Concrete pouring method capable of reducing cracks

Abstract

The invention provides a concrete pouring method for reducing generation of cracks, which belongs to the technical field of concrete pouring construction, and comprises the following steps: collecting parameters such as construction environment, concrete structure, concrete parameters and mix proportion, establishing and fitting a shrinkage stress equation set of concrete, calculating stress distribution at different time points and positions, and calculating a shrinkage stress equation set; the method comprises the steps of determining the maximum stress value and the occurrence time and position of the maximum stress value, adopting a pre-trained shrinkage joint arrangement model, determining the optimal shrinkage joint distance and position, adopting a pre-trained stress expanding agent model, and determining the type and the adding amount of a needed expanding agent and the optimal distribution of the expanding agent in concrete. And the thickness of each layer, the pouring sequence and the interval time of layered pouring are determined, the calculated expanding agent is added in the mixing process, and uniform distribution of the expanding agent is ensured by adopting an efficient mixing technology. Pouring is conducted according to a layered pouring plan, an optimized shrinkage joint is arranged, and it is ensured that concrete is compact through intelligent vibrating equipment. And curing the constructed concrete.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete pouring construction, and in particular relates to a concrete pouring method for reducing the generation of cracks. Background Art

[0002] Concrete is one of the most widely used materials in today's construction projects. It is widely used in various types of civil, industrial and infrastructure buildings. As a porous and heterogeneous composite material, concrete is prone to various shrinkage stresses during pouring and curing, leading to the appearance of structural cracks, which seriously threatens the quality and safety of buildings. Therefore, how to effectively control the generation of cracks during concrete pouring has always been a key issue of concern in the industry.

[0003] The existing concrete crack control technologies mainly include the following:

[0004] 1. Contraction joint setting technology. By setting the contraction joint in advance, the cracks are guided to occur along the preset position, thereby avoiding random cracks in the stress-bearing parts of the structure. This technology is the most common crack control method, but the determination of the contraction joint position and spacing is still highly subjective and cannot adapt well to different structures and environmental conditions.

[0005] 2. Expansive agent addition technology. Add expansive chemical additives to concrete and use the expansion stress generated during the hydration process to offset the shrinkage stress, thereby reducing the occurrence of cracks. This technology is simple and easy to implement, but the type of expansive agent, the amount of addition, and the distribution location require a lot of experimental verification, and it is not very targeted.

[0006] 3. Layered pouring technology. By rationally arranging the order of layered pouring of concrete and the time interval between each layer, it helps to reduce the stress gradient inside the structure and reduce local stress concentration. However, how to determine the best pouring process parameters still depends on engineering experience.

[0007] In general, existing crack control technologies mostly rely on engineering experience and a large number of experimental verifications, and are difficult to adapt to the crack control needs under different engineering conditions. Summary of the invention

[0008] In view of this, the present invention provides a concrete pouring method for reducing the generation of cracks, which can solve the technical problem that the existing crack control technology mostly relies on engineering experience and a large number of experimental verifications and is difficult to adapt to the crack control needs under different engineering conditions.

[0009] The present invention is achieved in that:

[0010] A first aspect of the present invention provides a concrete pouring method for reducing crack generation, which comprises the following steps:

[0011] S10, collecting construction environment parameters, concrete structure parameters, concrete parameters, mix ratio parameters, and curing condition parameters, and establishing and fitting a group of shrinkage stress equations for concrete, including an autogenous shrinkage stress equation, a drying shrinkage stress equation, a temperature stress equation, a creep stress equation, and an overall stress equation;

[0012] S20, calculating the stress distribution at different time points and different positions according to the shrinkage stress equation group, and determining the maximum stress value and the position and time at which it occurs;

[0013] S30, based on the stress distribution, using a pre-trained contraction joint arrangement model to calculate an optimal contraction joint spacing and position;

[0014] S40, based on the maximum stress value and its distribution, using a pre-trained stress expansion agent model to calculate the required expansion agent parameters, including the type of expansion agent, the amount added, and the preferred distribution in the concrete;

[0015] S50, according to the concrete structure parameters and the stress distribution, adopting the principle of minimum stress gradient and uniform stress distribution, determining the thickness of each layer of layered pouring, pouring sequence and interval time;

[0016] S60. During the concrete mixing process, the expansion agent is added according to the calculated required expansion agent parameters and efficient mixing technology is used to ensure its uniform distribution;

[0017] S70, pouring concrete according to the layered pouring plan, setting contraction joints according to the preferred contraction joint spacing and positions, and using intelligent vibrating equipment to ensure the density of the concrete;

[0018] S80. Maintain the completed concrete.

[0019] The efficient mixing technology specifically adopts a method combining a high shear force mixer and sonic wave assisted mixing technology to ensure uniform dispersion of the expansion agent in the concrete.

[0020] Furthermore, the intelligent vibrating equipment specifically adopts a variable frequency intelligent vibrating rod with a real-time density monitoring function, which automatically adjusts the vibration frequency and time by analyzing the feedback signal during the vibration process to achieve the best compaction effect.

[0021] Furthermore, the contraction joint arrangement model adopts a multi-layered neural network, including a feature extraction subnetwork, a spatial relationship subnetwork and a decision subnetwork.

[0022] Furthermore, the feature extraction sub-network is used to extract key features of concrete structure and stress distribution, the input is concrete structure parameters and stress distribution data, the output is a high-dimensional feature vector, and the structure is a convolutional neural network;

[0023] The spatial relationship sub-network is used to analyze the spatial relationship between contraction joints, the input is the output of the feature extraction sub-network, the output is the spatial relationship feature, and the structure is a graph convolutional neural network;

[0024] The decision subnetwork is used to finally determine the layout of the contraction joints. The input is the output of the feature extraction subnetwork and the spatial relationship subnetwork. The output is the spacing and position of the contraction joints. The structure is a fully connected neural network.

[0025] Furthermore, the stress expansion agent model adopts a residual neural network with a multi-branch structure, including a stress analysis branch network, a material property branch network, an environmental factor branch network and a fusion branch network.

[0026] Furthermore, the stress analysis branch network is used to analyze stress distribution characteristics, the input is stress distribution data, the output is a stress characteristic vector, and the structure is a one-dimensional convolutional neural network;

[0027] The material property branch network is used to analyze the concrete material properties, the input is the concrete parameters and the mix ratio parameters, the output is the material property vector, and the structure is a multi-layer perceptron;

[0028] The environmental factor branch network is used to analyze the impact of environmental factors. The input is the construction environment parameters and the maintenance condition parameters. The output is the environmental impact vector. The structure is a long short-term memory network.

[0029] The fusion branch network is used to integrate the outputs of each branch network and give the final expander parameters. The input is the output vector of each branch network, the output is the type, addition amount and distribution method of the expander, and the structure is a fully connected neural network enhanced by the attention mechanism.

[0030] Among them, the construction of the training data set and the training steps of the contraction joint arrangement model are described as follows:

[0031] Dataset construction:

[0032] 1) Collect a large amount of data from real engineering cases, including:

[0033] Concrete structural parameters (such as size, shape, reinforcement, etc.)

[0034] Stress distribution data (can be obtained from finite element analysis or actual measurement)

[0035] Practical shrinkage joint arrangement scheme and its effect evaluation

[0036] 2) Generate more samples using computer simulation to increase the size and diversity of the dataset:

[0037] Simulate concrete shrinkage behavior under different conditions based on fluid mechanics and material science principles

[0038] Generate various hypothetical shrinkage joint layouts and calculate their corresponding stress distributions

[0039] 3) Data preprocessing:

[0040] Normalize all input parameters

[0041] Convert structure and stress distribution data into a format suitable for CNN input

[0042] Construct graph structure data for GCN to represent the relationship between potential shrinkage joint locations

[0043] 4) Data Augmentation:

[0044] Make small perturbations to the structural parameters and stress distribution to generate more training samples

[0045] Apply transformations such as rotation and mirroring to increase data diversity

[0046] Training steps:

[0047] 1) Initialization: Randomly initialize the parameters of each sub-network

[0048] 2) Training in stages:

[0049] a. First, train the feature extraction sub-network (CNN) separately, using supervised learning methods to accurately extract structural and stress features

[0050] b. Train the spatial relation sub-network (GCN) to learn the spatial association between contraction seams

[0051] c. Train the decision subnetwork (FNN) to map the outputs of the first two subnetworks to the final contraction joint layout plan

[0052] 3) Joint fine-tuning:

[0053] Connect the three sub-networks and perform joint training in an end-to-end manner

[0054] Use a weighted loss function to comprehensively consider the shrinkage joint spacing, position accuracy and stress control effect

[0055] 4) Verification and testing:

[0056] Evaluating model performance using cross validation

[0057] Final evaluation is performed on an independent test set

[0058] 5) Iterative optimization:

[0059] Analyze the model's performance on different types of structures

[0060] Supplement training data in a targeted manner and repeat the training process

[0061] The construction of the training data set and the training steps of the stress expansion agent model are described as follows:

[0062] Dataset construction:

[0063] 1) Collect experimental data:

[0064] Conducted extensive laboratory tests to test the effects of different types and amounts of expansion agents on concrete

[0065] Record the performance of the expander under various environmental conditions

[0066] 2) Integration project cases:

[0067] Collect data on actual engineering cases using expansion agents

[0068] Including stress distribution, concrete parameters, environmental conditions and use of expansion agents

[0069] 3) Generate simulated data:

[0070] Based on the principles of material science, simulate the mechanism of action of the expander under different conditions

[0071] Generate a large number of virtual samples to expand the data set

[0072] 4) Data preprocessing:

[0073] Normalize all input parameters

[0074] Convert time series data (such as environmental parameters) into a format suitable for LSTM processing

[0075] Reduce the dimension of stress distribution data and extract key features

[0076] Training steps:

[0077] 1) Initialization: Randomly initialize the parameters of each branch network

[0078] 2) Branch network pre-training:

[0079] a. Train the stress analysis branch network (1D CNN) separately to learn and extract stress distribution features

[0080] b. Train the Material Property Branch Network (MLP) to learn the relationship between concrete parameters and properties

[0081] c. Train the environmental factor branch network (LSTM) to capture the temporal impact of environmental conditions

[0082] 3) Fusion network training:

[0083] Freeze pre-trained branch network parameters

[0084] Train the fusion branch network to learn and determine the optimal expansion agent parameters based on various factors

[0085] 4) End-to-end fine-tuning:

[0086] Unfreeze all network parameters and perform end-to-end joint training

[0087] Using a multi-task learning strategy to simultaneously optimize the prediction of bulking agent type, addition amount, and distribution pattern

[0088] 5) Verification and testing:

[0089] Evaluating model performance using cross validation

[0090] Final evaluation is performed on an independent test set

[0091] 6) Continuous optimization:

[0092] Regularly update models with new experimental data and engineering examples

[0093] Analyze the prediction error of the model and supplement the training data in a targeted manner

[0094] The training process of both models requires a large amount of data and computing resources. In practical applications, they may need to be adjusted and optimized according to specific circumstances. At the same time, due to the complexity of the model, attention should be paid to its generalization ability when using it, and the final decision should be made in combination with professional engineering experience.

[0095] The autogenous shrinkage stress equation is used to describe the stress generated by volume change due to internal chemical reactions during cement hydration;

[0096] The drying shrinkage stress equation is used to describe the stress generated by volume shrinkage due to water evaporation during the concrete hardening process;

[0097] The temperature stress equation is used to describe the stress generated by the temperature gradient caused by the heat of hydration and the ambient temperature change;

[0098] The creep stress equation is used to describe the stress relaxation caused by the deformation of concrete under long-term load;

[0099] The overall stress equation is used to integrate the above-mentioned various stresses and calculate the overall stress distribution in the concrete structure.

[0100] The following are the formulas and explanations of the equations:

[0101] 1. Autogenous shrinkage stress equation:

[0102] ;

[0103] In the formula, is the autogenous shrinkage stress at time t, is the elastic modulus at time t, is the creep coefficient from t0 to t, is the autogenous strain at time t, is a parameter characterizing the shrinkage rate, t is time (days), and t0 is the initial time;

[0104] 2. Drying shrinkage stress equation:

[0105] ;

[0106] In the formula, is the drying shrinkage stress at time t, is the humidity influence coefficient, is the size influence coefficient, is the influence coefficient of concrete strength, is the reference drying shrinkage strain, is a function describing the development of drying shrinkage over time, is the aging coefficient,

[0107] It’s time to start drying;

[0108] 3. Temperature stress equation:

[0109] ;

[0110] In the formula, is the temperature stress at position x and time t, is the linear thermal expansion coefficient, is the temperature at position x and time τ, is the creep function;

[0111] 4. Creep stress equation:

[0112] ;

[0113] In the formula, is the creep stress at time t, is the initial stress, is the relaxation time at time t, is the stress at time τ;

[0114] 5. Overall stress equation:

[0115] ;

[0116] In the formula, is the total stress at position x and time t, represents other possible stress components, and n is the number of additional stress components;

[0117] The following is a detailed description of the steps for calculating stress distribution at different time points and locations:

[0118] 1. Determine the computational grid: Divide the concrete structure into a three-dimensional grid of m×n×p, and each grid point is represented as ;

[0119] 2. Determine the time step: Divide the total computation time T into N time steps, each time step is ;

[0120] 3. Initialization parameters: Initialize the parameters in each equation according to the concrete mix ratio, environmental conditions, etc. These parameters can be obtained through experimental determination or empirical formulas;

[0121] 4. For each time step and each grid point , calculate the stress components:

[0122] a. Calculate autogenous shrinkage stress:

[0123] ;

[0124] b. Calculate drying shrinkage stress:

[0125] ;

[0126] c. Calculate temperature stress:

[0127] ;

[0128] d. Calculate creep stress:

[0129] ;

[0130] 5. Calculate total stress:

[0131] ;

[0132] in represent , , Three directions, It can be calculated by the central difference method:

[0133] ;

[0134] 6. Determine the maximum stress value and its location and time: Traverse all time steps and grid points to find The maximum value of and time .

[0135] Through the above steps, the stress distribution at different time points and different locations can be obtained, and the maximum stress value and its location and time of occurrence can be determined. This process involves a large amount of numerical calculations, which usually require the help of computer programs.

[0136] Furthermore, the rotation speed of the high shear mixer is controlled at 40 r / min.

[0137] Furthermore, the sound wave assisted mixing technology specifically uses a sound wave vibration device to emit sound waves with a frequency of 40 Hz and an amplitude of 0.5 mm.

[0138] Specifically, step S10 includes: collecting construction environment parameters including ambient temperature, relative humidity, wind speed, etc., collecting concrete structure parameters including structure size, component thickness, stress state, etc., collecting concrete parameters and mix ratio parameters including cement type, water-cement ratio, admixture type and amount, etc., collecting curing condition parameters including curing temperature, humidity, duration, etc., and establishing and fitting a set of mathematical equations describing concrete shrinkage stress based on the above-mentioned collected parameters, including an autogenous shrinkage stress equation, a drying shrinkage stress equation, a temperature stress equation, and a creep stress equation.

[0139] Specifically, step S20 includes: dividing the concrete structure into a three-dimensional finite element grid, selecting an appropriate time step to divide the total calculation time into multiple time steps, calculating the autogenous shrinkage stress, drying shrinkage stress, temperature stress and creep stress for each grid point and each time step, and superimposing various stresses to obtain the total stress distribution, and finding the maximum value of the total stress and its location and time of occurrence among all grid points and time steps.

[0140] Specifically, step S30 includes: using a multi-layer neural network as a contraction joint arrangement model, including a feature extraction subnetwork, a spatial relationship subnetwork and a decision subnetwork, the feature extraction subnetwork uses a convolutional neural network to extract key features of the concrete structure and stress distribution, the spatial relationship subnetwork uses a graph convolutional neural network to analyze the spatial relationship between contraction joints, and the decision subnetwork uses a fully connected neural network to give the preferred contraction joint spacing and position.

[0141] Specifically, step S40 includes: using a residual neural network with a multi-branch structure as a stress expansion agent model, including a stress analysis branch network, a material property branch network, an environmental factor branch network and a fusion branch network, the stress analysis branch network uses a one-dimensional convolutional neural network to extract the characteristics of stress distribution, the material property branch network uses a multi-layer perceptron to analyze the concrete material properties, the environmental factor branch network uses a long short-term memory network to analyze the impact of environmental factors, and the fusion branch network uses a fully connected neural network enhanced by an attention mechanism to give the optimal expansion agent type, addition amount and distribution method.

[0142] Specifically, the step S50 includes: analyzing the stress gradient at each position according to the stress distribution calculated in step S20, determining the optimal thickness of each layer of concrete by adopting the principle of minimum stress gradient, minimizing the stress difference between adjacent layers, and formulating the pouring sequence and the pouring interval time between each layer according to the uniformity of stress distribution to promote stress uniformity.

[0143] Specifically, step S60 includes: during the concrete mixing process, adding the expansive agent according to the expansive agent type, addition amount and optimal distribution position calculated in step S40, and using a method combining a high shear mixer and sonic wave assisted mixing technology to ensure that the expansive agent is fully dispersed and evenly distributed in the concrete.

[0144] Specifically, step S70 includes: pouring concrete in each layer in an orderly manner according to the layered pouring scheme determined in step S50, and setting contraction joints according to the determined positions and spacings during the concrete pouring process according to the preferred contraction joint arrangement scheme calculated in step S30, and using a variable frequency intelligent vibrator with real-time density monitoring function to automatically adjust the vibration frequency and time by analyzing the feedback signal during the vibration process to ensure the optimal density of the concrete.

[0145] Specifically, the step S80 includes: formulating a concrete curing plan including insulation, watering, etc. according to the curing condition parameters collected in step S10, and determining an appropriate curing duration, and performing regular inspections during the curing process and taking timely remedial measures to ensure the curing effect.

[0146] The method combining the high shear mixer and the sonic-assisted mixing technology can effectively destroy the agglomeration of the expansion agent and promote its full dispersion in the concrete, thereby ensuring the uniform distribution of the expansion agent in the concrete.

[0147] Compared with the prior art, the concrete pouring method provided by the present invention has the following beneficial effects of reducing the generation of cracks:

[0148] 1. Using accurate stress calculation models, we can comprehensively analyze the distribution of various shrinkage stresses inside the concrete structure, providing a solid mathematical foundation for subsequent process optimization. Compared with existing empirical methods, this model can more accurately predict the stress state of key parts, laying the foundation for targeted crack control measures.

[0149] 2. Using artificial intelligence technology, the layout of contraction joints and the parameters of the required expansion agent are automatically optimized, which greatly improves the accuracy and operability of crack control. The existing contraction joint setting and expansion agent use mostly rely on engineering experience and are less targeted. The intelligent optimization method of the present invention can provide the best crack control plan according to the specific structure and environmental conditions.

[0150] 3. Make full use of the layered pouring technology, combine the results of stress analysis, determine reasonable pouring process parameters, effectively reduce the stress gradient inside the structure, and promote uniform stress distribution. This layered pouring method based on stress distribution optimization can better control local stress concentration, thereby reducing the occurrence of cracks.

[0151] 4. In the process implementation stage, intelligent vibration technology and efficient mixing technology are used to ensure the optimal density of concrete and the uniform distribution of expansion agent, providing strong guarantee for crack control. These advanced process means combined with the aforementioned intelligent optimization method constitute a systematic crack control system.

[0152] To sum up, the concrete pouring method proposed in the present invention makes full use of mathematical modeling, artificial intelligence algorithms and traditional process technology to form a set of crack control measures with strong pertinence and good operability, which solves the technical problem that most of the existing crack control technologies rely on engineering experience and a large number of experimental verifications and are difficult to adapt to the crack control needs under different engineering conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0153] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0154] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0155] like Figure 1 As shown, it is a flow chart of a concrete pouring method for reducing cracks provided by the present invention. The method comprises the following steps:

[0156] S10, collecting construction environment parameters, concrete structure parameters, concrete parameters, mix ratio parameters, and curing condition parameters, and establishing and fitting a group of shrinkage stress equations for concrete, including an autogenous shrinkage stress equation, a drying shrinkage stress equation, a temperature stress equation, a creep stress equation, and an overall stress equation;

[0157] S20, calculating the stress distribution at different time points and different positions according to the shrinkage stress equation group, and determining the maximum stress value and the position and time at which it occurs;

[0158] S30, based on the stress distribution, using a pre-trained contraction joint arrangement model to calculate the optimal contraction joint spacing and position;

[0159] S40, based on the maximum stress value and its distribution, using a pre-trained stress expansion agent model to calculate the required expansion agent parameters, including the type of expansion agent, the amount added, and the preferred distribution in the concrete;

[0160] S50, according to the concrete structure parameters and stress distribution, adopting the principle of minimum stress gradient and uniform stress distribution, determine the thickness of each layer of layered pouring, pouring sequence and interval time;

[0161] S60. During the concrete mixing process, the expansion agent is added according to the calculated required expansion agent parameters and efficient mixing technology is used to ensure its uniform distribution;

[0162] S70, pouring concrete according to the layered pouring plan, setting the contraction joints according to the preferred contraction joint spacing and positions, and using intelligent vibrating equipment to ensure the density of the concrete;

[0163] S80. Maintain the completed concrete.

[0164] The specific implementation methods of the above steps are described in detail below:

[0165] Step S10: Collect construction environment parameters, concrete structure parameters, concrete parameters, mix ratio parameters, and curing condition parameters, and establish and fit the shrinkage stress equation group of concrete.

[0166] The purpose of this step is to obtain the key parameters that affect the shrinkage behavior of concrete and establish a mathematical model to describe the shrinkage stress of concrete. It includes the following sub-steps:

[0167] 1) Collect construction environment parameters: including ambient temperature, relative humidity, wind speed, etc. These environmental factors will significantly affect the drying shrinkage behavior of concrete.

[0168] 2) Collect concrete structure parameters: including structural dimensions, component thickness, stress state, etc. These geometric and load factors will affect the stress distribution at different locations.

[0169] 3) Collect concrete parameters: including cement type, water-cement ratio, admixture type and amount, etc. These mix ratio factors will affect the shrinkage behavior of concrete.

[0170] 4) Collect curing condition parameters: including curing temperature, humidity, duration, etc. These factors will affect the early temperature stress and drying shrinkage stress of concrete.

[0171] 5) According to the various parameters collected above, establish and fit the mathematical equations describing the shrinkage stress of concrete, including the autogenous shrinkage stress equation, drying shrinkage stress equation, temperature stress equation and creep stress equation, etc. These equations can be established through existing empirical formulas or finite element analysis methods.

[0172] Specifically, the following are the formula expressions and explanations of each equation:

[0173] 1. Autogenous shrinkage stress equation:

[0174] ;

[0175] In the formula, is the autogenous shrinkage stress at time t, is the elastic modulus at time t, is the creep coefficient from t0 to t, is the autogenous strain at time t, is a parameter characterizing the shrinkage rate, t is time (days), and t0 is the initial time;

[0176] 2. Drying shrinkage stress equation:

[0177] ;

[0178] In the formula, is the drying shrinkage stress at time t, is the humidity influence coefficient, is the size influence coefficient, is the influence coefficient of concrete strength, is the reference drying shrinkage strain, is a function describing the development of drying shrinkage over time, is the aging coefficient,

[0179] It’s time to start drying;

[0180] 3. Temperature stress equation:

[0181] ;

[0182] In the formula, is the temperature stress at position x and time t, is the linear thermal expansion coefficient, is the temperature at position x and time τ, is the creep function;

[0183] 4. Creep stress equation:

[0184] ;

[0185] In the formula, is the creep stress at time t, is the initial stress, is the relaxation time at time t, is the stress at time τ;

[0186] 5. Overall stress equation:

[0187] ;

[0188] In the formula, is the total stress at position x and time t, represents other possible stress components and n is the number of additional stress components.

[0189] The following is a detailed explanation of each variable and parameter’s source, acquisition method or calculation steps:

[0190] 1. Elastic modulus :

[0191] Obtained through experiments or calculated using prediction models. The experimental steps are as follows:

[0192] a) Preparation of standard concrete specimens

[0193] b) Perform compressive strength tests at different ages (such as 3 days, 7 days, 28 days, etc.)

[0194] c) Determine the elastic modulus using the initial slope of the stress-strain curve

[0195] d) Fitting time correlation function:

[0196] in is the limiting elastic modulus, is the fitting parameter

[0197] 2. Creep coefficient :

[0198] Calculated using the CEB-FIP model:

[0199] ;

[0200] In the formula,

[0201] ;

[0202] ;

[0203] ;

[0204] ;

[0205] ;

[0206] RH is the relative humidity, is the imaginary thickness, is the average compressive strength;

[0207] 3. Autogenous shrinkage strain :

[0208] Calculated using the Japan Society of Civil Engineering (JSCE) model:

[0209] ;

[0210] In the formula, ; ;w / c is the water-cement ratio;

[0211] 4. Shrinkage rate parameters :

[0212] It is obtained by fitting experimental data. The experimental steps are as follows:

[0213] a) Preparation of stress-free concrete specimens

[0214] b) Using strain gauge to measure the autogenous contraction strain at different time points

[0215] c) Fit the data to get value

[0216] 5. Humidity influence coefficient :

[0217] Calculated based on ambient relative humidity (RH):

[0218] ;

[0219] 6. Size influence coefficient :

[0220] Calculated based on the volume surface area ratio (V / S) of the component:

[0221] ;

[0222] 7. Concrete strength influence coefficient :

[0223] According to the 28-day compressive strength ( )calculate:

[0224] ;

[0225] 8. Baseline drying shrinkage strain :

[0226] It is obtained by standard shrinkage test. The experimental steps are as follows:

[0227] a) Prepare standard size specimens (e.g. 100×100×400mm);

[0228] b) Measure the long-term shrinkage strain under standard conditions (RH=60%, T=20°C);

[0229] c) Take the final stable value as ;

[0230] 9. Drying shrinkage time function :

[0231] Calculated using the CEB-FIP model:

[0232] ;

[0233] where h is the imaginary thickness

[0234] 10. Aging coefficient :

[0235] Calculated using the ACI 209R model:

[0236] ;

[0237] 11. Linear thermal expansion coefficient :

[0238] Through experimental determination, the experimental steps are as follows:

[0239] a) Preparation of long strip concrete specimens

[0240] b) Measure the change of specimen length at different temperatures

[0241] c) Calculate the strain caused by unit temperature change

[0242] 12. Temperature distribution :

[0243] Solve the heat conduction equation:

[0244] ;

[0245] in is the thermal diffusivity, is the internal heat source function

[0246] 13. Creep function :

[0247] Calculated using the double power law model:

[0248] ;

[0249] in , , is the material parameter, obtained by fitting the experimental data

[0250] 14. Relaxation time :

[0251] Calculated using the modified Maxwell model:

[0252] ;

[0253] in is the initial relaxation time, n is the fitting parameter

[0254] The acquisition of these parameters and variables involves multiple experiments and complex calculation processes. In practical applications, appropriate simplification or correction may be required according to the requirements and conditions of the specific project. At the same time, many parameters may need to be determined through field trials or laboratory tests to improve the accuracy and applicability of the model.

[0255] Step S20: Calculate the stress distribution at different time points and different positions according to the shrinkage stress equation group, and determine the maximum stress value and its position and time of occurrence.

[0256] The purpose of this step is to obtain the spatial and temporal distribution of various shrinkage stresses in the concrete structure through numerical calculations, so as to provide a basis for subsequent process optimization. It specifically includes the following sub-steps:

[0257] 1) Divide the concrete structure into three-dimensional finite element grids, and the coordinates of each grid point are expressed as express.

[0258] 2) Choose an appropriate time step , the total computation time Divide into time steps.

[0259] 3) According to the various stress equations established in step S10, for each grid point and each time step , calculate the autogenous shrinkage stress , Drying shrinkage stress , Temperature stress and creep stress wait.

[0260] 4) Superimpose various stresses to obtain the total stress distribution.

[0261] 5) Among all grid points and time steps, find The maximum value of , and record the location where it appears and time .

[0262] The following is a detailed description of the steps for calculating stress distribution at different time points and locations:

[0263] 1. Determine the computational grid: Divide the concrete structure into a three-dimensional grid of m×n×p, and each grid point is represented as ;

[0264] 2. Determine the time step: Divide the total computation time T into N time steps, each time step is ;

[0265] 3. Initialization parameters: Initialize the parameters in each equation according to the concrete mix ratio, environmental conditions, etc. These parameters can be obtained through experimental determination or empirical formulas;

[0266] 4. For each time step and each grid point , calculate the stress components:

[0267] a. Calculate autogenous shrinkage stress:

[0268] ;

[0269] b. Calculate drying shrinkage stress:

[0270] ;

[0271] c. Calculate temperature stress:

[0272] ;

[0273] d. Calculate creep stress:

[0274] ;

[0275] 5. Calculate total stress:

[0276] ;

[0277] in represent , , Three directions, It can be calculated by the central difference method:

[0278] ;

[0279] 6. Determine the maximum stress value and its location and time: Traverse all time steps and grid points to find The maximum value of and time .

[0280] Through the above steps, the stress distribution at different time points and different locations can be obtained, and the maximum stress value and its location and time of occurrence can be determined. This process involves a large amount of numerical calculations, which usually require the help of computer programs.

[0281] Through this step of calculation, we can understand the stress distribution in the concrete structure and provide a basis for subsequent process optimization.

[0282] Step S30: Based on the stress distribution, a pre-trained contraction joint arrangement model is used to calculate the preferred contraction joint spacing and position.

[0283] The purpose of this step is to use artificial intelligence technology to automatically optimize the layout of shrinkage joints based on the stress distribution characteristics of concrete structures, so as to better control the occurrence of cracks. It includes the following sub-steps:

[0284] 1) The shrinkage joint layout model adopts a multi-layer neural network, including a feature extraction subnetwork, a spatial relationship subnetwork and a decision subnetwork.

[0285] 2) The feature extraction subnetwork uses a convolutional neural network (CNN), with concrete structure parameters and stress distribution data as input and a high-dimensional feature vector as output to extract key stress distribution features.

[0286] 3) The spatial relationship sub-network uses a graph convolutional neural network (GCN) with the output of the feature extraction sub-network as input to analyze the spatial relationship between contraction joints.

[0287] 4) The decision subnetwork uses a fully connected neural network (FNN), with the input being the output of the feature extraction and spatial relationship subnetworks, and the output being the preferred shrinkage joint spacing and position.

[0288] The shrinkage joint arrangement model is based on a large number of training data from concrete pouring cases, and learns the law between concrete stress distribution and shrinkage joint arrangement. Through this model, a suitable shrinkage joint arrangement scheme can be quickly found to effectively control the occurrence of cracks.

[0289] Step S40: Based on the maximum stress value and its distribution, the required expansion agent parameters are calculated using a pre-trained stress expansion agent model.

[0290] The purpose of this step is to use artificial intelligence technology to automatically determine the type, amount and optimal distribution position of the required expansion agent based on the stress distribution characteristics in the concrete structure to effectively inhibit the occurrence of cracks. It specifically includes the following sub-steps:

[0291] 1) The stress expansion agent model adopts a residual neural network with a multi-branch structure, including a stress analysis branch network, a material property branch network, an environmental factor branch network, and a fusion branch network.

[0292] 2) The stress analysis branch network uses a one-dimensional convolutional neural network, with stress distribution data as input to extract the key features of stress distribution.

[0293] 3) The material property branch network uses a multi-layer perceptron, with concrete parameters and mix ratio parameters as input to analyze the concrete material properties.

[0294] 4) The environmental factor branch network uses a long short-term memory network (LSTM) with construction environment parameters and maintenance condition parameters as input to analyze the impact of environmental factors on shrinkage behavior.

[0295] 5) The fusion branch network uses a fully connected neural network enhanced by the attention mechanism to integrate the outputs of each branch network and give the optimal expander type, addition amount and distribution method.

[0296] The stress expansion agent model is trained based on a large amount of experimental data. It can accurately predict the stress distribution characteristics in concrete structures and provide targeted expansion agent usage plans to effectively inhibit the occurrence of cracks.

[0297] Step S50: According to the concrete structure parameters and the stress distribution, the thickness of each layer of layered pouring, the pouring sequence and the interval time are determined by adopting the principle of minimum stress gradient and uniform stress distribution.

[0298] The purpose of this step is to further reduce the stress gradient inside the concrete structure by reasonably arranging the process parameters of layered pouring, making the stress distribution more uniform and thus reducing the occurrence of cracks. It specifically includes the following sub-steps:

[0299] 1) According to the stress distribution calculated in step S20, analyze the stress gradient at each position.

[0300] 2) Adopt the principle of minimum stress gradient to determine the optimal thickness of each layer of concrete so that the stress difference between adjacent layers is minimized. Generally speaking, using a thinner casting layer thickness is conducive to reducing stress gradient.

[0301] 3) On the basis of determining the thickness of each layer, according to the uniformity of stress distribution, the pouring order and the pouring interval time between each layer are determined. Usually, the area with lower stress is poured first, and then gradually moves to the area with higher stress, and the pouring interval time of each layer is controlled to promote stress uniformity.

[0302] 4) The key to this step is to balance the stress differences between the layers so that the stress distribution within the entire structure is as uniform as possible, thereby effectively reducing the risk of local stress concentration.

[0303] Through this step of optimization, the stress gradient during concrete pouring can be greatly reduced, thereby reducing the occurrence of cracks.

[0304] Step S60: During the concrete mixing process, the expansion agent is added according to the required expansion agent parameters obtained by calculation, and efficient mixing technology is used to ensure its uniform distribution.

[0305] The purpose of this step is to add the expansion agent during the concrete mixing process according to the expansion agent usage scheme determined in step S40, and use efficient mixing technology to ensure its uniform dispersion in the concrete to achieve the best crack suppression effect. Specifically, it includes the following sub-steps:

[0306] 1) According to the expansion agent type, addition amount and optimal distribution position calculated in step S40, the expansion agent is added as required during the concrete mixing process.

[0307] 2) Use a combination of high shear mixer and sonic wave assisted mixing technology to ensure that the expansive agent is fully dispersed and evenly distributed in the concrete. The high shear mixer can generate strong shear force, which is conducive to destroying the agglomeration of the expansive agent; the sonic wave assisted technology can increase the vibration energy and promote the dispersion of the expansive agent in the concrete.

[0308] 3) By controlling parameters such as mixing time and rotation speed, ensure that the expansion agent achieves an ideal distribution effect in the concrete, laying the foundation for subsequent crack control.

[0309] The key to this step is to utilize efficient mixing technology to fully utilize the crack-inhibiting effect of the expansion agent and ensure its uniform distribution in the concrete.

[0310] Step S70: pouring concrete according to the layered pouring plan, setting shrinkage joints according to the preferred shrinkage joint spacing and positions, and using intelligent vibrating equipment to ensure the density of the concrete.

[0311] The purpose of this step is to pour concrete in layers in an orderly manner according to the aforementioned optimization plan, arrange shrinkage joints reasonably, and use intelligent vibrating equipment to ensure the compactness of concrete, thereby further reducing the risk of cracks. It specifically includes the following sub-steps:

[0312] 1) According to the layered pouring scheme determined in step S50, concrete pouring of each layer is carried out in order to ensure that the pouring interval time between each layer is controlled.

[0313] 2) According to the preferred contraction joint arrangement scheme calculated in step S30, during the concrete pouring process, contraction joints are arranged at the determined positions and intervals. The arrangement of contraction joints can effectively guide and control the generation of cracks.

[0314] 3) At the same time, intelligent vibrating equipment is used to compact the concrete. The intelligent vibrating equipment has a real-time density monitoring function and can automatically adjust the vibration frequency and time according to the feedback signal during the vibration process to achieve the best compaction effect.

[0315] The key to this step is to strictly follow the optimization plan for layered pouring and shrinkage joint arrangement, and use intelligent vibration technology to ensure the compactness of the concrete, thereby effectively controlling the occurrence of cracks.

[0316] Step S80: Curing the completed concrete.

[0317] The purpose of this step is to take appropriate curing measures to further reduce the temperature stress and drying shrinkage stress of concrete in the early stage, thereby reducing the risk of cracks. It includes the following sub-steps:

[0318] 1) Formulate a suitable curing plan based on the curing condition parameters collected in step S10, such as temperature, humidity, etc. In general, it is necessary to take measures such as insulation and watering to maintain good temperature and humidity conditions for concrete in the early stages.

[0319] 2) Determine the appropriate curing duration based on the actual conditions of the concrete structure. Generally speaking, large-volume concrete structures require a longer curing period to reduce the generation of temperature stress and drying shrinkage stress.

[0320] 3) During the maintenance process, regular inspections and timely remedial measures are required to ensure the maintenance effect. If local dryness is found, watering and other measures should be taken in time.

[0321] Through this step of maintenance measures, the stress accumulation of concrete structures in the early stages can be effectively reduced, thereby further reducing the risk of cracks.

[0322] In summary, this concrete pouring method that reduces cracks can effectively reduce the occurrence of cracks during concrete pouring by accurately calculating the distribution of concrete shrinkage stress and taking targeted process optimization measures, such as setting reasonable shrinkage joints, adding expansion agents, and layered pouring. This method makes full use of artificial intelligence technology, makes intelligent optimization in the layout of shrinkage joints and the use of expansion agents, and greatly improves the accuracy of crack control.

[0323] Specifically, the principle of the present invention is: a complete set of mathematical modeling and intelligent optimization technology is adopted to systematically solve various factors affecting the shrinkage behavior of concrete.

[0324] First, in step S10, a set of mathematical equations describing concrete shrinkage stress is established, including autogenous shrinkage stress equation, drying shrinkage stress equation, temperature stress equation and creep stress equation. These equations cover the key factors affecting concrete shrinkage, such as environmental conditions, structural parameters, mix characteristics and curing process. By fitting these equations, the stress distribution of each part in the concrete structure can be accurately predicted, providing a reliable mathematical basis for subsequent process optimization.

[0325] Secondly, in step S20, by numerically calculating the above stress equations, the stress distribution at each time point and each position in the concrete structure is obtained. The purpose of this step is to fully grasp the temporal and spatial characteristics of the internal stress of the structure and lay the foundation for subsequent intelligent optimization. In particular, the maximum value of stress and its location and time of occurrence are determined. These key information provide a basis for the layout of shrinkage joints and the use of expansion agents.

[0326] In steps S30 and S40, intelligent optimization algorithms based on neural networks are used to provide optimal solutions for shrinkage joint arrangement and expansion agent use. These algorithms can make full use of the stress distribution information obtained in step S20, learn the inherent laws between stress distribution and crack control measures, and automatically provide highly targeted and operable optimization results. This optimization method based on artificial intelligence greatly improves the accuracy and operability of crack control, and makes up for the shortcomings of existing empirical methods.

[0327] Finally, in steps S50, S60 and S70, the concrete pouring and curing process is further optimized by combining traditional process technologies such as layered pouring, intelligent vibration and efficient mixing. The purpose of layered pouring is to reduce the stress gradient inside the structure and promote the uniform distribution of stress; intelligent vibration and efficient mixing ensure the optimal density of concrete and the uniform dispersion of expansion agent, providing strong support for the aforementioned crack control measures. These process optimization methods are combined with the aforementioned mathematical modeling and intelligent algorithms to form a systematic crack control system.

[0328] In general, the core of the method of the present invention is to make full use of mathematical modeling and artificial intelligence technology, accurately analyze the stress distribution characteristics of concrete structures, and take targeted systematic control measures including shrinkage joint arrangement, expansion agent use and layered pouring. This technical solution conforms to the inherent mechanism of concrete crack generation, and can effectively reduce stress concentration and uneven distribution inside the structure, thereby greatly reducing the generation of cracks. Compared with the existing empirical crack control technology, the method of the present invention is more scientific and reasonable, and has stronger pertinence and operability.

[0329] In order to better understand and implement the present invention, a specific embodiment 1 of the present invention is provided below. The steps of this embodiment 1 are specifically described as follows:

[0330] Step S10: Collect construction environment parameters, concrete structure parameters, concrete parameters, mix ratio parameters, and curing condition parameters, and establish and fit the shrinkage stress equation group of concrete.

[0331] In this step, it is necessary to obtain the key parameters that affect the shrinkage behavior of concrete and establish a mathematical model to describe the shrinkage stress of concrete. Specifically, it includes:

[0332] 1. Collect construction environment parameters: including ambient temperature , relative humidity and wind speed Etc. These environmental factors will significantly affect the drying shrinkage behavior of concrete.

[0333] 2. Collect concrete structure parameters: including structural dimensions , Component thickness And the stress state Etc. These geometric and loading factors will affect the stress distribution at different locations.

[0334] 3. Collect concrete parameters and mix ratio parameters: including cement type , water-cement ratio , Type and dosage of admixtures Etc. These mix factors will affect the autogenous shrinkage behavior of concrete.

[0335] 4. Collect curing condition parameters: including curing temperature ,humidity and duration Etc. These factors will affect the early temperature stress and drying shrinkage stress of concrete.

[0336] 5. Establish and fit a set of mathematical equations describing concrete shrinkage stress, including:

[0337] Autogenous shrinkage stress equation:

[0338]

[0339] in, It's time The autogenous shrinkage stress, It's time The elastic modulus at is from arrive The creep coefficient when It's time The autogenous shrinkage strain, It is a parameter that characterizes the shrinkage rate.

[0340] Drying shrinkage stress equation:

[0341]

[0342] in, It's time Drying shrinkage stress, is the humidity influence coefficient, is the size influence coefficient, is the influence coefficient of concrete strength, is the reference drying shrinkage strain, is a function that describes the development of drying shrinkage over time, is the aging coefficient, It's time to start drying.

[0343] Temperature stress equation:

[0344]

[0345] in, It's location ,time The temperature stress at is the linear thermal expansion coefficient, It's location ,time The temperature at is the creep function.

[0346] Creep stress equation:

[0347]

[0348] in, It's time The creep stress at is the initial stress, It's time relaxation time, It's time The stress during .

[0349] These equations contain multiple influencing factors, and the specific parameter values ​​need to be determined through experimental measurements or empirical formulas. Establishing this complete set of stress equations provides the necessary mathematical basis for subsequent steps.

[0350] Step S20: Calculate the stress distribution at different time points and different positions according to the shrinkage stress equation group, and determine the maximum stress value and its position and time of occurrence.

[0351] In this step, the spatial and temporal distribution of various shrinkage stresses in the concrete structure will be obtained through numerical calculation. Specifically, it includes:

[0352] 1. Divide the concrete structure into three-dimensional finite element grids, and use the coordinates of each grid point as express.

[0353] 2. Choose an appropriate time step , the total computation time Divide into time steps.

[0354] 3. According to the various stress equations established in step S10, for each grid point and each time step , respectively calculate:

[0355] Autogenous shrinkage stress:

[0356]

[0357] Drying shrinkage stress:

[0358]

[0359] Temperature stress:

[0360]

[0361] Creep stress:

[0362]

[0363] 4. Superimpose various stresses to obtain the total stress Distribution:

[0364]

[0365] in represent , , Three directions, It can be calculated by the central difference method:

[0366]

[0367] 5. Among all grid points and time steps, find The maximum value of , and record the location where it appears and time .

[0368] Through this step of calculation, we can understand the stress distribution in the concrete structure and provide a basis for subsequent process optimization.

[0369] Step S30: Based on the stress distribution, a pre-trained contraction joint arrangement model is used to calculate the preferred contraction joint spacing and position.

[0370] In this step, artificial intelligence technology will be used to automatically optimize the layout of contraction joints based on the stress distribution characteristics of the concrete structure. Specifically:

[0371] 1. The shrinkage joint layout model adopts a multi-layer neural network, including a feature extraction subnetwork, a spatial relationship subnetwork and a decision subnetwork.

[0372] 2. The feature extraction sub-network uses a convolutional neural network (CNN) with concrete structure parameters as input and stress distribution data , the output is a high-dimensional feature vector , to extract key stress distribution features:

[0373]

[0374] 3. The spatial relationship sub-network uses a graph convolutional neural network (GCN), and the input is the output of the feature extraction sub-network , analyze the spatial relationship between contraction joints, and output the spatial relationship features :

[0375]

[0376] 4. The decision subnetwork uses a fully connected neural network (FNN), and the input is the output of the feature extraction and spatial relationship subnetworks and , the output is the optimal contraction joint spacing and location :

[0377]

[0378] The shrinkage joint arrangement model is based on a large number of training data from concrete pouring cases, and learns the law between concrete stress distribution and shrinkage joint arrangement. Through this model, a suitable shrinkage joint arrangement scheme can be quickly found to effectively control the occurrence of cracks.

[0379] Step S40: Based on the maximum stress value and its distribution, the required expansion agent parameters are calculated using a pre-trained stress expansion agent model.

[0380] In this step, artificial intelligence technology will be used to automatically determine the parameters of the required expansion agent based on the stress distribution characteristics in the concrete structure. Specifically:

[0381] 1. The stress expansion agent model adopts a residual neural network with a multi-branch structure, including a stress analysis branch network, a material property branch network, an environmental factor branch network, and a fusion branch network.

[0382] 2. The stress analysis branch network uses a one-dimensional convolutional neural network (1D-CNN) with stress distribution data as input , extract key features of stress distribution :

[0383]

[0384] 3. Material property branch network uses multi-layer perceptron (MLP) and input is concrete parameters and mix parameters , analyze the concrete material properties and output them as material property vectors :

[0385]

[0386] 4. The environmental factor branch network uses a long short-term memory network (LSTM) and the input is the construction environment parameters and curing condition parameters , analyze the impact of environmental factors on shrinkage behavior, and output the environmental impact vector :

[0387]

[0388] 5. The fusion branch network uses a fully connected neural network enhanced by the attention mechanism to combine the output of each branch network , and Taken together, the best expansion agent type is given , Addition amount and distribution :

[0389]

[0390] The stress expansion agent model is trained based on a large amount of experimental data. It can accurately predict the stress distribution characteristics in concrete structures and provide targeted expansion agent usage plans to effectively inhibit the occurrence of cracks.

[0391] Step S50: According to the concrete structure parameters and the stress distribution, the thickness of each layer of layered pouring, the pouring sequence and the interval time are determined by adopting the principle of minimum stress gradient and uniform stress distribution.

[0392] In this step, the stress gradient inside the concrete structure will be further reduced by reasonably arranging the process parameters of layered pouring, making the stress distribution more uniform and thus reducing the occurrence of cracks. Specifically, it includes:

[0393] 1. Stress distribution calculated according to step S20 , analyze the stress gradient size at each position:

[0394]

[0395] 2. Adopt the principle of minimum stress gradient to determine the optimal thickness of each layer of concrete , so that the stress difference between adjacent layers is minimized:

[0396]

[0397] Generally speaking, using a thinner casting layer thickness is beneficial to reducing stress gradients.

[0398] 3. Determine the thickness of each layer Based on the uniformity of stress distribution , determine the pouring sequence and the pouring interval between each layer Usually, the area with lower stress is poured first, and then gradually moved to the area with higher stress, and the interval time between pouring each layer is controlled to promote stress uniformity.

[0399]

[0400] 4. The key to this step is to balance the stress differences between the layers so that the stress distribution inside the entire structure is as uniform as possible, thereby effectively reducing the risk of local stress concentration.

[0401] Step S60: During the concrete mixing process, the expansion agent is added according to the required expansion agent parameters obtained by calculation, and efficient mixing technology is used to ensure its uniform distribution.

[0402] In this step, the expansion agent is added during the concrete mixing process according to the expansion agent usage scheme determined in step S40, and efficient mixing technology is used to ensure its uniform dispersion in the concrete to achieve the best crack suppression effect. Specifically, it includes:

[0403] 1. The expansion agent type calculated according to step S40 , Addition amount and the best distribution location , add expansion agent as required during concrete mixing process.

[0404] 2. Use a combination of high shear mixer and sonic assisted mixing technology to ensure that the expansion agent is fully dispersed and evenly distributed in the concrete. High shear mixer can generate strong shear force. , which is beneficial to destroy the agglomeration of the expansion agent:

[0405]

[0406] in, is the mixer speed, is the mixer power, is the density of concrete, is the volume of concrete, is the mixer diameter.

[0407] Sonic wave assisted technology can increase vibration energy , promote the dispersion of expansion agent in concrete:

[0408]

[0409] in, is the quality of concrete, is the vibration acceleration, It is the vibration time.

[0410] 3. By controlling the mixing time and speed Parameters such as pressure, pressure and pressure are adjusted to ensure that the expansion agent achieves an ideal distribution effect in the concrete, laying the foundation for subsequent crack control.

[0411] The key to this step is to utilize efficient mixing technology to fully utilize the crack-inhibiting effect of the expansion agent and ensure its uniform distribution in the concrete.

[0412] Step S70: pouring concrete according to the layered pouring plan, setting shrinkage joints according to the preferred shrinkage joint spacing and positions, and using intelligent vibrating equipment to ensure the density of the concrete.

[0413] In this step, according to the above optimization plan, the concrete will be poured in layers in an orderly manner, the shrinkage joints will be arranged reasonably, and intelligent vibration equipment will be used to ensure the compactness of the concrete, thereby further reducing the risk of cracks. Specifically, it includes:

[0414] 1. According to the layered pouring scheme determined in step S50, including the thickness of each layer , pouring sequence and interval time , pour concrete at each level in an orderly manner to ensure that the pouring interval time between each layer is controlled.

[0415] 2. The optimal contraction joint arrangement scheme calculated in step S30, including the contraction joint spacing and location ,During the concrete pouring process, shrinkage joints are set according to the determined positions and spacings. The setting of shrinkage joints can effectively guide and control the generation of cracks.

[0416] 3. At the same time, a variable frequency intelligent vibrator with real-time density monitoring function is used to compact the concrete. The intelligent vibrating equipment can monitor the feedback signal during the vibration process. , automatic adjustment of vibration frequency and time , in order to achieve the best compaction effect:

[0417]

[0418]

[0419]

[0420] in, is the vibration coefficient, is the vibration acceleration.

[0421] The key to this step is to strictly follow the optimization plan for layered pouring and shrinkage joint arrangement, and use intelligent vibration technology to ensure the compactness of the concrete, thereby effectively controlling the occurrence of cracks.

[0422] In summary, the concrete pouring method of this embodiment 1 makes full use of advanced mathematical modeling and artificial intelligence technology, and comprehensively optimizes key links such as shrinkage stress calculation, shrinkage joint arrangement, expansion agent use, and layered pouring, greatly improving the accuracy of crack control. At the same time, this method also combines traditional vibration and maintenance processes to form a set of systematic crack control measures. Through these comprehensive means, the generation of cracks in the concrete pouring process can be effectively reduced, providing a strong guarantee for the quality and safety of concrete structures.

[0423] In order to further better understand and implement the present invention, the following is a specific application scenario of the present invention, Example 2: A large shopping center construction project is located in the central area of ​​the city, with a total construction area of ​​about 30,000 square meters, including 6 floors above ground and 2 floors underground. The building adopts a reinforced concrete frame structure, and the concrete strength grade is C35. Due to the nature of the use of the shopping center, the appearance quality of the building is required to be high. Therefore, how to effectively control the generation of cracks during the concrete pouring process has become a major technical difficulty of the project.

[0424] According to the concrete pouring method of the present invention, the construction party has taken the following specific measures:

[0425] Step S10: Collect construction environment parameters, concrete structure parameters, concrete parameters, mix ratio parameters, and curing condition parameters, and establish a shrinkage stress equation group.

[0426] 1) Environmental parameter collection: The ambient temperature, relative humidity and wind speed data during the construction of the building were collected through on-site monitoring equipment. The ambient temperature fluctuated between 20 and 30°C, the relative humidity varied between 60% and 80%, and the wind speed was in the range of 2 to 5 m / s.

[0427] 2) Structural parameter collection: According to the design drawings of the building, the following structural parameters were collected: the height of the 6-story building is 24m, the depth of the 2-story underground is 8m, the total construction area is 30,000 square meters, the cross-sectional dimensions of the main load-bearing components are 800mm×800mm for columns and 600mm×400mm for beams. The stress state of the building is a multi-story frame structure.

[0428] 3) Concrete parameter collection: The concrete used in this project adopts Type II cement with a water-cement ratio of 0.45, and the admixture is a polycarboxylic acid-based high-performance water reducer with a dosage of 1% of the cement weight. Through laboratory tests, the material characteristic parameters of concrete were determined, such as: compressive strength 35MPa, elastic modulus 33GPa, drying shrinkage coefficient 800 micro-damage, and thermal expansion coefficient 10×10^-6 / ℃.

[0429] 4) Collection of curing condition parameters: According to the construction plan, wet film curing will be carried out after concrete pouring. The curing temperature will be controlled at around 20°C, the relative humidity will be maintained at above 80%, and the curing time will be no less than 14 days.

[0430] Based on the various parameters collected above, the construction party established and fitted a set of mathematical equations describing concrete shrinkage stress, including autogenous shrinkage stress equation, drying shrinkage stress equation, temperature stress equation and creep stress equation. These equations cover the key factors affecting concrete shrinkage behavior and lay the foundation for subsequent stress calculation and process optimization.

[0431] Step S20: According to the shrinkage stress equation group, calculate the stress distribution at different time points and different positions to determine the maximum stress value.

[0432] 1) The main structure of the shopping mall is divided into three-dimensional finite element grids, with a total of 5000 grid points. The time step is selected as 1 day, and the total calculation time is 60 days, that is, there are 60 time steps in total.

[0433] 2) According to the stress equations established in step S10, the autogenous shrinkage stress, drying shrinkage stress, temperature stress and creep stress of each grid point at each time step are calculated respectively. Taking the middle position of a typical column (x=10m, y=15m, z=3m) as an example, the stress distribution within 60 days is shown in Table 1 below:

[0434] Table 1 Stress distribution table

[0435]

[0436] 3) Superimpose each stress component to obtain the total stress distribution in the middle of the column. By traversing all grid points and time steps, it is found that the maximum total stress in the entire structure occurs at a height of about 3 meters above the ground and occurs on the 45th day after concrete pouring, with a stress value of 5.45 MPa.

[0437] Step S30: Based on the stress distribution, the pre-trained contraction joint arrangement model is used to calculate the preferred contraction joint spacing and position.

[0438] 1) The calculated stress distribution data and structural parameters are input into the pre-trained contraction joint layout model. The model uses a multi-layer neural network and gives the optimal contraction joint layout plan through feature extraction, spatial relationship analysis and decision optimization.

[0439] 2) For the shopping center building, the optimal shrinkage joint arrangement output by the model is as follows: 18 shrinkage joints are arranged in the longitudinal and transverse directions of the main structure, with a spacing of approximately 8 meters.

[0440] 3) The shrinkage joint arrangement scheme can effectively guide the stress concentration inside the structure, causing cracks to occur along preset positions, thus avoiding the occurrence of random cracks.

[0441] Step S40: Based on the maximum stress value, the required expansion agent parameters are calculated using the pre-trained stress expansion agent model.

[0442] 1) The maximum stress value of 5.45 MPa calculated in step S20, as well as the relevant concrete structure parameters and environmental parameters, are input into the pre-trained stress expansion agent model. The model uses a multi-branch neural network and can provide the best use plan for the required expansion agent based on the input stress distribution characteristics and material properties.

[0443] 2) For this shopping center project, the expansion agent usage plan output by the model is as follows:

[0444] - Expansive agent type: 3% expansive mineral admixture

[0445] - Addition amount: 3% of concrete weight

[0446] - Distribution location: A 50mm thick special expansion concrete layer is set at the bottom and top of the main structure.

[0447] 3) The expansion agent usage scheme can effectively offset the shrinkage stress inside the structure, especially in the bottom and top areas where the stress is concentrated, thereby significantly reducing the risk of cracks.

[0448] Step S50: Determine the process parameters for layered casting according to the stress distribution characteristics.

[0449] 1) According to the stress analysis results of step S20, the stress distribution of the building is relatively concentrated, mainly concentrated at a position about 3 meters above the ground. At the same time, the stress difference between adjacent layers is large, and there is a more obvious stress gradient.

[0450] 2) In order to reduce the stress gradient, the construction party adopted a layered pouring method. After optimization, the following layered pouring scheme was determined: - The bottom 3 meters uses a 100mm thick casting layer

[0451] - 3m~12m use 200mm thick casting layer - For heights above 12 meters, a 150mm thick casting layer is used

[0452] - The interval between pouring each layer is 2 days

[0453] 3) This layered casting scheme can effectively reduce the stress difference between adjacent layers and promote the uniform distribution of stress in the entire structure, thereby reducing the risk of local stress concentration.

[0454] Step S60: During the concrete mixing process, an expansion agent is added according to the calculation results, and a high-efficiency mixing technology is used.

[0455] 1) During the concrete mixing process, according to the result of step S40, 3% of expansive mineral admixture is added to the mixture.

[0456] 2) To ensure that the expansive agent is fully dispersed and evenly distributed in the concrete, the construction party adopted a method combining a high shear mixer and sonic wave assisted mixing technology. The speed of the high shear mixer is controlled at 40r / min, and the power is about 15kW, which can generate strong shear force, which is conducive to destroying the agglomeration of the expansive agent. At the same time, the vibration energy of the concrete is increased by sonic wave vibration equipment (frequency 40Hz, amplitude 0.5mm), which further promotes the dispersion of the expansive agent.

[0457] 3) After 10 minutes of efficient mixing, the expansive agent achieved an ideal dispersion effect in the concrete, laying the foundation for subsequent crack control.

[0458] Step S70: pour concrete according to the optimized layered pouring plan, set shrinkage joints, and use intelligent vibrating equipment.

[0459] 1) According to the layered pouring scheme determined in step S50, pour concrete in each layer in order. First, pour a 100 mm thick layer at the bottom 3 meters, then pour a 200 mm thick layer from 3 meters to 12 meters after an interval of 2 days, and then pour a 150 mm thick layer above 12 meters after an interval of 2 days.

[0460] 2) During the concrete pouring process, according to the contraction joint arrangement scheme determined in step S30, 18 contraction joints are arranged in the longitudinal and transverse directions with a spacing of about 8 meters to guide the cracks to form along the preset positions.

[0461] 3) At the same time, the construction party used intelligent vibrating equipment with real-time density monitoring function to compact the concrete. The vibrating equipment can automatically adjust the vibration frequency and time according to the feedback signal during the vibration process to ensure that each layer of concrete reaches the ideal density, providing guarantee for subsequent crack control.

[0462] Step S80: Curing the poured concrete.

[0463] 1) Based on the maintenance condition parameters collected in step S10, the construction party formulated the following maintenance measures:

[0464] - Temperature controlled at 20±2℃

[0465] - Relative humidity should be kept above 80%

[0466] - Use wet film curing, the duration is not less than 14 days

[0467] 2) During the curing process, regular inspections and timely watering were carried out to ensure that the concrete surface remained moist and prevent local drying. At the same time, the curing time of concrete was appropriately extended to effectively reduce the generation of early temperature stress and drying shrinkage stress.

[0468] By taking the above measures, the large shopping mall building basically had no crack problems during the concrete pouring process. After the construction was completed, a one-year follow-up monitoring was carried out, and the structural appearance quality was good, and no obvious crack defects were found.

[0469] In general, the concrete pouring method adopted in this embodiment 2 makes full use of mathematical modeling and artificial intelligence technology, and systematically optimizes key links such as shrinkage joint arrangement, expansion agent use, and layered pouring. Compared with the traditional empirical method, this method can more accurately analyze the shrinkage stress distribution of concrete and provide crack control measures with strong pertinence and good operability. At the same time, this embodiment also incorporates advanced intelligent vibration and efficient mixing technology to ensure the effective implementation of various optimization measures in actual construction. Through these comprehensive means, the large shopping center building basically did not have crack problems during the concrete pouring process, which fully demonstrated the effectiveness and practicality of the method of the present invention.

[0470] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A concrete pouring method for reducing crack generation, characterized in that: The following steps are involved: S10, collecting construction environment parameters, concrete structure parameters, concrete parameters, mix ratio parameters, and curing condition parameters, and establishing and fitting a group of shrinkage stress equations for concrete, including an autogenous shrinkage stress equation, a drying shrinkage stress equation, a temperature stress equation, a creep stress equation, and an overall stress equation; S20, calculating the stress distribution at different time points and different positions according to the shrinkage stress equation group, and determining the maximum stress value and the position and time at which it occurs; S30, based on the stress distribution, using a pre-trained contraction joint arrangement model to calculate an optimal contraction joint spacing and position; S40, based on the maximum stress value and its distribution, using a pre-trained stress expansion agent model to calculate the required expansion agent parameters, including the type of expansion agent, the amount added, and the preferred distribution in the concrete; S50, according to the concrete structure parameters and the stress distribution, adopting the principle of minimum stress gradient and uniform stress distribution, determining the thickness of each layer of layered pouring, pouring sequence and interval time; S60. During the concrete mixing process, the expansion agent is added according to the calculated required expansion agent parameters and efficient mixing technology is used to ensure its uniform distribution; S70, pouring concrete according to the layered pouring plan, setting contraction joints according to the preferred contraction joint spacing and positions, and using intelligent vibrating equipment to ensure the density of the concrete; S80. Maintain the completed concrete.

2. A concrete pouring method for reducing crack generation according to claim 1, characterized in that: The efficient mixing technology specifically adopts a method combining a high shear force mixer and a sonic wave assisted mixing technology to ensure uniform dispersion of the expansion agent in the concrete.

3. A concrete pouring method for reducing crack generation according to claim 2, characterized in that: The intelligent vibrating equipment specifically adopts a variable frequency intelligent vibrating rod with a real-time density monitoring function, which automatically adjusts the vibration frequency and time by analyzing the feedback signal during the vibration process to achieve the best compaction effect.

4. A concrete pouring method for reducing crack generation according to claim 3, characterized in that: The shrinkage joint arrangement model adopts a multi-layered neural network. It includes feature extraction subnetwork, spatial relationship subnetwork and decision subnetwork.

5. A concrete pouring method for reducing crack generation according to claim 4, characterized in that: The feature extraction sub-network is used to extract key features of concrete structure and stress distribution. The input is concrete structure parameters and stress distribution data, the output is a high-dimensional feature vector, and the structure is a convolutional neural network. The spatial relationship sub-network is used to analyze the spatial relationship between contraction joints, the input is the output of the feature extraction sub-network, the output is the spatial relationship feature, and the structure is a graph convolutional neural network; The decision subnetwork is used to finally determine the layout of the contraction joints. The input is the output of the feature extraction subnetwork and the spatial relationship subnetwork. The output is the spacing and position of the contraction joints. The structure is a fully connected neural network.

6. A concrete pouring method for reducing crack generation according to claim 5, characterized in that: The stress expansion agent model adopts a residual neural network with a multi-branch structure, including a stress analysis branch network, a material property branch network, an environmental factor branch network and a fusion branch network.

7. A concrete pouring method for reducing crack generation according to claim 6, characterized in that: The stress analysis branch network is used to analyze stress distribution characteristics, the input is stress distribution data, the output is a stress feature vector, and the structure is a one-dimensional convolutional neural network; The material property branch network is used to analyze the concrete material properties, the input is the concrete parameters and the mix ratio parameters, the output is the material property vector, and the structure is a multi-layer perceptron; The environmental factor branch network is used to analyze the impact of environmental factors. The input is the construction environment parameters and the maintenance condition parameters. The output is the environmental impact vector. The structure is a long short-term memory network. The fusion branch network is used to integrate the outputs of each branch network and give the final expander parameters. The input is the output vector of each branch network, the output is the type, addition amount and distribution method of the expander, and the structure is a fully connected neural network enhanced by the attention mechanism.

8. A concrete pouring method for reducing crack generation according to claim 1, characterized in that: The autogenous shrinkage stress equation is used to describe the stress generated by volume change due to internal chemical reactions during cement hydration; The drying shrinkage stress equation is used to describe the stress generated by volume shrinkage due to water evaporation during the concrete hardening process; The temperature stress equation is used to describe the stress generated by the temperature gradient caused by the heat of hydration and the ambient temperature change; The creep stress equation is used to describe the stress relaxation caused by the deformation of concrete under long-term load; The overall stress equation is used to integrate the above-mentioned various stresses and calculate the overall stress distribution in the concrete structure.

9. A concrete pouring method for reducing crack generation according to claim 2, characterized in that: The rotation speed of the high shear mixer is controlled at 40 r / min.

10. A concrete pouring method for reducing crack generation according to claim 9, characterized in that: The sonic wave assisted mixing technology specifically uses a sonic wave vibration device to emit sonic waves with a frequency of 40 Hz and an amplitude of 0.5 mm.

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