Bridge wet joint template mounting and dismounting method

Abstract

The invention provides a bridge wet joint formwork mounting and dismounting method which is suitable for the field of bridge formwork construction and comprises the steps of early-stage work preparation, wet joint formwork mounting, wet joint steel bar binding, concrete pouring work, concrete curing work, formwork dismounting and wet joint construction completion. According to the bridge wet joint formwork mounting and dismounting method, on the basis of recording of environmental conditions, strength grades and maintenance measures in the concrete pouring process, a machine learning technology is used for training a formwork dismounting time prediction model, micro-expansive concrete is adopted, steel fibers are added in the micro-expansive concrete, and the formwork dismounting time prediction model is obtained. And the connecting strength of the bridge wet joint is further guaranteed, and the method can be widely applied to the field of bridge formwork construction.

Description

Technical Field

[0001] The present invention relates to a method for installing and removing wet joint formwork of a bridge, which is applicable to the field of bridge formwork construction. Background Art

[0002] With the continuous development of modern transportation infrastructure, as an important transportation hub, the structural safety and durability of bridges have received increasing attention. During the construction of bridges, the wet joint, as an important part connecting two sections of concrete, its construction quality directly affects the overall performance and service life of the bridge. The wet joint of a bridge refers to the joint formed by wet construction between concrete components of the bridge, especially at the joints between beams and beams, beams and bridge decks, etc. This kind of joint is usually carried out during the concrete pouring process, that is, when pouring new concrete, it is mixed with the existing concrete to form an integral structure.

[0003] During the construction of the wet joint of a bridge, since the wet joint of the bridge is connected to the existing bridge deck, problems such as poor bonding are likely to occur between the newly poured concrete and the existing structure, thus forming cracks, which is a very common problem. How to solve the crack problem of the wet joint of the bridge is a current research hotspot. At present, most scholars believe that the generation of cracks is closely related to the wet joint formwork project. Traditional wet joint formwork construction often lacks a systematic standard process, resulting in unstable construction quality and inaccurate timing of formwork removal, which in turn affects the strength and durability of concrete. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem of cracks in the current wet joints of bridges. A method for installing and removing wet joint formwork of a bridge is proposed.

[0005] The purpose of the present invention can be achieved by adopting the following technical solutions:

[0006] The steps of a method for installing and removing wet joint formwork of a bridge are as follows:

[0007] S101 Preparation of preliminary work;

[0008] The preparation of the preliminary work includes completing the installation of the beam slab bridge, installing the prefabricated beam slab bridge to the designed position, ensuring that all necessary formwork, steel bars, concrete and other construction materials are in place, checking and confirming that their quality meets the design requirements, preparing for the installation of the wet joint formwork, and marking it as A according to the different bridge decks where the wet joint is located i , i = 1 to n;

[0009] S102 Install the wet joint formwork;

[0010] The installation of the wet joint formwork includes using a multi-functional formwork installation trolley for formwork installation work, and the formwork installation work includes formwork positioning, formwork assembly, fixing the formwork, and checking the installation quality;

[0011] S103 Tie the wet joint steel bars;

[0012] The tying of the wet joint steel bars includes carrying out steel bar tying work at the wet joint according to the design requirements. The tying method used for the steel bar tying work is the "cross" tying method, and after the steel bar tying work is completed, a special person conducts an inspection of the steel bar tying quality;

[0013] S104 Carry out concrete pouring work;

[0014] The carrying out of the concrete pouring work includes, during the pouring process, using a concrete vibrator for vibration operation to eliminate the air bubbles in the concrete, evenly pouring the concrete into the formwork, and at the same time, during the concrete pouring process, for wet joint A i Record the pouring ambient temperature T i , humidity H i , concrete strength grade C i ;

[0015] S105 Carry out concrete curing work;

[0016] The carrying out of the concrete curing work includes determining the curing plan according to the type of concrete and the local climate conditions. The curing plan includes the curing time, curing measures, and regular inspections. The regular inspections include regularly checking the wetness of the concrete surface to ensure the curing effect;

[0017] The carrying out of the concrete curing work also includes, for wet joint A i , its curing measure marking code is Y i , when the curing measure adopted is sprinkler curing, code Y i = 1, when the curing measure adopted is covering curing, code Y i = 2, when the curing measure adopted is other, code Y i = 3;

[0018] S106 Demold the formwork to complete the wet joint construction;

[0019] The demolding of the formwork to complete the wet joint construction includes, for wet joint A i of the pouring ambient temperature T i , humidity H i , concrete strength grade C i and the curing measure marking Y i, input this data into the formwork removal time prediction model to obtain the formwork removal time t, and remove the formwork according to the obtained formwork removal time t, finally completing the construction work of the wet joint;

[0020] The steps for removing the formwork and completing the construction of the wet joint also include the steps for obtaining the formwork removal time prediction model as follows:

[0021] a) Fabricate a bridge wet joint model,

[0022] b) Record the pouring environmental temperature T, humidity H, concrete strength grade C, and curing measure label Y during the fabrication of the wet joint model;

[0023] c) Adopt different formwork removal times for the wet joint model, evaluate the strength and crack development under different formwork removal times, and finally determine that there are a total of m optimal formwork removal wet joint models, denoted as M j , j = 1 to m and the corresponding optimal formwork removal time T j ;

[0024] d) Use the pouring environmental temperature T, humidity H, concrete strength grade C, and curing measure label Y corresponding to the optimal formwork removal wet joint model M j as the input quantities, and the corresponding optimal formwork removal time T j as the output quantity to form the input-output data set C;

[0025] e) Use the input-output data set C to train the formwork removal time prediction model, and finally obtain a formwork removal time prediction model that meets the requirements.

[0026] Furthermore, in the above step S104, the concrete pouring work includes using micro-expansion concrete, and the expansion agent dosage of the micro-expansion concrete is 8% - 12% of the total amount of cementitious materials. The contact surface between the new and old concrete is treated by high-pressure water jet scabbing, the scabbing pressure is 50 - 70 MPa, and the scabbing depth is not less than 4 mm to form a uniform exposed aggregate surface.

[0027] Even further, steel fibers are incorporated into the micro-expansion concrete, and the volume fraction of the steel fibers is 1.0% - 1.5%, the length is 30 - 50 mm, and the aspect ratio is 50 - 70.

[0028] Furthermore, in the above step S106, the steps for using the input-output data set C to train the formwork removal time prediction model and finally obtaining a formwork removal time prediction model that meets the requirements are as follows:

[0029] a) Perform data preprocessing on the input-output data set C, and the data preprocessing includes missing value processing, outlier processing, feature encoding, and normalization;

[0030] b) Dataset augmentation, where the dataset augmentation includes processing the input-output dataset C using data augmentation techniques to obtain the augmented dataset D;

[0031] c) Divide the augmented dataset D into a training set and a test set in a ratio of 8 to 2;

[0032] d) Create a random forest model with the following parameter settings: the number of trees is set to 50, the maximum depth of the trees is set to 10, the minimum number of samples required to split a node is set to 2, and the minimum number of samples required at a leaf node is set to 1;

[0033] e) Use the training set to train the random forest model to obtain the trained model;

[0034] f) Use the test set to predict the trained model, evaluate the model performance, and optimize the model according to the model performance evaluation results. Finally, the optimized formwork removal time prediction model obtained is the formwork removal time prediction model that meets the requirements.

[0035] Furthermore, the step of processing the input-output dataset C using data augmentation techniques to obtain the augmented dataset D is as follows:

[0036] a) Target class identification, which includes performing statistical analysis on the input-output dataset C to determine the minority class samples that need to be augmented;

[0037] b) Calculate neighboring samples. For each minority class sample, calculate its neighboring samples in the feature space;

[0038] c) Synthesize new samples, which includes randomly selecting one neighboring sample from the k nearest neighbors of each minority class sample, and then calculating a new synthesized sample based on the difference between the randomly selected neighboring sample and the current sample;

[0039] d) Add synthesized samples, which includes adding the newly synthesized minority class samples to the original dataset to finally form the augmented dataset D.

[0040] Furthermore, the steps of model optimization are as follows:

[0041] a) Determine the parameters to be optimized, where the parameters to be optimized are one or more of the number of trees parameter, the maximum depth of the trees parameter, the minimum number of samples required to split a node parameter, and the minimum number of samples required at a leaf node parameter of the random forest model;

[0042] b) Select the optimization method as the grid search algorithm;

[0043] c) Perform grid search, systematically test each parameter combination, evaluate the performance of the model under each combination using cross-validation, and record the performance metrics for each combination;

[0044] d) Select the hyperparameter combination with the best performance, retrain the model, and compare its performance with the untuned model to observe the improvement effect and ultimately achieve the purpose of model tuning.

[0045] The present invention has the following beneficial effects: Through reasonable preliminary work preparation, standardized formwork installation, steel bar binding, and concrete pouring, this method can ensure the high efficiency and convenience of the entire wet joint construction process, thereby shortening the construction period and saving time and costs. Adopting scientific concrete curing measures and accurate prediction of formwork removal time helps reduce the occurrence of cracks and other quality defects, improve the overall quality of the wet joint, and ensure the structural safety and service life of the bridge. By recording the environmental conditions, strength grades, and curing measures during the concrete pouring process and using machine learning techniques to train the formwork removal time prediction model, it is possible to provide a scientific basis based on data and optimize construction decisions; By using micro-expansion concrete and adding steel fibers to the micro-expansion concrete, the connection strength of the bridge wet joint is further ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flowchart of a method for installing and removing formwork of a bridge wet joint according to the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following provides a detailed description of specific embodiments of the present invention with reference to the accompanying drawings; it should be understood that the specific embodiments given here are only for illustrating and explaining the present invention and cannot be used to limit the present invention.

[0048] The following is a specific embodiment of a method for installing and removing formwork of a bridge wet joint.

[0049] S101 Preliminary work preparation;

[0050] The preliminary work preparation includes completing the installation of the beam slab bridge, fixing the prefabricated beam slab bridge to the design position, ensuring that all necessary formwork, steel bars, concrete, and other construction materials are in place, checking and confirming that their quality meets the design requirements, preparing for the installation of the wet joint formwork, and marking it as A according to the different bridge decks where the wet joint is located i , i = 1 to n;

[0051] S102 Install the wet joint formwork;

[0052] The installation of the wet joint formwork includes using a multi-functional formwork installation trolley to perform the formwork installation work, and the formwork installation work includes formwork positioning, formwork assembly, fixing the formwork, and checking the installation quality;

[0053] S103 Tie the wet joint reinforcement;

[0054] The tying of the wet joint reinforcement includes carrying out the reinforcement tying work at the wet joint according to the design requirements. The tying method used for the reinforcement tying work is the "cross" tying method. After the reinforcement tying work is completed, a special person is responsible for checking the quality of the reinforcement tying;

[0055] S104 Carry out the concrete pouring work;

[0056] The carrying out of the concrete pouring work includes, during the pouring process, using a concrete vibrator for vibrating operations to eliminate the air bubbles in the concrete and uniformly pouring the concrete into the formwork. At the same time, during the concrete pouring process, for wet joint A i Record the pouring ambient temperature T i and humidity H i and the concrete strength grade C i ;

[0057] Furthermore, in the above step S104, the carrying out of the concrete pouring work includes using micro-expansion concrete for the concrete. The admixture content of the expansion agent in the micro-expansion concrete is 8% - 12% of the total amount of cementitious materials. The contact surface between the new and old concretes is treated by high-pressure water jet scabbling. The scabbling pressure is 50 - 70 MPa, and the scabbling depth is not less than 4 mm to form a uniform exposed aggregate surface.

[0058] Even further, steel fibers are incorporated into the micro-expansion concrete. The volume admixture content of the steel fibers is 1.0% - 1.5%, the length is 30 - 50 mm, and the aspect ratio is 50 - 70.

[0059] S105 Carry out the concrete curing work;

[0060] The carrying out of the concrete curing work includes determining the curing plan according to the type of concrete and the local climate conditions. The curing plan includes the curing time, curing measures, and regular inspections. The regular inspections include regularly checking the wetness of the concrete surface to ensure the curing effect;

[0061] The carrying out of the concrete curing work also includes, for wet joint A i , the code for its curing measures is Y i . When the curing measure adopted is water spraying curing, the code Y i = 1. When the curing measure adopted is covering curing, the code Y i = 2. When the curing measure adopted is other, the code Y i = 3;

[0062] S106 Remove the formwork and complete the wet joint construction;

[0063] Demolish the formwork and complete the construction of the wet joint, including for wet joint A i The pouring ambient temperature T i , humidity H i , concrete strength grade C i And the curing measure mark Y i , input these data into the formwork removal time prediction model to obtain the formwork removal time t, and remove the formwork according to the obtained formwork removal time t, and finally complete the construction work of the wet joint;

[0064] Demolish the formwork and complete the construction of the wet joint, and the acquisition steps of the formwork removal time prediction model also include:

[0065] a) Fabricate a bridge wet joint model,

[0066] b) Record the pouring ambient temperature T, humidity H, concrete strength grade C and curing measure mark Y during the fabrication of the wet joint model;

[0067] c) Adopt different formwork removal times for the wet joint model, evaluate the strength and crack development under different formwork removal times, and finally determine that the total number of the best formwork removal wet joint models is m, denoted as M j , j = 1~m and the corresponding best formwork removal time T j ;

[0068] d) Use the pouring ambient temperature T, humidity H, concrete strength grade C and curing measure mark Y corresponding to the best formwork removal wet joint model M j as input quantities, and the corresponding best formwork removal time T j as the output quantity to form the input-output data set C;

[0069] e) Use the input-output data set C to train the formwork removal time prediction model, and finally obtain the formwork removal time prediction model that meets the requirements.

[0070] Furthermore, in the above step S106, the steps of using the input-output data set C to train the formwork removal time prediction model and finally obtaining the formwork removal time prediction model that meets the requirements are:

[0071] a) Perform data preprocessing on the input-output data set C, and the data preprocessing includes missing value processing, outlier processing, feature encoding and normalization;

[0072] b) Data set augmentation, and the data set augmentation includes using data augmentation technology to process the input-output data set C to obtain the data-augmented data set D;

[0073] c) Divide the data-augmented dataset D into a training set and a test set in a ratio of 8 to 2;

[0074] d) Create a random forest model with the following parameter settings: the number of trees is set to 50, the maximum depth of the trees is set to 10, the minimum number of samples required to split a node is set to 2, and the minimum number of samples required at a leaf node is set to 1;

[0075] e) Use the training set to train the random forest model to obtain the trained model;

[0076] f) Use the test set to predict the trained model, evaluate the model performance, and optimize the model according to the model performance evaluation results. Finally, the optimized formwork removal time prediction model obtained is the formwork removal time prediction model that meets the requirements.

[0077] Furthermore, the steps of using the data augmentation technology to process the input-output dataset C to obtain the data-augmented dataset D are as follows:

[0078] a) Target class recognition, which includes performing statistical analysis on the input-output dataset C to determine the minority class samples that need to be augmented;

[0079] b) Calculate neighboring samples. For each minority class sample, calculate its neighboring samples in the feature space;

[0080] c) Synthesize new samples, which includes randomly selecting one neighboring sample from the k nearest neighbors of each minority class sample, and then calculating a new synthesized sample based on the difference between the randomly selected neighboring sample and the current sample;

[0081] d) Add synthesized samples, which includes adding the newly synthesized minority class samples to the original dataset to finally form the data-augmented dataset D.

[0082] e) Furthermore, the steps of model optimization are as follows:

[0083] a) Determine the parameters to be optimized, and the optimized parameters are one or more of the number of trees parameter, the maximum depth of the trees parameter, the minimum number of samples required to split a node parameter, and the minimum number of samples required at a leaf node parameter of the random forest model;

[0084] b) Select the optimization method as the grid search algorithm;

[0085] c) Perform grid search, systematically test each parameter combination, evaluate the performance of the model under each combination using cross-validation, and record the performance metrics of each combination;

[0086] d) Select the combination of hyperparameters with the best performance, retrain the model, and compare the performance with the untuned model to observe the improvement effect, ultimately achieving the purpose of model tuning.

[0087] In the above embodiments, the present invention discloses a method for installing and removing the formwork of the wet joint of a bridge, including preparation of preliminary work, installation of the wet joint formwork, binding of the wet joint steel bars, concrete pouring work, concrete curing work, and removal of the formwork to complete the wet joint construction; this method proposes a method for installing and removing the formwork of the wet joint of a bridge. Based on recording the environmental conditions, strength grades, and curing measures during the concrete pouring process, a prediction model for the formwork removal time is trained using machine learning technology. Micro-expansion concrete is adopted, and steel fibers are added to the micro-expansion concrete, further ensuring the connection strength of the wet joint of the bridge and can be widely applied to the field of bridge formwork construction.

[0088] The above are the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for installing and removing the wet joint formwork of a bridge, characterized in that, It includes the following steps: S101 Preparation for preliminary work; The above-mentioned preliminary work preparation includes completing the installation of beams and slab bridges, fixing the prefabricated beams and slab bridges to the designed positions, ensuring that all necessary formworks, steel bars, concrete and other construction materials are in place, inspecting and confirming that their quality meets the design requirements, preparing for the installation of wet joint formworks, and marking them as A according to the different bridge decks where the wet joints are located i , i = 1 to n; S102 Install the wet joint formwork; The installation of the wet joint formwork includes using a multi-functional formwork installation trolley to install the formwork. The installation work of the formwork includes formwork positioning, formwork assembly, fixing the formwork, and inspecting the installation quality; S104 Bind the wet joint steel bars; The binding of the wet joint steel bars includes carrying out the steel bar binding work at the wet joint according to the design requirements. The binding method adopted for the steel bar binding work is the "cross" binding method. After the steel bar binding work is completed, a special person conducts the inspection of the steel bar binding quality; S106 Carry out the concrete pouring work; The concrete pouring work includes, during the pouring process, using a concrete vibrator for vibrating operations to eliminate air bubbles in the concrete, and evenly pouring the concrete into the formwork. At the same time, during the concrete pouring process, for the wet joint A i record the ambient temperature T i , humidity H i , concrete strength grade C i ; S107 Carry out the concrete curing work; The carrying out of the concrete curing work includes determining the curing plan according to the type of concrete and the local climate conditions. The curing plan includes the curing time, curing measures, and regular inspections. The regular inspections include regularly inspecting the wetness of the concrete surface to ensure the curing effect; The concrete curing work also includes wet joint A i , and its curing measure marking code is Y i . When the adopted curing measure is sprinkler curing, code Y i = 1. When the adopted curing measure is covering curing, code Y i = 2. When the adopted curing measure is others, code Y i = 3; S108 Remove the formwork and complete the wet joint construction; Demolish the formwork to complete the construction of the wet joint, including for wet joint A i The pouring ambient temperature T i , humidity H i , concrete strength grade C i And the maintenance measure mark Y i , input these data into the formwork demolition time prediction model to obtain the formwork demolition time t, and carry out formwork demolition according to the obtained formwork demolition time t, and finally complete the construction work of the wet joint; The removal of the formwork and the completion of the wet joint construction also include the steps for obtaining the formwork removal time prediction model: a) Fabricate a bridge wet joint model, b) Record the pouring environmental temperature T, humidity H, concrete strength grade C, and curing measure mark Y during the fabrication of the wet joint model; c) Different formwork removal times are adopted for the wet joint model, and the strength and crack development under different formwork removal times are evaluated. Finally, a total of m optimal wet joint models for formwork removal are determined, denoted as M j , where j = 1 to m and the corresponding optimal formwork removal time T j ; d) Taking the pouring ambient temperature T, humidity H, concrete strength grade C, and curing measure mark Y corresponding to the optimal form removal wet joint model M j as input quantities, and the corresponding optimal form removal time T j as the output quantity, to form the input-output data set C; e) Use the input-output dataset C to train the formwork removal time prediction model, and finally obtain a formwork removal time prediction model that meets the requirements.

2. A method for installing and removing the wet joint formwork of a bridge according to claim 1, characterized in that In the step S104, the carrying out of the concrete pouring work includes using micro-expansion concrete. The admixture amount of the expansion agent in the micro-expansion concrete is 8%-12% of the total amount of the cementitious materials. The contact surface between the new and old concretes is treated by high-pressure water jet scabbing. The scabbing pressure is 50-70 MPa, and the scabbing depth is not less than 4 mm to form a uniform exposed aggregate surface.

3. A method for installing and removing the wet joint formwork of a bridge according to claim 2, characterized in that Steel fibers are incorporated into the micro-expansion concrete. The volume admixture amount of the steel fibers is 1.0%-1.5%, the length is 30-50 mm, and the length-diameter ratio is 50-70.

4. A method for installing and disassembling the wet joint formwork of a bridge according to claim 1, characterized in that, In the step S106, the steps for using the input-output dataset C to train the formwork removal time prediction model and finally obtaining a formwork removal time prediction model that meets the requirements are: a) Conduct data preprocessing on the input-output dataset C. The data preprocessing includes missing value processing, outlier processing, feature encoding, and normalization; b) Dataset augmentation. The dataset augmentation includes using data augmentation techniques to process the input-output dataset C to obtain the data-augmented dataset D; c) Divide the data-augmented dataset D into a training set and a test set in a ratio of 8 to 2; d) Create a random forest model. The parameters of the random forest model are set as follows: the number of trees is set to 50, the maximum depth of the trees is set to 10, the minimum number of samples required to split a node is set to 2, and the minimum number of samples required for a leaf node is set to 1; e) Use the training set to train the random forest model to obtain the trained model; f) Use the test set to predict the trained model, evaluate the model performance, and optimize the model according to the model performance evaluation results. The finally obtained optimized formwork removal time prediction model is the formwork removal time prediction model that meets the requirements.

5. A method for installing and disassembling a wet joint formwork of a bridge according to claim 4, characterized in that, The steps of using the data augmentation technology to process the input-output data set C to obtain the data-augmented data set D are as follows: a) Target class recognition, where the target class recognition includes performing statistical analysis on the input-output data set C to determine the minority class samples that need to be augmented; b) Calculate neighboring samples. For each minority class sample, calculate its neighboring samples in the feature space; c) Synthesize new samples, where the synthesizing of new samples includes randomly selecting one neighboring sample from the k nearest neighbors of each minority class sample, and then calculating a new synthesized sample based on the difference between the randomly selected neighboring sample and the current sample; d) Add synthesized samples, where the adding of synthesized samples includes adding the newly synthesized minority class samples to the original data set to finally form the data-augmented data set D.

6. A method for installing and removing a wet joint formwork of a bridge according to claim 4, characterized in that The steps of the model optimization are as follows: a) Determine the parameters to be optimized. The parameters to be optimized are one or more of the number of trees parameter, the maximum depth parameter of the tree, the minimum number of samples required to split a node, and the minimum number of samples required for a leaf node in the random forest model; b) Select the optimization method as the grid search algorithm; c) Perform grid search, systematically test each parameter combination, evaluate the performance of the model under each combination using cross-validation, and record the performance metrics of each combination; d) Select the hyperparameter combination with the best performance, retrain the model, compare the performance with the unoptimized model, observe the improvement effect, and finally achieve the purpose of model optimization.

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