Variable cross-section bridge component parametric modeling and construction management and control method and system

Abstract

The invention provides a variable cross-section bridge component parametric modeling and construction management and control method and system, and relates to the technical field of engineering entity digital modeling. The system collects physical layer data in real time through a multi-source sensor network, the data of sensors are processed through a space-time dynamic binding module, space coordinates and construction timestamps are fused to generate a unique component ID, and a real-time mapping relation between the sensor data and a variable cross-section component is established. Based on the data, the damage evolution dual-prediction module can establish a current state diagnosis model, predict the residual life and quantify the amplification effect of foundation settlement on variable cross-section damage. And the lightweight decision output module generates an executable construction instruction according to diagnosis and prediction results, dynamically optimizes the mechanical requirements of the bridge, improves the construction quality and safety, and ensures the long-term stability and reliability of the bridge.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering entity digital modeling, and specifically to a parametric modeling and construction control method and system for variable-section bridge components. Background Art

[0002] In the field of digital modeling of engineering entities, the real-time geometric deformation of engineering entities caused by construction loads, material phase changes, and environmental coupling causes the physical position of embedded sensors to continuously deviate from the preset monitoring target area. Traditional static coordinate mapping models fail to establish dynamic feedback from deformation to position drift, resulting in the same sensor identifier continuously outputting data streams at different spatial locations on the component, causing a break in the spatiotemporal association chain between data and entities. While current digital modeling systems are committed to increasing sensor density or optimizing transmission protocols, they have not resolved the underlying contradictions between entity deformation, sensor position drift, and data stream binding failures. Ultimately, the model incorrectly associates data with bridge components, severely restricting the trusted decision-making capabilities of the engineering digital twin system. Summary of the Invention

[0003] The purpose of the present invention is to provide a parametric modeling and construction control method and system for variable-section bridge components, aiming to solve the above-mentioned problems; the purpose of the present invention can be achieved through the following technical solutions: A multi-source sensor network integrated in the variable-section components of the bridge collects physical layer data in real time. The sensor network includes strain gauges arranged in the stress concentration area of ​​the variable section, humidity sensors at the depth of the protective layer, crack meters at the junction of the web and bottom plate, and inclinometers on the foundation pedestal. The sensors each perform their respective functions. The strain gauges perceive tiny strain changes in the stress concentration area in real time, the humidity sensors accurately monitor the humidity conditions at the depth of the protective layer, the crack meters promptly detect and measure the generation and development of cracks at the junction of the web and bottom plate, and the inclinometers are used to accurately measure the inclination angle of the foundation pedestal, providing basic data support for data analysis and control.

[0004] The spatiotemporal dynamic binding module generates a unique component ID by fusing the spatial coordinates in the sensor network with the construction timestamp, and establishes a real-time mapping relationship between the sensor data and the variable-section component. Specifically, during the sensor installation phase, the initial spatial coordinates (X0, Y0, Z0) relative to the geometric centroid are measured based on the specific layout points of the sensor in the stress concentration area, the junction of the web and the bottom plate, the depth of the protective layer, and the foundation pedestal. During the construction process, the Y coordinate is corrected according to the change in the variable section curvature inverted by the strain gauge data, the Z coordinate is corrected according to the concrete expansion coefficient calculated by the humidity sensor data, and the X coordinate is corrected according to the settlement gradient calculated by the foundation inclinometer data, generating a dynamically compensated spatial coordinate (X0+ΔX m ,Y0+ΔY S ,Z0+ΔZ h); Based on the real-time strain data collected by the strain gauge in the stress concentration area, the instantaneous curvature change Δκ of the neutral axis of the component is inverted, and according to ΔY S =(ΔκX0²) / 2Calculate the Y-axis coordinate compensation ΔY S , correct the position of the sensor in the height direction of the cross section; the change in concrete moisture content Δω measured by the humidity sensor at the depth of the protective layer is calculated according to ε h =αΔωCalculate the expansion strain ε in the width direction of the variable section h , where α is the moisture expansion coefficient of concrete material at curing age and ambient temperature, and then through ΔZ h =ε h (Z0+b / 2) Calculate the Z-axis coordinate compensation ΔZ h , b is the initial half-width of the variable section of the bridge component, and the position of the sensor in the width gradient direction is corrected; according to the inclination angle θ monitored by the inclinometer on the foundation pedestal and its change gradient dθ / dt, the longitudinal settlement difference δ is calculated, and ΔX m =δ(X0 / L) Calculate the X-axis coordinate compensation ΔX m , where L is the total length of the component, and the position of the sensor along the neutral axis is corrected; after the dynamic space coordinates are generated by three-level sequential coupling compensation, they are hashed and fused with the timestamp to generate a unique component ID, thereby achieving precise binding of sensor data and variable-section components in the time and space dimensions, ensuring the accuracy and relevance of the data.

[0005] The damage evolution dual prediction module establishes a current state diagnosis model based on the data collected by the sensor network; the maximum strain ε of the variable section collected max Normalization is performed to convert strain data of different dimensions into dimensionless relative values, which is convenient for subsequent comprehensive analysis and comparison. At the same time, the daily variation of crack width ΔW is calculated. fThe humidity of the concrete and the protective layer are standardized and converted respectively to map them to the same dimensional space, eliminating the influence of the unit and dimensional differences between different physical quantities, so that these data can be integrated and analyzed under a unified framework; the weight coefficients α, β, and γ are dynamically adjusted during the construction stage, where β decays exponentially with the curing time during the initial setting stage of concrete. This is because the generation and development of cracks in the initial setting process of concrete are extremely sensitive to the time factor. With the passage of time, the concrete gradually hardens and this sensitivity gradually decreases, so the β weight coefficient decays exponentially; while γ increases linearly, because with the extension of the curing time, the influence of humidity on the performance of concrete gradually accumulates and becomes apparent, and its weight increases accordingly; in the prestressing stage, α is increased step by step according to the prestressing grade, because the degree of prestressing is directly related to the stress state and performance of the component, and different grades of prestressing have different degrees of influence on the component. By increasing the α weight coefficient in a step-by-step manner, it can more accurately reflect the dominant role of prestressing on the component state at this stage; based on this, a current state diagnosis model is constructed. , where S t is the current status diagnostic value, is the maximum strain of the variable section, is the daily variation of crack width, and RH is the humidity of the protective layer, so as to accurately evaluate the current state of the variable-section bridge component; and establish the remaining life prediction relationship based on the current state diagnosis model. , where T rem is the predicted value of remaining life, K d is the damage accumulation coefficient, θ is the daily inclination angle of the foundation, and quantifies the amplification effect of foundation settlement on variable section damage; its remaining life prediction relationship is associated with the daily inclination angle θ of the foundation, and the exponential penalty term of θ reflects the amplification effect of settlement on damage, that is, the greater the inclination of the foundation, the faster the damage accumulation rate of the variable section component, and the corresponding shortening of the remaining life; by converting the current state diagnostic value S t With K d Perform double feedback operation to obtain the remaining life T rem , to achieve dynamic prediction of the remaining life of variable-section bridge components, and provide a basis for maintenance and decision-making during the construction process.

[0006] The lightweight decision output module generates executable construction instructions based on the results of the damage evolution dual prediction module, and dynamically optimizes the mechanical requirements of the bridge through the executable construction instructions; when the current state diagnosis value exceeds the preset safety threshold, a dynamic adjustment instruction for the concrete process parameters is sent to the casting equipment bound to the component ID, and the dynamic correction coefficient is calculated by combining the protective layer humidity expansion strain inverted by the Z-axis coordinate compensation and the real-time concrete moisture content change. The dynamic correction coefficient reflects the performance change trend of the concrete under the current environment and stress conditions. Based on this, a concrete process parameter adjustment instruction including the dynamic correction coefficient of the water-cement ratio is sent to the casting equipment to enable it to accurately control the concrete pouring process and ensure that the quality and performance of the concrete meet the construction requirements; when the remaining life prediction value is lower than the maintenance critical value, a local prestressing strengthening scheme is generated according to the variable section curvature characteristics inverted by the component ID to strengthen the prestressing value and the curvature change The absolute value of the settlement amount is proportional to the product of the damage accumulation coefficient. By reasonably adjusting the prestress, the bearing capacity and crack resistance of the component are enhanced, and its service life is extended. When the foundation inclination angle is monitored to reach the settlement intervention threshold, the collaborative foundation stability compensation operation of the components in the same foundation ID group is triggered. According to the gradient distribution of the compensation amount of the X-axis coordinate of each component in the same foundation ID group, the differential settlement vector is calculated, and the settlement gradient distribution along the neutral axis of the component is established. The spatial change rate of the compensation amount of adjacent components is calculated to obtain the longitudinal settlement difference vector. This vector is used as the input parameter for multi-point synchronous grouting compensation operation. The grouting amount of each grouting point is proportional to the settlement vector and its dynamic compensation X-coordinate value, and inversely proportional to the total length of the component. At the same time, this vector is combined with the inclination angle change gradient monitored in real time by the foundation inclinometer for tensor synthesis to generate a vector representing the three-dimensional spatial differential settlement trend, thereby achieving effective compensation and control of foundation settlement and ensuring the overall stability of the bridge.

[0007] As a further technical solution, the spatial coordinates of the sensor network establish a local coordinate system based on the variable-section component, wherein the X-axis locates the longitudinal position along the neutral axis of the component, the Y-axis locates the vertical position along the height direction of the section, and the Z-axis locates the lateral position along the gradient direction of the section width. This coordinate system establishment method is consistent with the geometric shape and mechanical properties of the variable-section component, and can more accurately describe the positional relationship of the sensor on the component, providing support for subsequent spatial coordinate compensation and data fusion.

[0008] As a further technical solution, the mapping process realizes the real-time binding of spatial coordinates and component ID through a three-level dynamic compensation mechanism. Based on the real-time strain data collected by the strain gauge in the stress concentration area, the instantaneous curvature change Δκ of the neutral axis of the component is inverted and the Y-axis coordinate compensation ΔY is calculated. S, correct the position of the sensor in the height direction of the cross section; calculate the expansion strain ε in the width direction of the cross section by measuring the change in concrete moisture content Δω at the depth of the protective layer using the humidity sensor h , calculate the Z-axis coordinate compensation ΔZ h , correct the position of the sensor in the width gradient direction; according to the inclination angle θ and its change gradient dθ / dt monitored by the inclinometer on the foundation pedestal, solve the longitudinal settlement difference δ and calculate the X-axis coordinate compensation ΔX m , correct the position of the sensor along the neutral axis; after the dynamic space coordinates are generated by three-level sequential coupling compensation, they are hashed and fused with the timestamp to generate a unique component ID. The dynamic compensation mechanism takes into account the deformation of variable-section bridge components during construction and the influence of environmental factors, and acts on the real-time accuracy of the sensor space coordinates and the uniqueness and reliability of the component ID.

[0009] As a further technical solution, the establishment of the current state diagnostic model is to collect the maximum strain ε of the variable section. max Normalization is performed to convert the maximum strain value into a relative, dimensionless range, which is convenient for comprehensive analysis and comparison with other parameters. At the same time, the daily change of crack width ΔW f The humidity of the protective layer RH is standardized and mapped to the same dimensional space, eliminating the influence of unit and dimensional differences between different physical quantities, so that these parameters can be integrated and analyzed under a unified framework; the weight coefficients α, β, and γ are dynamically adjusted during the construction stage. In the initial setting stage of concrete, β decays exponentially with the curing time, while γ increases linearly; in the prestressing stage, α is increased in a step-by-step manner according to the prestressing grade. This dynamic adjustment of the weight coefficients takes into account the influence of various parameters on the component status at different construction stages, so that the current status diagnosis model can more accurately reflect the actual condition of the component.

[0010] As a further technical solution, the remaining life prediction relationship introduces the damage accumulation coefficient K based on the current state diagnosis model. d , its remaining life prediction relationship is related to the foundation daily inclination angle θ, and the exponential penalty term of θ reflects the amplification effect of settlement on damage. Finally, S t With K d Perform double feedback operation to obtain T rem This prediction method takes into account the current status diagnosis results of the components and fully quantifies the cumulative impact of foundation settlement on variable section damage, achieving a dynamic and accurate prediction of the remaining life, and providing forward-looking and scientific guidance for maintenance and decision-making during the construction process.

[0011] As a further technical solution, the executable construction instructions are generated based on the current state diagnosis value, the remaining life prediction value and the foundation inclination angle, and the corresponding dynamic compensation space coordinates and damage accumulation coefficient are called in real time according to the unique identification of the component, so as to achieve precise control of each component; when the state diagnosis value exceeds the preset safety threshold, the concrete process parameter adjustment instruction containing the dynamic correction coefficient of the water-cement ratio is sent to the casting equipment bound to the ID, combined with the protective layer humidity expansion strain inverted by the Z-axis coordinate compensation amount and the real-time collected concrete moisture content change.

[0012] As a further technical solution, the dynamic correction coefficient is calculated by solving the relationship between humidity expansion strain and moisture content change. This solution fully considers the performance changes of concrete materials under different humidity conditions and can accurately reflect the water-cement ratio adjustment requirements of concrete in actual construction environments. When the remaining life prediction value is lower than the maintenance threshold, based on the curvature change and damage accumulation coefficient corresponding to the ID, a local prestressing reinforcement scheme is generated for the area with increased curvature of the variable section, and the reinforcement prestressing value is proportional to the product of the absolute value of the curvature change and the damage accumulation coefficient. This reinforcement scheme accurately adjusts the prestressing according to the damaged area and damage degree of the component, enhancing the component's bearing capacity and crack resistance, and extending its service life. When the foundation inclination angle reaches the settlement intervention threshold, the differential settlement vector is calculated based on the gradient distribution of the X-axis coordinate compensation value of each component in the same foundation ID group, providing a precise parameter basis for the foundation stability compensation operation. Through multi-point synchronous grouting compensation operation, effective control of foundation settlement is achieved, ensuring the overall stability of the bridge.

[0013] The system of the present invention comprises: A multi-source sensor network integrated into the bridge's variable-section components collects physical layer data in real time. This sensor network includes strain gauges located in stress concentration areas of the variable sections, humidity sensors at the depth of the protective layer, crack gauges at the interface between the web and base plate, and inclinometers on the foundation cap. This provides real-time, accurate basic data support for the system's data-driven operation. The spatiotemporal dynamic binding module generates a unique component ID by fusing the spatial coordinates in the sensor network with the construction timestamp. This module then establishes a real-time mapping relationship between sensor data and variable-section components, achieving precise association between data and components in spatiotemporal dimensions and ensuring the accuracy and reliability of subsequent analysis and control. The damage evolution dual prediction module establishes a current status diagnosis model based on data collected by the sensor network. Through the fusion analysis of multiple data and dynamic weight adjustment, it can accurately diagnose the current status of the component, further establish a remaining life prediction relationship, quantify the amplification effect of foundation settlement on variable section damage, and provide a forward-looking basis for maintenance and decision-making during construction. The lightweight decision-making output module generates executable construction instructions based on the results of the damage evolution dual prediction module. By dynamically adjusting concrete process parameters, optimizing prestressing schemes, and implementing foundation stability compensation operations, it dynamically optimizes the mechanical requirements of the bridge, achieves precise control of variable-section bridge components, improves construction quality and safety, and ensures the long-term stability and reliability of the bridge.

[0014] The present invention provides a parametric modeling and construction control method and system for variable-section bridge components, which has the following beneficial effects: 1. The present invention achieves high-precision binding of data and physical components by associating the data collected by sensors in real time with the precise spatiotemporal position of variable-section components, significantly improving the effectiveness of status diagnosis and decision optimization, and enabling the system to accurately identify and locate key status information of components.

[0015] 2. The present invention establishes a remaining service life prediction relationship affected by foundation settlement through the correlation feedback of the damage accumulation coefficient and the foundation inclination angle, realizes the accurate dynamic prediction of the remaining service life of variable-section bridge components, and improves the risk warning capability during the construction process.

[0016] 3. The present invention realizes real-time dynamic optimization of bridge mechanical requirements through dynamic adjustment of construction process parameters and optimized design of local prestressed reinforcement scheme, thereby improving construction quality and long-term stability of the structure. Dynamic optimization enables timely adjustment of construction process according to the actual status and remaining life of the components, thereby extending the service life of the bridge and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] In the field of bridge construction, especially for the parametric modeling and construction control of variable-section bridge components, the present invention provides a parametric modeling and construction control method and system for variable-section bridge components; the implementation method and technical details of the present invention will be elaborated in detail through a specific embodiment below.

[0020] refer to Figure 1Taking a large-scale variable-section bridge construction project as an example, at the beginning of construction, the project team carried out detailed planning and deployment of the sensor network based on the bridge's design drawings and construction plan; in the stress concentration area, a strain gauge was installed every 0.5 meters to accurately monitor the tiny strain changes in the area; at the junction of the web and the bottom plate, a crack meter was set up every 1 meter to promptly detect and measure the occurrence and development of cracks; at the depth of the protective layer, a humidity sensor was installed every 1.5 meters to monitor the changes in the moisture content of the concrete; in addition, at the foundation pedestal, inclinometers were arranged according to the geological conditions to measure the inclination angle of the foundation.

[0021] During the sensor installation process, the project team used high-precision measuring instruments, such as total stations and levels, to determine the initial spatial coordinates (X0, Y0, Z0) of each sensor relative to the geometric centroid of the bridge. For example, the initial coordinates of the strain gauges in the stress concentration area were accurately measured and recorded. These coordinate data will serve as the basis for subsequent dynamic compensation. During construction, the sensors collected physical layer data in real time, including information such as strain, humidity, crack width, and inclination angle. The sampling frequency of the data acquisition equipment was set to once per minute to ensure the real-time and continuity of the data.

[0022] The spatiotemporal dynamic binding module generates a unique component ID by fusing the spatial coordinates of the sensor with the construction timestamp, thereby establishing a real-time mapping relationship between the sensor data and the variable-section component. Specifically, the system corrects the Y coordinate based on the change in the variable-section curvature inverted by the strain gauge data, corrects the Z coordinate based on the concrete expansion coefficient calculated by the humidity sensor data, and corrects the X coordinate based on the settlement gradient calculated by the foundation inclinometer data, thereby generating a dynamically compensated spatial coordinate (X0+ΔX m ,Y0+ΔY S ,Z0+ΔZ h For example, when the strain gauge detects a change in the strain data at a certain position, the system calculates the instantaneous curvature change Δκ of the neutral axis of the component. Assume that there are three strain gauges at a certain section, located at different heights y1, y2, and y2 from the neutral axis, and the strain values ​​ε1, ε2, and ε3 are measured; according to the plane bending theory, the strain is linearly distributed along the section height, that is, ε(y)=κy; where ε(y) is the strain at a distance of y from the neutral axis, and κ is the curvature of the neutral axis of the component; the collected strain data is linearly fitted using the least squares method to obtain a linear relationship between strain and position; according to the theory of material mechanics, the curvature κ is equal to the strain gradient, that is, the slope of the fitting line; by calculating the curvature κ at the current moment current With the initial curvature κ nitial The difference between the two can be used to obtain the instantaneous curvature change of the neutral axis Δκ=κ current -κ nitial ; Initial curvature κ nitialIt can be measured at the early stage of construction when the component is subjected to small stress and the deformation is stable; according to the formula , calculate the Y-axis coordinate compensation ΔY S , and then correct the position of the sensor in the height direction of the cross section; similarly, the change in concrete moisture content Δω measured by the humidity sensor is calculated by the formula ε h =αΔω to calculate the expansion strain ε in the width direction of the cross section h , where α is the humidity expansion coefficient of concrete material at curing age and ambient temperature; and then through the geometric relationship ΔZ h =ε h (Z0+b / 2) calculates the Z-axis coordinate compensation ΔZ h , correct the position of the sensor in the width gradient direction, where b is the initial half-width of the variable section; in addition, according to the inclination angle θ monitored by the inclinometer and its change gradient dθ / dt, the longitudinal settlement difference δ is calculated and the formula ΔX is used. m =δ(X0 / L) (where L is the total length of the component) to calculate the X-axis coordinate compensation ΔX m , correct the position of the sensor along the neutral axis.

[0023] After the dynamic spatial coordinates are generated through three-level sequential coupling compensation, the system performs a hash fusion operation on them with the timestamp to generate a unique component ID. This process enables the precise binding of sensor data and variable-section components in the time and space dimensions, greatly improving the accuracy and relevance of the data.

[0024] The establishment of the current state diagnosis model involves the normalization and standardization of the collected data; for example, the maximum strain ε of the variable section max Normalized to the dimensionless interval of 0-1, the formula is , where ε min is the initial strain value, ε maxlimit The strain limit is 150×10-6; at the same time, the daily change in crack width ΔW f and the cover humidity RH are converted by the Z-Score standardization method respectively; during the construction stage, the weight coefficients α, β, and γ are dynamically adjusted according to the construction progress; for example, in the initial setting stage of concrete, β decays exponentially with the curing time, and the formula is , where the initial value of β0 is 0.8, λ is 0.1 / h; γ increases linearly, and the formula is , the initial value of γ0 is 0.2, and k is 0.01 / h; in the prestressing stage, α is increased step by step according to the prestressing level, α=0.6 at level one and α=0.8 at level two; based on these processed data and weight coefficients, the current state diagnosis model is constructed, and the formula is ; For example, at a certain moment, the measured ε max=120×10-6, after normalization ε max =0.8; ΔW f =0.03mm, after normalization ΔW f =1.2; RH=80%, after standardization RH=1.5; in the initial setting stage of concrete, assuming the curing time is 10 hours, β≈0.29, γ=0.3, substituting into the formula to calculate S t ≈1.278; when S t When the preset safety threshold of 1.0 is exceeded, the system will trigger an early warning mechanism to prompt construction personnel to take appropriate measures; the remaining life prediction relationship includes a damage accumulation coefficient K d , whose expression is K d =K d0 +ηS t , where K d0 The initial value is 0.1, η is 0.001 / h; the remaining life prediction value T rem The calculation formula is ,Through the remaining life prediction relationship, construction personnel can ,understand the remaining service life of bridge components in advance, ,thus providing a scientific basis for maintenance and decision-making.

[0025] The lightweight decision output module generates executable construction instructions based on the results of the damage evolution dual prediction module. When the current state diagnosis value exceeds the preset safety threshold, the system will send concrete process parameter adjustment instructions to the bound pouring equipment. For example, the dynamic correction coefficient is calculated by combining the protective layer moisture expansion strain inverted by the Z-axis coordinate compensation and the real-time concrete moisture content change. When the remaining life prediction value is lower than the maintenance critical value, the system generates a local prestressed reinforcement plan based on the cross-sectional curvature characteristics of the component ID inversion. When the foundation inclination angle reaches the settlement intervention threshold, the system will trigger the coordinated foundation stability compensation operation of the components in the same foundation ID group. For example, based on the gradient distribution of the X-axis coordinate compensation of each component in the same foundation ID group, the differential settlement vector δ is solved. S =(ΔX m1 -ΔX m2 ) / L; Assume that ΔX of two adjacent components m1 =0.005m, ΔX m2 =0.002m, spacing L=5m, then δ S =0.0006m, and use this vector as input parameter to perform multi-point synchronous grouting compensation operation. The grouting amount of each grouting point , where k = 1000 kg / m³, ΔX m =0.005m, L=5m, then the grouting volume Q≈0.0006kg; at the same time, the vector is tensor-synthesized with the inclination angle gradient monitored in real time by the foundation inclinometer to generate a vector representing the three-dimensional spatial differential settlement trend.

[0026] Through the coordinated work of various modules, the system of the present invention can achieve precise control of variable-section bridge components, effectively improve construction quality and safety, and ensure the long-term stability and reliability of the bridge.

[0027] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A parametric modeling and construction control method for variable cross-section bridge components, characterized in that: The following steps are involved: S1. A sensor network integrated into the bridge's variable-section components collects physical layer data in real time. The sensor network includes strain gauges located in stress concentration areas of the variable sections, humidity sensors at the depth of the protective layer, crack gauges at the interface between the web and base plates, and inclinometers on the foundation cap. S2. A unique component ID is generated by fusing the spatial coordinates in the sensor network with the construction timestamp, and a real-time mapping relationship between the sensor data and the variable-section component is established; S3. Establish a current state diagnostic model based on data collected by the sensor network; establish a remaining life prediction relationship based on the current state diagnostic model and quantify the amplification effect of foundation settlement on variable section damage; S4. Generate executable construction instructions based on the results of step S3, and dynamically optimize the mechanical requirements of the bridge through the executable construction instructions; Instruction I: When the current state diagnosis value exceeds the preset safety threshold, a dynamic adjustment instruction for concrete process parameters is sent to the pouring equipment bound to the component ID; Instruction II: When the remaining life prediction value is lower than the maintenance critical value, a local prestressing strengthening scheme is generated according to the variable section curvature characteristics of the component ID inversion; Instruction III: When the monitored foundation inclination angle reaches the settlement intervention threshold, the coordinated foundation stability compensation operation of the components in the same foundation ID group is triggered.

2. The parametric modeling and construction control method for variable cross-section bridge components according to claim 1, characterized in that: The spatial coordinates of the sensor network establish a local coordinate system based on the variable-section component, in which the X-axis locates the longitudinal position along the neutral axis of the component, the Y-axis locates the vertical position along the height direction of the section, and the Z-axis locates the transverse position along the gradient direction of the section width; during the sensor installation phase, the initial spatial coordinates (X0, Y0, Z0) relative to the geometric centroid are measured based on the specific layout points of the sensor in the stress concentration area, the junction of the web and the bottom plate, the depth of the protective layer, and the foundation pedestal; when fused with the timestamp during the construction process, the Y coordinate is corrected according to the variable section curvature change inverted by the strain gauge data, the Z coordinate is corrected according to the concrete expansion coefficient calculated by the humidity sensor data, and the X coordinate is corrected according to the settlement gradient calculated by the foundation inclinometer data to generate a dynamically compensated spatial coordinate (X0+ΔX m ,Y0+ΔY S ,Z0+ΔZ h ); The compensated spatial coordinates are used as the spatial parameters of the component ID, so that the ID maps the real-time physical position of the sensor on the variable cross-section.

3. The parametric modeling and construction control method for variable-section bridge components according to claim 2, characterized in that: The mapping process realizes the real-time binding of spatial coordinates and component ID through a three-level dynamic compensation mechanism; based on the real-time strain data collected by the strain gauge in the stress concentration area, the instantaneous curvature change Δκ of the neutral axis of the component is inverted. Calculate the Y-axis coordinate compensation ΔY S , correct the position of the sensor in the height direction of the cross section; secondly, the change in concrete moisture content Δω measured by the humidity sensor at the depth of the protective layer is calculated according to , calculate the expansion strain ε in the width direction of the section h , where α is the moisture expansion coefficient of concrete material at curing age and ambient temperature; through the geometric relationship Calculate the Z-axis coordinate compensation ΔZ h , b is the initial half-width of the variable section, and the position of the sensor in the width gradient direction is corrected; according to the inclination angle θ monitored by the inclinometer on the foundation pedestal and its change gradient dθ / dt, the longitudinal settlement difference δ is calculated, and ΔX m =δ(X0 / L) to calculate the X-axis coordinate compensation ΔX m , where L is the total length of the component, and the position of the sensor along the neutral axis is corrected; after the dynamic space coordinates are generated by three-level sequential coupling compensation, they are hashed and fused with the timestamp to generate a unique component ID.

4. The parametric modeling and construction control method for variable cross-section bridge components according to claim 1, characterized in that: The current state diagnosis model is established as follows: the maximum strain ε of the variable section is collected max Normalization is performed; the daily variation of crack width ΔW f The humidity of the protective layer RH is standardized and mapped to the same dimensional space; the weight coefficients α, β, and γ are dynamically adjusted according to the construction stage. In the initial setting stage of concrete, β decays exponentially with the curing time, and γ increases linearly; in the prestressing stage, α is increased step by step according to the prestressing grade; based on this, a current state diagnosis model is constructed. , where S t is the current state diagnostic value, t is the service time of the bridge, ε max is the maximum strain of the variable section, ΔW f is the daily variation of crack width, and RH is the humidity of the protective layer.

5. The parametric modeling and construction control method for variable cross-section bridge components according to claim 4 is characterized by: The remaining life prediction relationship is based on the current state diagnosis model and introduces the damage accumulation coefficient K d , its remaining life prediction relationship is related to the foundation daily inclination angle θ, and the exponential penalty term of θ reflects the amplification effect of settlement on damage; finally, S t With K d The remaining life prediction relationship is obtained by performing double feedback operation, specifically: , where T rem is the predicted value of remaining life, K d is the damage accumulation coefficient, and θ is the daily inclination angle of the foundation.

6. The parametric modeling and construction control method for variable cross-section bridge components according to claim 1, characterized in that: The executable construction instructions are generated based on the status diagnosis value, remaining life prediction value and foundation inclination angle of step S3; the corresponding dynamic compensation space coordinates and damage accumulation coefficient are called in real time according to the unique ID of the component; when the status diagnosis value exceeds the preset safety threshold, the concrete process parameter adjustment instruction including the dynamic correction coefficient of the water-cement ratio is sent to the casting equipment bound to the ID in combination with the protective layer humidity expansion strain inverted by the Z-axis coordinate compensation amount and the real-time collected concrete moisture content change.

7. The parametric modeling and construction control method for variable cross-section bridge components according to claim 6, characterized in that: The dynamic correction coefficient is calculated by solving the relationship between humidity expansion strain and moisture content change; when the remaining life prediction value is lower than the maintenance critical value, based on the curvature change and damage accumulation coefficient corresponding to the ID, a local prestressing reinforcement scheme is generated for the area with increased curvature of the variable section, and the reinforcement prestressing value is proportional to the product of the absolute value of the curvature change and the damage accumulation coefficient; when the foundation inclination angle reaches the settlement intervention threshold, the differential settlement vector is calculated based on the gradient distribution of the X-axis coordinate compensation amount of each component in the same foundation ID group.

8. The parametric modeling and construction control method for variable cross-section bridge components according to claim 7, characterized in that: The differential settlement vector is solved based on the dynamic compensation X-axis coordinate compensation of each component in the same foundation ID group, and the settlement gradient distribution along the neutral axis of the component is established. The longitudinal settlement difference vector is solved by calculating the spatial change rate of the compensation amount of adjacent components, and a multi-point synchronous grouting compensation operation is performed with this vector as an input parameter. The grouting amount of each grouting point is proportional to the settlement vector and its dynamic compensation X-coordinate value at the point, and inversely proportional to the total length of the component. The vector is combined with the inclination angle change gradient monitored in real time by the foundation inclinometer for tensor synthesis to generate a vector representing the three-dimensional spatial differential settlement trend.

9. A system for parametric modeling and construction control of variable cross-section bridge components according to any one of claims 1 to 8, characterized in that: include: A multi-source sensor network integrated into the bridge's variable-section components collects physical layer data in real time. The sensor network includes strain gauges placed in stress concentration areas of the variable sections, humidity sensors at the depth of the protective layer, crack gauges at the interface between the web and base plate, and inclinometers on the foundation cap. The spatiotemporal dynamic binding module generates a unique component ID by fusing the spatial coordinates in the sensor network with the construction timestamp, thus establishing a real-time mapping relationship between sensor data and variable-section components. The damage evolution dual prediction module establishes a current state diagnosis model based on data collected by the sensor network. Based on the current state diagnosis model, it establishes a remaining life prediction relationship and quantifies the amplification effect of foundation settlement on variable section damage. The lightweight decision-making output module generates executable construction instructions based on the results of the damage evolution dual prediction module, and dynamically optimizes the mechanical requirements of the bridge through the executable construction instructions.

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