Prefabricated concrete bridge deck slab reinforcing steel bar pre-tightening method

Abstract

The invention provides a prefabricated concrete bridge deck slab reinforcing steel bar pre-tightening method which is suitable for the field of bridge construction and comprises the steps of construction preparation, reinforcing steel bar installation, anchorage device and pre-tightening force monitoring point position, reinforcing steel bar pre-tightening, follow-up work, acquisition of pre-tightening force monitoring data, analysis of the pre-tightening force monitoring data and early warning of the pre-tightening force monitoring data. According to the prefabricated concrete bridge deck slab reinforcing steel bar pre-tightening method, the systematized reinforcing steel bar pre-tightening method and the intelligent monitoring technology are adopted, the construction quality and safety of a bridge deck slab are ensured, long-term monitoring and early warning are carried out after construction is completed, the monitoring capacity for bridge structure health is further enhanced, and the safety of the bridge deck slab is improved. The method improves the timely recognition and processing of long-term potential construction quality problems, and improves the long-term safety and benefits of the whole project.

Description

Technical Field

[0001] This invention relates to a method for pre-tightening reinforcing bars in precast concrete bridge decks, applicable to the field of bridge construction. Background Technology

[0002] In recent years, with rapid economic development and accelerated urbanization, infrastructure construction has become increasingly important. Bridges, as crucial components of transportation, directly impact people's quality of life and the smooth flow of economic activities due to their safety and durability. However, numerous quality problems persist during bridge construction and maintenance, posing potential safety hazards to traffic. In the construction of precast concrete bridge decks, the pre-tightening process of reinforcing bars is a critical step in ensuring structural strength and stability. As tensile members, the quality of the pre-tightening force of the reinforcing bars directly affects the load-bearing capacity, durability, and crack resistance of the bridge deck. Traditional rebar pre-tightening methods often rely on manual operation, depending on the experience and judgment of construction workers. This method not only struggles to guarantee the accuracy of the pre-tightening force but may also lead to construction defects caused by human factors, thus affecting the overall performance and long-term service life of the bridge. In recent years, with advancements in construction technology and the introduction of intelligent technologies, online monitoring and data analysis during the rebar pre-tightening process have gradually become an important research direction. Many studies have shown that adopting modern monitoring methods can effectively improve construction accuracy and reduce safety hazards caused by construction quality problems. For example, using strain gauges and other sensor devices to monitor the preload of reinforcing bars in real time over a long period can achieve dynamic feedback on the actual preload. Against this backdrop, there is an urgent need for a scientific and rational method for preloading reinforcing bars that comprehensively utilizes modern monitoring technologies and intelligent analysis methods. Summary of the Invention

[0003] The purpose of this invention is to address the urgent need for a scientific and reasonable method for pre-tightening reinforcing bars that comprehensively utilizes modern monitoring technology and intelligent analysis methods. Therefore, this invention proposes a method for pre-tightening reinforcing bars in precast concrete bridge decks.

[0004] The objective of this invention can be achieved by adopting the following technical solutions:

[0005] The steps of a method for pre-tightening reinforcing bars in a precast concrete bridge deck are as follows:

[0006] S101 Construction Preparation;

[0007] The construction preparation includes reviewing the bridge deck design drawings, determining the reinforcement layout and reinforcement pretensioning requirements, carrying out the pretensioning work of the precast concrete bridge deck reinforcement, preparing qualified reinforcement and anchorages that meet the design requirements, preparing the necessary construction equipment and machinery, and organizing personnel.

[0008] S102 Installation points for reinforcing bars, anchorages, and preload monitoring;

[0009] The installation of reinforcing bars, anchors, and preload monitoring points includes installing the reinforcing bars and anchors in the required positions according to design requirements, arranging preload monitoring points at key positions of the reinforcing bars, using strain gauges at the preload monitoring points, and testing the connection between the monitoring equipment and the data acquisition system after the preload monitoring points are installed to ensure that the equipment can work normally.

[0010] The installation of reinforcing bars, anchorages, and preload monitoring points, including the number of preload monitoring points, is n, and is marked as Y. i , i = 1 to n;

[0011] S103 is used for pre-tightening of reinforcing bars;

[0012] The process of pre-tightening the reinforcing bars includes using hydraulic equipment to uniformly tension the reinforcing bars according to the designed tension force, recording the tension force value and the elongation of the reinforcing bars to ensure that they meet the design requirements.

[0013] S104 follow-up work;

[0014] The subsequent work includes, after completing the pre-tightening of the reinforcing bars, fixing the anchorage to ensure reliable anchoring of the reinforcing bars, checking the position and condition of the anchorage, and ensuring that the reinforcing bars are fixed to prevent loosening;

[0015] Acquisition of S105 preload monitoring data;

[0016] The acquisition of the preload monitoring data includes, based on the preload monitoring point Y i， According to the time interval T at fixed intervals j Obtain the preload data at the preload monitoring points, denoted as Y. ij ;

[0017] Analysis of S106 preload monitoring data;

[0018] The analysis of the preload monitoring data includes the following steps:

[0019] a) Based on the obtained time T j Preload data Y at each detection point ij The representative value of preload Y at time Tj was calculated using the base-truncated averaging method. j ,

[0020] b) Based on the obtained times T1~T j The preload values ​​Y1 to Y2 are representative values. j Perform data analysis to obtain time T j+1 ~T j+ Predicted preload value Y of m j+1 ~Y j+m ;

[0021] Early warning of S107 preload monitoring data;

[0022] The early warning of the preload monitoring data includes, based on the obtained time T j+1 ~T j+m Predicted preload Y j+1 ~Y j+m The system uses the preload threshold ε and comparative analysis to issue early warnings, achieving the goal of early warning and timely handling.

[0023] Furthermore, in step S105 above, the preload data Y of the preload monitoring point is obtained. ij The method is to calculate using equation (1).

[0024] Y ij =E·x ij (1)

[0025] In the formula, E represents the elastic modulus of the steel reinforcement, x ij For the preload data Y ij The displacement of the steel bar at the corresponding preload detection point.

[0026] Furthermore, in step S106 above, the time T is calculated using the truncated averaging method. j The preload value Y j The steps are as follows:

[0027] a) Organize the data and obtain time T j Preload data Y at each monitoring point ij ;

[0028] b) The preload data Y ij Sort the data from smallest to largest to obtain the data sequence L. ij ;

[0029] c) Truncate the data sequence L ij The data from the front and back ends (5% each) are used to obtain the final data Z after truncation. j ;

[0030] d) Calculate data Z ij The arithmetic mean of the values ​​at time T is used as the time interval. j The preload value Y j The calculation formula is shown in equation (2);

[0031]

[0032] In the formula, η is the reduction coefficient, which takes the value of 0.9.

[0033] Furthermore, in step S106 above, the data analysis step is as follows:

[0034] a) Obtain time T1~T j and its corresponding preload values ​​Y1~Y j Perform data organization;

[0035] b) Data preprocessing, which includes data cleaning, normalization and data serialization. Data cleaning includes outlier identification, missing value identification and outlier and missing value imputation. Data preprocessing also includes dividing the data into training set and test set, with a division ratio of 7:3.

[0036] c) Setting up the prediction model and selecting the hyperparameters to be optimized, including setting the prediction model to use a long short-term memory neural network for prediction model construction, and selecting the number of layers, number of units per layer, and batch size of the long short-term memory neural network model to be optimized.

[0037] d) Obtain the optimal hyperparameters using optimization algorithms, including obtaining the optimal hyperparameters using particle swarm optimization algorithms;

[0038] e) Construct a prediction model using the obtained optimal hyperparameters, wherein constructing a prediction model using the obtained optimal hyperparameters includes constructing a prediction model using Hodgson's optimal hyperparameters, denoted as M;

[0039] f) Model training and testing, wherein the model training and testing includes training the model M using the training set data, testing the model training results using the test set data after training and making adjustments based on the test results, and finally obtaining an applicable prediction model.

[0040] g) Model application, wherein the model application includes using the applicable prediction model to perform model prediction and obtain time T. j+1 ~T j+m Predicted preload Y j+1 ~Y j+m .

[0041] Furthermore, the steps described above for obtaining the optimal hyperparameters using the particle swarm optimization algorithm are as follows:

[0042] a) Determine the loss function, wherein determining the loss function includes determining that the loss function is the mean squared error, and the expression of the loss function f(x) is Equation (3).

[0043]

[0044] In the formula, n is the total number of samples, and y i y' is the true value of the i-th sample.i It is the predicted value of the i-th sample;

[0045] b) Initialize the particle swarm, wherein initializing the particle swarm includes selecting a number of particles, initializing the position and velocity of each particle, wherein the position of each particle contains a combination of hyperparameters;

[0046] c) Calculate the fitness value of each particle using a loss function. The fitness value is calculated by using a long short-term memory neural network model constructed with the hyperparameter combination information contained in the particle for prediction, and combining the prediction results with the actual test results and the loss function f(x) to calculate the fitness value of the particle.

[0047] d) The fitness value of each particle and the best position P it has experienced i The fitness value is compared, and if it is better, it is taken as the current best position;

[0048] e) The fitness value of each particle and the best position P experienced globally g If the comparison is successful, then take it as the best position globally at this point.

[0049] f) Calculate the velocity and position of the particle. If the termination condition is not met, return to step b. Otherwise, output the position information of the optimal particle, which is the optimal hyperparameter.

[0050] This invention offers the following advantages: By setting scientific monitoring points and using strain gauges for real-time monitoring, the preload can be accurately measured, ensuring that the preload during rebar tensioning meets design requirements and avoiding errors that may occur in traditional methods. By collecting and analyzing preload data at fixed time intervals, combined with mathematical methods such as truncated averaging, outliers are effectively removed, improving data reliability and providing accurate data for subsequent early warning systems. Through analysis, prediction, and comparison with preload thresholds, real-time early warnings can be achieved, potential problems can be detected promptly, safety hazards caused by rebar loosening or improper fixing can be reduced, and construction safety can be improved. Using particle swarm optimization to optimize the hyperparameters of the LSTM model significantly improves the accuracy of the prediction model, making the prediction of preload more scientific. Attached Figure Description

[0051] Figure 1 This is a flowchart of a method for pre-tightening reinforcing bars in a precast concrete bridge deck according to the present invention. Detailed Implementation

[0052] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings; it should be understood that the specific embodiments given herein are only for illustration and explanation of the present invention and cannot be used to limit the present invention.

[0053] The following is a specific embodiment of a method for pre-tightening the reinforcing bars of a precast concrete bridge deck.

[0054] S101 Construction Preparation;

[0055] The construction preparation includes reviewing the bridge deck design drawings, determining the reinforcement layout and reinforcement pretensioning requirements, carrying out the pretensioning work of the precast concrete bridge deck reinforcement, preparing qualified reinforcement and anchorages that meet the design requirements, preparing the necessary construction equipment and machinery, and organizing personnel.

[0056] S102 Installation points for reinforcing bars, anchorages, and preload monitoring;

[0057] The installation of reinforcing bars, anchors, and preload monitoring points includes installing the reinforcing bars and anchors in the required positions according to design requirements, arranging preload monitoring points at key positions of the reinforcing bars, using strain gauges at the preload monitoring points, and testing the connection between the monitoring equipment and the data acquisition system after the preload monitoring points are installed to ensure that the equipment can work normally.

[0058] The installation of reinforcing bars, anchorages, and preload monitoring points, including the number of preload monitoring points, is n, and is marked as Y. i , i = 1 to n;

[0059] S103 is used for pre-tightening of reinforcing bars;

[0060] The process of pre-tightening the reinforcing bars includes using hydraulic equipment to uniformly tension the reinforcing bars according to the designed tension force, recording the tension force value and the elongation of the reinforcing bars to ensure that they meet the design requirements.

[0061] S104 follow-up work;

[0062] The subsequent work includes, after completing the pre-tightening of the reinforcing bars, fixing the anchorage to ensure reliable anchoring of the reinforcing bars, checking the position and condition of the anchorage, and ensuring that the reinforcing bars are fixed to prevent loosening;

[0063] Acquisition of S105 preload monitoring data;

[0064] The acquisition of the preload monitoring data includes, based on the preload monitoring point Y i， According to the time interval T at fixed intervals j Obtain the preload data at the preload monitoring points, denoted as Y. ij ;

[0065] Furthermore, in step S105 above, the preload data Y of the preload monitoring point is obtained. ij The method is to calculate using equation (1).

[0066] Y ij =E·xij (1)

[0067] In the formula, E represents the elastic modulus of the steel reinforcement, x ij For the preload data Y ij The displacement of the steel bar at the corresponding preload detection point.

[0068] Analysis of S106 preload monitoring data;

[0069] The analysis of the preload monitoring data includes the following steps:

[0070] a) Based on the obtained time T j Preload data Y at each detection point ij The representative value of preload Y at time Tj was calculated using the base-truncated averaging method. j ,

[0071] b) Based on the obtained times T1~T j The preload values ​​Y1 to Y2 are representative values. j Perform data analysis to obtain time T j+1 ~T j+ Predicted preload value Y of m j+1 ~Y j+m ;

[0072] Furthermore, in step S106 above, the time T is calculated using the truncated averaging method. j The preload value Y j The steps are as follows:

[0073] a) Organize the data and obtain time T j Preload data Y at each monitoring point ij ;

[0074] b) The preload data Y ij Sort the data from smallest to largest to obtain the data sequence L. ij ;

[0075] c) Truncate the data sequence L ij The data from the front and back ends (5% each) are used to obtain the final data Z after truncation. j ;

[0076] d) Calculate data Z ij The arithmetic mean of the values ​​at time T is used as the time interval. j The preload value Y j The calculation formula is shown in equation (2);

[0077]

[0078] In the formula, η is the reduction coefficient, which takes the value of 0.9.

[0079] Furthermore, in step S106 above, the data analysis step is as follows:

[0080] a) Obtain time T1~T j and its corresponding preload values ​​Y1~Y j Perform data organization;

[0081] b) Data preprocessing, which includes data cleaning, normalization and data serialization. Data cleaning includes outlier identification, missing value identification and outlier and missing value imputation. Data preprocessing also includes dividing the data into training set and test set, with a division ratio of 7:3.

[0082] c) Setting up the prediction model and selecting the hyperparameters to be optimized, including setting the prediction model to use a long short-term memory neural network for prediction model construction, and selecting the number of layers, number of units per layer, and batch size of the long short-term memory neural network model to be optimized.

[0083] d) Obtain the optimal hyperparameters using optimization algorithms, including obtaining the optimal hyperparameters using particle swarm optimization algorithms;

[0084] e) Construct a prediction model using the obtained optimal hyperparameters, wherein constructing a prediction model using the obtained optimal hyperparameters includes constructing a prediction model using Hodgson's optimal hyperparameters, denoted as M;

[0085] f) Model training and testing, wherein the model training and testing includes training the model M using the training set data, testing the model training results using the test set data after training and making adjustments based on the test results, and finally obtaining an applicable prediction model.

[0086] g) Model application, wherein the model application includes using the applicable prediction model to perform model prediction and obtain time T. j+1 ~T j+m Predicted preload Y j+1 ~Y j+m .

[0087] Furthermore, the steps described above for obtaining the optimal hyperparameters using the particle swarm optimization algorithm are as follows:

[0088] a) Determine the loss function, wherein determining the loss function includes determining that the loss function is the mean squared error, and the expression of the loss function f(x) is Equation (3).

[0089]

[0090] In the formula, n is the total number of samples, and y i y' is the true value of the i-th sample. i It is the predicted value of the i-th sample;

[0091] b) Initialize the particle swarm, wherein initializing the particle swarm includes selecting a number of particles, initializing the position and velocity of each particle, wherein the position of each particle contains a combination of hyperparameters;

[0092] c) Calculate the fitness value of each particle using a loss function. The fitness value is calculated by using a long short-term memory neural network model constructed with the hyperparameter combination information contained in the particle for prediction, and combining the prediction results with the actual test results and the loss function f(x) to calculate the fitness value of the particle.

[0093] d) The fitness value of each particle and the best position P it has experienced i The fitness value is compared, and if it is better, it is taken as the current best position;

[0094] e) The fitness value of each particle and the best position P experienced globally g If the comparison is successful, then take it as the best position globally at this point.

[0095] f) Calculate the velocity and position of the particle. If the termination condition is not met, return to step b. Otherwise, output the position information of the optimal particle, which is the optimal hyperparameter.

[0096] Early warning of S107 preload monitoring data;

[0097] The early warning of the preload monitoring data includes, based on the obtained time T j+1 ~T j+m Predicted preload Y j+1 ~Y j+m The system uses the preload threshold ε and comparative analysis to issue early warnings, achieving the goal of early warning and timely handling.

[0098] In the above embodiments, the present invention discloses a method for pre-tightening the reinforcing bars of precast concrete bridge decks, including construction preparation, installation of reinforcing bars, anchorage and pre-tightening force monitoring points, pre-tightening of reinforcing bars, subsequent work, acquisition of pre-tightening force monitoring data, analysis of pre-tightening force monitoring data, and early warning of pre-tightening force monitoring data. This method proposes a pre-tightening method for precast concrete bridge decks, adopting a systematic reinforcing bar pre-tightening method and intelligent monitoring technology to ensure the construction quality and safety of the bridge decks. After construction is completed, long-term monitoring and early warning are implemented, further enhancing the monitoring capability of bridge structural health, improving the timely identification and handling of long-term potential construction quality problems, and improving the long-term safety and benefits of the overall project.

[0099] The above description is a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for pre-tightening reinforcing bars in precast concrete bridge decks, characterized in that, Includes the following steps: S101 Construction Preparation; S102 Installation points for reinforcing bars, anchorages, and preload monitoring; S103 is used for pre-tightening of reinforcing bars; S104 follow-up work; Acquisition of S105 preload monitoring data; Analysis of S106 preload monitoring data; Early warning of S107 preload monitoring data; The construction preparation includes reviewing the bridge deck design drawings, determining the reinforcement layout and reinforcement pretensioning requirements, carrying out the pretensioning work of the precast concrete bridge deck reinforcement, preparing qualified reinforcement and anchorages that meet the design requirements, preparing the necessary construction equipment and machinery, and organizing personnel. The installation of reinforcing bars, anchors, and preload monitoring points includes installing the reinforcing bars and anchors in the required positions according to design requirements, arranging preload monitoring points at key positions of the reinforcing bars, using strain gauges at the preload monitoring points, and testing the connection between the monitoring equipment and the data acquisition system after the preload monitoring points are installed to ensure that the equipment can work normally. The installation of reinforcing bars, anchorages, and preload monitoring points, including the number of preload monitoring points, is n, and is marked as Y. i , i = 1 to n; The process of pre-tightening the reinforcing bars includes using hydraulic equipment to uniformly tension the reinforcing bars according to the designed tension force, recording the tension force value and the elongation of the reinforcing bars to ensure that they meet the design requirements. The subsequent work includes, after completing the pre-tightening of the reinforcing bars, fixing the anchorage to ensure reliable anchoring of the reinforcing bars, checking the position and condition of the anchorage, and ensuring that the reinforcing bars are fixed to prevent loosening; The acquisition of the preload monitoring data includes, based on the preload monitoring point Y i， According to the time interval T at fixed intervals j Obtain the preload data at the preload monitoring points, denoted as Y. ij ; The analysis of the preload monitoring data includes the following steps: a) Based on the obtained time T j Preload data Y at each detection point ij The representative value of preload Y at time Tj was calculated using the base-truncated averaging method. j , b) Based on the obtained times T1~T j The preload values ​​Y1 to Y2 are representative values. j Perform data analysis to obtain time T j+1 ~T j+ Predicted preload value Y of m j+1 ~Y j+m ; The early warning of the preload monitoring data includes, based on the obtained time T j+1 ~T j+m Predicted preload Y j+1 ~Y j+m The system uses the preload threshold ε and comparative analysis to issue early warnings, achieving the goal of early warning and timely handling.

2. The method for pre-tightening reinforcing bars in a precast concrete bridge deck according to claim 1, characterized in that, In step S105, the preload data Y of the preload monitoring point is obtained. ij The method is to calculate using equation (1). AND ij =E·x ij (1) In the formula, E represents the elastic modulus of the steel reinforcement, x ij For the preload data Y ij The displacement of the steel bar at the corresponding preload detection point.

3. A method for pre-tightening reinforcing bars in a precast concrete bridge deck according to claim 1, characterized in that, In step S106, time T is calculated using the truncated averaging method. j The preload value Y j The steps are as follows: a) Organize the data and obtain time T j Preload data Y at each monitoring point ij ; b) The preload data Y ij Sort the data from smallest to largest to obtain the data sequence L. ij ; c) Truncate the data sequence L ij The data from the front and back ends (5% each) are used to obtain the final data Z after truncation. j ; d) Calculate data Z ij The arithmetic mean of the values ​​at time T is used as the time. j The preload value Y j The calculation formula is shown in equation (2); In the formula, η is the reduction coefficient, which takes the value of 0.

9.

4. A method for pre-tightening reinforcing bars in a precast concrete bridge deck according to claim 1, characterized in that, In step S106, the data analysis steps are as follows: a) Obtain time T1~T j and its corresponding preload values ​​Y1~Y j Perform data organization; b) Data preprocessing, which includes data cleaning, normalization and data serialization. Data cleaning includes outlier identification, missing value identification and outlier and missing value imputation. Data preprocessing also includes dividing the data into training set and test set, with a division ratio of 7:

3. c) Setting up the prediction model and selecting the hyperparameters to be optimized, including setting the prediction model to use a long short-term memory neural network for prediction model construction, and selecting the number of layers, number of units per layer, and batch size of the long short-term memory neural network model to be optimized. d) Obtain the optimal hyperparameters using optimization algorithms, including obtaining the optimal hyperparameters using particle swarm optimization algorithms; e) Construct a prediction model using the obtained optimal hyperparameters, wherein constructing a prediction model using the obtained optimal hyperparameters includes constructing a prediction model using Hodgson's optimal hyperparameters, denoted as M; f) Model training and testing, wherein the model training and testing includes training the model M using the training set data, testing the model training results using the test set data after training and making adjustments based on the test results, and finally obtaining an applicable prediction model. g) Model application, wherein the model application includes using the applicable prediction model to perform model prediction and obtain time T. j+1 ~T j+m Predicted preload Y j+1 ~Y j+m .

5. A method for pre-tightening reinforcing bars in a precast concrete bridge deck according to claim 4, characterized in that, The steps for obtaining the optimal hyperparameters using the particle swarm optimization algorithm are as follows: a) Determine the loss function, wherein determining the loss function includes determining that the loss function is the mean squared error, and the expression of the loss function f(x) is Equation (3). In the formula, n is the total number of samples, and y i y is the true value of the i-th sample. i ' is the predicted value of the i-th sample; b) Initialize the particle swarm, wherein initializing the particle swarm includes selecting a number of particles, initializing the position and velocity of each particle, wherein the position of each particle contains a combination of hyperparameters; c) Calculate the fitness value of each particle using a loss function. The fitness value is calculated by using the hyperparameter combination information contained in the particle to build a long short-term memory neural network model for prediction, and combining the prediction results with the actual test results and the loss function f(x) to calculate the fitness value of the particle. d) The fitness value of each particle and the best position P it has experienced i The fitness value is compared, and if it is better, it is taken as the current best position; e) The fitness value of each particle and the best position P experienced globally g If the comparison is successful, then take it as the best position globally at this point. f) Calculate the velocity and position of the particle. If the termination condition is not met, return to step b. Otherwise, output the position information of the optimal particle. The position information of the optimal particle is the best hyperparameter.

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