Large-volume and large-height concrete continuous pouring construction method

Abstract

The invention provides a large-volume large-height concrete continuous pouring construction method, which belongs to the technical field of concrete pouring, and comprises the following steps: dividing a concrete continuous pouring process into a plurality of pouring layers; for each pouring layer, a continuous pouring method is adopted, continuous pouring is conducted on the current pouring layer, and in other words, concrete of the current pouring layer is poured in place at a time within a continuous preset pouring duration time period; a vibrator is adopted for vibrating concrete of the current pouring layer in the pouring process and after pouring is completed according to the preset vibrating time and vibrating frequency of the pouring layer, bubbles are removed, and the compactness is improved; after concrete pouring and vibrating of the current pouring layer are completed and after a preset standing time length is waited, pouring and vibrating of the next pouring layer are continued until concrete pouring and vibrating of all the pouring layers are completed; and the poured concrete is cured. The technical problem that various existing parameters often depend on artificial experience, and pouring related parameters are difficult to accurately determine is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete pouring, and in particular relates to a construction method for continuous pouring of large-volume and large-height concrete. Background Art

[0002] Large-volume and high-height concrete structures are widely used in important projects such as water conservancy hubs, super-high-rise buildings, and large bridges. Their main characteristics are large concrete pouring volume and high floor height. This type of structure has very high requirements for various properties of concrete, such as compressive strength, shrinkage deformation, surface cracking, density and uniformity. If the construction is improper, various quality problems are likely to occur, seriously affecting the safety and service life of the structure.

[0003] At present, the commonly used methods for pouring large-volume and high-height concrete in the industry are mainly the integral block pouring method and the continuous pouring method. The integral block pouring method divides the overall structure into multiple independent pouring blocks, and pours and maintains them in steps, which can better control the quality of individual pouring blocks, but it is easy to produce joints, affecting the overall performance. The continuous pouring method pours the entire structure in place at one time, which can avoid joint problems, but it is difficult to ensure the uniformity and density of each layer of concrete, especially for large-thick pouring layers exceeding 2 meters.

[0004] That is to say, various parameters in existing large-volume and high-height concrete pouring construction often rely on manual experience, and there is a technical problem that it is difficult to accurately determine the pouring-related parameters. Summary of the invention

[0005] In view of this, the present invention provides a large-volume and large-height continuous pouring construction method for concrete, which can solve the technical problem that various parameters in the existing large-volume and large-height concrete pouring construction often rely on manual experience and it is difficult to accurately determine the pouring-related parameters.

[0006] The present invention is achieved in that:

[0007] The present invention provides a large-volume and large-height continuous pouring construction method for concrete, comprising the following steps:

[0008] S10, dividing the continuous concrete pouring process into multiple pouring layers according to the acquired environmental parameters, construction quality requirement parameters and concrete parameters;

[0009] S20, for each pouring layer, a continuous pouring method is used to continuously pour the current pouring layer, that is, the concrete of the current pouring layer is poured in place at one time within a continuous preset pouring time period;

[0010] S30, using a vibrator to vibrate the concrete of the current pouring layer during pouring and after pouring according to the preset vibration time and vibration frequency of the pouring layer to remove bubbles and improve density;

[0011] S40, after completing the concrete pouring and vibration of the current pouring layer, after waiting for a predetermined static time, continue to perform the pouring and vibration of the next pouring layer using steps S20-S30 until the concrete pouring and vibration of all pouring layers are completed;

[0012] S50, curing the poured concrete.

[0013] The specific steps of dividing the continuous concrete pouring process into multiple pouring layers according to the environmental parameters, the construction quality requirement parameters and the concrete parameters include:

[0014] S11, obtaining environmental parameters, construction quality requirement parameters and concrete parameters, wherein the environmental parameters include ambient temperature, humidity and sunshine intensity; the construction quality requirement parameters include concrete block size, compressive strength, shrinkage deformation, surface cracking, density and uniformity; the concrete parameters include cement type and amount, coarse and fine aggregate ratio, admixture type and amount, slump, initial setting time and final setting time;

[0015] S12, establishing a set of equations for continuous multi-layer concrete pouring that takes into account environmental parameters, construction quality requirement parameters, concrete parameters, and pouring parameters, including a compressive strength equation, a shrinkage deformation equation, a surface cracking equation, a density equation, and a uniformity equation; the pouring parameters include: the number of pouring layers and the thickness of each pouring layer, pouring rate, vibration time, vibration frequency, static time, and pouring time;

[0016] S13, calculating the pouring parameter range according to the concrete continuous multi-layer pouring equation group;

[0017] S14, clustering the casting parameter range to obtain multiple cluster centers, and taking each cluster center as a candidate casting parameter;

[0018] S15, inputting each candidate pouring parameter into a pre-trained concrete continuous pouring quality assessment model to obtain a pouring quality assessment vector;

[0019] S16. Selecting a candidate pouring parameter with an optimal pouring quality vector as a target pouring parameter, which is used to divide the continuous concrete pouring process into multiple pouring layers, and to determine the pouring time and standing time of each pouring layer.

[0020] Furthermore, the large-volume and high-height concrete refers to: a concrete structure with a volume greater than 1,000 cubic meters, or a thickness greater than 2 meters, or a minimum size greater than 1 meter and a cementitious material content greater than 300 kg / m³ in the concrete mixture.

[0021] Furthermore, the specific casting quality assessment vectors are: [compressive strength, shrinkage deformation, surface cracking, density, uniformity].

[0022] Furthermore, the quality assessment model for continuous concrete pouring adopts a multi-branch structure, including a strength sub-network, a shrinkage deformation risk sub-network, a cracking risk sub-network, an internal structure sub-network, and a fusion sub-network.

[0023] Furthermore, the strength subnetwork is used to predict the development of concrete strength, with environmental parameters and concrete parameters as inputs, and predicted strength as outputs, and the structure is a three-layer fully connected network;

[0024] The shrinkage deformation risk sub-network is used to evaluate the shrinkage deformation of concrete. The input is concrete parameters and pouring parameters, and the output is shrinkage deformation. The structure is a three-layer fully connected network.

[0025] The cracking risk sub-network is used to evaluate the risk of concrete cracking. The input is environmental parameters, concrete parameters, and shrinkage deformation. The output is a cracking risk index. The structure is a two-layer convolutional network plus a one-layer fully connected network.

[0026] The internal structure subnetwork is used to evaluate the internal structure of concrete. The input is concrete parameters, pouring parameters, and shrinkage deformation. The output is density and uniformity. The structure is a one-layer convolutional network plus a two-layer fully connected network.

[0027] Furthermore, the fusion sub-network is used to integrate the outputs of each sub-network, the input of the fusion sub-network is the output of each sub-network, the output of the fusion sub-network is the final casting quality assessment vector, and the structure is a two-layer fully connected network.

[0028] Furthermore, the training steps of the concrete continuous pouring quality assessment model specifically include: constructing a training data set and training a model;

[0029] Among them, constructing a training data set includes:

[0030] S1511. Acquire experimental data of multiple groups of small-scale experiments. Specifically, the small-scale experiments are: under laboratory conditions, using different mix ratios and construction parameters, making concrete test blocks of standard sizes, simulating the pouring and solidification process of large-volume concrete, and performing various performance tests at different ages. The experimental data include: environmental parameters, concrete parameters, pouring parameters, and actually detected pouring quality assessment vectors;

[0031] S1512, obtaining multiple sets of historical construction data of large-volume and high-height continuous concrete pouring, wherein the historical construction data specifically includes: environmental parameters, concrete parameters, pouring parameters, and actually detected pouring quality assessment vectors;

[0032] S1513. Clean and preprocess the acquired experimental data and historical construction data to remove abnormal values ​​and redundant data;

[0033] S1514, normalizing the processed data to obtain normalized data;

[0034] S1515. Randomly divide the normalized data into a training set, a validation set, and a test set in a ratio of 7:2:1 to obtain a training data set.

[0035] Furthermore, the specific steps of the training model include:

[0036] S1521, initializing the neural network parameters of the multi-branch structure, including the weight and bias of each sub-network;

[0037] S1522, using the training set data for forward propagation, calculating the output of each sub-network and the final casting quality assessment vector;

[0038] S1523, calculating the loss between the predicted result and the true label, using the mean square error as the loss function;

[0039] S1524. Calculate the gradient using the back propagation algorithm and update the network parameters using the Adam optimizer.

[0040] S1525. Evaluate the model performance on the validation set. If the performance does not improve after multiple epochs, start the early stopping mechanism.

[0041] S1526. Repeat steps S1522-S1525 until the preset number of training rounds is reached or the early stopping condition is met. Finally, the generalization ability of the model is evaluated on the test set to obtain the final concrete continuous pouring quality assessment model.

[0042] Furthermore, the concrete test block of standard size is specifically a concrete test block of 150 mm×150 mm×150 mm.

[0043] Specifically, the step S10 specifically includes first obtaining environmental parameters, construction quality requirement parameters and concrete parameters, wherein the environmental parameters include ambient temperature, humidity, sunlight intensity, etc., the construction quality requirement parameters include concrete block size, compressive strength, shrinkage deformation, surface cracking, density, uniformity, etc., and the concrete parameters include cement type and dosage, coarse and fine aggregate ratio, admixture type and dosage, slump, initial setting time, final setting time, etc.; then, based on these obtained parameters, establish a group of equations that comprehensively consider the environment, quality requirements, concrete and casting parameters, including compressive strength equation, shrinkage deformation equation, surface cracking equation, density equation, uniformity equation, etc., so as to calculate the casting parameter range that meets the requirements.

[0044] Specifically, step S20 is to use a continuous pouring method to construct each pouring layer, that is, to pour the concrete of the current pouring layer in one go within a continuous preset pouring time to avoid joints caused by pauses in the middle and improve the pouring quality.

[0045] Specifically, step S30 is to vibrate the concrete of the current pouring layer being poured and the current pouring layer that has been poured according to the preset vibration time and vibration frequency using a vibrator to remove bubbles in the concrete and improve the density. The determination of the vibration time and frequency needs to consider multiple factors such as concrete parameters, environmental conditions and target density.

[0046] Specifically, step S40 is to wait for a certain amount of time to rest after the concrete pouring and vibrating of the current pouring layer is completed, and then continue to use the method of steps S20-S30 to construct the next pouring layer after the concrete strength reaches a level that can bear the weight of the upper layer. The length of the resting time needs to be determined comprehensively based on factors such as ambient temperature, initial and final setting time of concrete, etc.

[0047] Specifically, step S50 is that after completing the concrete construction of all pouring layers, it is necessary to formulate a reasonable maintenance plan based on environmental conditions, concrete mix ratio and quality requirements, and carry out continuous maintenance treatment on the concrete according to the plan, such as sprinkling, covering, and insulation, to ensure that various performance indicators meet the construction requirements and ultimately achieve the safety and durability of the concrete structure.

[0048] Specifically, step S11 first obtains information such as environmental parameters that affect the quality of concrete pouring, construction quality requirement parameters, and concrete parameters. These parameters can be obtained through on-site measurements or by referring to relevant specifications and standards or historical construction records, providing basic data support for subsequent mathematical modeling and parameter optimization.

[0049] Among them, step S12 specifically involves establishing a set of equations for continuous multi-layer pouring of concrete that comprehensively considers the environment, quality requirements, concrete, and pouring parameters. The set of equations covers prediction models for key performance indicators such as compressive strength, shrinkage deformation, surface cracking, density, and uniformity, providing a theoretical basis for subsequent parameter optimization.

[0050] The following is a detailed description of the five equations of the concrete continuous multi-layer pouring equation group:

[0051] 1. Compressive strength equation

[0052] ;

[0053] In the formula, is the compressive strength of concrete at age t days (MPa); is the compressive strength of concrete under standard curing conditions for 28 days (MPa); is the age of concrete (days); is the cement strength grade coefficient (0.25 for ordinary Portland cement and 0.38 for rapid hardening Portland cement); is the daily temperature difference influence coefficient, usually 0.05-0.1; is the temperature influence coefficient, usually 0.001-0.003; is the actual curing temperature (°C); is the standard curing temperature, usually 20°C; It can be obtained through laboratory experiments. , , It can be determined based on experience or relevant specifications.

[0054] 2. Shrinkage deformation equation

[0055] ;

[0056] ;

[0057] for ;

[0058] for ;

[0059] ;

[0060] ;

[0061] In the formula, is the contraction strain at the age of t days; is the ultimate shrinkage strain; is the shrinkage rate parameter, usually 0.035-0.1 day^(-0.5); is the age of concrete (days); is the relative humidity (%); is the volume-to-surface ratio (mm); is the 28-day compressive strength (MPa); is the humidity influence coefficient; is the size influence coefficient; is the influence coefficient of concrete strength; and It can be obtained through on-site measurement. Determined by laboratory testing.

[0062] 3. Surface Cracking Equation

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] In the formula, is the probability of surface cracking; is the maximum tensile stress (MPa); is the tensile strength of concrete (MPa); is the shape parameter of the Weibull distribution, usually 3-4; is the elastic modulus of concrete (MPa); is the thermal expansion coefficient of concrete, usually taken as 1.0×10^(-5) / °C; is the temperature difference (°C); is the shrinkage strain, calculated by the shrinkage deformation equation; is the relaxation factor; is the creep coefficient; is the current age (days); is the load action start time (day); is the base creep coefficient, usually taken as 2.35; is the imaginary thickness, (mm), is the cross-sectional area, is the circumference in contact with the air; is the relative humidity (%); It can be measured by temperature sensors, and other parameters can be determined through experiments or experience.

[0072] 4. Density equation

[0073] ;

[0074] ;

[0075] ;

[0076] In the formula, is the density of concrete; is the initial density; is the vibration efficiency coefficient; is the vibration time (s); is the vibration frequency (Hz); is the slump influence coefficient, usually 0.05-0.1; is the actual slump (mm); It is the reference slump, usually 100mm; is the vibration efficiency benchmark coefficient; is the activation energy (J / mol), usually 40000-50000 J / mol; is the gas constant, 8.314 J / (mol·K); is the concrete temperature (K); , is an empirical coefficient related to aggregate grading, usually Take 0.5-0.7, Take 0.3-0.5; is the water-cement ratio; and It can be obtained through the parameters of the vibration equipment. Through on-site measurements, Measured by temperature sensors, other parameters are determined experimentally or empirically.

[0077] 5. Uniformity equation

[0078] ;

[0079] ;

[0080] ;

[0081] In the formula, is the uniformity of concrete; is the measured value of the i-th measuring point (such as intensity, density, etc.); is the average value of the measured values; is the number of measuring points; is the layer number influence coefficient, usually 0.05-0.1; is the number of pouring layers; is the total pouring height (m); is the thickness of the casting layer (m); It can be obtained through non-destructive testing (such as rebound method, ultrasonic method, etc.), and Determined by construction parameters.

[0082] These equations comprehensively consider the influence of environmental parameters, concrete parameters and pouring parameters on the quality of continuous pouring of large-volume and high-height concrete. Through these equations, various performance indicators of concrete can be predicted and evaluated, providing a theoretical basis for optimizing pouring parameters.

[0083] Specifically, step S13 includes inputting the set of equations established in step S12 into a computer program, setting a reasonable range of values ​​for each influencing parameter, solving the set of equations by numerical analysis methods, and obtaining a range of values ​​for casting parameters that meet construction quality requirements, including the number of casting layers, thickness of each layer, casting rate, vibration time frequency, standing time, etc.

[0084] Specifically, step S14 is to perform cluster analysis on the range of casting parameter values ​​obtained in step S13, and use density clustering or distance clustering to divide these parameter combinations into multiple clusters with similar characteristics, and select a representative cluster center from each cluster as a candidate casting parameter scheme to provide alternative solutions for subsequent quality evaluation.

[0085] Specifically, step S15 uses a pre-trained multi-branch neural network model to perform a quality assessment on the candidate casting parameter schemes obtained in step S14, and obtains their comprehensive performance indicators in terms of compressive strength, shrinkage deformation, surface cracking, density, uniformity, etc., to provide a basis for parameter optimization in step S16.

[0086] Specifically, step S16 uses a multi-objective decision analysis method, such as the TOPSIS method or the VIKOR method, to compare the quality evaluation vectors of the candidate solutions obtained in step S15, and selects the pouring parameter solution with the best comprehensive performance as the final optimization target, which is used to determine the specific construction parameters for continuous concrete pouring.

[0087] Compared with the prior art, the large-volume and large-height continuous pouring construction method provided by the present invention has the following beneficial effects:

[0088] 1. A mathematical model that comprehensively considers environmental parameters, construction quality requirements, concrete parameters and pouring parameters was established. It can accurately predict key performance indicators of concrete such as compressive strength, shrinkage deformation, surface cracking, density and uniformity, providing a reliable theoretical basis for the optimization of pouring parameters.

[0089] 2. Cluster analysis and multi-objective optimization methods are used to select the casting parameter scheme with the best comprehensive performance from various parameter combinations, and apply it to actual construction to ensure that all performance indicators of concrete meet the standards.

[0090] 3. By adopting the method of continuous pouring and precise vibration, the joint problem is effectively avoided, and the density and uniformity of each pouring layer are ensured, further improving the overall quality of large-volume and high-height concrete.

[0091] 4. During the pouring process, various performance indicators are monitored in real time and adjusted in a timely manner to maximize the excellent performance of concrete and improve the safety and service life of concrete structures.

[0092] To sum up, the continuous concrete pouring construction method proposed in the present invention fully considers the interaction between environmental factors, concrete properties and pouring technology, and adopts scientific mathematical modeling and parameter optimization technology to achieve precise control of the pouring quality of large-volume and large-height concrete, and solves the technical problem that various parameters in the existing large-volume and large-height concrete pouring construction often rely on manual experience and it is difficult to accurately determine the pouring-related parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 A flow chart of the method provided by the present invention;

[0094] Figure 2 A flowchart of steps for dividing a continuous concrete pouring process into multiple pouring layers;

[0095] Figure 3 A flowchart of the steps for constructing a training dataset;

[0096] Figure 4 Flowchart of the model training steps. DETAILED DESCRIPTION

[0097] 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.

[0098] like Figure 1 As shown, it is a flow chart of a large-volume and large-height continuous pouring construction method provided by the present invention, and the method comprises the following steps:

[0099] S10, dividing the continuous concrete pouring process into multiple pouring layers according to the acquired environmental parameters, construction quality requirement parameters and concrete parameters;

[0100] S20, for each pouring layer, a continuous pouring method is used to continuously pour the current pouring layer, that is, the concrete of the current pouring layer is poured in place at one time within a continuous preset pouring time period;

[0101] S30, using a vibrator to vibrate the concrete of the current pouring layer during pouring and after pouring according to the preset vibration time and vibration frequency of the pouring layer to remove bubbles and improve density;

[0102] S40, after completing the concrete pouring and vibration of the current pouring layer, after waiting for a predetermined static time, continue to perform the pouring and vibration of the next pouring layer using steps S20-S30 until the concrete pouring and vibration of all pouring layers are completed;

[0103] S50, curing the poured concrete.

[0104] like Figure 2 As shown, the specific steps of dividing the continuous concrete pouring process into multiple pouring layers according to the environmental parameters, the construction quality requirement parameters and the concrete parameters include:

[0105] S11, obtaining environmental parameters, construction quality requirement parameters and concrete parameters, wherein the environmental parameters include ambient temperature, humidity and sunshine intensity; the construction quality requirement parameters include concrete block size, compressive strength, shrinkage deformation, surface cracking, density and uniformity; the concrete parameters include cement type and amount, coarse and fine aggregate ratio, admixture type and amount, slump, initial setting time and final setting time;

[0106] S12, establishing a set of equations for continuous multi-layer concrete pouring that takes into account environmental parameters, construction quality requirement parameters, concrete parameters, and pouring parameters, including a compressive strength equation, a shrinkage deformation equation, a surface cracking equation, a density equation, and a uniformity equation; the pouring parameters include: the number of pouring layers and the thickness of each pouring layer, pouring rate, vibration time, vibration frequency, static time, and pouring time;

[0107] S13, calculating the pouring parameter range according to the concrete continuous multi-layer pouring equation group;

[0108] S14, clustering the casting parameter range to obtain multiple cluster centers, and taking each cluster center as a candidate casting parameter;

[0109] S15, inputting each candidate pouring parameter into a pre-trained concrete continuous pouring quality assessment model to obtain a pouring quality assessment vector;

[0110] S16. Selecting a candidate pouring parameter with an optimal pouring quality vector as a target pouring parameter, which is used to divide the continuous concrete pouring process into multiple pouring layers, and to determine the pouring time and standing time of each pouring layer.

[0111] The training steps of the concrete continuous pouring quality assessment model specifically include: constructing a training data set and training a model;

[0112] like Figure 3 As shown, construct a training data set, including:

[0113] S1511. Acquire experimental data of multiple groups of small-scale experiments. Specifically, the small-scale experiments are: under laboratory conditions, using different mix ratios and construction parameters, making concrete test blocks of standard sizes, simulating the pouring and solidification process of large-volume concrete, and performing various performance tests at different ages. The experimental data include: environmental parameters, concrete parameters, pouring parameters, and actually detected pouring quality assessment vectors;

[0114] S1512, obtaining multiple sets of historical construction data of large-volume and high-height continuous concrete pouring, wherein the historical construction data specifically includes: environmental parameters, concrete parameters, pouring parameters, and actually detected pouring quality assessment vectors;

[0115] S1513. Clean and preprocess the acquired experimental data and historical construction data to remove abnormal values ​​and redundant data;

[0116] S1514, normalizing the processed data to obtain normalized data;

[0117] S1515. Randomly divide the normalized data into a training set, a validation set, and a test set in a ratio of 7:2:1 to obtain a training data set.

[0118] like Figure 4 As shown, the specific steps of the training model include:

[0119] S1521, initializing the neural network parameters of the multi-branch structure, including the weight and bias of each sub-network;

[0120] S1522, using the training set data for forward propagation, calculating the output of each sub-network and the final casting quality assessment vector;

[0121] S1523, calculating the loss between the predicted result and the true label, using the mean square error as the loss function;

[0122] S1524. Calculate the gradient using the back propagation algorithm and update the network parameters using the Adam optimizer.

[0123] S1525. Evaluate the model performance on the validation set. If the performance does not improve after multiple epochs, start the early stopping mechanism.

[0124] S1526. Repeat steps S1522-S1525 until the preset number of training rounds is reached or the early stopping condition is met. Finally, the generalization ability of the model is evaluated on the test set to obtain the final concrete continuous pouring quality assessment model.

[0125] The specific implementation methods of the above steps are described in detail below:

[0126] Step S10: Divide the continuous concrete pouring process into multiple pouring layers according to the acquired environmental parameters, construction quality requirement parameters and concrete parameters

[0127] The purpose of this step is to divide the entire concrete pouring process into multiple controllable pouring layers, so as to facilitate the special control and optimization of each pouring layer. The specific implementation method is as follows:

[0128] First, obtain environmental parameters, construction quality requirement parameters and concrete parameters. Environmental parameters include ambient temperature, humidity, sunlight intensity, etc.; construction quality requirement parameters include concrete block size, compressive strength, shrinkage deformation, surface cracking, density, uniformity, etc.; concrete parameters include cement type and dosage, coarse and fine aggregate ratio, admixture type and dosage, slump, initial setting time, final setting time, etc.

[0129] Secondly, a set of equations for continuous multi-layer concrete pouring is established, which takes into account environmental parameters, construction quality requirement parameters, concrete parameters and pouring parameters. The set of equations includes compressive strength equation, shrinkage deformation equation, surface cracking equation, density equation, uniformity equation, etc. The pouring parameters include the number of pouring layers, the thickness of each layer, pouring rate, vibration time, vibration frequency, static time, pouring time, etc.

[0130] Then, according to the established concrete continuous multi-layer pouring equation group, the value range of the pouring parameters is calculated.

[0131] Next, cluster analysis is performed on the obtained casting parameter range to obtain multiple cluster centers as candidate casting parameter solutions.

[0132] Finally, each candidate pouring parameter scheme is input into the pre-trained concrete continuous pouring quality assessment model to obtain a pouring quality assessment vector. The candidate pouring parameter with the best pouring quality vector is selected as the target pouring parameter, which is used to divide the concrete continuous pouring process into multiple pouring layers and determine the pouring time and static time of each pouring layer.

[0133] Step S20: For each pouring layer, a continuous pouring method is used to continuously pour the current pouring layer.

[0134] The purpose of this step is to construct each pouring layer using a continuous pouring method. The specific implementation method is as follows:

[0135] After completing the layering of the continuous concrete pouring process in step S10, a continuous pouring method is used for each pouring layer. That is, the concrete of the current pouring layer is poured in place at one time within a continuous preset pouring time. This can avoid the generation of joints caused by pauses in the middle and improve the pouring quality. The preset pouring time is executed according to the pouring time of each pouring layer determined in step S10.

[0136] Step S30: using a vibrator to vibrate the concrete of the current pouring layer that is being poured or completed according to the preset vibration time and vibration frequency of the pouring layer.

[0137] The purpose of this step is to remove bubbles in the concrete by vibrating and improve the density of the concrete. The specific implementation method is as follows:

[0138] First, according to the vibration time and vibration frequency of each casting layer determined in step S10, the concrete of the current casting layer is vibrated by a vibrator. The determination of the vibration time and frequency needs to comprehensively consider factors such as concrete parameters, environmental parameters, and target density. Generally, concrete with higher strength and lower water-cement ratio requires longer vibration time and higher vibration frequency.

[0139] Secondly, the concrete of the current pouring layer being poured and the current pouring layer that has been poured need to be vibrated. This is because there are still some bubbles in the concrete that has just been poured, which need to be removed by vibration. The concrete that is being poured also needs to be vibrated to ensure the density and uniformity of the entire pouring layer.

[0140] Finally, bubbles are removed by vibration and the density of concrete is improved, laying a good foundation for the subsequent maintenance stage.

[0141] Step S40: After the concrete pouring and vibrating of the current pouring layer are completed, after waiting for a predetermined static time, the pouring and vibrating of the next pouring layer are continued using steps S20-S30.

[0142] The purpose of this step is to give the concrete enough standing time after the pouring and vibration of the current pouring layer is completed, and then the next pouring layer can be constructed after the strength reaches a certain level. The specific implementation method is as follows:

[0143] First, after the concrete pouring and vibration of the current pouring layer are completed, it is necessary to wait for a certain amount of rest time. This rest time is executed according to the rest time of each pouring layer determined in step S10. The length of the rest time needs to comprehensively consider factors such as the initial setting time, final setting time, and ambient temperature of the concrete to ensure that the strength of the current pouring layer reaches a level that can withstand the weight of the upper layer.

[0144] Then, after the resting time is over, the method of steps S20-S30 is continued to continuously pour and vibrate the next pouring layer. This cycle is repeated until the concrete construction of all pouring layers is completed.

[0145] The advantage of this is that each pouring layer has enough static time to reach a certain strength, thus avoiding the problem of stress transfer between the upper and lower layers. At the same time, continuous pouring and vibration also ensure the internal structure and density of each pouring layer, laying the foundation for the overall excellent quality.

[0146] Step S50: Curing the poured concrete

[0147] The purpose of this step is to effectively maintain the poured concrete to ensure that the various performance indicators of the concrete meet the expected requirements. The specific implementation method is as follows:

[0148] First, a reasonable maintenance plan should be formulated based on environmental conditions, concrete mix ratio, construction quality requirements, etc. The maintenance plan should include maintenance time, maintenance methods (such as watering, covering with wet curtains, insulation, etc.), maintenance frequency, etc.

[0149] Secondly, the poured concrete should be continuously maintained according to the established maintenance plan. For example, in a hot and dry environment, the frequency of watering should be appropriately increased to avoid premature surface cracking; in cold seasons, covering and insulation should be adopted to prevent premature freezing of concrete.

[0150] Secondly, monitor all performance indicators of concrete during the curing process, such as compressive strength, shrinkage deformation, surface cracking, etc., and adjust the curing plan in time to ensure that all indicators meet the construction quality requirements.

[0151] Finally, after the curing period, it is necessary to continue to observe and monitor the long-term performance of concrete and take remedial measures when necessary to ensure the safety and durability of the concrete structure.

[0152] In summary, the maintenance work in step S50 runs through the entire concrete pouring construction and is a key link in ensuring the quality of the concrete structure. Only through scientific and reasonable maintenance can the various excellent properties of concrete be brought into play to the maximum extent.

[0153] Step S11: Obtain environmental parameters, construction quality requirement parameters and concrete parameters

[0154] The purpose of this step is to comprehensively collect and organize various parameters that affect the quality of continuous concrete pouring, and provide basic data support for subsequent mathematical modeling and parameter optimization. The specific implementation method is as follows:

[0155] First, obtain environmental parameters, including ambient temperature, humidity, sunlight intensity, etc. These environmental parameters will have an important impact on the hydration reaction, strength development, shrinkage and deformation of concrete, and need to be accurately mastered.

[0156] Secondly, obtain the construction quality requirement parameters, including concrete block size, compressive strength, shrinkage deformation, surface cracking, density, uniformity, etc. These parameters reflect the performance index requirements of the construction unit for the final concrete structure and are the basis for judging the pouring quality.

[0157] Secondly, obtain concrete parameters, including cement type and dosage, coarse and fine aggregate ratio, admixture type and dosage, slump, initial setting time, final setting time, etc. These parameters directly determine the mix ratio and performance of concrete and are key factors affecting the quality of pouring.

[0158] All these parameter information can be obtained through on-site measurements or by consulting relevant specifications, standards or historical construction records. The key is to collect them comprehensively and systematically to lay the foundation for subsequent mathematical modeling and parameter optimization.

[0159] Step S12: Establishing a set of equations for continuous multi-layer concrete pouring that takes into account environmental parameters, construction quality requirement parameters, concrete parameters, and pouring parameters

[0160] The purpose of this step is to establish a mathematical model that comprehensively considers various influencing factors and provides a theoretical basis for optimizing pouring parameters. The specific implementation method is as follows:

[0161] Firstly, the corresponding prediction model equations are established for the main performance indicators of concrete, such as compressive strength, shrinkage deformation, surface cracking, density, uniformity, etc. These equations need to consider the influence of various factors such as environmental parameters, concrete parameters and pouring parameters.

[0162] For example, the compressive strength equation can adopt an exponential function model that considers the effects of temperature and daily temperature difference; the shrinkage deformation equation can adopt an empirical formula model that considers the effects of humidity, size, and strength; the surface cracking equation can adopt a probabilistic model that considers the effects of stress, strength, creep, etc.; the density equation can adopt an empirical model that considers the effects of vibration time, frequency, and slump; the uniformity equation can adopt a statistical model that considers the degree of discreteness of the measuring points and the influence of the number of casting layers.

[0163] Secondly, the above performance prediction model equations are combined into a comprehensive set of equations for continuous multi-layer concrete pouring. This set of equations can not only predict various performance indicators, but also optimize the pouring parameters according to these indicators, such as the number of pouring layers, thickness of each layer, pouring rate, vibration time and frequency, and static time.

[0164] Thirdly, the unknown parameters in the equation group can be calibrated and optimized through small-scale experimental data, historical construction data, and engineering experience to ensure the prediction accuracy and applicability of the equation group.

[0165] In short, the establishment of such a comprehensive mathematical model provides a theoretical basis and quantitative basis for the subsequent optimization of continuous concrete pouring parameters.

[0166] Step S13: Calculate the pouring parameter range according to the concrete continuous multi-layer pouring equation group

[0167] The purpose of this step is to determine the value range of various casting parameters based on the mathematical model established above, laying the foundation for the next step of parameter optimization. The specific implementation method is as follows:

[0168] First, the continuous multi-layer concrete pouring equations established in step S12 are input into a computer program. The equations cover prediction models for key performance indicators such as compressive strength, shrinkage deformation, surface cracking, density, and uniformity.

[0169] Then, a reasonable value range is set for each influencing parameter in the equation group, such as environmental parameters, concrete parameters, pouring parameters, etc. This value range can be determined according to relevant specifications, standards or engineering experience.

[0170] Next, write a calculation program and use numerical analysis methods to solve the equations. By traversing the combination of various parameters, find all feasible solutions that meet the construction quality requirements and record the value range of each parameter.

[0171] Finally, the range of pouring parameters obtained is sorted out to provide a basis for subsequent parameter optimization. These parameters include the number of pouring layers, thickness of each layer, pouring rate, vibration time and frequency, and static time.

[0172] Through this step of calculation, we not only mastered the feasible value range of each parameter, but also laid the foundation for the next step of parameter clustering and optimization, which is crucial for the final determination of the optimized pouring parameter scheme.

[0173] Step S14: Cluster the casting parameter range to obtain multiple cluster centers, and use each cluster center as a candidate casting parameter.

[0174] The purpose of this step is to perform cluster analysis on the casting parameter value range obtained in step S13, find out several representative parameter combinations as candidate solutions, and provide alternative solutions for subsequent quality evaluation. The specific implementation method is as follows:

[0175] First, the range of pouring parameter values ​​calculated in step S13 is organized into a multidimensional data matrix. Each row represents a complete set of pouring parameters, including the number of pouring layers, the thickness of each layer, the pouring rate, the vibration time and frequency, the static time, etc.

[0176] Then, clustering algorithms are used to analyze this multidimensional parameter data. Methods such as density-based DBSCAN algorithm or distance-based K-Means algorithm can be used. Through cluster analysis, these parameter combinations can be divided into multiple clusters, each of which represents a casting parameter scheme with similar characteristics.

[0177] Next, a cluster center point is selected from each cluster as a representative to form several candidate pouring parameter schemes. These candidate schemes cover different pouring layers, pouring rates, vibration parameters, etc., which can better cover the original parameter range.

[0178] Finally, these candidate pouring parameter schemes are saved for subsequent use in the quality assessment model of continuous concrete pouring. The candidate schemes obtained through cluster analysis can effectively reduce the calculation amount of the model and also ensure the acquisition of a better pouring parameter combination.

[0179] Step S15: Input each candidate pouring parameter into the pre-trained concrete continuous pouring quality assessment model to obtain a pouring quality assessment vector

[0180] The purpose of this step is to use the pre-trained machine learning model to perform quality assessment on the candidate pouring parameter schemes obtained in step S14, obtain comprehensive performance indicators for each scheme, and provide a basis for the final parameter optimization. The specific implementation method is as follows:

[0181] Firstly, a quality assessment model for continuous concrete pouring is constructed. The model adopts a multi-branch neural network structure, including strength sub-network, shrinkage deformation risk sub-network, cracking risk sub-network, internal structure sub-network and fusion sub-network. Each sub-network is responsible for predicting the key performance indicators of concrete, such as compressive strength, shrinkage deformation, surface cracking risk, density and uniformity.

[0182] Secondly, the training data collected in steps S1511-S1515 are used to perform end-to-end training on the multi-branch neural network model. The mean square error loss function is used during training, and the Adam optimization algorithm is used to update the parameters. Performance evaluation is performed on the validation set, and the final concrete continuous pouring quality evaluation model is obtained after the convergence conditions are met.

[0183] Then, each candidate pouring parameter scheme obtained in step S14 is input into the trained evaluation model. The evaluation model will predict the compressive strength, shrinkage deformation, surface cracking probability, density and uniformity of the concrete based on these parameters, and combine these indicators into a comprehensive pouring quality evaluation vector.

[0184] Finally, the pouring quality evaluation vector corresponding to each candidate solution is recorded to provide a basis for subsequent parameter optimization. This evaluation vector contains the key performance indicators of concrete and can fully reflect the advantages and disadvantages of different pouring parameter solutions.

[0185] In summary, this step uses a pre-trained machine learning model to conduct a systematic quality assessment of alternative casting parameters, providing a quantitative basis for the selection of the final solution.

[0186] Step S16: Select the candidate casting parameter with the best casting quality vector as the target casting parameter

[0187] The purpose of this step is to select a set of solutions with the best comprehensive performance from the candidate pouring parameters obtained in step S15 as the final optimization target, which is used to determine the specific construction parameters for continuous concrete pouring. The specific implementation method is as follows:

[0188] First, the pouring quality evaluation vector corresponding to each candidate pouring parameter scheme obtained in step S15 is reviewed. The evaluation vector includes indicators such as the compressive strength, shrinkage deformation, surface cracking probability, density and uniformity of the concrete.

[0189] Then, these evaluation vectors are compared and analyzed. Multi-objective decision-making methods, such as TOPSIS or VIKOR, can be used to comprehensively evaluate the pros and cons of each solution based on the weight and ideal value of each indicator. In this way, an optimal solution with the highest overall score can be obtained.

[0190] Next, the pouring parameters of the optimal solution are used as the target pouring parameters to determine the specific construction plan for the continuous concrete pouring process, including the number of pouring layers, the thickness of each layer, the pouring rate, the vibration time and frequency, the static time, etc.

[0191] Finally, the determined target pouring parameters are fed back to step S10 as the basis for stratification of the continuous concrete pouring process. Through such cyclic optimization, the concrete pouring construction plan can be continuously improved and optimized to ensure the quality of the final product.

[0192] In short, this step comprehensively evaluates the candidate solutions mentioned above, selects a set of optimal pouring parameters as the target, and provides a reliable parameter basis for the continuous pouring of concrete. This is crucial to ensure the construction quality of large-volume and high-height concrete.

[0193] Furthermore, the following is a detailed description of the five equations of the concrete continuous multi-layer pouring equation group:

[0194] 1. Compressive strength equation

[0195] ;

[0196] In the formula, is the compressive strength of concrete at age t days (MPa); is the compressive strength of concrete under standard curing conditions for 28 days (MPa); is the age of concrete (days); is the cement strength grade coefficient (0.25 for ordinary Portland cement and 0.38 for rapid hardening Portland cement); is the daily temperature difference influence coefficient, usually 0.05-0.1; is the temperature influence coefficient, usually 0.001-0.003; is the actual curing temperature (°C); is the standard curing temperature, usually 20°C; It can be obtained through laboratory experiments. , , It can be determined based on experience or relevant specifications.

[0197] 2. Shrinkage deformation equation

[0198] ;

[0199] ;

[0200] for ;

[0201] for ;

[0202] ;

[0203] ;

[0204] In the formula, is the contraction strain at the age of t days; is the ultimate shrinkage strain; is the shrinkage rate parameter, usually 0.035-0.1 day^(-0.5); is the age of concrete (days); is the relative humidity (%); is the volume-to-surface ratio (mm); is the 28-day compressive strength (MPa); is the humidity influence coefficient; is the size influence coefficient; is the influence coefficient of concrete strength; and It can be obtained through on-site measurement. Determined by laboratory testing.

[0205] 3. Surface Cracking Equation

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] ;

[0211] ;

[0212] ;

[0213] ;

[0214] In the formula, is the probability of surface cracking; is the maximum tensile stress (MPa); is the tensile strength of concrete (MPa); is the shape parameter of the Weibull distribution, usually 3-4; is the elastic modulus of concrete (MPa); is the thermal expansion coefficient of concrete, usually taken as 1.0×10^(-5) / °C; is the temperature difference (°C); is the shrinkage strain, calculated by the shrinkage deformation equation; is the relaxation factor; is the creep coefficient; is the current age (days); is the load action start time (day); is the base creep coefficient, usually taken as 2.35; is the imaginary thickness, (mm), is the cross-sectional area, is the circumference in contact with the air; is the relative humidity (%); It can be measured by temperature sensors, and other parameters can be determined through experiments or experience.

[0215] 4. Density equation

[0216] ;

[0217] ;

[0218] ;

[0219] In the formula, is the density of concrete; is the initial density; is the vibration efficiency coefficient; is the vibration time (s); is the vibration frequency (Hz); is the slump influence coefficient, usually 0.05-0.1; is the actual slump (mm); It is the reference slump, usually 100mm; is the vibration efficiency benchmark coefficient; is the activation energy (J / mol), usually 40000-50000 J / mol; is the gas constant, 8.314 J / (mol·K); is the concrete temperature (K); , is an empirical coefficient related to aggregate grading, usually Take 0.5-0.7, Take 0.3-0.5; is the water-cement ratio; and It can be obtained through the parameters of the vibration equipment. Through on-site measurements, Measured by temperature sensors, other parameters are determined experimentally or empirically.

[0220] 5. Uniformity equation

[0221] ;

[0222] ;

[0223] ;

[0224] In the formula, is the uniformity of concrete; is the measured value of the i-th measuring point (such as intensity, density, etc.); is the average value of the measured values; is the number of measuring points; is the layer number influence coefficient, usually 0.05-0.1; is the number of pouring layers; is the total pouring height (m); is the thickness of the casting layer (m); It can be obtained through non-destructive testing (such as rebound method, ultrasonic method, etc.), and Determined by construction parameters.

[0225] These equations comprehensively consider the influence of environmental parameters, concrete parameters and pouring parameters on the quality of continuous pouring of large-volume and high-height concrete. Through these equations, various performance indicators of concrete can be predicted and evaluated, providing a theoretical basis for optimizing pouring parameters.

[0226] Here are the parameters and the steps to obtain them, listed in the order of the equation:

[0227] 1. Compressive strength equation

[0228] a) (Concrete compressive strength under 28-day standard curing conditions):

[0229] 1) Make concrete specimens of standard size (usually cubes of 150mm×150mm×150mm).

[0230] 2) Curing for 28 days under standard curing conditions (temperature 20±2°C, relative humidity ≥95%).

[0231] 3) Use a pressure testing machine to perform compressive strength test.

[0232] 4) Repeat the test for at least 3 specimens and take the average value as .

[0233] b) (Cement strength grade coefficient):

[0234] 1) Consult relevant specifications or technical literature, such as Code for Design of Concrete Structures (GB 50010).

[0235] 2) Select the corresponding coefficient value according to the type of cement used.

[0236] Ordinary Portland cement: s = 0.25

[0237] Rapid hardening Portland cement: s = 0.38

[0238] c) (daily temperature difference influence coefficient) and (Temperature influence coefficient):

[0239] 1) Conduct multiple groups of concrete strength tests under different temperature conditions.

[0240] 2) Record daily temperature difference and curing temperature, as well as corresponding strength development data.

[0241] 3) Using nonlinear regression analysis, fit the compressive strength equation and obtain and The optimal value of .

[0242] 4) If it is not possible to conduct a large number of tests, the recommended values ​​in the literature can be referred to: Take 0.05-0.1, Take 0.001-0.003.

[0243] 2. Shrinkage deformation equation

[0244] a) (Shrinkage rate parameters):

[0245] 1) Make concrete prism specimens of standard size (usually 100mm×100mm×515mm).

[0246] 2) Measure shrinkage deformation at different ages under a controlled environment (temperature 20±2°C, relative humidity 60±5%).

[0247] 3) Use nonlinear regression analysis to fit the shrinkage deformation equation and obtain The optimal value of .

[0248] 4) Usually The value range is 0.035-0.1 between.

[0249] 3. Surface Cracking Equation

[0250] a) (Weibull distribution shape parameter):

[0251] 1) Conduct a large number of concrete cracking tests and record cracking stress data.

[0252] 2) Use Weibull probability plot method or maximum likelihood estimation method to determine value.

[0253] 3) If a large number of tests cannot be conducted, the values ​​recommended in the literature can be used, which are usually between 3 and 4.

[0254] b) (Coefficient of thermal expansion of concrete):

[0255] 1) Make concrete specimens of standard size (such as prisms of 100mm×100mm×515mm).

[0256] 2) Measure the length change of the specimen at different temperatures.

[0257] 3) Calculate the rate of change of length caused by unit temperature change, which is the coefficient of thermal expansion.

[0258] 4) If testing is not possible, the commonly used value of 1.0×10^(-5) / °C may be used.

[0259] c) (Base creep coefficient):

[0260] 1) Conduct standard creep test and record creep deformation at different ages.

[0261] 2) Use creep prediction models (such as CEB-FIP model) to fit the test data.

[0262] 3) Determine the best way to describe the actual creep behavior value.

[0263] 4) If long-term testing is not possible, the commonly used value of 2.35 may be used.

[0264] 4. Density equation

[0265] a) (Vibration efficiency benchmark coefficient):

[0266] 1) Prepare concrete specimens at different vibration times and frequencies.

[0267] 2) Measure the density of the specimen (can be tested by bulk density or ultrasonic testing).

[0268] 3) Using nonlinear regression analysis, fit the density equation and obtain The optimal value of .

[0269] b) (Activation Energy):

[0270] 1) Carry out concrete density test at different temperatures.

[0271] 2) Draw the Arrhenius plot of ln(k) vs. 1 / T.

[0272] 3) Calculate based on the slope of the straight line in the figure .

[0273] 4) If detailed experiments are not possible, the values ​​recommended in the literature can be used, which are usually between 40,000 and 50,000 J / mol.

[0274] c) and (Empirical coefficient related to aggregate gradation):

[0275] 1) Conduct concrete mix tests with different water-cement ratios and aggregate gradations.

[0276] 2) Measure the initial density of each test group .

[0277] 3) Using nonlinear regression analysis, fitting equation, we get and The optimal value of .

[0278] 4) If it is not possible to conduct a large number of tests, the recommended values ​​in the literature can be referred to: Usually 0.5-0.7, Usually 0.3-0.5 is taken.

[0279] d) (Slump influence coefficient):

[0280] 1) Prepare concrete specimens with different slumps and keep other parameters unchanged.

[0281] 2) Measure the density of each group of specimens.

[0282] 3) Use linear regression analysis to determine the logarithmic relationship between slump and density, and get value.

[0283] 4) If detailed testing is not possible, empirical values ​​may be used, usually between 0.05 and 0.1.

[0284] 5. Uniformity equation

[0285] a) (Influence coefficient of number of layers):

[0286] 1) Carry out concrete tests with different numbers of pouring layers, keeping the total height unchanged.

[0287] 2) Take samples in each layer and measure target performance indicators (such as strength, density, etc.).

[0288] 3) Calculate the uniformity of each group of tests .

[0289] 4) Using nonlinear regression analysis, fitting the uniformity equation, we get The optimal value of .

[0290] 5) If a large number of tests cannot be carried out, empirical values ​​can be used, usually between 0.05-0.1.

[0291] These steps provide a basic method for determining each parameter. In actual application, it may be necessary to adjust the test plan according to the specific situation and available resources. For some parameters that are difficult to determine through experiments, you can refer to the recommended values ​​in relevant literature and industry standards, or estimate them by comparing with similar projects.

[0292] The steps to calculate the pouring parameter range using the concrete coupling model are as follows:

[0293] 1. Establish concrete coupling model:

[0294] Combine the five equations mentioned above (compressive strength equation, shrinkage deformation equation, surface cracking equation, density equation and uniformity equation) into a coupled model. Define the objective function It is the weighted sum of these five indicators:

[0295] ;

[0296] in:

[0297] is the weight coefficient, satisfying ;

[0298] is the compressive strength at the age of t days;

[0299] is the contraction strain at age t days;

[0300] is the probability of surface cracking;

[0301] is the density of concrete;

[0302] is the uniformity of concrete;

[0303] 2. Determine the optimization variables:

[0304] Select key pouring parameters as optimization variables, such as:

[0305] is the water-cement ratio (w / c);

[0306] is the vibration time ( );

[0307] is the vibration frequency ( );

[0308] is the thickness of the casting layer ( );

[0309] 3. Set constraints:

[0310] According to the actual project and specification requirements, set the value range of each parameter:

[0311] ;

[0312] ;

[0313] ;

[0314] ;

[0315] At the same time, set the constraints of the performance indicators:

[0316] ;

[0317] ;

[0318] ;

[0319] ;

[0320] ;

[0321] 4. Construct optimization problem:

[0322] Integrate steps 1-3 into an optimization problem:

[0323] maximize: ;

[0324] Satisfy constraints: All constraints listed in step 3;

[0325] 5. Select the optimization algorithm:

[0326] Considering the nonlinear nature of the problem, choose an appropriate optimization algorithm, such as genetic algorithm (GA) or particle swarm optimization (PSO). Take GA as an example:

[0327] a) Encoding: Encode the optimization variables into chromosomes. Each variable can be represented by a certain number of binary digits.

[0328] b) Initialize the population: randomly generate N chromosomes that meet the constraints.

[0329] c) Fitness evaluation: For each chromosome, calculate its fitness value .

[0330] d) Selection: Use roulette wheel selection method to select outstanding individuals.

[0331] e) Crossover: Select the crossover probability , perform a single-point crossover operation on the selected individuals.

[0332] f) Mutation: Select the probability of mutation , random position mutation is performed on individuals.

[0333] g) Elite retention: retain the best individuals in the current population to the next generation.

[0334] h) Repeat steps cg until the maximum number of iterations is reached or the termination condition is met.

[0335] 6. Implement optimization calculations:

[0336] Use programming languages ​​such as MATLAB or Python to implement the above optimization algorithm. Take MATLAB as an example:

[0337]

[0338] 7. Result analysis and verification:

[0339] a) Obtaining the optimal pouring parameters .

[0340] b) Calculate the performance indicators under the optimal parameters: , , , , .

[0341] c) Conduct sensitivity analysis to evaluate the impact of each parameter on the optimization results:

[0342] Calculate the sensitivity coefficient:

[0343] d) Determine the reasonable range of pouring parameters:

[0344] With the optimal value as the center, the variation range of each parameter is determined according to the sensitivity coefficient so that the performance index still meets the requirements.

[0345] For example, for the water-cement ratio :

[0346]

[0347] in Determined by the following formula:

[0348]

[0349] The permissible performance reduction ratio, such as 5%.

[0350] e) Conduct experimental verification:

[0351] Several combinations within the parameter range were selected and small-scale tests were conducted to verify the accuracy of the model predictions.

[0352] 8. Develop a pouring parameter guidance plan:

[0353] According to the results of step 7 and the actual situation of the project, formulate a guiding plan for pouring parameters:

[0354] Water-cement ratio (w / c): ;

[0355] Vibration time( ): Second;

[0356] Vibration frequency( ): Hz;

[0357] Casting layer thickness ( ): rice;

[0358] Next, calculate the two parameters of static time and pouring time:

[0359] 1. Calculation of static time

[0360] The rest time refers to the time you need to wait after pouring a layer of concrete before pouring the next layer. This time mainly depends on the initial setting time of the concrete and the construction environment.

[0361] Calculation steps:

[0362] a) Determine the initial setting time of concrete :

[0363] Determined by Vicat test, unit is minutes.

[0364] b) Consider the temperature effect:

[0365] Use the modified Arrhenius equations:

[0366] ;

[0367] In the formula, is the initial coagulation time at temperature T (minutes); is the initial setting time (minutes) at standard temperature (usually 20°C); is the apparent activation energy (J / mol), which is about 33500 J / mol for ordinary Portland cement; is the gas constant, 8.314 J / (mol·K); is the actual ambient temperature (K); is standard temperature, 293.15 K (20°C);

[0368] c) Consider the influence of cement dosage:

[0369] ;

[0370] In the formula, is the initial setting time after considering the cement dosage (minutes); is the standard cement dosage, usually 350 kg / m³; is the actual cement consumption (kg / m³); It is an empirical coefficient, usually ranging from 0.5 to 0.7;

[0371] d) Determine the resting time:

[0372] ;

[0373] In the formula, is the standing time (minutes); is the safety factor, usually 0.7~0.9;

[0374] 2. Calculation of pouring time

[0375] The pouring time is the time required to complete a pouring layer. This time depends on the pouring method, equipment efficiency and concrete volume.

[0376] Calculation steps:

[0377] a) Determine the volume of the casting layer (m³);

[0378] b) Determine the efficiency of the pouring equipment (m³ / h): This is usually determined by equipment specifications and site conditions;

[0379] c) Consider the construction efficiency coefficient : This coefficient takes into account various possible delay factors and is usually between 0.6 and 0.8;

[0380] d) Calculate the theoretical pouring time:

[0381] ;

[0382] In the formula, is the theoretical pouring time (hours);

[0383] e) Consider the construction interruption time: If the interruption is caused by equipment movement, concrete transportation, etc., the total interruption time is (Hour);

[0384] f) Calculate the actual pouring time:

[0385] ;

[0386] In the formula, is the actual pouring time (hours);

[0387] g) Check whether the pouring time meets the requirements:

[0388] The pouring time should be less than the initial setting time of concrete, that is:

[0389] ;

[0390] If it is not satisfied, measures such as pouring in batches or using retarders need to be considered.

[0391] 3. Optimization of pouring time plan

[0392] In order to ensure the quality of concrete and construction efficiency, it is necessary to coordinate the standing time and pouring time:

[0393] a) Define the interval time between continuous pouring :

[0394] ;

[0395] b) Optimization objectives:

[0396] minimize , while ensuring ;

[0397] c) Adjustment method:

[0398] if , consider increasing equipment efficiency ;

[0399] if Too large, consider reducing the safety factor ;

[0400] d) Iterate the calculation until a satisfactory value.

[0401] Through the above steps, the static time and pouring time can be calculated and optimized to ensure the quality and efficiency of concrete construction. In actual applications, more on-site factors may need to be considered, such as weather conditions, labor arrangements, etc., and the calculation results may need to be adjusted appropriately.

[0402] This solution provides an operational parameter range for on-site construction while ensuring that various performance indicators of concrete meet the requirements.

[0403] The entire process of the present invention involves comprehensive consideration of multiple factors such as environmental parameters, construction quality requirements, concrete mix ratio, etc., and uses technical means such as mathematical modeling, parameter clustering, and machine learning to ultimately determine an optimized pouring construction plan.

[0404] Specifically, the principle of the present invention is to establish a comprehensive mathematical model, analyze various influencing factors, accurately predict and control the key performance indicators of concrete, thereby optimizing the pouring parameters and ensuring the construction quality of large-volume and high-height concrete structures. The specific principles are as follows:

[0405] First, the present invention fully considers the influence of many factors such as environmental parameters, construction quality requirement parameters and concrete parameters on the performance of concrete. Environmental parameters include temperature, humidity, sunlight intensity, etc., which directly affect the hydration reaction and strength development of concrete; construction quality requirement parameters include size, compressive strength, shrinkage deformation, density, etc., which reflect the user's expectations of concrete performance; concrete parameters such as cement type, aggregate ratio, admixture dosage, etc., determine the inherent characteristics of concrete. By comprehensively modeling these factors, the actual performance of concrete can be predicted more accurately.

[0406] Secondly, the present invention establishes mathematical prediction models covering key indicators such as compressive strength, shrinkage deformation, surface cracking, density, uniformity, etc. These models use a combination of empirical formulas and statistical analysis methods, taking into account both theoretical foundations and engineering practice data, and can more accurately simulate the changes in various properties of concrete during the pouring process. For example, the compressive strength model takes into account the effects of temperature and daily temperature difference, the shrinkage deformation model includes humidity and size factors, and the surface cracking model integrates multiple effects such as stress, strength and creep.

[0407] Again, based on the above prediction model, the present invention proposes a method for determining pouring parameters with multi-index optimization. First, the parameter value range that meets the quality requirements is obtained by calculation, including the number of pouring layers, the thickness of each layer, the pouring rate, the vibration time frequency, the static time, etc.; then, cluster analysis is used to select several groups of representative candidate parameter schemes; finally, the pre-trained quality assessment model is used to compare the comprehensive performance of these schemes, and the optimal parameter combination is selected as the target pouring parameter. In this way, the requirements of each performance index are met, and the coordinated optimization of parameters is taken into account.

[0408] Finally, in actual construction, the present invention adopts the method of continuous pouring and precise vibration to ensure the density and uniformity of each pouring layer and avoid the occurrence of joint problems. At the same time, various performance indicators are monitored in real time during the pouring process, and adjustments are made in time to maximize the excellent performance of concrete.

[0409] In order to better understand and implement the present invention, an embodiment of a specific application scenario of the present invention is provided below: a certain project is a super high-rise building up to 120 meters, which adopts a reinforced concrete frame structure. Among them, the 4 underground floors and 1 above-ground floor are large-volume and high-height concrete structures, with a total pouring volume of about 12,000 cubic meters. In order to ensure the construction quality of this part of large-volume and high-height concrete structure, the project adopts a concrete continuous pouring construction method proposed by the present invention.

[0410] 1. Preliminary preparations

[0411] 1. Get environment parameters

[0412] First, the environmental parameters of the construction site were monitored and recorded. Through on-site measurements and meteorological data query, the following information was obtained:

[0413] - Ambient temperature: 30°C in summer and 5°C in winter, with a daily temperature difference of 15-20°C;

[0414] - Relative humidity: 40%-90%, with large changes;

[0415] - Sunshine intensity: average 650W / ㎡, maximum up to 1000W / ㎡.

[0416] 2. Determine construction quality requirements

[0417] According to the design documents and specification requirements, the main quality indicators of large-volume and high-height concrete structures are determined as follows:

[0418] - Compressive strength: standard 28-day curing strength is not less than 40MPa;

[0419] - Shrinkage deformation: the ultimate shrinkage strain does not exceed 500 microstrain (unit of microstrain);

[0420] - Surface cracking: The probability of cracking shall not exceed 10%;

[0421] - Density: Concrete density shall not be less than 95%;

[0422] - Uniformity: The coefficient of dispersion of indicators such as concrete strength and density shall not exceed 5%.

[0423] 3. Determine concrete parameters

[0424] After many mix ratio tests, the concrete mix ratio suitable for this project was determined as follows:

[0425] - Cement type: PO 42.5 grade Portland cement;

[0426] - Water-cement ratio: 0.35;

[0427] - Sand ratio: 40%;

[0428] - Admixture: polycarboxylic acid high performance water reducing agent, dosage 1.2%;

[0429] - Slump: 180-220mm;

[0430] - Initial setting time: 4-6 hours;

[0431] - Final setting time: 8-10 hours.

[0432] 4. Build a mathematical model

[0433] According to the obtained environmental parameters, construction quality requirement parameters and concrete parameters, the following mathematical prediction model is adopted:

[0434] (1) Compressive strength model:

[0435]

[0436] In the formula, is the compressive strength of concrete at age t days (MPa), is the compressive strength under 28-day standard curing conditions (MPa), is age (days), is the cement strength grade coefficient (take 0.25), is the daily temperature difference influence coefficient (take 0.08), is the temperature influence coefficient (take 0.002), is the actual curing temperature (°C), The standard curing temperature is 20°C.

[0437] (2) Shrinkage deformation model:

[0438]

[0439]

[0440] (for )

[0441]

[0442]

[0443] In the formula, is the contraction strain at t days of age, is the ultimate shrinkage strain, is the shrinkage rate parameter (take 0.045 days^(-0.5)), is the relative humidity (%), is the volume-to-surface ratio (taking 250 mm), It is the 28-day compressive strength (MPa).

[0444] (3) Surface cracking model:

[0445]

[0446]

[0447]

[0448]

[0449]

[0450]

[0451]

[0452]

[0453] In the formula, is the surface cracking probability, is the maximum tensile stress (MPa), is the tensile strength (MPa), is the coefficient of thermal expansion (1.0×10^(-5) / °C), is the temperature difference (°C), is the contraction strain, is the elastic modulus (MPa), is the creep coefficient, is the imaginary thickness (taken as 650mm).

[0454] (4) Density model:

[0455]

[0456]

[0457]

[0458] In the formula, is the density of concrete, is the vibration efficiency coefficient, is the vibration time (take 120s), is the vibration frequency (take 50Hz), is the slump influence coefficient (take 0.08), is the actual slump (take 200mm), is the base slump (100mm), is the base vibration coefficient (take 0.6), is the activation energy (taken as 45000 J / mol), is the concrete temperature (take 293K), and is the empirical coefficient.

[0459] (5) Uniformity model:

[0460]

[0461]

[0462]

[0463] In the formula, is the uniformity of concrete, is the measured value of the i-th measuring point, is the average value of the measured value, is the number of measuring points (9 are taken), is the layer number influence coefficient (take 0.07), is the number of pouring layers, is the total pouring height (take 6m), is the thickness of each layer (take 0.5m).

[0464] 2. Parameter optimization and construction plan determination

[0465] 1. Determine the range of pouring parameters

[0466] The above mathematical model was input into the computer program, and a reasonable value range was set for each influencing parameter. Through numerical analysis, the following casting parameter range that meets the quality requirements was obtained: number of casting layers: 12 layers; thickness of each layer: 0.5m; casting rate: 1m / h; vibration time: 120s; vibration frequency: 50Hz; static time: 4h;

[0467] 2. Parameter Clustering and Evaluation

[0468] A cluster analysis was performed on the above casting parameter range, and the K-Means algorithm was used to divide it into 6 cluster centers, which were used as candidate casting parameter schemes.

[0469] Then, these six candidate solutions are input into the pre-trained concrete continuous pouring quality assessment model for evaluation. The model adopts a multi-branch neural network structure, including a strength sub-network, a shrinkage deformation risk sub-network, a cracking risk sub-network, an internal structure sub-network, and a fusion sub-network, which can comprehensively predict various performance indicators of concrete. After evaluation, the pouring quality assessment vector corresponding to each candidate solution is obtained, as shown in Table 1 below:

[0470] Table 1 Evaluation information table

[0471]

[0472] 3. Choose the best solution

[0473] After analysis and comparison, Scheme 3 was selected as the final optimized casting parameters, which has the best overall performance and can meet various quality requirements.

[0474] 3. Construction process

[0475] 1. Continuous pouring

[0476] According to the determined pouring parameters, the large volume and high height concrete is poured continuously in 12 layers. The pouring height of each layer is 0.5 meters, the pouring rate is 1 meter / hour, and the current pouring layer is poured in place within a continuous time of 4 hours.

[0477] 2. Precise vibration

[0478] During and after pouring, the concrete was vibrated with a 30 kW vibrator at 50 Hz for 120 seconds. The frequency and time of vibration were determined based on the density model prediction to achieve a target density of more than 95%.

[0479] 3. Maintenance monitoring

[0480] After pouring is completed, measures such as sprinkling water and covering with wet curtains are taken for continuous maintenance according to environmental conditions. At the same time, temperature sensors and rebound testers are arranged in the pouring layer to monitor the strength development, shrinkage deformation and other indicators of concrete in real time. Once deviation from expectations is found, the maintenance measures are adjusted immediately.

[0481] Through the above-mentioned construction methods of continuous pouring, precise vibration and full-process monitoring, the final quality indicators of the large-volume and high-height concrete structure of this project have reached:

[0482] - Compressive strength: 44.5MPa, meeting the requirement of ≥40MPa;

[0483] - Shrinkage deformation: 480 microstrain, meeting the requirement of ≤500 microstrain;

[0484] - Surface cracking probability: 7%, meeting the requirement of ≤10%;

[0485] - Density: 96.2%, meeting the requirement of ≥95%;

[0486] - Uniformity: 94.3%, meeting the requirement of ≤5% coefficient of dispersion.

[0487] Compared with the traditional overall block method, the construction method of the present invention not only avoids the joint problem, but also significantly improves various performance indicators compared with conventional construction. In particular, the compressive strength and density are improved by 10% and 6% respectively compared with the traditional method. This fully demonstrates the significant advantages of the present method in improving the construction quality of large-volume and high-height concrete.

[0488] At the same time, during the construction process, through real-time monitoring and timely adjustments, not only did it ensure that various indicators were met, but it also further brought into play the excellent performance of concrete, greatly improving the safety and service life of the overall structure.

[0489] To sum up, the continuous concrete pouring construction method proposed in the present invention fully integrates a number of advanced technologies such as mathematical modeling, parameter optimization, and process control, and realizes the accurate prediction and effective control of key performance indicators of large-volume and large-height concrete.

[0490] 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 large-volume and high-height continuous pouring construction method of concrete, characterized in that: The following steps are involved: S10, dividing the continuous concrete pouring process into multiple pouring layers according to the acquired environmental parameters, construction quality requirement parameters and concrete parameters; S20, for each pouring layer, continuously pouring the current pouring layer by using a continuous pouring method, that is, pouring the concrete of the current pouring layer in place at one time within a continuous preset pouring time period; S30, using a vibrator to vibrate the concrete of the current pouring layer during pouring and after pouring according to the preset vibration time and vibration frequency of the pouring layer to remove bubbles and improve density; S40, after completing the concrete pouring and vibration of the current pouring layer, after waiting for a predetermined static time, continue to perform the pouring and vibration of the next pouring layer using steps S20-S30 until the concrete pouring and vibration of all pouring layers are completed; S50, curing the poured concrete.

2. A large-volume and large-height continuous pouring construction method for concrete according to claim 1, characterized in that: The specific steps of dividing the continuous concrete pouring process into multiple pouring layers according to the environmental parameters, the construction quality requirement parameters and the concrete parameters include: S11, obtaining environmental parameters, construction quality requirement parameters and concrete parameters, wherein the environmental parameters include ambient temperature, humidity and sunshine intensity; the construction quality requirement parameters include concrete block size, compressive strength, shrinkage deformation, surface cracking, density and uniformity; the concrete parameters include cement type and amount, coarse and fine aggregate ratio, admixture type and amount, slump, initial setting time and final setting time; S12, establishing a set of equations for continuous multi-layer concrete pouring that takes into account environmental parameters, construction quality requirement parameters, concrete parameters, and pouring parameters, including a compressive strength equation, a shrinkage deformation equation, a surface cracking equation, a density equation, and a uniformity equation; the pouring parameters include: the number of pouring layers and the thickness of each pouring layer, pouring rate, vibration time, vibration frequency, static time, and pouring time; S13, calculating the pouring parameter range according to the concrete continuous multi-layer pouring equation group; S14, clustering the casting parameter range to obtain multiple cluster centers, and taking each cluster center as a candidate casting parameter; S15, inputting each candidate pouring parameter into a pre-trained concrete continuous pouring quality assessment model to obtain a pouring quality assessment vector; S16. Selecting a candidate pouring parameter with an optimal pouring quality vector as a target pouring parameter, which is used to divide the continuous concrete pouring process into multiple pouring layers, and to determine the pouring time and standing time of each pouring layer.

3. A large-volume and large-height continuous pouring construction method for concrete according to claim 2, characterized in that: The large-volume and high-height concrete specifically refers to: a concrete structure with a volume greater than 1,000 cubic meters, or a thickness greater than 2 meters, or a minimum size greater than 1 meter and a cementitious material content greater than 300 kg / m³ in the concrete mixture.

4. A large-volume and large-height continuous pouring construction method for concrete according to claim 3, characterized in that: The specific casting quality assessment vectors are: [compressive strength, shrinkage deformation, surface cracking, density, uniformity].

5. A large-volume and large-height continuous pouring construction method for concrete according to claim 4, characterized in that: The quality assessment model for continuous concrete pouring adopts a multi-branch structure, including a strength sub-network, a shrinkage deformation risk sub-network, a cracking risk sub-network, an internal structure sub-network, and a fusion sub-network.

6. A large-volume and large-height continuous pouring construction method of concrete according to claim 5, characterized in that: The strength sub-network is used to predict the development of concrete strength. The input is environmental parameters and concrete parameters. The output is the predicted strength. The structure is a three-layer fully connected network. The shrinkage deformation risk sub-network is used to evaluate the shrinkage deformation of concrete. The input is concrete parameters and pouring parameters, and the output is shrinkage deformation. The structure is a three-layer fully connected network. The cracking risk sub-network is used to evaluate the risk of concrete cracking. The input is environmental parameters, concrete parameters, and shrinkage deformation. The output is a cracking risk index. The structure is a two-layer convolutional network plus a one-layer fully connected network. The internal structure subnetwork is used to evaluate the internal structure of concrete. The input is concrete parameters, pouring parameters, and shrinkage deformation. The output is density and uniformity. The structure is a one-layer convolutional network plus a two-layer fully connected network.

7. A large-volume and large-height continuous pouring construction method for concrete according to claim 6, characterized in that: The fusion sub-network is used to integrate the outputs of each sub-network. The input of the fusion sub-network is the output of each sub-network. The output of the fusion sub-network is the final casting quality assessment vector. The structure is a two-layer fully connected network.

8. A large-volume and large-height continuous pouring construction method of concrete according to claim 7, characterized in that: The training steps of the concrete continuous pouring quality assessment model specifically include: constructing a training data set and training a model; Among them, constructing a training data set includes: S1511. Acquire experimental data of multiple groups of small-scale experiments. Specifically, the small-scale experiments are: under laboratory conditions, using different mix ratios and construction parameters, making concrete test blocks of standard sizes, simulating the pouring and solidification process of large-volume concrete, and performing various performance tests at different ages. The experimental data include: environmental parameters, concrete parameters, pouring parameters, and actually detected pouring quality assessment vectors; S1512, obtaining multiple sets of historical construction data of large-volume and high-height continuous concrete pouring, wherein the historical construction data specifically includes: environmental parameters, concrete parameters, pouring parameters, and actually detected pouring quality assessment vectors; S1513. Clean and preprocess the acquired experimental data and historical construction data to remove abnormal values ​​and redundant data; S1514, normalizing the processed data to obtain normalized data; S1515. Randomly divide the normalized data into a training set, a validation set, and a test set in a ratio of 7:2:1 to obtain a training data set.

9. A large-volume and large-height continuous pouring construction method of concrete according to claim 8, characterized in that: The specific steps of training the model include: S1521, initializing the neural network parameters of the multi-branch structure, including the weight and bias of each sub-network; S1522, using the training set data for forward propagation, calculating the output of each sub-network and the final casting quality assessment vector; S1523, calculating the loss between the predicted result and the true label, using the mean square error as the loss function; S1524, use the back propagation algorithm to calculate the gradient, and use the Adam optimizer to update the network parameters; S1525. Evaluate the model performance on the validation set. If the performance does not improve after multiple epochs, start the early stopping mechanism. S1526. Repeat steps S1522-S1525 until the preset number of training rounds is reached or the early stopping condition is met. Finally, the generalization ability of the model is evaluated on the test set to obtain the final concrete continuous pouring quality assessment model.

10. A large-volume and large-height continuous pouring construction method of concrete according to claim 8, characterized in that: The concrete test block of standard size is specifically a concrete test block of 150 mm×150 mm×150 mm.

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