Steel anchor beam pre-installation construction method

Abstract

The invention relates to the technical field of bridge engineering construction, and discloses a steel anchor beam pre-installation construction method which comprises the following steps: S1, performing trial assembly for many times in a factory to ensure that the size and the shape of a steel anchor beam completely meet design requirements, and introducing a third-party detection mechanism for independent inspection, it is ensured that various indexes of the geometric dimension, the board flatness and the butt joint deviation are qualified; s2, a high-precision total station or a laser locator is used for confirming the position of the embedded part, and a measured data automatic recording and analyzing system is adopted for timely perceiving so as to correct errors; and S3, the stainless steel plate is precisely machined through a numerical control machine tool. By introducing high-precision equipment and complex algorithm control, the traceability and controllability of quality are realized, the material waste is reduced, the construction risk and cost are reduced, meanwhile, the safety and durability of the structure are enhanced, each step in the construction process is ensured to strictly meet the design requirements, and the overall quality of a project is improved.

Description

Technical Field

[0001] The invention relates to the technical field of bridge engineering construction, in particular to a steel anchor beam pre-installation construction method. Background Art

[0002] In the construction of large-span bridges, steel anchor beams are key structural components. The accuracy and quality of their pre-installation construction are directly related to the safety and durability of the bridge structure. However, the existing technology still has shortcomings in the pre-installation process of steel anchor beams, which will affect the overall quality and service life of the bridge project.

[0003] Firstly, the traditional method of installing steel anchor beams relies on manual operation and conventional measuring tools for positioning and installation accuracy, which is prone to errors, resulting in poor connectivity between the steel anchor beams and the cable towers, affecting the overall stability of the structure. In addition, due to the lack of precise real-time monitoring and control methods, the tensioning force of the cable-stayed cable is unevenly distributed, resulting in unbalanced structural force and increasing safety risks during construction and use.

[0004] Secondly, the existing technology has obvious shortcomings in construction efficiency and cost control. The traditional method relies on a large amount of manual operation, the construction process is cumbersome and time-consuming, and the stability of the construction progress cannot be ensured. At the same time, due to the lack of support from intelligent and automated equipment, material waste and resource waste are prone to occur during the construction process, increasing the overall cost of the project.

[0005] In addition, in terms of construction environment and environmental protection requirements, traditional methods cannot meet the high standards of modern engineering for environmental protection and construction environment management. Dust, noise and other pollution problems generated during the construction process have not been effectively controlled, which in turn affects the working environment of construction workers and has an adverse impact on the surrounding environment.

[0006] Therefore, those skilled in the art provide a steel anchor beam pre-installation construction method to solve the problems raised in the above background technology. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides a steel anchor beam pre-installation construction method to solve the problems raised in the above background technology.

[0008] To achieve the above purpose, the present invention is implemented through the following technical scheme: a steel anchor beam pre-installation construction method, comprising the following steps:

[0009] S1. Conduct multiple trial assemblies in the factory to ensure that the size and shape of the steel anchor beam fully meet the design requirements, and introduce a third-party testing agency to conduct independent inspections to ensure that the various indicators of geometric dimensions, plate surface flatness and docking deviation are qualified;

[0010] S2. Use a high-precision total station or laser locator to confirm the position of embedded parts, and use an automatic recording and analysis system for measurement data. The analysis system is an intelligent system for accurately locating and correcting the position of embedded parts.

[0011] S3. Use CNC machine tools to perform precision processing on stainless steel plates, test the friction coefficient of sliding friction pairs, and use laser alignment systems and wireless remote control equipment to ensure the accuracy and safety of hoisting positioning;

[0012] S4. Use digital tensioning equipment to control the tensioning force of the inclined cable to monitor and record the tensioning force in real time;

[0013] S5. Select high-performance concrete materials, conduct trial mixing and testing before construction to ensure that their fluidity, cohesion and strength meet the requirements, and then use an automatic concrete mixing station to mix the high-performance concrete materials;

[0014] S6. Use layered pouring and vibration compaction technology to ensure uniform pouring and compaction of concrete, and set up a monitoring system to monitor the temperature, humidity and vibration frequency during the concrete pouring process in real time;

[0015] S7. After the cable is tensioned, tighten the high-strength bolts on one side into place;

[0016] S8. Use computer simulation and analysis software to simulate the cable tensioning process in advance, and set up a multi-point synchronous tensioning system to record each tensioning process in detail;

[0017] S9. Use the structural health monitoring system to monitor the stress and deformation of the tower in real time.

[0018] Preferably, in the step S1, the geometric dimension inspection algorithm formula is:

[0019] D=|D measured -D design |,

[0020] Among them, D measured is the measured geometric size, D design is the design size, D is the deviation value;

[0021] The inspection algorithm formula of the board surface flatness is:

[0022] F=max(|h 1 -h 2 |),

[0023] Among them, h 1 and h 2 is the height value on the same measuring line, and F is the deviation of flatness;

[0024] The inspection algorithm formula of the docking deviation is:

[0025]

[0026] Among them, X 1 , Y 1 and X 2 , Y 2 are the coordinates of the docking end, and E is the docking deviation.

[0027] Preferably, in the step S2, the algorithm formula for real-time monitoring and correction of the error is:

[0028] C=ΔE=E new -E old ,

[0029] Among them, E new is the error value of the latest measurement, E old is the error value of the previous measurement, and C is the change in error;

[0030] The algorithm formula of the error accumulation analysis is:

[0031]

[0032] Among them, E i is the error of the ith measurement, E total is the cumulative error;

[0033] The algorithm formula for automatic data recording and analysis is:

[0034]

[0035] Among them, E i is the error value of each measurement, μ E is the average value of the error.

[0036] Preferably, in the step S3, the friction coefficient of the sliding friction pair is tested using a contact mechanics model, and its algorithm formula is:

[0037]

[0038] Among them, δ is the deformation, F is the contact force, ν is the Poisson's ratio of the material, E is the elastic modulus of the material, and R is the contact curvature radius;

[0039]

[0040] Where μ is the friction coefficient, F t is the tangential friction force, F n is the normal load.

[0041] Preferably, in the step S3, the precision control of the hoisting positioning is controlled by a Kalman filter algorithm, and the Kalman filter includes a state prediction equation, a measurement update equation and a Kalman gain calculation:

[0042] The state prediction equation:

[0043]

[0044] in, is the state prediction value at time k, A is the state transfer matrix, B is the control input matrix, u k-1 To control the input quantity;

[0045] The measurement update equation:

[0046]

[0047] in, is the updated state estimate, K k is the Kalman gain, z k is the measurement value, H is the observation matrix;

[0048] The Kalman gain is calculated as:

[0049]

[0050] Among them, P k|k-1 is the prediction covariance matrix, and R is the measurement noise covariance matrix.

[0051] Preferably, in step S4, a collaborative control algorithm in the digital tensioning device is used for control, and the collaborative control algorithm includes a consensus algorithm and a delay compensation algorithm:

[0052] The consensus algorithm:

[0053]

[0054] in, is the state of the i-th control node, a ij is the connection weight between neighbor nodes;

[0055] The delay compensation algorithm:

[0056]

[0057] Among them, τ is the delay.

[0058] Preferably, in step S4, a support vector machine is used for tension force prediction, and the support vector machine algorithm includes a loss function, constraints and a back propagation algorithm:

[0059] The loss function:

[0060]

[0061] Among them, w is the weight vector, b is the bias, ξ i is the slack variable, C is the penalty parameter;

[0062] The constraints are:

[0063] y i (w T φ(x i )+b)≥1-ξ i ,ξ i ≥0,

[0064] Among them, φ(x i ) is the kernel function mapping;

[0065]

[0066] Among them, y i is the true value, is the output of the neural network, θ is the network parameter;

[0067] The back-propagation algorithm:

[0068]

[0069] Among them, η is the learning rate.

[0070] Preferably, in the step S6, the temperature range during the concrete pouring process is 5° C. to 35° C., the humidity range during the concrete pouring process is 50% to 90%, and the vibration frequency range during the concrete pouring process is 100 Hz to 200 Hz.

[0071] Preferably, in step S9, the algorithm used in structural health monitoring is a damage identification algorithm, and the operation steps of the damage identification algorithm are as follows:

[0072] Frequency Change:

[0073]

[0074] Where Δf i is the change of the frequency of the ith mode;

[0075] Curvature Mode Method:

[0076]

[0077] Among them, C i is the modal curvature, φ i is the i-th mode vibration shape;

[0078] Wavelet transform is used for signal denoising:

[0079]

[0080] Among them, W(a,b) is the wavelet coefficient, ψ is the mother wavelet function, a is the scale parameter, and b is the translation parameter.

[0081] Preferably, in step S8, the computer simulation and analysis software is operated by a time series analysis algorithm, and the time series analysis algorithm includes an autoregressive model, a moving average model and a discrete Fourier transform:

[0082] The autoregressive model:

[0083]

[0084] Among them, X t is the time series data, φ i is the model parameter, ∈ t is the white noise term;

[0085] The moving average model:

[0086]

[0087] Among them, θ i is the model parameter, ∈ t is the white noise term;

[0088] The discrete Fourier transform:

[0089]

[0090] Among them, X(k) is the frequency domain signal, x(n) is the time domain signal, and N is the signal length.

[0091] The present invention provides a steel anchor beam pre-installation construction method, which has the following beneficial effects:

[0092] 1. The present invention achieves quality traceability and controllability by introducing high-precision equipment and complex algorithm control, reduces material waste, reduces construction risks and costs, and at the same time, enhances the safety and durability of the structure, ensures that each step in the construction process strictly complies with the design requirements, and improves the overall quality of the project.

[0093] 2. The present invention improves the construction environment through automated equipment and environmental protection measures, reduces dependence on manpower and labor intensity, promotes technological progress and innovation in construction methods in the field of bridge engineering, improves the construction level of the project, and provides valuable experience and reference for similar projects in the future, which helps to achieve long-term stable operation and sustainable development of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0095] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0096] The present invention is described in detail below in conjunction with the accompanying drawings:

[0097] Example:

[0098] Please refer to the attached Figure 1 The embodiment of the present invention provides a steel anchor beam pre-installation construction method, comprising the following steps:

[0099] S1. Conduct multiple trial assemblies in the factory to ensure that the size and shape of the steel anchor beam fully meet the design requirements, and introduce a third-party testing agency to conduct independent inspections to ensure that the various indicators of geometric dimensions, plate surface flatness and docking deviation are qualified;

[0100] S2. Use a high-precision total station or laser locator to confirm the position of embedded parts, and use an automatic recording and analysis system for measurement data. The analysis system is an intelligent system for accurately locating and correcting the position of embedded parts.

[0101] S3. Use CNC machine tools to perform precision processing on stainless steel plates, test the friction coefficient of sliding friction pairs, and use laser alignment systems and wireless remote control equipment to ensure the accuracy and safety of hoisting positioning;

[0102] S4. Use digital tensioning equipment to control the tensioning force of the inclined cable to monitor and record the tensioning force in real time;

[0103] S5. Select high-performance concrete materials, conduct trial mixing and testing before construction to ensure that their fluidity, cohesion and strength meet the requirements, and then use an automatic concrete mixing station to mix the high-performance concrete materials;

[0104] S6. Use layered pouring and vibration compaction technology to ensure uniform pouring and compaction of concrete, and set up a monitoring system to monitor the temperature, humidity and vibration frequency during the concrete pouring process in real time;

[0105] S7. After the cable is tensioned, tighten the high-strength bolts on one side into place;

[0106] S8. Use computer simulation and analysis software to simulate the cable tensioning process in advance, and set up a multi-point synchronous tensioning system to record each tensioning process in detail;

[0107] S9. Use the structural health monitoring system to monitor the stress and deformation of the tower in real time.

[0108] Benefit S1 improves the manufacturing accuracy and installation matching of steel anchor beams, reduces the adjustment workload at the installation site, ensures the smooth progress of subsequent construction, and improves the quality and safety of the overall structure; Benefit S2 reduces installation deviations caused by inaccurate positioning, improves the docking accuracy between steel anchor beams and other structural components, and ensures the stability of the overall structure; Benefit S3 improves installation accuracy, reduces quality problems caused by processing errors and installation positioning errors, enhances safety during construction, and reduces construction risks; Benefit S4 ensures uniform distribution of tensioning force of the inclined cable, prevents unbalanced force on the structure, improves the overall stability and safety of the bridge, and provides traceable construction data for easy quality control; Benefit S5 improves the quality of concrete, ensures its operability during construction and final structural strength, reduces Reduce material waste during construction and reduce construction costs; S6 benefits improve the construction quality of concrete, prevent the formation of voids and cracks inside concrete, ensure the durability and stability of the structure, and control the construction environment in real time through the monitoring system to avoid quality defects; S7 benefits ensure the stability of the steel anchor beam after the cable is tensioned, prevent structural instability caused by looseness or improper fixation, and ensure the overall safety of the structure; S8 benefits effectively prevent uneven force distribution during tensioning, avoid structural stress concentration and deformation, improve construction quality, and provide complete quality control data through records to facilitate subsequent maintenance and inspection; S9 benefits can timely discover and warn of potential problems during construction and use, prevent structural failure, extend the service life of the bridge, and ensure the long-term stability and safety of the bridge.

[0109] In step S1, the geometric dimension inspection algorithm formula is:

[0110] D=|D measured -D design |,

[0111] Among them, D measured is the measured geometric size, D design is the design size, D is the deviation value;

[0112] The inspection algorithm formula for the flatness of the board surface is:

[0113] F=max(|h 1 -h 2 |),

[0114] Among them, h 1 and h 2is the height value on the same measuring line, and F is the deviation of flatness;

[0115] The algorithm formula for checking docking deviation is:

[0116]

[0117] Among them, X 1 , Y 1 and X 2 , Y 2 are the coordinates of the docking end, and E is the docking deviation.

[0118] The dimension inspection algorithm can accurately evaluate the manufacturing accuracy of the steel anchor beam by calculating the deviation between the measured geometric dimensions and the designed dimensions to ensure that it meets the design requirements. This formula can effectively identify errors in the manufacturing process, ensure the dimensional accuracy of the steel anchor beam, reduce the need for adjustments during the subsequent installation process, and ensure the accuracy and reliability of the overall structure.

[0119] The plate surface flatness inspection algorithm evaluates the flatness of the steel anchor beam surface by calculating the height difference between points on the same measurement line. This formula can be used to detect uneven areas on the plate surface in a timely manner to prevent the surface unevenness from having an adverse effect on subsequent installation and structural performance, thereby ensuring the surface quality and aesthetics of the structure.

[0120] The algorithm formula for checking docking deviation calculates the three-dimensional spatial distance between the docking endpoints to accurately evaluate the deviation of the steel anchor beam during the docking process. This formula can effectively detect docking errors, ensure the precise docking of each part of the steel anchor beam, reduce misalignment and mismatch during installation, ensure the integrity and mechanical properties of the structure, and improve the overall quality and safety of the project.

[0121] In step S2, the algorithm formula for real-time monitoring and correction of errors is:

[0122] C=ΔE=E new -E old ,

[0123] Among them, E new is the error value of the latest measurement, E old is the error value of the previous measurement, and C is the change in error;

[0124] The algorithm formula of error accumulation analysis is:

[0125]

[0126] Among them, E i is the error of the ith measurement, E total is the cumulative error;

[0127] Algorithm formula for automatic data recording and analysis:

[0128]

[0129] Among them, E i is the error value of each measurement, μ E is the average value of the error.

[0130] The algorithm formula for real-time monitoring and correction of errors is used to monitor the error changes in the construction process in real time and promptly identify the increase or decrease trend of errors. By real-time monitoring and analyzing the error changes, timely adjustments and corrections can be made during the construction process to prevent quality problems caused by error accumulation, thereby improving construction accuracy and ensuring that the project proceeds smoothly according to design requirements.

[0131] The algorithm formula of error accumulation analysis evaluates the overall accumulation of errors by calculating the square root of the sum of squares of measurement errors. Error accumulation analysis can help identify the cumulative effect of errors during the construction process, prevent the cumulative errors from exceeding the allowable range, take corrective measures in time, avoid serious quality problems, and improve the overall reliability of the project.

[0132] The algorithm formula for automatic data recording and analysis is used to calculate the average error value in multiple measurement data and evaluate the overall error level in the construction process. By automatically recording and analyzing error data, the engineering team can better understand the quality status of the construction process, identify the existence of systematic errors in the construction process, and improve the efficiency and effectiveness of construction quality management.

[0133] In step S3, the friction coefficient of the sliding friction pair is tested in the contact mechanics model, and its algorithm formula is:

[0134]

[0135] Among them, δ is the deformation, F is the contact force, ν is the Poisson's ratio of the material, E is the elastic modulus of the material, and R is the contact curvature radius;

[0136]

[0137] Where μ is the friction coefficient, F t is the tangential friction force, F n is the normal load.

[0138] The Hertz contact formula is used to calculate the deformation of the contact surface under pressure. The Hertz contact theory provides a basis for understanding the contact behavior of sliding friction pairs under stress conditions. By calculating the deformation, the contact condition of the friction pair in use can be evaluated. By accurately calculating the contact deformation, the design of the friction pair can be optimized, friction loss and surface damage can be reduced, the service life of the equipment can be extended, and the reliability and efficiency of its operation can be improved.

[0139] The friction coefficient calculation formula is used to calculate the friction coefficient of the sliding friction pair. The friction coefficient is the ratio of the tangential friction force to the normal load during the sliding contact process, reflecting the friction characteristics between the material surfaces. The calculation of the friction coefficient can help engineers accurately evaluate the performance of the sliding friction pair. When designing and selecting materials, optimization can be performed based on the friction coefficient to reduce energy loss, improve transmission efficiency, and reduce wear, thereby improving the overall performance and durability of the equipment.

[0140] In step S3, the accuracy control of the hoisting positioning is controlled by the Kalman filter algorithm. The Kalman filter includes the state prediction equation, the measurement update equation and the Kalman gain calculation:

[0141] State prediction equation:

[0142]

[0143] in, is the state prediction value at time k, A is the state transfer matrix, B is the control input matrix, u k-1 To control the input quantity;

[0144] Measurement update equation:

[0145]

[0146] in, is the updated state estimate, K k is the Kalman gain, z k is the measurement value, H is the observation matrix;

[0147] Kalman gain calculation:

[0148] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ,

[0149] Among them, P k|k-1 is the prediction covariance matrix, and R is the measurement noise covariance matrix.

[0150] The state prediction equation takes into account the dynamic behavior of the system. The equation can predict the hoisting position and posture in real time during the hoisting process. Using the state prediction equation, engineers can foresee the deviations that may occur during the hoisting process and take adjustment measures in advance to improve the accuracy of hoisting positioning and avoid construction quality problems caused by error accumulation.

[0151] The measurement update equation compares the actual measured value with the predicted value and uses the Kalman gain to correct the predicted value to obtain a more accurate state estimate. This process continuously corrects the real-time positioning error during the lifting process. The application of the measurement update equation can adjust the prediction error in real time, significantly improve the accuracy of lifting positioning, ensure the consistency between the actual positioning and the designed positioning, effectively reduce the deviation and error in the construction process, and improve the construction quality.

[0152] Kalman gain calculation is used to balance the weight coefficient between the prediction model and the actual measurement. By calculating the Kalman gain, the best state estimation can be achieved. The calculation of the Kalman gain can effectively reduce the error caused by measurement noise, improve the data fusion quality in the lifting and positioning process, and improve the consistency between the prediction and the actual state.

[0153] In step S4, the collaborative control algorithm in the digital tensioning device is used for control. The collaborative control algorithm includes a consensus algorithm and a delay compensation algorithm:

[0154] Consensus Algorithm:

[0155]

[0156] in, is the state of the i-th control node, a ij is the connection weight between neighbor nodes;

[0157] Delay compensation algorithm:

[0158]

[0159] Among them, τ is the delay.

[0160] The consensus algorithm is used to coordinate the status of multiple control nodes. Through information exchange between adjacent nodes, the status of each node is gradually adjusted to the same state as that of neighboring nodes. By applying the consensus algorithm, the synchronous operation of multiple tensioning points can be ensured, improving the stability and security of the overall structure.

[0161] The delay compensation algorithm is used to deal with the control input lag problem caused by communication delay or system lag. By considering the impact of delay, the algorithm can adjust the control input to compensate for the error caused by delay. In the multi-point synchronous tensioning system, the delay compensation algorithm can effectively reduce or eliminate the operation asynchrony problem between different tensioning points caused by delay. Through delay compensation, the synchronization of each node is ensured, further improving the accuracy and safety of construction.

[0162] In step S4, support vector machine is used for tension prediction. The support vector machine algorithm includes loss function, constraint conditions and back propagation algorithm:

[0163] Loss function:

[0164]

[0165] Among them, w is the weight vector, b is the bias, ξ i is the slack variable, C is the penalty parameter;

[0166] Constraints:

[0167] y i (w T φ(x i )+b)≥1-ξ i ,ξ i ≥0,

[0168] Among them, φ(x i ) is the kernel function mapping;

[0169]

[0170] Among them, y i is the true value, is the output of the neural network, θ is the network parameter;

[0171] Back Propagation Algorithm:

[0172]

[0173] Among them, η is the learning rate.

[0174] Loss function of support vector machine By optimizing the loss function, SVM can query the optimal segmentation hyperplane in tension prediction to ensure that the prediction model has good generalization ability and accuracy. This method can handle high-dimensional data and is used for complex tension prediction problems, providing more accurate control decision support;

[0175] The constraints of SVM ensure that during the prediction process, the data points are correctly classified or regressed and the deviation is within the allowable range. The kernel function mapping is used to map the original data to a higher dimensional space to ensure the precise control of the tension force.

[0176] The loss function of the neural network is used to measure the error between the model prediction output and the true value. As the objective function optimized during the model training process, the parameters of the neural network can be optimized by minimizing the loss function, so that the network can better fit the training data. The neural network can learn and capture the complex relationship between tension and input features, and provide more accurate predictions, which can improve construction quality.

[0177] The back-propagation algorithm gradually updates the parameters of the neural network by calculating the gradient of the loss function, so that the model error gradually decreases. The learning rate η determines the step size of each parameter update and affects the convergence speed and stability. The back-propagation algorithm enables the neural network to effectively adjust internal parameters and improve the fitting ability and generalization performance of the model. It is crucial for dynamically adjusting the tensioning force and responding to changes in the construction environment in real time, ensuring the accuracy and stability of the tensioning operation.

[0178] In step S6, the temperature range during the concrete pouring process is 5° C. to 35° C., the humidity range during the concrete pouring process is 50% to 90%, and the vibration frequency range during the concrete pouring process is 100 Hz to 200 Hz.

[0179] In step S9, the algorithm used in structural health monitoring is a damage identification algorithm. The operation steps of the damage identification algorithm are as follows:

[0180] Frequency Change:

[0181]

[0182] Where Δf i is the change of the frequency of the ith mode;

[0183] Curvature Mode Method:

[0184]

[0185] Among them, C i is the modal curvature, φ i is the i-th mode vibration shape;

[0186] Wavelet transform is used for signal denoising:

[0187]

[0188] Among them, W(a,b) is the wavelet coefficient, ψ is the mother wavelet function, a is the scale parameter, and b is the translation parameter.

[0189] The frequency change formula can quickly identify the potential damage location and extent of the structure by monitoring the changes in modal frequency, provide early warning, and prevent further structural degradation. This method is very effective for health monitoring of large structures such as long-span bridges, which can extend the service life of the structure and reduce maintenance costs.

[0190] The curvature modal method can accurately locate the damaged area of ​​the structure, especially when the damage is small and difficult to detect through frequency changes. The curvature modal method provides higher sensitivity, making damage identification more accurate, which helps to take repair measures in time to ensure the safety and reliability of the structure;

[0191] Wavelet transform is used in the process of signal denoising to retain important features in the signal, reduce false alarms caused by noise, and enhance the accuracy of damage identification. It is particularly important in complex structural health monitoring because environmental noise and measurement errors often interfere with the damage identification process. Through wavelet transform, the accuracy of signal processing can be improved, thereby enhancing the reliability and stability of damage identification.

[0192] In step S8, the computer simulation and analysis software is operated by a time series analysis algorithm, wherein the time series analysis algorithm includes an autoregressive model, a moving average model, and a discrete Fourier transform:

[0193] Autoregressive Model:

[0194]

[0195] Among them, X t is the time series data, φ i is the model parameter, ∈ t is the white noise term;

[0196] Moving Average Model:

[0197]

[0198] Among them, θ i is the model parameter, ∈ t is the white noise term;

[0199] Discrete Fourier Transform:

[0200]

[0201] Among them, X(k) is the frequency domain signal, x(n) is the time domain signal, and N is the signal length.

[0202] The autoregressive model can effectively identify and utilize the correlation in the time series for short-term prediction. It is very useful for data recording and trend analysis in structural health monitoring, and can help predict the future structural state and prevent potential problems in advance. The moving average model can effectively filter out random fluctuations in the time series and extract more meaningful trend signals. In structural health monitoring, the MA model helps to detect abnormal fluctuations, identify potential structural health problems, and enhance the accuracy and reliability of data analysis.

[0203] Discrete Fourier transform helps to identify periodic vibrations or frequency characteristics in structural health monitoring. Through frequency domain analysis, potential faults in the structure, such as abnormal vibration or fatigue cracks, can be detected, providing early warnings and ensuring the long-term safety of the structure.

[0204] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A steel anchor beam pre-installation construction method, characterized in that: The steps include: S1. Conduct multiple trial assemblies in the factory to ensure that the size and shape of the steel anchor beam fully meet the design requirements, and introduce a third-party testing agency to conduct independent inspections to ensure that the various indicators of geometric dimensions, plate surface flatness and docking deviation are qualified; S2. Use a high-precision total station or laser locator to confirm the position of embedded parts, and use an automatic recording and analysis system for measurement data. The analysis system is an intelligent system for accurately locating and correcting the position of embedded parts. The intelligent system uses a high-precision total station or laser locator to ensure the accurate positioning of the embedded parts. The system includes data acquisition, storage, analysis and correction modules, which can automatically record measurement data and analyze it in real time. Through high-precision algorithms, it provides positioning and correction solutions and generates detailed measurement reports. It is applied to building construction, bridge construction and equipment installation scenarios. S3. Use CNC machine tools to perform precision processing on stainless steel plates, test the friction coefficient of sliding friction pairs, and use laser alignment systems and wireless remote control equipment to ensure the accuracy and safety of hoisting positioning; S4. Use digital tensioning equipment to control the tensioning force of the inclined cable to monitor and record the tensioning force in real time; S5. Select high-performance concrete materials, conduct trial mixing and testing before construction to ensure that their fluidity, cohesion and strength meet the requirements, and then use an automatic concrete mixing station to mix the high-performance concrete materials; S6. Use layered pouring and vibration compaction technology to ensure uniform pouring and compaction of concrete, and set up a monitoring system to monitor the temperature, humidity and vibration frequency during the concrete pouring process in real time; S7. After the cable is tensioned, tighten the high-strength bolts on one side into place; S8. Use computer simulation and analysis software to simulate the cable tensioning process in advance, and set up a multi-point synchronous tensioning system to record each tensioning process in detail; S9. Use the structural health monitoring system to monitor the stress and deformation of the tower in real time.

2. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In the step S1, the geometric dimension inspection algorithm formula is: D=|D measured -D design |, Among them, D measured is the measured geometric size, D design is the design size, D is the deviation value; The inspection algorithm formula of the board surface flatness is: F = max(|h1-h2|), Among them, h1 and h2 are the height values ​​on the same measuring line, and F is the deviation of flatness; The inspection algorithm formula of the docking deviation is: Among them, X1, Y1 and X2, Y2 are the coordinates of the docking end, and E is the docking deviation.

3. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In the step S2, the error is calculated by a real-time monitoring and correction algorithm, and the real-time monitoring and correction algorithm formula is: C=ΔE=E new -E old , Among them, E new is the error value of the latest measurement, E old is the error value of the previous measurement, and C is the change in error; In step S2, the calculated error is analyzed and processed by an error accumulation analysis algorithm, and the error accumulation analysis algorithm formula is: Among them, E i is the error of the ith measurement, E total is the cumulative error; In the step S2, the calculated data is recorded and analyzed by an automatic data recording and analysis algorithm, and the formula of the automatic data recording and analysis algorithm is: Among them, E i is the error value of each measurement, μ E is the average value of the error.

4. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In the step S3, the friction coefficient of the sliding friction pair is tested in the contact mechanics model, and its algorithm formula is: Among them, δ is the deformation, F is the contact force, ν is the Poisson's ratio of the material, E is the elastic modulus of the material, and R is the contact curvature radius; Where μ is the friction coefficient, F t is the tangential friction force, F n is the normal load.

5. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In the step S3, the precision control of the hoisting positioning is controlled by the Kalman filter algorithm, and the Kalman filter includes the state prediction equation, the measurement update equation and the Kalman gain calculation: The state prediction equation: in, is the state prediction value at time k, A is the state transfer matrix, B is the control input matrix, u k-1 To control the input quantity; The measurement update equation: in, is the updated state estimate, K k is the Kalman gain, z k is the measurement value, H is the observation matrix; The Kalman gain is calculated as: Among them, P k|k-1 is the prediction covariance matrix, and R is the measurement noise covariance matrix.

6. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In step S4, the collaborative control algorithm in the digital tensioning device is used for control, and the collaborative control algorithm includes a consensus algorithm and a delay compensation algorithm: The consensus algorithm: in, is the state of the i-th control node, a ij is the connection weight between neighbor nodes; The delay compensation algorithm: Among them, τ is the delay.

7. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In step S4, a support vector machine is used for tension prediction. The support vector machine algorithm includes a loss function, constraints and a back propagation algorithm: The loss function: Among them, w is the weight vector, b is the bias, ξ i is the slack variable, C is the penalty parameter; The constraints are: y i (w T φ(x i )+b)≥1-ξ i ,x i ≥0, Among them, φ(x i ) is the kernel function mapping; Among them, y i is the true value, is the output of the neural network, θ is the network parameter; The back-propagation algorithm: Among them, η is the learning rate.

8. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In the step S6, the temperature range of the concrete pouring process is 5° C. to 35° C., the humidity range of the concrete pouring process is 50% to 90%, and the vibration frequency range of the concrete pouring process is 100 Hz to 200 Hz.

9. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In step S9, the algorithm used in structural health monitoring is a damage identification algorithm, and the operation steps of the damage identification algorithm are as follows: Frequency Change: Where Δf i is the change of the frequency of the ith mode; Curvature Mode Method: Among them, C i is the modal curvature, φ i is the i-th mode vibration shape; Wavelet transform is used for signal denoising: Among them, W(a,b) is the wavelet coefficient, ψ is the mother wavelet function, a is the scale parameter, and b is the translation parameter.

10. A steel anchor beam pre-installation construction method according to claim 1, characterized in that: In step S8, the computer simulation and analysis software is operated by a time series analysis algorithm, wherein the time series analysis algorithm includes an autoregressive model, a moving average model and a discrete Fourier transform: The autoregressive model: Among them, X y is the time series data, φ i is the model parameter, ∈ t is a white noise term; the moving average model: Among them, θ i is the model parameter, ∈ t is the white noise term; The discrete Fourier transform: Among them, X(k) is the frequency domain signal, x(n) is the time domain signal, and N is the signal length.

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