Parametric Modeling and Construction Control Methods and Systems for Variable Cross-Section Bridge Components

Abstract

This invention provides a method and system for parametric modeling and construction control of variable cross-section bridge components, belonging to the field of digital modeling technology for engineering entities. The system collects physical layer data in real time through a multi-source sensor network. This sensor data is processed by a spatiotemporal dynamic binding module, fusing spatial coordinates with construction timestamps to generate a unique component ID, establishing a real-time mapping relationship between sensor data and variable cross-section components. Based on this data, a dual-prediction module for damage evolution can establish a current state diagnostic model and predict remaining lifespan, while quantifying the amplification effect of foundation settlement on variable cross-section damage. A lightweight decision output module generates executable construction instructions based on the diagnostic and prediction results, dynamically optimizing the mechanical requirements of the bridge, improving construction quality and safety, and ensuring the long-term stability and reliability of the bridge.

Description

Technical Field

[0001] This invention relates to the field of digital modeling technology for engineering entities, specifically to a parametric modeling and construction control method and system for variable cross-section bridge components. Background Technology

[0002] In the field of digital modeling of engineering entities, the real-time geometric deformation of engineering entities under construction loads, material phase changes, and environmental coupling causes the physical positions of embedded sensors to continuously deviate from the preset monitoring target area. Traditional static coordinate mapping models, lacking dynamic feedback on deformation position drift, result in the same sensor identifier continuously outputting data streams from different spatial locations on the component, causing a break in the spatiotemporal correlation chain between data and the entity. While current digital modeling systems strive to increase sensor density or optimize transmission protocols, they have not resolved the underlying contradictions of entity deformation, sensor position drift, and data stream binding failures. Ultimately, this leads to the model incorrectly associating data with bridge components, severely restricting the reliable decision-making capabilities of engineering digital twin systems. Summary of the Invention

[0003] The purpose of this invention is to provide a parametric modeling and construction control method and system for variable cross-section bridge components, aiming to solve the above-mentioned problems; the purpose of this invention can be achieved through the following technical solutions:

[0004] A multi-source sensor network integrated into the variable cross-section components of a bridge collects physical layer data in real time. The sensor network includes strain gauges deployed in the stress concentration zone of the variable cross-section, humidity sensors at the depth of the protective layer, crack gauges at the junction of the web and the base plate, and inclinometers for the foundation abutment. Each sensor performs its specific function: the strain gauges sense minute strain changes in the stress concentration zone in real time; the humidity sensors accurately monitor the humidity at the depth of the protective layer; the crack gauges promptly detect and measure the generation and development of cracks at the junction of the web and the base plate; and the inclinometers are used to accurately measure the tilt angle of the foundation abutment, providing basic data support for data analysis and management.

[0005] The spatiotemporal dynamic binding module establishes a real-time mapping relationship between sensor data and variable cross-section components by fusing spatial coordinates in the sensor network with construction timestamps to generate unique component IDs. Specifically, during sensor installation, initial spatial coordinates (X0, Y0, Z0) relative to the geometric centroid are measured based on the specific placement points of the sensors in stress concentration areas, at the interface between the web and the base plate, at the depth of the protective layer, and at the foundation cap. During construction, the Y-coordinate is corrected based on the change in curvature of the variable cross-section derived from strain gauge data, the Z-coordinate is corrected based on the concrete expansion coefficient calculated from humidity sensor data, and the X-coordinate is corrected based on the settlement gradient calculated from foundation inclinometer data, generating dynamically compensated spatial coordinates (X0 + ΔX). m ,Y0+ΔY S ,Z0+ΔZ hBased on real-time strain data collected by strain gauges in the stress concentration zone, the instantaneous curvature change Δκ of the neutral axis of the component is inverted, and based on ΔY... S =(ΔκX0²) / 2 Calculate the Y-axis coordinate compensation ΔY S Correct the sensor's position in the cross-sectional height direction; use the change in concrete moisture content Δω measured by the humidity sensor at the depth of the protective layer, based on ε h =αΔω calculates the expansion strain ε in the direction of the variable cross-section width. h Where α is the coefficient of hygroscopic expansion of concrete material during curing age and at ambient temperature, and then through ΔZ h =ε h Calculate the Z-axis coordinate compensation ΔZ using (Z0+b / 2). h 'b' represents the initial half-width of the variable cross-section of the bridge component, correcting the sensor's position in the width-gradient direction; based on the tilt angle θ and its gradient dθ / dt monitored by the inclinometer on the foundation abutment, the longitudinal settlement difference δ is calculated, and then expressed through ΔX. m =δ(X0 / L) Calculate the X-axis coordinate compensation amount ΔX m Where L is the total length of the component, the position of the sensor along the neutral axis is corrected; after generating dynamic spatial coordinates through three-level sequential coupling compensation, the coordinates are hashed and fused with the timestamp to generate a unique component ID, thereby realizing the precise binding of sensor data and variable cross-section components in the spatiotemporal dimension, ensuring the accuracy and relevance of the data.

[0006] The damage evolution dual prediction module establishes a current state diagnostic model based on data collected by a sensor network; it also analyzes the maximum strain ε of the variable cross section. max Normalization was performed to convert strain data with different dimensions into dimensionless relative values, facilitating subsequent comprehensive analysis and comparison; simultaneously, the daily variation of crack width ΔW was normalized. fThe humidity (RH) of the protective layer was standardized and converted to map it to the same dimensional space, eliminating the influence of differences in units and dimensions between different physical quantities. This allowed for the fusion and analysis of these data within a unified framework. During the construction phase, the weighting coefficients α, β, and γ were dynamically adjusted. In the initial setting stage of concrete, β decreased exponentially with curing time. This is because the formation and development of cracks during the initial setting process are extremely sensitive to time factors. As the concrete hardens, this sensitivity gradually decreases, hence the exponential decrease in the β weighting coefficient. γ increased linearly because the impact of humidity on concrete performance gradually accumulates and becomes apparent with prolonged curing time, leading to a corresponding increase in its weight. In the prestressing stage, α was increased in steps according to the prestressing level. Since the degree of prestressing directly affects the stress state and performance of the component, and different levels of prestress have different impacts, the stepwise increase in the α weighting coefficient more accurately reflects the dominant role of prestress in the component's state at this stage. Based on this, a current state diagnostic model was constructed. S t This is the diagnostic value for the current state. For the maximum strain of the variable cross section, The daily variation of crack width and RH represent the humidity of the protective layer, thus enabling an accurate assessment of the current state of variable cross-section bridge components; a remaining life prediction relationship is established based on the current state diagnostic model. T rem K represents the predicted remaining lifespan. d Let θ be the damage accumulation coefficient, and let θ be the daily tilt angle of the foundation. This quantifies the amplification effect of foundation settlement on variable cross-section damage. The remaining life prediction relationship is related to the daily tilt angle θ of the foundation. The exponential penalty term of θ reflects the amplification effect of settlement on damage; that is, the greater the tilt of the foundation, the faster the damage accumulation rate on the variable cross-section component, and the shorter the remaining life accordingly. This is achieved by using the current state diagnostic value S... t With K d Perform a dual feedback calculation to obtain the remaining lifetime T. rem This enables dynamic prediction of the remaining life of variable cross-section bridge components, providing a basis for maintenance and decision-making during construction.

[0007] The lightweight decision output module generates executable construction instructions based on the results of the damage evolution dual prediction module. These instructions dynamically optimize the bridge's mechanical requirements. When the current state diagnostic value exceeds a preset safety threshold, a dynamic adjustment instruction for concrete process parameters is sent to the pouring equipment bound to the component ID. Combining the protective layer's humidity expansion strain inverted by the Z-axis coordinate compensation and the real-time collected concrete moisture content change, a dynamic correction coefficient is calculated. This coefficient reflects the performance change trend of concrete under current environmental and stress conditions. Based on this, a concrete process parameter adjustment instruction containing the water-cement ratio dynamic correction coefficient is sent to the pouring equipment, enabling precise control of the concrete pouring process and ensuring that the concrete quality and performance meet construction requirements. When the remaining life prediction value is lower than the maintenance critical value, a local prestressing strengthening scheme is generated based on the variable cross-section curvature characteristics inverted by the component ID. The strengthening prestress value is correlated with the curvature change... The product of the absolute value of the prestressing amount and the damage accumulation coefficient is directly proportional. By reasonably adjusting the prestress, the bearing capacity and crack resistance of the components are enhanced, and their service life is extended. When the foundation tilt angle is detected 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 X-axis coordinate compensation amount of each component in the same foundation ID group, the differential settlement vector is calculated, 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, and the longitudinal settlement difference vector is obtained. The vector is used as the input parameter to perform multi-point synchronous grouting compensation operation. The grouting amount of each grouting point is directly proportional to the settlement vector of that point and its dynamic compensation X-coordinate value, and inversely proportional to the total length of the component. At the same time, the vector is tensor synthesized with the tilt angle change gradient monitored in real time by the foundation inclinometer to generate a vector characterizing the three-dimensional spatial differential settlement trend, thereby realizing effective compensation and control of foundation settlement and ensuring the overall stability of the bridge.

[0008] As a further technical solution, the spatial coordinates of the sensor network are based on a local coordinate system established by the variable cross-section component. The X-axis is positioned longitudinally along the neutral axis of the component, the Y-axis is positioned vertically along the height of the cross-section, and the Z-axis is positioned laterally along the direction of the gradual change in the width of the cross-section. This method of establishing the coordinate system is consistent with the geometry and mechanical properties of the variable cross-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.

[0009] As a further technical solution, the mapping process achieves real-time binding of spatial coordinates and component IDs 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 component's neutral axis is inverted, and the Y-axis coordinate compensation amount ΔY is calculated. SThe position of the sensor in the height direction of the cross section is corrected; the expansion strain ε in the width direction of the cross section is calculated by using the change in concrete moisture content Δω measured by the humidity sensor at the depth of the protective layer. h Calculate the Z-axis coordinate compensation amount ΔZ h Correct the sensor's position in the width gradient direction; based on the tilt angle θ and its gradient dθ / dt monitored by the inclinometer on the foundation platform, calculate the longitudinal settlement difference δ, and calculate the X-axis coordinate compensation ΔX. m The sensor position along the neutral axis is corrected; after generating dynamic spatial coordinates through three-level sequential coupling compensation, the coordinates are hashed and fused with the timestamp to generate a unique component ID. The dynamic compensation mechanism takes into account the deformation of the variable cross-section bridge component during construction and the influence of environmental factors, which affects the real-time accuracy of the sensor spatial coordinates and the uniqueness and reliability of the component ID.

[0010] As a further technical solution, the establishment of the current state diagnostic model involves analyzing the collected maximum strain ε of the variable cross-section. max Normalization was performed to transform the maximum strain value into a relative, dimensionless range, facilitating comprehensive analysis and comparison with other parameters; simultaneously, the daily variation of crack width ΔW was also normalized. f The humidity (RH) of the protective layer is standardized and converted to map it to the same dimensional space, eliminating the influence of differences in units and dimensions between different physical quantities. This allows these parameters to be integrated and analyzed within a unified framework. During the construction phase, the weighting coefficients α, β, and γ are dynamically adjusted. In the initial setting stage of concrete, β decreases exponentially with curing time, while γ increases linearly. During the prestressing stage, α is increased stepwise according to the prestressing level. This dynamic adjustment of weighting coefficients takes into account the influence of various parameters on the component's state at different construction stages, making the current state diagnosis model more accurately reflect the actual condition of the component.

[0011] As a further technical solution, the remaining lifetime prediction relationship introduces a damage accumulation coefficient K based on the current state diagnostic model. d Its remaining lifetime prediction relationship is related to the daily tilt angle θ of the foundation. The exponential penalty term of θ reflects the amplification effect of settlement on damage. Finally, S t With K d T is obtained by performing a dual feedback operation. rem This prediction method takes into account the current state diagnosis results of the components and fully quantifies the cumulative impact of foundation settlement on variable cross-section damage, realizing dynamic and accurate prediction of the remaining life, and providing forward-looking and scientific guidance for maintenance and decision-making during construction.

[0012] As a further technical solution, the executable construction instructions generated based on the current state diagnostic value, the remaining life prediction value, and the foundation tilt angle, and the corresponding dynamic compensation spatial coordinates and damage accumulation coefficients of the components are called in real time according to the unique identifier of the components, which can achieve precise control over each component; when the state diagnostic value exceeds the preset safety threshold, the concrete process parameter adjustment instruction containing the water-cement ratio dynamic correction coefficient is sent to the pouring 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 amount.

[0013] As a further technical solution, the dynamic correction coefficient is calculated by the relationship between humidity expansion strain and moisture content change. This calculation method fully considers the performance change law of concrete materials under different humidity conditions and can accurately reflect the water-cement ratio adjustment needs of concrete in the actual construction environment. When the predicted remaining service life is lower than the maintenance critical value, based on the curvature change and damage accumulation coefficient corresponding to the ID, a local prestressing strengthening scheme is generated for the area with increased curvature of variable cross section, and the strengthening prestress value is proportional to the product of the absolute value of the curvature change and the damage accumulation coefficient. This strengthening scheme accurately adjusts the prestress for the damaged area and degree of the component, enhances the load-bearing capacity and crack resistance of the component, and extends its service life. When the foundation tilt angle reaches the settlement intervention threshold, the differential settlement vector is calculated according to the gradient distribution of the X-axis coordinate compensation of each component in the same foundation ID group, providing accurate parameter basis for 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.

[0014] The system of the present invention includes:

[0015] A multi-source sensor network integrated into the variable cross-section components of the bridge collects physical layer data in real time. The sensor network includes strain gauges deployed in the stress concentration area of ​​the variable cross-section, humidity sensors at the depth of the protective layer, crack gauges at the junction of the web and the bottom plate, and inclinometers of the foundation cap, providing real-time and accurate basic data support for the data-driven system.

[0016] The spatiotemporal dynamic binding module generates a unique component ID by fusing spatial coordinates in the sensor network with construction timestamps, establishes a real-time mapping relationship between sensor data and variable cross-section components, and realizes precise association between data and components in the spatiotemporal dimension, ensuring the accuracy and reliability of subsequent analysis and control.

[0017] The damage evolution dual prediction module establishes a current state diagnostic model based on data collected by sensor networks. Through the fusion analysis of multiple data and dynamic weight adjustment, it achieves accurate diagnosis of the current state of the component and further establishes the remaining life prediction relationship, quantifies the amplification effect of foundation settlement on variable cross-section damage, and provides a forward-looking basis for maintenance and decision-making during construction.

[0018] The lightweight decision 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 application schemes, and implementing foundation stability compensation operations, it dynamically optimizes the mechanical requirements of the bridge, achieves precise control over variable cross-section bridge components, improves construction quality and safety, and ensures the long-term stability and reliability of the bridge.

[0019] This invention provides a method and system for parametric modeling and construction control of variable cross-section bridge components, which has the following beneficial effects:

[0020] 1. This invention achieves high-precision binding between data and physical components by associating real-time data collected by sensors with the precise spatiotemporal position of variable cross-section components, which significantly improves the effectiveness of state diagnosis and decision optimization, enabling the system to accurately identify and locate the key state information of components.

[0021] 2. This invention establishes a predictive relationship for the remaining service life of foundation settlement by correlating the damage accumulation coefficient with the foundation tilt angle, thereby achieving accurate dynamic prediction of the remaining service life of variable cross-section bridge components and improving the risk warning capability during construction.

[0022] 3. This invention achieves real-time dynamic optimization of bridge mechanical requirements through dynamic adjustment of construction process parameters and optimization design of local prestressing strengthening schemes, thereby improving construction quality and long-term structural stability. Dynamic optimization enables timely adjustment of construction processes based on the actual state and remaining life of components, thereby extending the service life of bridges and reducing maintenance costs. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In the field of bridge construction, especially for parametric modeling and construction control of variable cross-section bridge components, this invention provides a method and system for parametric modeling and construction control of variable cross-section bridge components. The following will illustrate the implementation and technical details of this invention through a specific embodiment.

[0026] refer to Figure 1 Taking a large variable cross-section bridge construction project as an example, in the early stages of construction, the project team planned and deployed the sensor network in detail according to 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 minute strain changes in the area. At the junction of the web and the base plate, a crack gauge was set every 1 meter to detect and measure the generation and development of cracks in a timely manner. 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 location of the foundation abutment, inclinometers were arranged according to the geological conditions to measure the inclination angle of the foundation.

[0027] During sensor installation, 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 center of the bridge. For example, the initial coordinates of strain gauges in stress concentration areas were precisely 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 tilt angle. The sampling frequency of the data acquisition equipment was set to once per minute to ensure the real-time nature and continuity of the data.

[0028] The spatiotemporal dynamic binding module generates a unique component ID by fusing the spatial coordinates of sensors with construction timestamps, thereby establishing a real-time mapping relationship between sensor data and variable cross-section components. Specifically, the system corrects the Y-coordinate based on the change in curvature of the variable cross-section derived from strain gauge data, corrects the Z-coordinate based on the concrete expansion coefficient calculated from humidity sensor data, and corrects the X-coordinate based on the settlement gradient calculated from foundation inclinometer data, thus generating dynamically compensated spatial coordinates (X0 + ΔX). m ,Y0+ΔY S ,Z0+ΔZ hFor example, when a strain gauge detects a change in strain data at a certain location, the system calculates the instantaneous change in curvature Δκ of the component's neutral axis. Assuming there are three strain gauges at a certain cross-section, located at different heights (y1, y2, y3) from the neutral axis, measuring strain values ​​ε1, ε2, and ε3; according to plane bending theory, the strain is linearly distributed along the cross-section height, i.e., ε(y) = κy; where ε(y) is the strain at a distance y from the neutral axis, and κ is the curvature of the component's neutral axis; the least squares method is used to linearly fit the collected strain data to obtain the linear relationship between strain and position; according to the theory of mechanics of materials, the curvature κ is equal to the strain gradient, i.e., the slope of the fitted line; by calculating the curvature κ at the current moment... current With initial curvature κ nitial The difference can be used to obtain the instantaneous curvature change of the neutral axis, Δκ=κ. current -κ nitial Initial curvature κ nitial It can be measured in the early stages of construction, when the component is under relatively low stress and the deformation is stable; according to the formula Calculate the Y-axis coordinate compensation amount ΔY S This corrects the sensor's position in the cross-sectional height direction; similarly, the change in concrete moisture content Δω measured by the humidity sensor is calculated using the formula ε. h =αΔω to calculate the expansion strain ε in the width direction of the cross section. h Where α is the coefficient of hygroscopic expansion of concrete material under curing age and ambient temperature; and then through geometric relationship ΔZ h =ε h The Z-axis coordinate compensation ΔZ is calculated using (Z0+b / 2). h The position of the sensor in the width-gradient direction is corrected, where b is the initial half-width of the variable cross-section; in addition, the longitudinal settlement difference δ is calculated based on the tilt angle θ monitored by the inclinometer and its change gradient dθ / dt, and then expressed using the formula ΔX. m The X-axis coordinate compensation amount ΔX is calculated using δ(X0 / L) (where L is the total length of the component). m Correct the position of the sensor along the neutral axis.

[0029] After generating dynamic spatial coordinates through three-level sequential coupling compensation, the system performs hash fusion calculations with timestamps to generate unique component IDs. This process enables precise binding of sensor data and variable cross-section components in the spatiotemporal dimension, greatly improving the accuracy and relevance of the data.

[0030] The establishment of the current state diagnostic model involves the normalization and standardization of the collected data; for example, the maximum strain ε of the variable cross section. max Normalized to the dimensionless interval of 0-1, the formula is: , where ε min The initial strain value is ε.maxlimit The strain limit is 150 × 10⁻⁶; simultaneously, the daily variation of crack width ΔW f The humidity (RH) of the protective layer was converted using the Z-Score standardization method. During the construction phase, the weighting coefficients α, β, and γ were dynamically adjusted according to the construction progress. For example, during the initial setting stage of concrete, β decreased exponentially with curing time, as shown in the formula: Where β0 is initially 0.8, λ is 0.1 / h; γ increases linearly, as shown in the formula. The initial value of γ0 is 0.2, and k is 0.01 / h. During the prestressing application stage, α is increased stepwise according to the prestressing level: α=0.6 for level one and α=0.8 for level two. Based on these processed data and weighting coefficients, a current state diagnostic model is constructed, and the formula is as follows: For example, at a certain moment, the measurement is...

[0031] ε max =120×10⁻⁶, after normalization ε max =0.8; ΔW f =0.03mm, after standardization ΔW f =1.2; RH=80%, after standardization RH=1.5; In the initial setting stage of concrete, assuming a curing time of 10 hours, β≈0.29, γ=0.3 are calculated, and S is obtained by substituting into the formula. 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 incorporates a damage accumulation coefficient K. d Its expression is K d =K d0 +ηS t K d0 The initial value is 0.1, and η is 0.001 / h; the predicted remaining lifetime value T rem The calculation formula is: By using the remaining life prediction relationship, construction personnel can understand the remaining service life of bridge components in advance, thereby providing a scientific basis for maintenance and decision-making.

[0032] The lightweight decision output module generates executable construction instructions based on the results of the damage evolution dual prediction module. When the current state diagnostic value exceeds the preset safety threshold, the system sends concrete process parameter adjustment instructions to the bound pouring equipment. For example, it calculates a dynamic correction coefficient by combining the protective layer humidity expansion strain and real-time concrete moisture content change based on the Z-axis coordinate compensation. When the remaining life prediction value is lower than the maintenance critical value, the system generates a local prestressing strengthening scheme based on the cross-sectional curvature characteristics of the component ID. When the foundation tilt angle reaches the settlement intervention threshold, the system triggers a collaborative foundation stability compensation operation for components in the same foundation ID group. For example, it calculates the differential settlement vector δ based on the gradient distribution of the X-axis coordinate compensation of each component in the same foundation ID group. S =(ΔX m1 -ΔX m2 ) / L; Assuming the ΔX of two adjacent components m1 =0.005m, ΔX m2 =0.002m, spacing L=5m, then δ S =0.0006m, using this vector as input parameter, multi-point synchronous grouting compensation operation is performed, and the grouting volume at each grouting point is... Where k = 1000 kg / m³, ΔX m =0.005m, L=5m, then the grouting volume Q≈0.0006kg; at the same time, this vector is combined with the gradient of the tilt angle change monitored in real time by the foundation inclinometer to generate a vector characterizing the three-dimensional spatial differential settlement trend.

[0033] Through the coordinated operation of each module, the system of this invention can achieve precise control over variable cross-section bridge components, effectively improve construction quality and safety, and ensure the long-term stability and reliability of the bridge.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for parametric modeling and construction control of variable cross-section bridge components, characterized in that, Includes the following steps: S1. A sensor network integrated into the variable cross-section components of the bridge to collect physical layer data in real time. The sensor network includes strain gauges deployed in the stress concentration area of ​​the variable cross-section, humidity sensors at the depth of the protective layer, crack gauges at the junction of the web and the bottom plate, and inclinometers for the foundation cap. S2. By fusing spatial coordinates in the sensor network with construction timestamps to generate unique component IDs, a real-time mapping relationship between sensor data and variable cross-section components 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 cross-section damage; S4. Based on the results of step S3, generate executable construction instructions, and dynamically optimize the mechanical requirements of the bridge through the executable construction instructions; Instruction I: When the current state diagnostic value exceeds the preset safety threshold, send a dynamic adjustment instruction for concrete process parameters to the pouring equipment bound to the component ID; Instruction II: When the predicted remaining life is lower than the maintenance threshold, a local prestressing strengthening scheme is generated based on the variable cross-section curvature characteristics derived from component ID. Command III: When the foundation tilt angle is detected to reach the settlement intervention threshold, trigger a coordinated foundation stability compensation operation for components in the same foundation ID group; The current state diagnostic model is established by: analyzing the maximum strain of the acquired variable cross-section. Normalization was performed; the daily variation of crack width ΔW was analyzed. f The humidity (RH) of the protective layer is standardized and converted to map it to the same dimension space. The weighting coefficients α, β, and γ are dynamically adjusted according to the construction stage. During the initial setting stage of concrete, β decreases exponentially with curing time, while γ increases linearly. During the prestressing stage, α is increased in a stepwise manner according to the prestressing level. Based on this, a current state diagnostic model is constructed. S t The current diagnostic value is t, where t is the bridge's service time, and ε is the bridge's current service value. max For the maximum strain of the variable cross section, ΔW f The daily variation of crack width is represented by RH, and the humidity of the protective layer is represented by RH. The remaining lifetime prediction relationship is based on the current state diagnostic model and introduces a damage accumulation coefficient K. d Its remaining life prediction relationship is related to the daily tilt angle θ of the foundation, and the exponential penalty term of θ reflects the amplification effect of settlement on damage; finally, S t With K d The remaining lifetime prediction relationship is obtained by performing a dual feedback calculation, specifically as follows: T rem K represents the predicted remaining lifespan. d θ is the damage accumulation coefficient, and θ is the daily tilt angle of the foundation.

2. The method for parametric modeling and construction control of variable cross-section bridge components according to claim 1, characterized in that: The spatial coordinates of the sensor network are established based on a local coordinate system for the variable cross-section component. The X-axis positions the longitudinal position along the neutral axis of the component, the Y-axis positions the vertical position along the cross-section height, and the Z-axis positions the lateral position along the direction of the gradual change in cross-section width. During sensor installation, the initial spatial coordinates (X0, Y0, Z0) relative to the geometric centroid are measured based on the specific placement points at stress concentration zones, the junction of the web and base plate, the depth of the protective layer, and the foundation cap. During construction and time-stamp integration, the Y-coordinate is corrected based on the change in curvature of the variable cross-section derived from strain gauge data, the Z-coordinate is corrected based on the concrete expansion coefficient calculated from humidity sensor data, and the X-coordinate is corrected based on the settlement gradient calculated from foundation inclinometer data, generating dynamically compensated spatial coordinates (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. The process of establishing the real-time mapping relationship between the sensor data and the variable cross section component is called the mapping process.

3. The method for parametric modeling and construction control of variable cross-section bridge components according to claim 2, characterized in that: The mapping process achieves real-time binding of spatial coordinates and component IDs 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 component's neutral axis is inverted, according to... Calculate the Y-axis coordinate compensation amount ΔY s First, the position of the sensor in the cross-sectional height direction is corrected; second, the change in concrete moisture content Δω measured by the humidity sensor at the depth of the protective layer is used to... Calculate the expansion strain ε in the width direction of the cross section. h Where α is the coefficient of hygroscopic expansion of concrete material under curing age and ambient temperature; through geometric relationships Calculate the Z-axis coordinate compensation amount ΔZ h b is the initial half-width of the variable cross-section, correcting the sensor's position in the width-gradient direction; based on the tilt angle θ and its gradient d monitored by the inclinometer on the foundation platform. θ / d t Calculate the longitudinal settlement difference δ using 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 generating dynamic spatial coordinates through three-level sequential coupling compensation, the coordinates are hashed and fused with the timestamp to generate a unique component ID.

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

5. The parametric modeling and construction control method for variable cross-section bridge components according to claim 4, characterized in that: The dynamic correction coefficient is calculated by the relationship between humidity expansion strain and moisture content change. When the remaining life prediction value is lower than the maintenance critical value, a local prestressing strengthening scheme is generated for the variable cross-section curvature increase area based on the curvature change and damage accumulation coefficient corresponding to the ID. The strengthening prestress value is proportional to the product of the absolute value of the curvature change and the damage accumulation coefficient. When the foundation tilt angle reaches the settlement intervention threshold, the differential settlement vector is calculated according to the gradient distribution of the X-axis coordinate compensation of each component in the same foundation ID group. The calculated differential settlement vector is called the solved differential settlement vector.

6. The parametric modeling and construction control method for variable cross-section bridge components according to claim 5, characterized in that: The differential settlement vector is calculated based on the dynamic compensation X-axis coordinate compensation of each component within the same foundation ID group, establishing a settlement gradient distribution along the neutral axis of the component. By calculating the spatial variation rate of the compensation of adjacent components, the longitudinal settlement difference vector is calculated, and a multi-point synchronous grouting compensation operation is performed with this vector as the input parameter. The grouting volume at each grouting point is directly proportional to the settlement vector at that point and its dynamic compensation X-coordinate value, and inversely proportional to the total length of the component. This vector is then tensor-synthesized with the tilt angle change gradient monitored in real time by the foundation inclinometer to generate a vector characterizing the three-dimensional spatial differential settlement trend.

7. The system for parametric modeling and construction control of variable cross-section bridge components according to any one of claims 1-6, characterized in that, include: A multi-source sensor network integrated into the variable cross-section components of a bridge collects physical layer data in real time. The sensor network includes strain gauges deployed in the stress concentration area of ​​the variable cross-section, humidity sensors at the depth of the protective layer, crack gauges at the junction of the web and the bottom plate, and inclinometers for the foundation cap. The spatiotemporal dynamic binding module generates a unique component ID by fusing spatial coordinates in the sensor network with construction timestamps, thus establishing a real-time mapping relationship between sensor data and variable cross-section components. The damage evolution dual prediction module establishes a current state diagnostic model based on data collected by a sensor network; it establishes a remaining life prediction relationship based on the current state diagnostic model and quantifies the amplification effect of foundation settlement on variable cross-section damage. 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.

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