity M i (t) and the expected state quantity P derived based on the stress-free state quantity S0. i The difference between (S0, t) is normalized:
[0085]
[0086] (7)
[0087] in, The allowable deviation value for the element.
[0088] Example 2:
[0089] This embodiment proposes a specific application based on the above embodiment one;
[0090] A steel-concrete composite arch bridge with a span of 418m is used as an example. Known conditions:
[0091] Number of linear segments in the main arch: 40;
[0092] Number of grappling hooks: 34;
[0093] Total number of stress measurement points: 88;
[0094] Weighting: w1 = 0.45 (linearity), w2 = 0.35 (cable force), w3 = 0.2 (stress).
[0095] Basic redundancy coefficient: R = 1.5;
[0096] Monitoring complexity assumptions: Complexity(c1) = 1.5 (linearity, requiring high-precision measurement), Complexity(c2) = 2 (cable force, complex equipment), Complexity(c3) = 2 (stress, complex layout)
[0097] Step 1: Determine the targeted twin mapping based on the stress-free state benchmark.
[0098] In this embodiment, the construction stage τ is defined as the "main arch installation stage". The load field L(t, τ) mainly includes the front and back cable forces; the structural response field R(t, τ) mainly includes the main arch alignment, cable forces, and stresses at key sections. By establishing a twin mapping based on stress-free state quantities (target stress-free curvature and stress-free cable length), stress-free state quantities that are not easily measured directly are converted into construction control variables that can be monitored and adjusted online, forming a unified control benchmark during the construction process.
[0099] Step 2: Establish a targeted element decision model based on the stress-free state control mechanism.
[0100] Based on the stress-free state control theory, this embodiment primarily uses linear control, supplemented by cable force control, with stress monitoring serving as a safety verification method to determine the target set for targeted control.
[0101] O(τ) = {Main arch alignment, cable tension, stress at key sections}
[0102] Step 3: Stress-free state-aware resource allocation method for target elements.
[0103] 1. Calculation of the number of linear sensors for the main arch:
[0104] Base quantity: Base Number(C1) =40 (one control point per segment)
[0105]
[0106] Conclusion: The calculation results show that 41 prisms need to be arranged, which is consistent with the 40 prisms arranged in the example, verifying the guiding role of the formula in the allocation of linear monitoring resources.
[0107] 2. Calculation of the number of cable tension sensors:
[0108] Base quantity: Base Number(C2) =34 (one measuring point for each cable)
[0109]
[0110] Conclusion: The calculation results indicate that the number of cable tension monitoring sensors configured is 36. In the actual embodiment, 34 cables were arranged to correspond to 34 measuring points. The error between the calculated value and the actual value is 5.8%, which is within the acceptable range for engineering.
[0111] 3. Calculation of the number of cross-sectional stress sensors:
[0112] Base quantity: Base Number(C3) =88
[0113]
[0114] Conclusion: The calculation results show that, under the current target weight configuration, the optimal number of sensors for cross-sectional stress is 53. This result differs from the 88 sensors actually deployed in the embodiment, which demonstrates the optimization guidance role of the configuration model proposed in this invention: it reveals that stress monitoring resources can be further intensively configured while ensuring structural safety. According to the model guidance, stress sensors only need to be deployed at nine key control sections, including the two arch abutments, L / 8, L / 4, 3L / 8, and L / 2, to effectively monitor the structural stress safety status, thereby achieving the optimal balance between the economy of monitoring resources and structural safety.
[0115] Step 4: Implement construction control and reliability evaluation based on stress-free state benchmark.
[0116]
[0117]
[0118] DE i(1)mean The linear average deviation control index indicates that the average control level is less than the allowable deviation, the predicted construction control reliability index is close to 1, the overall elevation control level is good, and the control accuracy reaches the millimeter level.
[0119]
[0120]
[0121] DE i(2)mean This indicates the average deviation control index of the cable tension, meaning that the average control level is less than the allowable deviation (±10%), and the overall deviation is within the specification range (JTG / T 3650-2020 specification).
[0122]
[0123]
[0124] DE i(3)mean This represents the stress average deviation control index, indicating that the average control level is less than the allowable deviation and the cross-sectional stress is in a safe state.
[0125] In summary, this embodiment verifies the superior performance of the proposed targeted digital twin construction method in the construction of long-span steel-concrete composite arch bridges. This method, with stress-free state quantity control as its core, fundamentally ensures the precise realization of the main arch's phased bridge alignment against the design objectives, effectively eliminating the interference of time-varying environmental factors on the final structural state. Based on the targeted twin concept, through precise mapping of sensor configuration via element weight allocation, it not only achieves full-process control within the specifications but also achieves millimeter-level accuracy in the main arch alignment, fully demonstrating the efficient matching between twin element selection and construction control precision.
[0126] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.
Claims
1. A method for targeted digital twin construction for long-span arch bridge construction control, characterized in that: At least the following steps are included: S1: determine the target twin mapping based on the stress-free state reference, establish the mapping relationship Φ between the physical construction space and the virtual model space, and determine the stress-free state quantity As the consistency axiom of the mapping, that is, to ensure that the stress-free state quantity of the physical space is always consistent with the stress-free state quantity of the virtual space ; S2: Establish a targeted element decision model based on the stress-free state control mechanism, aiming at the construction control target of the current construction stage τ Based on the mechanical sensitivity analysis, dynamically decide the most critical targeted element set to achieve the stress-free state quantity ; S3: Stress state-aware resource allocation towards targeting elements, according to the importance weight of each element in the set of targeting elements importance weight of each element , compute and configure the required sensor type, number and placement S4: Implement construction control and reliability evaluation based on stress-free state benchmark.
2. The method of claim 1, wherein the method is a method of constructing a target digital twin for construction control of a long-span arch bridge. S1 includes at least the following steps: S1.1: The physical construction space is P-Space, and the virtual model space is V-Space. First, define the targeted twin mapping A between the physical construction space and the virtual model space, and its mathematical expression is: (1) The mapping must respect the twin consistency axiom, i.e. guaranteeing the stress-free state of the physical space and the virtual space Always be consistent: (2) in, Represents the stress-free state quantity of physical space. Represents the stress-free state quantity in virtual space; Stress-free state quantities Including the stress-free curvature and stress-free cable length of the component, stress-free state quantities. It is the inherent geometric shape of a component under stress-free conditions; stress-free state quantity. As an invariant independent of the construction path and load history, based on this axiom, we construct the full state vector X(t,τ): (3) Where t is time, a continuous variable describing the process; τ is the construction stage, defining the control targets for different construction stages; E(t) is the environmental field vector, defining the time-varying physical environmental factors acting on the bridge structure, which include at least temperature field, wind field, and humidity; L(t, τ) is the load field vector, defining the load combination applied to the structure during construction, mainly consisting of permanent loads and construction loads; R(t, τ) is the structural response field vector, which is the direct basis for assessing the state and making control decisions.
3. The targeted digital twin construction method for construction control of long-span arch bridges according to claim 2, characterized in that: S2 includes at least the following steps: S2.1: Establish a deterministic mapping from construction control objectives to target elements. This mapping relationship is based on a predefined mechanical transmission path and prior knowledge of construction technology, and the expression is as follows: (4) in, Let j be the construction control objectives; τ be the construction stage; This is a candidate set of target elements relevant to this construction phase. S2.2: Define the decision algebraic expression for the target feature set, where the target feature set C(τ) is determined by the following set operations: (5) S2.3: Define the importance weight of the targeting element, element c i In the importance weight w of the targeting set i (τ) is determined by its mechanical sensitivity to the stress-free state quantity control target, and the relative rationality of weight allocation is ensured by normalization processing: (6) wherein Z is a normalization factor whose value is the sum of the mechanical sensitivities of all elements in the targeted set C(τ); Sensitivityi(τ) is the mechanical sensitivity; Mechanical Elements( τ ) is the set of structural elements most sensitive to the implementation error of the unstressed state quantity S0 for the construction phase τ; The set of mechanically sensitive elements was determined through mechanical sensitivity analysis, whereby the mechanical sensitivity Sensitivityi(τ) quantitatively describes the target element. The effect of unit state change on the control objective The extent of the impact.
4. The targeted digital twin construction method for construction control of long-span arch bridges according to claim 3, characterized in that: Element c i Required number of sensors N Sensors( c i) Is determined by the following equation: (7) Where R is the system's basic redundancy coefficient; For element c i The importance weight, which is directly determined by its mechanical sensitivity to achieving the stress-free state quantity S0; Complexity(c i ) is the target element c i Monitor complexity factors; Base Number(ci) This is the baseline quantity.
5. The targeted digital twin construction method for construction control of long-span arch bridges according to claim 4, characterized in that: The S4 includes at least the following steps: Computing a targeting element c i a deviation control index DE i(t) ; Deviation control index DE i(t) The core of the method is to monitor the difference between the actual state quantity M i (t) and the expected state quantity P i (S0, t) derived on the basis of the stress-free state quantity S0 and normalize it: ; （7） in, The allowable deviation value for the element.