An optimization method for closure jacking force of low tower cable-stayed bridge facing environmental adaptability

Abstract

The application discloses a kind of short tower cable-stayed bridge closure jacking force optimization methods for environmental adaptability, belong to jacking force calculation technical field, comprising: the coupling model of environmental temperature field and structural response is constructed, temperature gradient and strain data are collected in real time by arranging distributed temperature and strain sensor array, temperature-strain mapping function is established;Define multi-objective optimization function, with jacking force as decision variable, comprehensively consider structural stress, design linear deviation and construction safety risk, realize multi-objective optimization by dynamically adjusting weight coefficient;Set constraint condition including structural stress, elevation deviation and axis error;Improved particle swarm optimization algorithm is used to solve optimal jacking force;Output optimal solution set, select the solution with the lowest construction safety risk in Pareto frontier as the final jacking force.The method improves the precision and efficiency of jacking force optimization, enhances the adaptability under complex environment, and provides a scientific and reliable solution for bridge construction.

Description

Technical Field

[0001] This invention belongs to the field of thrust calculation technology, specifically relating to an optimization method for the closure thrust of a low-tower cable-stayed bridge that is environmentally adaptable. Background Technology

[0002] In the construction of low-tower cable-stayed bridges, optimizing the jacking force of the closure segment is a crucial step in ensuring the structural safety and construction quality of the bridge. However, traditional optimization methods often neglect the impact of environmental factors (such as temperature changes) on the structural response, leading to insufficient adaptability and reliability of the optimization results. Furthermore, the fixed setting of weight coefficients in multi-objective optimization problems is difficult to adapt to the complex and ever-changing construction environment, easily causing the optimization results to deviate from actual requirements. In existing technologies, particle swarm optimization algorithms suffer from slow convergence speed and insufficient global search capability when dealing with complex multi-objective optimization problems, making it difficult to meet the high-precision requirements of practical engineering.

[0003] Therefore, there is an urgent need for a jacking force optimization method that can comprehensively consider environmental adaptability, multi-objective optimization, and efficient solution to improve the safety and reliability of bridge construction. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an optimization method for the closure thrust of a low-tower cable-stayed bridge that is environmentally adaptable, in order to solve the problems of slow convergence speed and insufficient global search capability in the prior art, which makes it difficult to meet the high precision requirements of actual engineering.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention provides an optimization method for the closure thrust of a low-tower cable-stayed bridge with environmental adaptability, comprising the following steps:

[0007] S1: Distributed temperature sensor arrays and strain sensor arrays are arranged on both sides of the closure section to collect real-time temperature gradient distribution data and corresponding strain values ​​of the beam structure. The collected raw temperature data is then used to generate a continuous three-dimensional temperature model through a spatiotemporal interpolation algorithm. And establish a temperature-strain mapping function; where, For spatial coordinates, It is a time variable;

[0008] S2: Using the top thrust as the decision variable F, establish the following multi-objective optimization function:

[0009]

[0010] In the formula, The standard deviation of the structural Mises equivalent stress. To determine the Euclidean distance between the designed alignment and the measured alignment. The construction safety risk index is calculated based on tilt and pressure sensors. , , These are the weighting coefficients, and + + ; The ambient temperature is the variable.

[0011] S3: Set constraints: , , In the formula, , , These are the design allowable thresholds for component stress, elevation deviation, and axis error, respectively. , , These are the maximum calculated stress of the component, the calculated deviation of the main beam elevation, and the calculated deviation of the main beam transverse axis, respectively.

[0012] S4: Solving using an improved PSO algorithm: Initializing particle swarm positions Through fitness function To evaluate the quality of particles, the formula is as follows: The objective function value, For the penalty function;

[0013] S5: Output the optimal solution set: Select the solution with the lowest construction safety risk in the Pareto front as the final jacking force. .

[0014] Furthermore, in step S4, the improved PSO algorithm introduces a temperature adaptive mechanism, the specific steps of which are as follows:

[0015] S4.1 Particle Coding Extension: Extending the particle position vector into three dimensions In the formula, This is the temperature influence factor, and its initial value is taken as the current temperature. Thermal strain under the following conditions;

[0016] S4.2, Dynamic inertia weight adjustment: according to Adjust the weights, reducing them when temperature fluctuations are drastic. To enhance local search; among which, The attenuation coefficient;

[0017] S4.3 Temperature-oriented velocity update: A temperature prediction term is added to the standard PSO velocity update to guide particles to move toward the future temperature optimal solution;

[0018] S4.4, Constraint Violation Adaptive Penalty: According to Calculate the penalty coefficient to double the penalty for excessive tensile stress in the structure under heating conditions; where, As a benchmark reference value, This is a function of the direction of temperature deviation. This refers to the effective temperature range.

[0019] Furthermore, in step S5, the Pareto solution set selection employs a temperature-related fuzzy decision algorithm:

[0020] S5.1: Define the satisfaction function:

[0021]

[0022] In the formula, As the indicator number, , , The first The actual value, maximum allowable value, and minimum allowable value of each indicator. For the first The normalized utility value of the indicator;

[0023] S5.2: Introducing a temperature weight vector:

[0024]

[0025] in,

[0026]

[0027]

[0028]

[0029] S5.3: Calculate overall satisfaction

[0030]

[0031] In the formula, The dynamic weight of the k-th indicator. Let k be the utility function of the index. Key indicators; Based on the weights, For linear weights, For safety weights, The attenuation coefficient is... Characteristic temperature, This is the proportionality coefficient. This refers to the maximum permissible temperature variation range.

[0032] S5.4: Selection Corresponding solution And requires .

[0033] Furthermore, in step S5, a digital twin module is embedded during the PSO iteration process for virtual verification:

[0034] A1: Construct a parametric finite element model based on the BIM model, and input parameters. ;

[0035] A2: Real-time synchronization of physical sensor data to update the twin's status;

[0036] A3: For each particle Calculation of stress contour distribution in twins Beam end displacement vector ; buckling characteristic value ;

[0037] A4: Define the confidence index for twins ,when At that time, twin data was used to replace some physical sensor data in the PSO evaluation.

[0038] Furthermore, the optimization results are implemented through the following closed-loop control system:

[0039] The central controller receives the PSO output. Generate hydraulic instruction set to Taiwan jack;

[0040] Displacement sensors collect closure gap width in real time ,when At this time, PSO is triggered to re-optimize;

[0041] Establish the top thrust-displacement sensing function A PID controller is used to compensate for system lag.

[0042] In the formula, Let be the system's transfer function, representing the relationship between the output and input in the complex frequency domain; For the Laplace transform of the output, For the Laplace transform of the input, For static gain, It is a time constant. For complex frequency domain variables, For pure time delay, For the designed closure width, This is the allowable width deviation threshold.

[0043] Furthermore, in step S5, the output is... Then, a risk control instruction set is generated simultaneously:

[0044] B1: The jacking process is controlled in stages. Decomposed into Three stages of force values, among which, , In accordance with Increasing rate Duration , The structural relaxation time constant;

[0045] B2: Real-time safety margin monitoring: A pressure sensor array is embedded in the hydraulic system of the jacking equipment. When the measured pressure P deviates from the theoretical value... Automatically switch to safe mode: If Then activate the pressure relief valve, with a pressure relief rate ≤30kN / s; if Then the compensation pump will be triggered, with a compensation amount ≤8%. ;

[0046] B3: Establish an early warning system for sudden changes in tilt angle, when the tilt angle difference between the two beams is... or time-varying rate At that time, pause the jacking and start the laser scanner to check the line shape.

[0047] Furthermore, in step S2, the weighting coefficients , , Adopt an environmental risk-driven adjustment strategy:

[0048] C1: Establish a weighted influence factor library:

[0049] Structural stress sensitive factor Calculate the partial derivative of the Mises stress standard deviation with respect to the thrust F based on historical data;

[0050] Temperature Sudden Change Warning Factor Take the maximum absolute value of the rate of change of the temperature gradient over 3 consecutive hours;

[0051] Construction safety attenuation factor The ratio of safety risk indicators based on tilt sensor data to its threshold;

[0052] C2: Design weight dynamic calculation rules:

[0053] when ≥3℃ / hour and When ≥0.8, automatically increase Reduce to 150% of the original value at the same time Up to 70% of the original value;

[0054] when ≤0.5 and real-time linear deviation exceeds When it reaches 80%, Increase to 120% of the original value;

[0055] Each weight needs to be renormalized after adjustment, and the adjustment range should not exceed ±30% of the original value.

[0056] C3: Perform weight updates every 15 minutes and verify the robustness of the weight combination through Monte Carlo simulation.

[0057] Furthermore, the constraint condition described in step S3 adopts a time-varying elastic threshold strategy:

[0058] Component stress threshold Dynamic correction: When the concrete age is less than 7 days, Take 80% of the design value; when the average daily temperature exceeds 30℃, Decrease by 3% for every 5°C increase;

[0059] Elevation deviation threshold The grading is based on the width of the closure opening: when the width of the closure opening... Rice time, Pick millimeters; when the width of the closure joint is greater than 2 meters, Expand by 0.5 meters per increment millimeters;

[0060] Axis error threshold Introducing a temperature compensation mechanism: When the temperature difference between the main beam and the pier exceeds 10℃, Allowed to be relaxed to 120% of the design value;

[0061] The assessment of constraint violation increases the safety margin factor for stress constraints. The amount of violation is multiplied by a penalty weight of 1.5.

[0062] Furthermore, in step S1, the arrangement of the distributed temperature sensor array and strain sensor array adopts a regional dynamic encryption strategy, specifically including:

[0063] D1: Based on the finite element thermo-mechanical coupling analysis of the beams on both sides of the closure section, the monitoring area is divided into three sensitive areas: high-sensitivity area, medium-sensitivity area and low-sensitivity area. The sensor density in the high-sensitivity area is twice that of the medium-sensitivity area and four times that of the low-sensitivity area.

[0064] D2: The sensor array adopts a heterogeneous networking architecture: the temperature sensor uses fiber optic grating type to improve the temperature gradient resolution accuracy to 0.1℃, the strain sensor uses capacitive micro-strain gauge to eliminate electromagnetic interference, and the spatial distance between the two types of sensors is controlled within 50 mm to achieve accurate matching of physical quantities.

[0065] D3: Deploy an adaptive sampling mechanism: When the temperature difference between adjacent sensors exceeds 2°C or the strain rate of change is higher than 5... At a rate of / minute, local area encrypted sampling is automatically triggered to 10 Hz, and the temperature-strain mapping function Φ(ΔT)→ε is corrected in real time through edge computing nodes.

[0066] The beneficial effects of this invention are as follows:

[0067] This invention uses temperature influence factors By embedding particle coding, a dynamic correlation between temperature changes and inertia weights is established, and a temperature prediction guidance term is introduced into the velocity update. This overcomes the shortcomings of traditional PSO methods that ignore dynamic environmental changes; it innovatively establishes a temperature-dependent weighting function. (S-shaped growth) and (Linear growth), combined with key indicators and fuzzy satisfaction This enables dynamic priority decision-making under temperature conditions; the BIM-FEM twin is embedded in the PSO iteration loop, using confidence indexes... Achieve physical-virtual data fusion to solve the problem of missing sensor data under complex working conditions.

[0068] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0069] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0070] Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0071] like Figure 1 As shown, this invention provides an optimization method for the closure thrust of a low-tower cable-stayed bridge with environmental adaptability, comprising the following steps:

[0072] S1: Distributed temperature sensor arrays and strain sensor arrays are arranged on both sides of the closure section to collect real-time temperature gradient distribution data and corresponding strain values ​​of the beam structure. The collected raw temperature data is then used to generate a continuous three-dimensional temperature model through a spatiotemporal interpolation algorithm. And establish a temperature-strain mapping function; where, For spatial coordinates, It is a time variable;

[0073] S2: Using the top thrust as the decision variable F, establish a multi-objective optimization function:

[0074]

[0075] In the formula, The standard deviation of the structural Mises equivalent stress. To determine the Euclidean distance between the designed alignment and the measured alignment. The construction safety risk index is calculated based on tilt and pressure sensors. , , These are the weighting coefficients, and + + ; The ambient temperature is the variable.

[0076]

[0077]

[0078]

[0079] In the formula, For the first Feng, one unit Mises stress, For all units of Feng The mean value of the Mises stress; For the total length, These are the position coordinates along the length of the structure. To design the theoretical height curve, This is the actual deformation height curve. This is the temperature-deformation coupling coefficient; The maximum rotation angle of the critical node of the structure. The actual force applied during tensioning construction. The maximum allowable load-bearing capacity of the structure. , These are the weighting coefficients;

[0080] S3: Set constraints: , , In the formula, , , These are the design allowable thresholds for component stress, elevation deviation, and axis error, respectively.

[0081] S4: Solving using an improved PSO algorithm: Initializing particle swarm positions Through fitness function To evaluate the quality of particles, the formula is as follows: The objective function value, For the penalty function;

[0082] In the formula,

[0083]

[0084] In the formula, The total number of units, These are the weighting coefficients. For the first The solution for each particle. For the first Feng, one unit Mises stress, For all units of Feng The mean value of the Mises stress; For the total length, These are the position coordinates along the length of the structure. To design the theoretical height curve, This is the actual deformation height curve. This is the temperature-deformation coupling coefficient; The maximum rotation angle of the critical node of the structure. The actual force applied during tensioning construction. The maximum allowable load-bearing capacity of the structure. , These are the weighting coefficients;

[0085]

[0086] In the formula, Handling inequality constraints, Handling equality constraints, Let represent the number of inequality constraints. For inequality constraints that violate the metric, For the first j Inequality constraint functions, The number of equality constraints. For the first l Weighting coefficients of each equality constraint. This violates the metric due to equality constraints.

[0087] S5: Output the optimal solution set: Select the solution with the lowest construction safety risk in the Pareto front as the final jacking force. .

[0088] In step S5, the Pareto solution set selection uses a temperature-related fuzzy decision algorithm.

[0089] S5.1: Define the satisfaction function:

[0090]

[0091] In the formula, As the indicator number, , , The first The actual value, maximum allowable value, and minimum allowable value of each indicator. For the first The normalized utility value of the indicator;

[0092] S5.2: Introducing a temperature weight vector:

[0093]

[0094] in,

[0095]

[0096]

[0097]

[0098] S5.3: Calculate overall satisfaction

[0099]

[0100] S5.4: Selection Corresponding solution And requires ;

[0101] In the formula, The dynamic weight of the k-th indicator. Let k be the utility function of the index. Key indicators; Based on the weights, For linear weights, For safety weights, The attenuation coefficient is... Characteristic temperature, This is the proportionality coefficient. This refers to the maximum permissible temperature variation range.

[0102] In one embodiment of the present invention, in step S5, a digital twin module is embedded during the PSO iteration process to perform virtual verification:

[0103] A1: Construct a parametric finite element model based on the BIM model, and input parameters. ;

[0104] A2: Real-time synchronization of physical sensor data to update the twin's status;

[0105] A3: For each particle Calculation of stress contour distribution in twins Beam end displacement vector ; buckling characteristic value ;

[0106] A4: Define the confidence index for twins:

[0107]

[0108] when At that time, twin data was used to replace some physical sensor data in the PSO evaluation. For the consistency of digital twins, This represents the total number of physical sensors. Number the physical sensors; For the first The actual measured values ​​of each physical sensor For the first Digital twin predictions of physical sensors.

[0109] In one embodiment of the present invention, in step S4, the improved PSO algorithm introduces a temperature adaptive mechanism, specifically as follows:

[0110] S4.1 Particle Coding Extension: Extending the particle position vector into three dimensions ,in, This is the temperature influence factor, and its initial value is taken as the current temperature. Thermal strain under the following conditions;

[0111] S4.2, Dynamic Inertia Weight Adjustment: According to Adjust the weights, reducing them when temperature fluctuations are drastic. To enhance local search; among which, The attenuation coefficient;

[0112] S4.3 Temperature-oriented velocity update: Add a temperature prediction term to the standard PSO velocity update. This guides the particles to move toward the optimal temperature solution in the future.

[0113] S4.4, Constraint Violation Adaptive Penalty: According to Calculate the penalty coefficient to double the penalty for excessive tensile stress in the structure under heating conditions; where, As a benchmark reference value, This is a function of the direction of temperature deviation. This refers to the effective temperature range.

[0114] In this scheme, the temperature influence factor is... By directly embedding particle codes, a digital correlation is established between mechanical response and temperature, through the rate of temperature change. Dynamically adjust inertia weight This enables the algorithm to automatically focus on a fine-grained local search during drastic temperature changes, incorporates future temperature predictions, drives particles to migrate towards the optimal temperature solution in advance, avoids delayed responses, and utilizes sign functions. Differentiate between heating / cooling conditions and impose higher penalties for exceeding tensile stress limits caused by heating.

[0115] This scheme enhances the ability of the solution space to represent temperature sensitivity by expanding particle encoding, thereby accelerating the convergence speed; improves the accuracy of local search during drastic temperature changes by adjusting dynamic inertia weights, avoiding getting trapped in local optima; improves the temperature adaptability of the construction scheme by temperature-guided updates combined with meteorological forecast data; and reduces the probability of tensile stress exceeding limits under heating conditions by adaptive penalties, thereby improving the structural safety redundancy.

[0116] In one embodiment of the present invention, the optimization result is executed through the following closed-loop control system:

[0117] The central controller receives the PSO output. Generate hydraulic instruction set to Taiwan jack;

[0118] Displacement sensors collect closure gap width in real time ,when At this time, PSO is triggered to re-optimize;

[0119] Establish the top thrust-displacement sensing function A PID controller is used to compensate for system lag.

[0120] In the formula, Let be the system's transfer function, representing the relationship between the output and input in the complex frequency domain (s-domain); For the Laplace transform of the output, For the Laplace transform of the input, For static gain, It is a time constant. For complex frequency domain variables, For pure time delay, For the designed closure width, This is the allowable width deviation threshold.

[0121] In one embodiment of the present invention, the output is performed in step S5. Then, a risk control instruction set is generated simultaneously:

[0122] B1: The jacking process is controlled in stages. Decomposed into Three stages of force values, among which, , In accordance with Increasing rate Duration , The structural relaxation time constant;

[0123] B2: Real-time safety margin monitoring: A pressure sensor array is embedded in the hydraulic system of the jacking equipment. When the measured pressure P deviates from the theoretical value... Automatically switch to safe mode: If Then activate the pressure relief valve, with a pressure relief rate ≤30kN / s; if Then the compensation pump will be triggered, with a compensation amount ≤8%. ;

[0124] B3: Establish an early warning system for sudden changes in tilt angle, when the tilt angle difference between the two beams is... or time-varying rate At that time, pause the jacking and start the laser scanner to check the line shape.

[0125] This solution forms a closed-loop control chain of "optimization-execution-feedback" to eliminate the risk of exceeding tolerances.

[0126] In one embodiment of the present invention, in step S2, the weighting coefficient , , Adopt an environmental risk-driven adjustment strategy:

[0127] C1: Establish a weighted influence factor library:

[0128] Structural stress sensitivity factor Calculate the partial derivative of the Mises stress standard deviation with respect to the thrust F based on historical data;

[0129] Temperature Sudden Change Warning Factor Take the maximum absolute value of the rate of change of the temperature gradient over 3 consecutive hours;

[0130] Construction safety attenuation factor The ratio of safety risk indicators based on tilt sensor data to its threshold;

[0131] C2: Design weight dynamic calculation rules:

[0132] when ≥3℃ / hour and When ≥0.8, automatically increase Reduce to 150% of the original value at the same time Up to 70% of the original value;

[0133] when ≤0.5 and real-time linear deviation exceeds When it reaches 80%, Increase to 120% of the original value;

[0134] Each weight needs to be renormalized after adjustment, and the adjustment range should not exceed ±30% of the original value.

[0135] C3: Perform weight updates every 15 minutes and verify the robustness of the weight combination through Monte Carlo simulation.

[0136] This solution overcomes the adaptability defects of fixed weights under abrupt temperature fields, ensures structural safety priority under extreme operating conditions, and creatively proposes a non-uniform sensor network + data fusion algorithm to solve the industry pain point of insufficient temperature field monitoring accuracy.

[0137] In one embodiment of the present invention, the constraint condition in step S3 adopts a time-varying elastic threshold strategy:

[0138] Component stress threshold Dynamic correction: When the concrete age is less than 7 days, Take 80% of the design value; when the average daily temperature exceeds 30℃, The temperature is reduced by 3% for every 5°C increase; this effectively adapts to the characteristic of low early-stage concrete strength.

[0139] Elevation deviation threshold The grading is based on the width of the closure opening: when the width of the closure opening... Rice time, Pick millimeters; when the width of the closure joint is greater than 2 meters, Expand by 0.5 meters per increment Millimeters; This hierarchical setting method can better adapt to the construction needs under different working conditions and improve the practicality of the optimization results;

[0140] Axis error threshold Introducing a temperature compensation mechanism: When the temperature difference between the main beam and the pier exceeds 10℃, It allows for a relaxation of up to 120% of the design value; effectively avoids misjudgment of axial error caused by temperature differences; further strengthens the control of structural stress and reduces the risk of structural damage;

[0141] The assessment of constraint violation increases the safety margin factor for stress constraints. The amount of violation is multiplied by a penalty weight of 1.5.

[0142] This solution, through flexible design, allows constraints to better adapt to changes in multiple factors such as temperature, age, and closure width, significantly improving the applicability and reliability of the optimization method under complex environmental conditions; it also combines fuzzy logic with real-time engineering parameters to achieve dynamic weight adjustment, breaking through the limitations of static weight systems.

[0143] In one embodiment of the present invention, in step S1, the arrangement of the distributed temperature sensor array and the strain sensor array adopts a regional dynamic encryption strategy, specifically including:

[0144] D1: Based on the finite element thermo-coupling analysis of the beams on both sides of the closure section, the monitoring area is divided into three sensitive areas: high-sensitivity area (mid-span section, near the anchor point of the cable stay), medium-sensitivity area (1 / 4 span away from the pier), and low-sensitivity area (top of the pier abutment). The sensor density in the high-sensitivity area is twice that of the medium-sensitivity area and four times that of the low-sensitivity area.

[0145] D2: The sensor array adopts a heterogeneous networking architecture: the temperature sensor uses fiber optic grating type to improve the temperature gradient resolution accuracy to 0.1℃, the strain sensor uses capacitive micro-strain gauge to eliminate electromagnetic interference, and the spatial distance between the two types of sensors is controlled within 50 mm to achieve accurate matching of physical quantities.

[0146] D3: Deploy an adaptive sampling mechanism: When the temperature difference between adjacent sensors exceeds 2°C or the strain rate of change is higher than 5... At a rate of / minute, local area encrypted sampling is automatically triggered to 10 Hz, and the temperature-strain mapping function Φ(ΔT)→ε is corrected in real time through edge computing nodes.

[0147] This scheme divides the monitoring area into three sensitive zones—high, medium, and low—and employs differentiated sensor density configurations. The sensor density in the high-sensitivity zone is twice that of the medium-sensitivity zone and four times that of the low-sensitivity zone, effectively solving the monitoring blind spot problem caused by traditional uniform point distribution schemes. Particularly in key areas such as the mid-span sections of the beams on both sides of the closure segment and near the anchorage points of the stay cables, the high-density sensor arrangement significantly improves the monitoring capability of local stress concentration, providing more accurate data support for optimizing the jacking force. The temperature sensor uses a fiber optic grating type, improving the resolution accuracy to 0.1℃, and can accurately capture temperature gradient changes. The strain sensor uses a capacitive micro-strain gauge, effectively avoiding the influence of electromagnetic interference on the measurement results. The spatial spacing between the two types of sensors is controlled within 50 mm to ensure the physical quantity matching of temperature and strain data, avoiding data deviation. The adaptive sampling mechanism automatically triggers local area encrypted sampling to 10 Hz by monitoring the temperature difference and strain change rate of adjacent sensors, significantly improving the response speed to sudden temperature changes and strain abrupt changes. Simultaneously, the temperature-strain mapping function is corrected in real time through edge computing nodes, ensuring the accuracy and consistency of the monitoring data. High-precision temperature and strain field monitoring can promptly identify the risk of local stress concentration in the closure section, providing a scientific basis for optimizing the jacking force and thus reducing the possibility of structural damage during construction.

[0148] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. An optimization method for the closure thrust of a low-tower cable-stayed bridge with environmental adaptability, characterized in that, Includes the following steps: S1: Distributed temperature sensor arrays and strain sensor arrays are arranged on both sides of the closure section to collect real-time temperature gradient distribution data and corresponding strain values ​​of the beam structure. The collected raw temperature data is then used to generate a continuous three-dimensional temperature model through a spatiotemporal interpolation algorithm. And establish a temperature-strain mapping function; where, For spatial coordinates, It is a time variable; S2: Using the top thrust as the decision variable F, establish the following multi-objective optimization function: In the formula, The standard deviation of the structural Mises equivalent stress. To determine the Euclidean distance between the designed alignment and the measured alignment. The construction safety risk index is calculated based on tilt and pressure sensors. , , These are the weighting coefficients, and + + ; The ambient temperature is the variable. S3: Set constraints: , , In the formula, , , These are the design allowable thresholds for component stress, elevation deviation, and axis error, respectively. , , These are the maximum calculated stress of the component, the calculated deviation of the main beam elevation, and the calculated deviation of the main beam transverse axis, respectively. S4: Solving using an improved PSO algorithm: Initializing particle swarm positions Through fitness function To evaluate the quality of particles, the formula is as follows: The objective function value, For the penalty function; S5: Output the optimal solution set: Select the solution with the lowest construction safety risk in the Pareto front as the final jacking force. ; In step S2, the weighting coefficients , , Adopt an environmental risk-driven adjustment strategy: C1: Establish a weighted influence factor library: Structural stress sensitivity factor Calculate the partial derivative of the Mises stress standard deviation with respect to the thrust F based on historical data; Temperature Sudden Change Warning Factor Take the maximum absolute value of the rate of change of the temperature gradient over 3 consecutive hours; Construction safety attenuation factor The ratio of safety risk indicators based on tilt sensor data to its threshold; C2: Design weight dynamic calculation rules: when ≥3℃ / hour and When ≥0.8, automatically increase Reduce to 150% of the original value at the same time Up to 70% of the original value; when ≤0.5 and real-time linear deviation exceeds When it reaches 80%, Increase to 120% of the original value; Each weight needs to be renormalized after adjustment, and the adjustment range should not exceed ±30% of the original value. C3: Perform weight updates every 15 minutes and verify the robustness of the weight combination through Monte Carlo simulation; In step S4, the improved PSO algorithm introduces a temperature adaptive mechanism, the specific steps of which are as follows: S4.1 Particle Coding Extension: Extending the particle position vector into three dimensions In the formula, This is the temperature influence factor, and its initial value is taken as the current temperature. Thermal strain under the following conditions; S4.2, Dynamic Inertia Weight Adjustment: According to Adjust the weights, reducing them when temperature fluctuations are drastic. To enhance local search; among which, The attenuation coefficient; S4.3 Temperature-oriented velocity update: A temperature prediction term is added to the standard PSO velocity update to guide particles to move toward the future temperature optimal solution; S4.4, Constraint Violation Adaptive Penalty: According to Calculate the penalty coefficient to double the penalty for excessive tensile stress in the structure under heating conditions; where, As a benchmark reference value, This is a function of the direction of temperature deviation. This refers to the effective temperature range.

2. The method for optimizing the closure thrust of a low-tower cable-stayed bridge with environmental adaptability according to claim 1, characterized in that: In step S5, the Pareto solution set selection uses a temperature-related fuzzy decision algorithm. S5.1: Define the satisfaction function: In the formula, As the indicator number, , , The first The actual value, maximum allowable value, and minimum allowable value of each indicator. For the first The normalized utility value of the indicator; S5.2: Introducing a temperature weight vector: in, S5.3: Calculate overall satisfaction In the formula, The dynamic weight of the k-th indicator. Let k be the utility function of the index. Key indicators; S5.4: Selection Corresponding solution And requires .

3. The method for optimizing the closure thrust of a low-tower cable-stayed bridge with environmental adaptability according to claim 1, characterized in that: In step S5, a digital twin module is embedded during the PSO iteration process for virtual verification: A1: Construct a parametric finite element model based on the BIM model, and input parameters. ; A2: Real-time synchronization of physical sensor data to update the twin's status; A3: For each particle Calculation of stress contour distribution in twins Beam end displacement vector ; buckling characteristic value ; A4: Define the confidence index for twins ,when At that time, twin data was used to replace some physical sensor data in the PSO evaluation.

4. The method for optimizing the closure thrust of a low-tower cable-stayed bridge with environmental adaptability according to claim 1, characterized in that: The optimization results are implemented through the following closed-loop control system: The central controller receives the PSO output. Generate hydraulic instruction set to Taiwan jack; Displacement sensors collect closure gap width in real time ,when At this time, PSO is triggered to re-optimize; Establish the top thrust-displacement sensing function A PID controller is used to compensate for system lag. In the formula, Let be the system's transfer function, representing the relationship between the output and input in the complex frequency domain (s-domain); For the Laplace transform of the output, For the Laplace transform of the input, For static gain, It is a time constant. For complex frequency domain variables, For pure time delay, For the designed closure width, This is the allowable width deviation threshold.

5. The optimization method for the closure thrust of a low-tower cable-stayed bridge with environmental adaptability according to claim 1, characterized in that: Output in step S5 Then, a risk control instruction set is generated simultaneously: B1: The jacking process is controlled in stages. Decomposed into Three stages of force values, among which, , In accordance with Increasing rate Duration , The structural relaxation time constant; B2: Real-time safety margin monitoring: A pressure sensor array is embedded in the hydraulic system of the jacking equipment. When the measured pressure P deviates from the theoretical value... Automatically switch to safe mode: If Then activate the pressure relief valve, with a pressure relief rate ≤30kN / s; if Then the compensation pump will be triggered, with a compensation amount ≤8%. ; B3: Establish an early warning system for sudden changes in tilt angle, when the tilt angle difference between the two beams is... or time-varying rate At that time, pause the jacking and start the laser scanner to check the line shape.

6. The method for optimizing the closure thrust of a low-tower cable-stayed bridge with environmental adaptability according to claim 1, characterized in that: The constraint condition described in step S3 adopts a time-varying elastic threshold strategy: Component stress threshold Dynamic correction: When the concrete age is less than 7 days, Take 80% of the design value; when the average daily temperature exceeds 30℃, Decrease by 3% for every 5°C increase; Elevation deviation threshold The grading is based on the width of the closure opening: when the width of the closure opening... Rice time, Pick millimeters; when the width of the closure joint is greater than 2 meters, Expand by 0.5 meters per increment millimeters; Axis error threshold Introducing a temperature compensation mechanism: When the temperature difference between the main beam and the pier exceeds 10℃, Allowed to be relaxed to 120% of the design value; The assessment of constraint violation increases the safety margin factor for stress constraints. The amount of violation is multiplied by a penalty weight of 1.

5.

7. The optimization method for the closure thrust of a low-tower cable-stayed bridge with environmental adaptability according to claim 1, characterized in that: In step S1, the distributed temperature sensor array and strain sensor array are arranged using a regional dynamic encryption strategy, specifically including: D1: Based on the finite element thermo-mechanical coupling analysis of the beams on both sides of the closure section, the monitoring area is divided into three sensitive areas: high-sensitivity area, medium-sensitivity area and low-sensitivity area. The sensor density in the high-sensitivity area is twice that of the medium-sensitivity area and four times that of the low-sensitivity area. D2: The sensor array adopts a heterogeneous networking architecture: the temperature sensor uses fiber optic grating type to improve the temperature gradient resolution accuracy to 0.1℃, the strain sensor uses capacitive micro-strain gauge to eliminate electromagnetic interference, and the spatial distance between the two types of sensors is controlled within 50 mm to achieve accurate matching of physical quantities. D3: Deploy an adaptive sampling mechanism: When the temperature difference between adjacent sensors exceeds 2°C or the strain rate of change is higher than 5... At a rate of / minute, local area encrypted sampling is automatically triggered to 10 Hz, and the temperature-strain mapping function Φ(ΔT)→ε is corrected in real time through edge computing nodes.

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