Crack control construction method for ten-thousand-square-meter-level high-precision heavy-load air-floating terrace

Abstract

The invention provides a ten-thousand-square-meter-level high-precision heavy-load air-floating floor crack control construction method, and belongs to the technical field of air-floating floor cracks. Bearing uniformity optimization is achieved by establishing an air-floating hole layout multi-target topological optimization model, construction joint constraint stress is reduced through cooperation of skip-bay pouring and a double-layer cooling pipe network, and the construction efficiency is improved. Coupling regulation and control of a temperature field and a humidity field are achieved through a cooling water circulation temperature control and spray maintenance collaborative game optimization model, shrinkage deformation is compensated through an expansion strengthening band, a crack expansion path is predicted through a phase field simulation analysis system, and maintenance parameters are dynamically adjusted. The technical problem that the crack control precision of a 10000-square-meter heavy-load air floating floor is insufficient under the multi-factor coupling effect is solved.

Description

Technical Field

[0001] This invention belongs to the field of air-floor floor cracking technology, specifically, it relates to a construction method for controlling cracks in air-floor floors with high precision and heavy load at the 10,000-square-meter level. Background Technology

[0002] In large-scale logistics warehousing and precision manufacturing, heavy-duty air-floored concrete flooring covering tens of thousands of square meters achieves low-friction movement of equipment through air-floored hole arrays, requiring extremely high flatness and crack control precision. Traditional construction methods employ a technical approach of uniformly arranging air-floored holes, continuously pouring concrete, and using a single temperature control measure. However, in actual engineering projects, this approach faces multiple problems, including uneven load-bearing capacity, concentrated temperature stress, and surface drying cracking. Existing technologies suffer from uneven pressure distribution due to insufficient optimization of the air-floored hole layout, a lack of coordination between temperature control and humidity curing, and weak crack propagation prediction capabilities. This makes it difficult to effectively control temperature cracks caused by hydration heat, plastic shrinkage cracks due to surface dehydration, and constraint stress cracks at construction joints during large-area flooring construction. In other words, existing technologies suffer from insufficient crack control precision due to the coupled effects of multiple factors. Summary of the Invention

[0003] In view of this, the present invention provides a construction method for controlling cracks in heavy-duty air-floor concrete with high precision at the 10,000-square-meter level, which can solve the technical problem of insufficient crack control precision in the existing technology for heavy-duty air-floor concrete with 10,000-square-meter level under the coupled effect of multiple factors.

[0004] This invention is implemented as follows: It provides a construction method for controlling cracks in high-precision, heavy-duty air-floor concrete flooring with a capacity of tens of thousands of square meters. This includes: establishing a multi-objective topology optimization model for the air-floor hole layout and using an improved NSGA-III algorithm for iterative optimization to obtain the air-floor hole layout scheme; establishing a three-dimensional coordinate database using an RTK-GPS base station and a laser total station, and performing automatic drilling operations using a six-degree-of-freedom robotic arm; dividing the flooring into cell units and adopting a skip-cell pouring sequence, with HDPE cooling pipe networks pre-embedded simultaneously during the first batch of cell unit concrete pouring; establishing a collaborative game optimization model for cooling water circulation temperature control and spray curing to obtain a collaborative optimization parameter combination; activating a temperature monitoring system after concrete pouring, and introducing cooling water when the internal and external temperature difference reaches a threshold; deploying an infrared temperature and humidity sensor array for surface microenvironment monitoring and activating an intelligent spray curing system; setting an expansion reinforcement zone in the construction joint area; and establishing a crack propagation path phase-field simulation analysis system, adjusting curing parameters when the simulated predicted crack propagation rate exceeds a threshold.

[0005] Specifically, the establishment of the multi-objective topology optimization model for the air-bearing hole layout involves taking the air-bearing hole position as a design variable and setting objective functions for maximizing load uniformity, minimizing the number of holes, and optimizing edge distance.

[0006] Specifically, the implementation of the improved NSGA-III algorithm involves generating an initial population, performing non-dominated sorting and crowding calculation, using tournament selection for the selection operation, employing a simulated binary crossover operator for the crossover operation, and using an adaptive mutation operator for the mutation operation.

[0007] Specifically, the selection of the air flotation hole layout scheme involves screening out a set of solutions with pressure non-uniformity less than a set value from multiple non-dominated solutions at the Pareto front, and then selecting the scheme with the fewest holes from the set of solutions that meet the pressure uniformity requirements.

[0008] Specifically, the drilling control of the six-degree-of-freedom robotic arm is achieved through real-time pose feedback via a vision system and a laser rangefinder, three-point positioning calibration before drilling, and real-time monitoring of the drill bit's force and vibration during drilling.

[0009] After drilling is completed, the hole position coordinates are verified by three-dimensional scanning. For holes with deviations exceeding the set value, a combination of hole enlargement and filling is used for correction.

[0010] The HDPE cooling pipe network is arranged in a double-layer serpentine pattern. The distances between the upper pipe and the concrete surface and between the lower pipe and the bottom are determined by finite element heat conduction simulation analysis.

[0011] Specifically, the determination of the skip-pouring sequence involves using finite element structural analysis software to establish a floor shrinkage stress model, simulating the stress distribution under different pouring sequences, and selecting the scheme that minimizes the stress concentration coefficient at the construction joint.

[0012] The collaborative game optimization model is a two-layer optimization structure. The upper-layer cooling optimization model determines the circulating water flow rate, circulating water temperature, and cooling duration, while the lower-layer maintenance optimization model determines the spray frequency, droplet size, and spray duration.

[0013] The upper cooling optimization model and the lower maintenance optimization model interact with each other by using the surface temperature drop as a coupling term, and are solved iteratively using the Stackelberg game framework.

[0014] The temperature monitoring system collects internal temperature data in real time through a pre-embedded thermocouple sensor array, controls the cooling rate within a set range, and covers the surface with an insulation blanket to maintain the internal and external temperature difference less than a set value.

[0015] Specifically, the control logic of the intelligent spray maintenance system involves an infrared temperature and humidity sensor array collecting data at set intervals and transmitting it to the central controller. Spraying is activated when the relative humidity is below a set threshold and the surface temperature is above the set ambient temperature.

[0016] The expansion agent is added to the concrete within the expansion reinforcement strip. The amount of expansion agent is determined by preparing concrete specimens with different admixture amounts and testing the restricted expansion rate and compressive strength.

[0017] The expansion reinforcement strip has reserved shrinkage deformation space on both sides. The width of the shrinkage deformation space is determined by calculation based on the concrete shrinkage strain, constraint release coefficient and safety factor.

[0018] The phase field simulation analysis system introduces phase field variables to characterize the crack state evolution process and uses implicit Euler time integration scheme and quadrilateral isoparametric elements for finite element discretization.

[0019] The crack propagation rate threshold is determined by collecting on-site monitoring data of early crack propagation in concrete and performing statistical analysis using digital image correlation techniques.

[0020] This invention achieves synergistic optimization of load-bearing uniformity and hole count by establishing a multi-objective topology optimization model for the air-float hole layout. It reduces construction joint constraint stress through skip-stage casting combined with a double-layer cooling pipe network. Furthermore, it achieves coupled regulation of temperature and humidity fields through a collaborative game theory optimization model of cooling water circulation temperature control and spray curing. Finally, it predicts crack propagation paths and dynamically adjusts curing parameters through a phase-field simulation analysis system. This invention addresses the shortcomings of traditional technologies, such as unreasonable air-float hole layout, independent temperature control and humidity curing, and insufficient crack prediction capabilities. This invention integrates air-float hole optimization, temperature control, humidity curing, and crack prediction into a unified construction method. Through the organic combination of multi-objective optimization algorithms, collaborative game theory models, and phase-field simulation technology, it establishes a full-chain control system from layout design to construction process and crack prediction, thereby solving the technical problem of insufficient crack control accuracy under the coupled effects of multiple factors mentioned in the background technology. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention.

[0022] Figure 2 This is a finite element analysis cloud map of the surface pressure distribution of the air-floored floor in the embodiment.

[0023] Figure 3 This is a diagram illustrating the iterative convergence process of the collaborative game optimization model in the embodiment.

[0024] Figure 4 This is a comparison chart of the test results for limiting the expansion rate of the expansion reinforcement belt in the embodiments.

[0025] Figure 5 The image shows the phase field simulation results of the crack propagation path in the embodiment. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below.

[0027] like Figure 1 As shown, this invention provides a construction method for controlling cracks in high-precision heavy-duty air-floor flooring with a capacity of tens of thousands of square meters, comprising:

[0028] S10. Establish a multi-objective topology optimization model for the air-float hole layout, taking the air-float hole position as the design variable. Set the weight of the objective function for maximizing load uniformity to 0.5, the weight of the objective function for minimizing the number of holes to 0.3, and the weight of the objective function for optimizing edge distance to 0.2. Use the improved NSGA-III algorithm for 200 iterations to obtain the Pareto front containing multiple non-dominated solutions. Select the optimal solution with pressure non-uniformity reduced to 4.2% and the number of holes reduced by 18% as the air-float hole layout scheme.

[0029] S20. An RTK-GPS base station is used in conjunction with a laser total station to establish a three-dimensional coordinate database. A six-degree-of-freedom robotic arm performs automatic drilling operations with a verticality deviation of less than 0.3°, achieving a positioning accuracy of air-floating holes within ±2mm. After drilling is completed, the hole position coordinates are verified by three-dimensional scanning. Holes with deviations exceeding 3mm are corrected.

[0030] S30. Divide the ground into 30m×30m cell units and adopt a skip-cell pouring sequence. Simultaneously embed HDPE cooling pipe network with a spacing of 1.5m when pouring the first batch of cell unit concrete. The HDPE cooling pipe network is arranged in a double-layer serpentine pattern, with the upper layer pipe 200mm from the concrete surface and the lower layer pipe 150mm from the bottom. Pour the intermediate skip cell area after an interval of 7 days.

[0031] S40. Establish a collaborative game optimization model for cooling water circulation temperature control and spray maintenance. The collaborative game optimization model includes an upper-layer cooling optimization model with the goal of achieving optimal temperature field uniformity and a lower-layer maintenance optimization model with the goal of maintaining optimal surface humidity. The objective function input of the upper-layer cooling optimization model includes circulating water flow rate, circulating water temperature, and cooling duration, and the output is the deviation of internal and external temperature difference control. The objective function input of the lower-layer maintenance optimization model includes spray frequency, droplet size, and spray duration, and the output is the fluctuation range of surface relative humidity. Through iterative solution, a collaborative optimization parameter combination is obtained with a circulating water flow rate of 12 cubic meters / h, a circulating water temperature of 15℃, a cooling duration of 72h, a spray frequency of once every 15 minutes, a droplet size of 75μm, and a spray duration of 180s.

[0032] S50. Immediately after the concrete pouring is completed, the temperature monitoring system is activated. The internal temperature data is collected in real time through the pre-embedded thermocouple sensor array. When the internal and external temperature difference reaches 18℃, cooling water with a circulating water temperature of 15℃ and a circulating water flow rate of 12 cubic meters / h obtained in step S40 is introduced. The cooling rate is controlled between 1.5℃ / h and 2℃ / h. At the same time, a 50mm thick insulation blanket is covered on the surface to maintain the internal and external temperature difference of less than 20℃. The cooling duration obtained in step S40 is 72h.

[0033] S60. Deploy an infrared temperature and humidity sensor array to monitor the surface microenvironment. The sensor spacing of the infrared temperature and humidity sensor array is set to 10m. When the surface relative humidity is detected to be lower than 85% or the surface temperature is higher than the ambient temperature by 5°C, the intelligent spray maintenance system is automatically activated. The spray frequency of the intelligent spray maintenance system is once every 15 minutes as obtained in step S40, the droplet size is 75μm as obtained in step S40, and the spray duration is 180s as obtained in step S40, so as to reduce the surface temperature by 3°C to 5°C and maintain a moist state.

[0034] S70. An expansion reinforcement strip with a width of 800mm is set in the construction joint area with high stress concentration. The amount of expansion agent added to the concrete in the expansion reinforcement strip is 8% of the total amount of cementitious material. The expansion agent is a calcium sulfoaluminate material, and the expansion rate is controlled within the range of 0.025% to 0.035%. A shrinkage deformation space of 15mm is reserved on both sides of the expansion reinforcement strip. The expansion reinforcement strip is poured after the concrete of the adjacent cell unit has been cured for 14 days.

[0035] S80. Establish a phase field simulation analysis system for crack propagation path, introduce phase field variables to characterize the crack state evolution process, set the regularized length scale parameter to 8mm, use implicit Euler time integration scheme and quadrilateral isoparametric elements for finite element discretization, simulate the complete process of crack propagation from the initial defect within 30 hours after pouring, when the simulated crack propagation rate exceeds 3mm / min, adjust the curing parameters of the corresponding area and increase the surface spraying frequency from once every 15 minutes obtained in step S40 to once every 8 minutes.

[0036] The high evaporation risk state refers to a state in which the rate of water evaporation from the concrete surface exceeds the rate of water replenishment. Under the high evaporation risk state, negative pressure is generated in the capillaries, which triggers plastic shrinkage cracks.

[0037] The skip-cell pouring sequence refers to dividing the floor into multiple cell units and pouring them in a skip-cell manner, pouring some cell units first, and then pouring the skipped cell units after a period of time, thereby reducing the constraint stress at the construction joint.

[0038] The Stackelberg game framework refers to a two-level optimization structure where the upper-level decision-maker, as the leader, makes the first decision, and the lower-level decision-maker, as the follower, optimizes the response given the upper-level decision. The equilibrium of the two decisions is achieved through iterative solution.

[0039] The coupling term refers to a shared variable that simultaneously affects the upper-level cooling optimization model and the lower-level maintenance optimization model. The coupling term enables information interaction and collaborative decision-making between the two optimization models.

[0040] The critical humidity threshold refers to the critical value of the relative humidity of the concrete surface. When the relative humidity of the surface is lower than the critical humidity threshold, the risk of plastic shrinkage cracks increases significantly, and when it is higher than the critical humidity threshold, the risk of plastic shrinkage cracks is lower.

[0041] The specific implementation methods of the above steps are described in detail below.

[0042] In step S10, the process of establishing the multi-objective topology optimization model for the air flotation hole layout is as follows: the ground plane is divided into grid cells with a precision of 100mm×100mm, the position coordinates of each air flotation hole are used as a design variable, and the objective function is established to include three sub-objectives. The first sub-objective is to maximize the uniformity of load bearing, which is evaluated by calculating the variance of the outlet pressure of all air flotation holes. The smaller the variance, the more uniform the load bearing. The second sub-objective is to minimize the number of holes, which is achieved by setting a penalty coefficient for the number of holes. The third sub-objective is to optimize the edge distance, which requires that the minimum distance between the air flotation hole and the edge of the ground is greater than 150mm. A high-weight penalty is applied to layout schemes that do not meet the edge distance constraint.

[0043] The implementation process of the improved NSGA-III algorithm in step S10 is as follows: First, an initial population containing 200 individuals is generated, each individual is encoded as a sequence of air vent coordinates using real number encoding. Then, non-dominated sorting and crowding calculation are performed. The selection operation adopts tournament selection, the crossover operation adopts the simulated binary crossover SBX operator, the crossover probability is set to 0.9, the distribution index is set to 20, and the mutation operation adopts the adaptive mutation operator, the initial mutation rate is set to 0.15, which linearly decreases to 0.03 as the number of generations increases, and the mutation distribution index is set to 20. After 200 generations of evolution, the population converges to the Pareto front, obtaining multiple non-dominated optimal solutions.

[0044] In step S10, the selection criteria for the optimal solution are as follows: among the multiple non-dominated solutions in the Pareto front, the solution set with a pressure non-uniformity of less than 5% is first selected. The pressure non-uniformity is defined as the ratio of the standard deviation of the outlet pressure of all air flotation holes to the average pressure. Then, in the solution set that meets the pressure uniformity requirement, the scheme with the fewest holes is selected. The final selected scheme reduces the pressure non-uniformity from 12% before optimization to 4.2%, while reducing the number of holes by 18% compared with the uniform arrangement scheme. The hole spacing varies from 1.8m to 2.3m. The hole spacing is smaller in areas with higher load-bearing capacity requirements and appropriately increased in edge and corner areas.

[0045] In step S20, the drilling control process of the six-degree-of-freedom robotic arm is as follows: a high-speed drilling spindle is installed at the end of the six-degree-of-freedom robotic arm, the spindle speed is set to 3000 r / min, the feed speed is set to 80 mm / min, the drill bit diameter is 45 mm, and the drilling depth is controlled at 85% of the concrete thickness. The six-degree-of-freedom robotic arm provides real-time pose feedback through a vision system and a laser rangefinder. Three-point positioning calibration is performed before drilling to ensure that the perpendicularity deviation between the drilling axis and the ground plane is less than 0.3°. During the drilling process, the force and vibration of the drill bit are monitored in real time, and the system automatically pauses and alarms when an abnormality is detected.

[0046] The method for hole position verification and correction in step S20 is as follows: a three-dimensional laser scanner is used to scan the entire surface of the floor after drilling, with a scanning accuracy of 0.5mm. The hole position coordinates obtained by scanning are compared with the design coordinates to calculate the spatial deviation. For holes with a deviation between 2mm and 3mm, the deviation information is recorded for pressure compensation during subsequent air flotation system commissioning. For holes with a deviation exceeding 3mm, a combination of hole enlargement and filling is used for correction. First, the deviation hole is enlarged to a diameter of 60mm, and then filled with high-strength rapid repair mortar. After curing for 24 hours, the hole is re-drilled in the correct position.

[0047] The method for determining the layout parameters of the HDPE cooling pipe network in step S30 is as follows: A concrete hydration heat and temperature field model is established through finite element heat conduction simulation analysis, with a cement dosage of 350 kg / m³. Boundary conditions of water-cement ratio 0.42, pouring temperature 28℃, and ambient temperature 25℃ were used to simulate the temperature distribution under different pipe spacings. The results showed that when the pipe spacing was 1.5m, the highest temperature inside the concrete was controlled at 68℃, and the temperature field uniformity was optimal. The upper pipe was set 200mm from the surface because the surface dissipates heat faster and requires a weaker cooling intensity. The lower pipe was set 150mm from the bottom because the bottom is constrained by the foundation and requires a stronger cooling intensity. The cooling efficiency of the double-layer pipe network is more than 40% higher than that of the single-layer pipe network.

[0048] The determination of the skip-pour pouring sequence in step S30 is based on the following: a finite element structural analysis software is used to establish a floor shrinkage stress model to simulate the stress distribution under different pouring sequences. The input parameters are: concrete elastic modulus 30 GPa, Poisson's ratio 0.2, and shrinkage strain 280 × 10⁻⁶. With a constraint coefficient of 0.65, a comparative analysis was conducted on three schemes: continuous pouring, intermittent pouring, and intermittent pouring. The results showed that the intermittent pouring scheme reduced the stress concentration factor at the construction joint from 2.3 to 1.6, reduced the risk of shrinkage cracks by more than 50%, and met the requirement of stable shrinkage deformation of the pre-poured cell unit during the 7-day interval. The measured shrinkage strain reached more than 70% of the total shrinkage after 7 days.

[0049] In step S40, the collaborative game optimization model is a two-layer optimization structure. The upper-layer cooling optimization model determines the circulating water flow rate, circulating water temperature, and cooling duration, while the lower-layer maintenance optimization model determines the spray frequency, droplet size, and spray duration. The upper-layer cooling optimization model and the lower-layer maintenance optimization model interact with each other through surface temperature as a coupling term. The surface temperature output by the upper-layer cooling optimization model affects the evaporation rate calculation of the lower-layer maintenance optimization model, and the surface temperature reduction output by the lower-layer maintenance optimization model is fed back to the upper-layer cooling optimization model to adjust the cooling intensity.

[0050] In step S40, the objective function of the upper-level cooling optimization model is used to minimize the internal and external temperature difference control deviation. The inputs include circulating water flow rate, circulating water temperature, and cooling duration, and the output is the internal and external temperature difference control deviation. The objective function of the upper-level cooling optimization model is expressed as: the internal and external temperature difference control deviation divided by 20℃ equals the circulating water flow rate divided by 15. The upper cooling optimization model is constrained by the following formula: (1 / h)^0.6 multiplied by the circulating water temperature, divided by 20℃^1.2, divided by the cooling duration, divided by 80h^0.8, then divided by the surface temperature drop, divided by 4℃^0.5. The constraints include a circulating water flow rate range of 8... / h to 18 / h, circulating water temperature range of 12℃ to 18℃, cooling duration range of 60h to 90h, and internal and external temperature difference control deviation of less than 2℃.

[0051] In step S40, the objective function of the lower-level maintenance optimization model is used to minimize the surface relative humidity fluctuation range. The inputs include spray frequency, droplet size, and spray duration, and the output is the surface relative humidity fluctuation range. The objective function of the lower-level maintenance optimization model is expressed as follows: the surface relative humidity fluctuation range divided by 10% equals the spray frequency divided by 6 times per hour to the power of -0.7, the droplet size divided by 80μm to the power of 0.4, the spray duration divided by 200s to the power of -0.5, and the surface temperature divided by 30℃ to the power of 0.6. The constraints of the lower-level maintenance optimization model include a spray frequency range of 3 to 8 times per hour, a droplet size range of 50μm to 100μm, a spray duration range of 120s to 240s, and a surface relative humidity fluctuation range of less than 8%.

[0052] The surface temperature drop in step S40 is calculated as follows: the surface temperature drop is the surface temperature before spraying minus the surface temperature after spraying. The surface temperature before spraying is obtained by measuring an infrared temperature and humidity sensor array, and the surface temperature after spraying is obtained 5 minutes after the spraying ends. The surface temperature drop, as a coupling term, simultaneously affects the decisions of the upper cooling optimization model and the lower maintenance optimization model. When the surface temperature drop is less than 3°C, the upper cooling optimization model increases the circulating water flow rate. When the surface temperature drop is greater than 5°C, the lower maintenance optimization model reduces the spray frequency.

[0053] The solution process for the collaborative game optimization model in step S40 is as follows: Using the Stackelberg game framework, the upper-level cooling optimization model acts as the leader and makes the first decision. The lower-level maintenance optimization model acts as the follower and responds according to the decision of the upper-level cooling optimization model. The iterative solution process is as follows: In the first iteration, the upper-level cooling optimization model initially sets the circulating water flow rate to 10. With a circulating water temperature of 15℃ and a cooling duration of 70h, the lower-layer maintenance optimization model optimizes the solution based on the surface temperature output by the upper-layer cooling optimization model, obtaining a spray frequency of once every 16 minutes, a droplet size of 80μm, and a spray duration of 200s. The calculated surface temperature reduction output by the lower-layer maintenance optimization model is 3.5℃. The upper-layer cooling optimization model adjusts the decision variables based on the surface temperature reduction and enters the second round of iteration. After multiple rounds of iteration, the change in the objective function is less than 0.5%, and a converged solution is obtained.

[0054] The verification method for the collaborative optimization parameter combination in step S40 is as follows: in an area of ​​200 The test area was based on a circulating water flow rate of 12 Construction was carried out using parameters including a spray frequency of 15℃, a circulating water temperature of 15℃, a cooling duration of 72h, a spray frequency of once every 15 minutes, a droplet size of 75μm, and a spray duration of 180s. Temperature and humidity sensors were used to monitor the actual effects. Test results showed that the internal and external temperature difference control deviation was 1.6℃, and the surface relative humidity fluctuation was 6.8%, both meeting the constraint requirements. No temperature cracks or plastic shrinkage cracks were found, verifying the effectiveness of the proposed synergistic optimization parameter combination.

[0055] The basis for controlling the cooling rate in step S50 within the range of 1.5℃ / h to 2℃ / h is as follows: Concrete temperature stress tests were conducted, and specimens with dimensions of 1000mm×1000mm×300mm were prepared. Cooling pipes and temperature sensors were pre-embedded in the specimens. Different cooling rates were applied, and the occurrence of cracks was tested. The test results showed that when the cooling rate was less than 1.5℃ / h, the cooling cycle was too long, resulting in a higher peak temperature. When the cooling rate was greater than 2℃ / h, the temperature gradient was too large, causing the surface tensile stress to exceed the tensile strength, resulting in cracks. When the cooling rate was controlled within the range of 1.5℃ / h to 2℃ / h, the internal and external temperature difference was always less than 20℃, and no cracks were observed. The cooling rate range was obtained through statistical analysis of multiple sets of test data, with a confidence level of 95%.

[0056] The method for determining the internal and external temperature difference threshold of 18℃ in step S50 is as follows: A temperature stress calculation model is established. When calculating the self-constraining stress caused by the internal temperature of the concrete, the coefficient of linear expansion of the concrete is taken as [value missing]. / ℃, elastic modulus is 30GPa, Poisson's ratio is 0.2, constraint coefficient is 0.65, and ultimate tensile strain is... By calculating the stress and strain under different temperature differences, the results show that when the temperature difference is 18℃, the resulting tensile strain is... The tensile strain is close to but does not exceed the ultimate tensile strain. When the temperature difference exceeds 20°C, the tensile strain reaches... If the temperature exceeds the limit by 30%, the risk of cracking increases significantly. Therefore, 18°C ​​is set as the control threshold for starting cooling, with a safety margin of 2°C.

[0057] In step S60, the control logic of the intelligent spray curing system is as follows: the infrared temperature and humidity sensor array collects data every 5 minutes and transmits it to the central controller. The central controller is equipped with a dual-parameter joint judgment algorithm. When the relative humidity is below 85% and the surface temperature is 5°C higher than the ambient temperature, it is determined to be a high evaporation risk state, and the intelligent spray curing system is immediately started. The spraying duration is dynamically adjusted according to the humidity recovery rate. When the relative humidity rises to above 90%, the spraying stops. The spraying interval is not less than 15 minutes to avoid surface water accumulation. The selection of droplet size from 50μm to 100μm is based on fluid dynamics calculations. The evaporation loss rate of droplets within the droplet size range before reaching the concrete surface at a spraying height of 1.5m is less than 20%, and the droplet kinetic energy is insufficient to damage the surface slurry.

[0058] The determination of the surface relative humidity threshold of 85% in step S60 is based on the following: a concrete plastic shrinkage crack test was conducted. A modified plate test method was used to prepare a specimen with a thickness of 80 mm and an area of ​​600 mm × 600 mm. Under the conditions of an ambient temperature of 30℃ and a wind speed of 2 m / s, different surface relative humidities were controlled, and the crack appearance time and crack area were tested. The test results showed that when the relative humidity was higher than 85%, no visible cracks appeared within 4 hours. When the relative humidity was between 75% and 85%, fine cracks appeared within 2 to 3 hours. When the relative humidity was lower than 75%, obvious cracks appeared within 1 hour. Through regression analysis of multiple sets of parallel test data, 85% was determined as the critical humidity threshold. Under the critical humidity threshold, the capillary negative pressure decreased to 15 MPa, which is lower than the critical value of 25 MPa that induces plastic cracking.

[0059] The method for determining the 8% dosage of the expansive agent in step S70 is as follows: Concrete specimens with different dosages of expansive agent are prepared, and the restricted expansion rate and compressive strength are tested. The specimen size is 100mm×100mm×300mm. The specimens are cured in a restricted expansion rate test device, and the restricted expansion rate is tested after 7 days and 28 days. The results show that when the dosage of expansive agent is 6%, the restricted expansion rate is 0.018%, which is insufficient to compensate for shrinkage. When the dosage is 10%, the restricted expansion rate is 0.042%, which is too large and causes new stress. When the dosage is 8%, the restricted expansion rate is 0.030%, which is consistent with the concrete shrinkage strain of 280× The basic match is achieved, and the compressive strength reaches 45MPa after 28 days, meeting the design requirements. The dosage of the expansion agent was determined through optimization analysis of multiple sets of tests with different mix proportions.

[0060] The calculation method for the 15mm shrinkage deformation space width in step S70 is as follows: According to the theory of concrete shrinkage deformation, when calculating the total shrinkage deformation of a 30m span cell unit, the concrete shrinkage strain is taken as 280× The effective shrinkage strain under the constraint condition is the shrinkage strain multiplied by the constraint release coefficient, which is 0.45. The effective shrinkage deformation of the 30m span is calculated to be 12.6mm. Considering the construction deviation and the safety factor of 1.2, the reserved space width is finally determined to be 15mm. The shrinkage deformation space width not only meets the shrinkage deformation requirements, but also avoids the problem of cold joints in the expansion reinforcement concrete caused by excessive space.

[0061] In step S80, the physical meaning of the phase field variable is as follows: the phase field variable is a function of spatial coordinates and time, and its value ranges from 0 to 1. A value of 1 indicates that the material is in an intact and undamaged state, a value of 0 indicates that the material is completely cracked and forms a crack, and a value between 0 and 1 indicates that the material is in a transitional damage state. The core idea of ​​the phase field method is to approximate the discrete crack interface with a continuous phase field variable field, avoiding the difficulty of tracking the crack tip in traditional fracture mechanics. The evolution of the phase field variable follows the principle of energy minimization. The total energy of the system includes three parts: elastic strain energy, crack fracture energy, and work done by external load.

[0062] The selection of the regularization length scale parameter of 8mm in step S80 is based on the following: the regularization length scale parameter controls the spatial diffusion width of the phase field variable, which physically characterizes the size of the crack process zone. The smaller the regularization length scale parameter, the closer the phase field simulation result is to the real discrete crack, but the computational grid needs to be more refined, and the computational cost increases sharply. By comparing with the actual crack propagation test results, different regularization length scale parameters are selected for simulation. When the regularization length scale parameter is 8mm, the deviation between the simulated crack path and the experimental observation path is less than 5mm, the prediction error of the crack propagation rate is less than 10%, and the calculation time meets the requirements of engineering applications. When the regularization length scale parameter is greater than 12mm, the crack diffusion zone is too wide, which leads to a decrease in prediction accuracy. When the regularization length scale parameter is less than 6mm, the number of computational grid cells increases several times, and the calculation time exceeds the requirements of engineering applications.

[0063] The method for determining the crack propagation rate threshold of 3 mm / min in step S80 is as follows: collect on-site monitoring data of early crack propagation in concrete, and use the digital image correlation (DIC) technology to track cracks on the floor surface in real time. In multiple different engineering projects, the propagation process of more than 100 cracks was monitored. Statistical analysis showed that when the crack propagation rate was less than 3 mm / min, most cracks stopped propagating before reaching 50 mm by strengthening maintenance measures. When the crack propagation rate was greater than 3 mm / min, only a small number of cracks were controlled by maintenance measures, and most cracks continued to propagate and formed through cracks. Therefore, 3 mm / min was set as the warning threshold. When the simulation prediction exceeded the warning threshold, the surface spraying frequency in the corresponding area was immediately increased from once every 15 minutes obtained in step S40 to once every 8 minutes, and the surface wind speed was reduced to below 0.5 m / s.

[0064] It should be noted that this invention also solves the following technical problems: First, the technical problem of insufficient drilling positioning accuracy of air-float holes leading to a decrease in the performance of the air-float system. This invention uses an RTK-GPS base station in conjunction with a laser total station to establish a three-dimensional coordinate database. A six-degree-of-freedom robotic arm performs automatic drilling operations with a verticality deviation of less than 0.3 degrees, achieving air-float hole positioning accuracy control within ±2 millimeters. After drilling is completed, the hole position coordinates are verified by three-dimensional scanning. Holes with deviations exceeding 3 millimeters are corrected. By combining a high-precision positioning system with automated drilling equipment, the problems of large positioning errors and difficulty in ensuring verticality in traditional manual drilling are solved, ensuring the uniformity of pressure distribution and load-bearing performance of the air-float system. Secondly, the invention addresses the technical problem of frequent temperature cracks caused by the difficulty in precisely controlling early-stage temperature stress in concrete. This invention utilizes a pre-embedded HDPE cooling pipe network spaced 1.5 meters apart, arranged in a double-layer serpentine pattern to achieve three-dimensional temperature field control. The differentiated arrangement—with the upper pipe 200 mm from the concrete surface and the lower pipe 150 mm from the bottom—controls the highest internal temperature of the concrete at 68 degrees Celsius, significantly improving temperature field uniformity. Combined with a real-time temperature monitoring system and dynamic cooling water flow adjustment, the cooling rate is controlled between 1.5 and 2 degrees Celsius per hour, with the internal and external temperature difference consistently less than 20 degrees Celsius. Through precise temperature field control and gradient optimization, temperature cracks caused by hydration heat are effectively suppressed.

[0065] Specifically, the principle of this invention is as follows: The technical principle of this invention lies in achieving precise crack control through multi-level optimization and multi-physics coupling. First, the multi-objective topology optimization model for the air-float hole layout solves the three objective functions of load uniformity, hole number, and edge distance using an improved NSGA-III algorithm, obtaining the Pareto optimal solution. This reduces the pressure non-uniformity from 12% to 4.2%, eliminating local stress concentration caused by uneven load distribution and laying the foundation for crack control. Second, the skip-stage pouring sequence combined with the design of a double-layer cooling pipe network reduces the stress concentration coefficient at the construction joint from 2.3 to 1.6. The differentiated arrangement of the double-layer pipe network achieves uniform temperature field control, avoiding excessive tensile stress caused by excessive temperature gradients. Furthermore, the collaborative game optimization model for cooling water circulation temperature control and spray curing adopts the Stackelberg game framework. By using the surface temperature drop as a coupling term, it achieves information interaction between the upper and lower optimization models, solving the problem of insufficient synergy caused by the independence of temperature control and humidity curing. This ensures that the deviation in internal and external temperature control is less than two degrees Celsius, and the fluctuation range of surface relative humidity is less than eight percent, while simultaneously suppressing temperature cracks and plastic shrinkage cracks. Finally, the phase-field simulation analysis system introduces phase-field variables to characterize the crack state evolution, simulating the complete process of crack propagation from the initial defect. When the predicted crack propagation rate exceeds three millimeters per minute, the curing parameters are dynamically adjusted, achieving proactive crack prevention rather than passive repair. Therefore, this invention can solve the technical problem of insufficient crack control accuracy under the coupling effect of multiple factors.

[0066] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0067] The specific implementation methods of steps S20, S30, and S60 are the same as those described above, and will not be repeated in detail here.

[0068] The specific implementation of step S10 involves establishing a multi-objective topology optimization model for the air flotation hole layout. The ground plane is divided into grid cells with a precision of 100mm × 100mm. The position coordinates of each air flotation hole are used as a design variable. An objective function containing three sub-objectives is established. The first sub-objective is to maximize the uniformity of load bearing, which is evaluated by calculating the variance of the outlet pressure of all air flotation holes. The objective function is expressed as follows:

[0069] ;

[0070] In the formula, The objective function value for load uniformity is dimensionless; the smaller the value, the more uniform the load. The standard deviation of the outlet pressure of all air flotation orifices is given in Pa. This represents the average outlet pressure of all air flotation orifices, in Pa. The standard deviation is... The calculation formula is expressed as follows:

[0071] ;

[0072] In the formula, The standard deviation of the outlet pressure of all air flotation orifices is given in Pa. This represents the total number of air flotation holes, dimensionless. For the first The outlet pressure of each air flotation hole, in Pa; This is the average outlet pressure of all air flotation orifices, in Pa. The calculation formula is expressed as follows:

[0073] ;

[0074] In the formula, This is the average outlet pressure of all air flotation holes, in Pa. This represents the total number of air flotation holes, dimensionless. For the first The outlet pressure of each air flotation orifice is measured in Pa. The second sub-objective is to minimize the number of orifices, which is achieved by setting a penalty coefficient for the number of orifices. Its objective function is expressed as follows:

[0075] ;

[0076] In the formula, The objective function value for minimizing the number of holes is dimensionless. The number of air flotation holes in the current layout scheme is dimensionless. The reference number of holes is dimensionless and is usually taken as the number of holes when they are evenly distributed. is the hole number penalty coefficient, dimensionless, with an empirical value of 10; This is the Heaviside step function, which is dimensionless. It takes the value 1 when the expression inside the parentheses is greater than 0, and otherwise takes the value 0. The maximum allowed number of holes is dimensionless. The third sub-objective is edge distance optimization, requiring the minimum distance between the air flotation holes and the edge of the floor to be greater than 150mm. Layout schemes that do not meet the edge distance constraint are penalized with a high weight. Its objective function is expressed as follows:

[0077] ;

[0078] In the formula, The objective function for optimizing the edge distance is dimensionless. The index for the air flotation holes is dimensionless. The number of air flotation holes in the current layout scheme is dimensionless. This is the edge distance penalty coefficient, which is dimensionless and has an empirical value of 20. For the first The minimum distance between each air flotation hole and the edge of the floor, in mm; Let be the Heaviside step function, which is dimensionless. The overall objective function of the multi-objective topology optimization model is in weighted sum form, as follows:

[0079] ;

[0080] In the formula, The value of the overall objective function is dimensionless. The weight coefficients for each sub-objective function are dimensionless and are set to 0.5, 0.3, and 0.2 respectively. The values ​​are reference values ​​for each sub-objective function, dimensionless, and based on the function values ​​of the initial uniform layout scheme. The implementation process of the improved NSGA-III algorithm is as follows: First, an initial population of 200 individuals is generated, each encoded as a sequence of air vent coordinates using real-number encoding. Then, non-dominated sorting and crowding calculation are performed. Tournament selection is used for selection, and the crossover operation uses the simulated binary crossover SBX operator with a crossover probability of 0.9 and a distribution exponent of 20. An adaptive mutation operator is used with an initial mutation rate of 0.15, which linearly decreases to 0.03 with each generation. The mutation rate decay formula is expressed as follows:

[0081] ;

[0082] In the formula, For the first The rate of variation of generations, dimensionless; The current evolutionary generation is dimensionless. The initial mutation rate is dimensionless and has a value of 0.15. The final mutation rate is dimensionless and has a value of 0.03. The maximum number of generations is dimensionless and takes the value 200. After 200 generations of evolution, the population converges to the Pareto front, obtaining multiple non-dominated optimal solutions. Among these non-dominated solutions at the Pareto front, the solution set with a pressure non-uniformity of less than 5% is first selected. The formula is defined as follows:

[0083] ;

[0084] In the formula, Pressure non-uniformity, expressed in % %. The standard deviation of the outlet pressure of all air flotation orifices, in Pa; The value is the average outlet pressure of all air flotation holes, in Pa. Then, in the solution set that meets the pressure uniformity requirement, the scheme with the fewest holes is selected. The final selected scheme reduces the pressure non-uniformity from 12% before optimization to 4.2%, while reducing the number of holes by 18% compared to the uniform arrangement scheme.

[0085] The specific implementation of step S40 is to establish a collaborative game optimization model for cooling water circulation temperature control and spray curing. This collaborative game optimization model includes an upper-layer cooling optimization model with the objective of optimizing temperature field uniformity and a lower-layer curing optimization model with the objective of optimizing surface humidity. The objective function of the upper-layer cooling optimization model is used to minimize the deviation of internal and external temperature difference control, and its objective function is expressed as follows:

[0086] ;

[0087] In the formula, The deviation of internal and external temperature control is expressed in °C, which refers to the difference between the actual internal and external temperature difference and the target internal and external temperature difference of 20 °C. This refers to the circulating water flow rate, in units of... The range is 8 up to 18 ; The temperature of the circulating water is expressed in °C, ranging from 12°C to 18°C. The cooling duration is expressed in hours and ranges from 60 to 90 hours. The surface temperature drop, expressed in °C, represents the difference in surface temperature before and after spraying. This formula considers the combined effects of circulating water flow rate, circulating water temperature, and cooling duration on the internal and external temperature difference. The 0.6 power of the circulating water flow rate reflects the nonlinear effect of flow rate on cooling efficiency; the 1.2 power of the circulating water temperature reflects the amplification effect of the temperature difference driving force on heat transfer intensity; the 0.8 power of the cooling duration in the denominator reflects the cumulative effect of long-term cooling; and the 0.5 power of the surface temperature drop in the denominator reflects the regulating effect of spray curing on surface temperature. This formula achieves coupling and balance between different parameters through a polynomial ratio, accurately predicting the deviation in internal and external temperature difference control. The constraint condition is that the deviation in internal and external temperature difference control should be less than 2 °C. Surface temperature drop The calculation method is to subtract the surface temperature after spraying from the surface temperature before spraying. The calculation formula is as follows:

[0088] ;

[0089] In the formula, The surface temperature drop is expressed in °C. The surface temperature before spraying is measured in °C and is obtained by an array of infrared temperature and humidity sensors. The surface temperature after spraying is expressed in °C and was measured 5 minutes after spraying ended. The objective function of the lower-layer curing optimization model is used to minimize the fluctuation range of surface relative humidity, and its objective function is expressed as follows:

[0090] ;

[0091] In the formula, The fluctuation range of surface relative humidity, expressed in %, refers to the difference between the maximum and minimum values ​​of surface relative humidity within the monitoring period; The spray frequency is measured in sprays per hour and ranges from 3 to 8 sprays per hour. The droplet size is expressed in μm and ranges from 50 μm to 100 μm. The duration of the spray is measured in seconds and ranges from 120 seconds to 240 seconds. The average surface temperature, expressed in °C, represents the average surface temperature over the monitoring period. This formula considers the combined effects of spray frequency, droplet size, and spray duration on surface relative humidity. The -0.7 power of spray frequency reflects the suppressive effect of frequent spraying on humidity fluctuations; the 0.4 power of droplet size reflects the trend of increased humidity fluctuations due to slower evaporation caused by larger droplet size; the -0.5 power of spray duration reflects the promoting effect of extended spray time on humidity stability; and the 0.6 power of average surface temperature reflects the effect of increased temperature accelerating evaporation and thus increasing humidity fluctuations. This formula achieves nonlinear coupling between different parameters through power-law combinations, accurately predicting the amplitude of surface relative humidity fluctuations. The constraint condition includes a surface relative humidity fluctuation amplitude of less than 8%. The upper cooling optimization model and the lower maintenance optimization model interact with each other through surface temperature as a coupling term. The surface temperature drop, also a coupling term, influences the decisions of both models. When the surface temperature drop is less than 3°C, the upper cooling optimization model increases the circulating water flow rate; when the surface temperature drop is greater than 5°C, the lower maintenance optimization model decreases the spray frequency. The collaborative game optimization model is solved using the Stackelberg game framework. The upper cooling optimization model, as the leader, makes the first decision, and the lower maintenance optimization model, as the follower, responds based on the upper cooling optimization model's decision. Through iterative solving, the circulating water flow rate is obtained as 12. The optimal combination of parameters is as follows: / h, circulating water temperature is 15℃, cooling duration is 72h, spray frequency is once every 15 minutes, droplet size is 75μm, and spray duration is 180s.

[0092] The specific implementation of step S50 is to immediately activate the temperature monitoring system after the concrete pouring is completed, and collect internal temperature data in real time through a pre-embedded thermocouple sensor array. When the internal and external temperature difference reaches 18°C, the circulating water obtained in step S40 with a temperature of 15°C and a flow rate of 12 is introduced. Cooling water is supplied at a rate controlled between 1.5℃ / h and 2℃ / h, while a 50mm thick insulation blanket is applied to the surface to maintain an internal and external temperature difference of less than 20℃. The cooling duration obtained in step S40 is 72 hours. The internal and external temperature difference threshold of 18℃ is determined based on a temperature stress calculation model, specifically the self-constraining stress caused by the internal temperature of the concrete. The calculation formula is expressed as follows:

[0093] ;

[0094] In the formula, This refers to temperature stress, measured in Pa. This is the coefficient of linear expansion of concrete, expressed in units of 1 / ℃, and its value is [value missing]. / ℃; This refers to the elastic modulus of concrete, measured in Pa, with a value of 30 GPa. Pa; The temperature difference between the inside and outside of concrete, expressed in °C, refers to the difference between the internal temperature and the surface temperature of concrete. The constraint coefficient, dimensionless, is 0.65, reflecting the degree of constraint exerted by the foundation and boundaries on concrete deformation. The tensile strain corresponding to temperature stress is... The calculation formula is expressed as follows:

[0095] ;

[0096] In the formula, For temperature-induced tensile strain, dimensionless; This is the coefficient of linear expansion of concrete, expressed in units of 1 / ℃, and its value is [value missing]. / ℃; The temperature difference between the inside and outside of the concrete is expressed in °C. The constraint coefficient is dimensionless and has a value of 0.65. Stress-strain calculations under different temperature differences show that the tensile strain generated at a temperature difference of 18℃ is... Approaching but not exceeding the ultimate tensile strain When the temperature difference exceeds 20℃, the tensile strain reaches If the temperature exceeds the limit by 30%, the risk of cracking increases significantly. Therefore, 18°C ​​is set as the control threshold for starting cooling, with a safety margin of 2°C.

[0097] The specific implementation of step S70 involves setting an 800mm wide expansion reinforcement strip in the construction joint area with high stress concentration. The amount of expansion agent added to the concrete within the expansion reinforcement strip is 8% of the total cementitious material. The expansion agent is a calcium sulfoaluminate-based material, and the expansion rate is controlled within the range of 0.025% to 0.035%. A 15mm wide space is reserved on both sides of the expansion reinforcement strip for shrinkage deformation. This width is calculated based on the theory of concrete shrinkage deformation. The effective shrinkage deformation calculation formula for a 30m span cell unit is expressed as follows:

[0098] ;

[0099] In the formula, The effective shrinkage deformation is expressed in mm. The span of the storage cell is in mm, and the value is 30m, which is 30000mm. For concrete shrinkage strain, dimensionless, the value is taken as... ; The constraint release coefficient is dimensionless and has a value of 0.45, reflecting the degree of release of the expansion reinforcement zone's constraint on the contraction of adjacent compartments. The safety factor, dimensionless, is set to 1.2 to account for construction deviations and uncertainties. Substituting the parameters into the formula, the effective shrinkage deformation is calculated to be 12.6 mm. After considering construction deviations, the final reserved space width is determined to be 15 mm.

[0100] The specific implementation of step S80 involves establishing a phase-field simulation analysis system for crack propagation paths, introducing phase-field variables to characterize the crack state evolution process. These phase-field variables are functions of spatial coordinates and time, with values ​​ranging from 0 to 1. The evolution of the phase-field variables follows the principle of energy minimization. The total system energy comprises three parts: elastic strain energy, crack fracture energy, and work done by external loads. The functional expression of the total energy is as follows:

[0101] ;

[0102] In the formula, The total energy of the system is expressed in J. The calculation area is in units of ; is a phase field variable, dimensionless, with a value range of 0 to 1. A value of 1 indicates that the material is in a complete and undamaged state, while a value of 0 indicates that the material is completely cracked and has formed a crack. The residual stiffness coefficient is dimensionless, and its empirical value is [value missing]. This is used to avoid numerical singularities; This is the stress tensor, with units of Pa. The strain tensor is dimensionless; Critical fracture energy, in J / The value is 100J / ; This is the regularization length scale parameter, in meters, with a value of 8 mm, or 0.008 m. The gradient of the phase field variables is expressed in units of 1 / m. This is the Euclidean norm, and the unit of the result is the same as the unit of the variable in parentheses; For volume infinitesimal elements, the unit is . ; Work done on the external load is expressed in J. The first term of the formula is the integral over the computational domain of the product of the elastic strain energy density and the degradation function, where... The degenerate function is dimensionless and represents the process of material stiffness decreasing as cracks propagate; it is the dot product of the stress tensor and the strain tensor. The unit is Pa, and the second term is the fracture energy, which consists of two parts. This represents the energy density contribution of the crack surface, expressed in units of 1 / m. The energy density contribution term in the fracture process zone is expressed in units of 1 / m, and is the sum of the two terms multiplied by the critical fracture energy. The unit is J / The formula derives the phase field evolution equation and force balance equation through variational principles. Finite element discretization is performed using an implicit Euler time integration scheme and quadrilateral isoparametric elements to simulate the complete process of crack propagation from the initial defect within 30 hours after casting. Crack propagation rate. The calculation formula, based on the time-varying rate of change of the phase field variable, is as follows:

[0103] ;

[0104] In the formula, The crack propagation rate is expressed in mm / min. The distance the crack tip advances within a time interval, in mm, is determined by tracing the contour lines of the phase field variables. The position change is obtained; The time interval, in minutes, is the time window used to calculate the rate. When the simulated crack propagation rate exceeds 3 mm / min, the maintenance parameters for the corresponding area are adjusted and the surface spraying frequency is increased from once every 15 minutes obtained in step S40 to once every 8 minutes.

[0105] It should be noted that the variables involved in this embodiment are explained in detail in Table 1.

[0106] Table 1. Variable Explanation Table

[0107]

[0108] To better understand and implement this invention, the following is a specific application scenario of this invention, Example 2: The technical team first established a multi-objective topology optimization model for the air flotation hole layout, and then optimized the 12000... The ground surface is divided into 100mm × 100mm grid cells, totaling 1.2 million grid cells. The location coordinates of each air flotation hole are used as design variables, and the initial plan requires the placement of 2800 air flotation holes. The established objective function contains three sub-objectives: the first sub-objective is to maximize load uniformity, with a weight of 0.5. Load uniformity is evaluated by calculating the variance of the outlet pressure of all air flotation holes; the smaller the variance, the more uniform the pressure distribution. The second sub-objective is to minimize the number of holes, with a weight of 0.3. A penalty coefficient is applied to the number of holes to optimize the number of holes. The third sub-objective is to optimize the edge distance, with a weight of 0.2. The minimum distance between the air flotation hole and the edge of the ground surface must be greater than 150mm. An improved NSGA-III algorithm is used for optimization. First, an initial population of 200 individuals is generated, and each individual is encoded as a sequence of air flotation hole coordinates using real number encoding. The crossover operation employed the simulated binary crossover SBX operator with a crossover probability of 0.9 and a distribution exponent of 20. The mutation operation used an adaptive mutation operator with an initial mutation rate of 0.15, which linearly decreased to 0.03 with each generation. After 200 generations of iterative optimization, the population converged to the Pareto front, yielding 58 non-dominated solutions. From these non-dominated solutions, the team selected 32 solutions with a pressure non-uniformity of less than 5%, defined as the ratio of the standard deviation of the outlet pressure of all air flotation holes to the average pressure. Within the solution set satisfying the pressure uniformity requirement, the scheme with the fewest holes was selected. The final optimal scheme reduced the pressure non-uniformity from 12% to 4.2%, and the number of holes from 2800 to 2296, a reduction of 18%. The optimized air flotation hole layout features a hole spacing of 1.8m in the central area with higher load-bearing capacity and 2.3m in the edge and corner areas. Figure 2 As shown.

[0109] After obtaining the air-bearing hole layout scheme, the technical team used an RTK-GPS base station in conjunction with a laser total station to establish a three-dimensional coordinate database, inputting the design coordinates of 2296 air-bearing holes into the database. A six-degree-of-freedom robotic arm was used to perform automated drilling operations, with a high-speed drilling spindle mounted at the end of the robotic arm, rotating at 3000 RPM. Feed rate 80 The drill bit diameter was 45mm, and the drilling depth was 255mm, controlled at 85% of the concrete thickness. The robotic arm used a vision system and laser rangefinder for real-time posture feedback, performing three-point positioning calibration before drilling to ensure the perpendicularity deviation between the drilling axis and the ground plane was less than 0.3°. During drilling, the drill bit's stress and vibration parameters were monitored in real time. The system automatically paused and triggered an alarm when a sudden change in drill bit stress or vibration amplitude exceeded a set threshold. After 72 hours of continuous drilling, all 2296 air flotation holes were completed. A 3D laser scanner was used to comprehensively scan the completed ground surface with a scanning accuracy of 0.5mm. The obtained hole coordinates were compared with the design coordinates to calculate the spatial deviation. Statistical results showed that the deviation of 2178 holes was within 2mm, accounting for 94.9%; the deviation of 86 holes was between 2mm and 3mm; and the deviation of 32 holes exceeded 3mm. For the 32 holes with a deviation of more than 3 mm, a combination of hole enlargement and filling was used for correction. First, the deviation hole was enlarged to a diameter of 60 mm, and then filled with high-strength fast repair mortar. After curing for 24 hours, the hole was re-drilled in the correct position. The deviation of the corrected hole was controlled within 2 mm.

[0110] The technical team divided the concrete floor into 30m x 30m compartments, totaling 13 compartments plus one irregular boundary unit. A staggered pouring sequence was adopted, with the first batch of 7 compartments poured, followed by the remaining 6 compartments and the boundary unit after a 7-day interval. HDPE cooling pipe networks were pre-embedded simultaneously during the first batch of compartment concrete pouring. The cooling pipe network was arranged in a double-layer serpentine pattern, with a pipe spacing of 1.5m. The upper layer pipe was 200mm from the concrete surface, and the lower layer pipe was 150mm from the bottom. The total length of the pre-embedded cooling pipes in each compartment was approximately 800m, with a pipe diameter of 25mm and a wall thickness of 2.3mm. Finite element heat conduction simulation analysis verified that with a pipe spacing of 1.5m, the highest internal temperature of the concrete could be controlled at 68℃, with optimal temperature field uniformity. The cooling efficiency of the double-layer network was 42% higher than that of the single-layer network.

[0111] A collaborative game-theoretic optimization model for cooling water circulation temperature control and spray maintenance was established. This model employs the Stackelberg game framework, with the upper-level cooling optimization model acting as the leader, deciding on the circulation water flow rate, circulation water temperature, and cooling duration. The lower-level maintenance optimization model acts as the follower, deciding on the spray frequency, droplet size, and spray duration. The upper-level cooling optimization model aims for optimal temperature field uniformity, with the objective function minimizing the deviation in internal and external temperature difference control. Constraints include a circulation water flow rate range of 8... up to 18 The circulating water temperature ranges from 12℃ to 18℃, the cooling duration ranges from 60h to 90h, and the internal and external temperature difference deviation is controlled to be less than 2℃. The lower-level maintenance optimization model aims to maintain optimal surface humidity. The objective function minimizes the fluctuation range of surface relative humidity. Constraints include a spray frequency range of 3 to 8 times per hour, a droplet size range of 50μm to 100μm, a spray duration range of 120s to 240s, and a surface relative humidity fluctuation range of less than 8%. The two optimization models interact through a surface temperature reduction term. The surface temperature output by the upper-level model affects the evaporation rate calculation of the lower-level model, and the surface temperature reduction output by the lower-level model is fed back to the upper-level model to adjust the cooling intensity. During the iterative solution process, the initial circulating water flow rate of the upper-level model is set to 10 in the first iteration. With a circulating water temperature of 15℃ and a cooling duration of 70 hours, the lower-level model optimizes the solution based on the surface temperature output from the upper-level model, obtaining a spray frequency of once every 16 minutes, a droplet size of 80μm, and a spray duration of 200 seconds, resulting in a calculated surface temperature reduction of 3.5℃. The upper-level model adjusts the decision variables based on the surface temperature reduction and enters a second iteration. After 18 iterations, the change in the objective function is less than 0.5%, yielding a converged solution. The final co-optimized parameter combination is a circulating water flow rate of 12... The circulating water temperature is 15℃, the cooling duration is 72 hours, the spray frequency is once every 15 minutes, the droplet size is 75μm, and the spray duration is 180 seconds. Figure 3 As shown.

[0112] Immediately after the concrete pouring was completed, the temperature monitoring system was activated, collecting internal temperature data in real time through a pre-embedded array of thermocouple sensors. Each compartment contained 16 pre-embedded thermocouple sensors, arranged at four levels: upper, middle, lower, and bottom, with four sensors in a square distribution at each level. Sensor data was collected every 10 minutes and transmitted wirelessly to the central monitoring system in real time. Monitoring data showed that the internal temperature rose rapidly 8 hours after concrete pouring, reaching a peak temperature of 62℃ at 12 hours, at which point the temperature difference between the inside and outside reached 18℃. The technical team immediately began introducing circulating water at a temperature of 15℃ and a flow rate of 12... Cooling water was used, with the cooling rate controlled between 1.5℃ / h and 2℃ / h. Simultaneously, a 50mm thick insulation blanket was placed over the surface to maintain an internal and external temperature difference of less than 20℃. After 72 hours of continuous cooling, the internal temperature of the concrete dropped to 35℃, and the internal and external temperature difference decreased to 10℃. Throughout the cooling process, the internal and external temperature difference was consistently controlled within 20℃, as shown in Table 2.

[0113] Table 2 Concrete Temperature Monitoring Data

[0114] Time (h) Internal temperature (°C) Surface temperature (°C) Ambient temperature (°C) Temperature difference between inside and outside (°C) 0 28 28 28 0 6 52 36 27 16 12 62 44 26 18 18 58 42 26 16 24 54 40 27 14 36 48 36 28 12 48 42 33 28 9 60 38 31 27 7 72 35 30 28 5

[0115] The technical team deployed an infrared temperature and humidity sensor array to monitor the surface microenvironment. The sensors were spaced 10 meters apart, with a total of 120 sensors covering the entire floor surface. The sensors collected data every 5 minutes and transmitted it to the central controller. The controller was equipped with a dual-parameter joint judgment algorithm. When the relative humidity was below 85% and the surface temperature was 5°C higher than the ambient temperature, a high evaporation risk state was identified, and the intelligent spray curing system was immediately activated. The spray curing system used parameters of a spray frequency of once every 15 minutes, a droplet size of 75μm, and a spray duration of 180 seconds. Monitoring data showed that in the first 24 hours after concrete pouring, the surface relative humidity repeatedly dropped below 85%, and the spray curing system automatically activated 68 times. After each spray, the surface temperature decreased by 3°C to 5°C, and the relative humidity recovered to above 90%. The surface temperature drop data was fed back to the upper-layer cooling optimization model. When the surface temperature drop was less than 3°C, the upper-layer model increased the circulating water flow; when the surface temperature drop was greater than 5°C, the lower-layer model decreased the spray frequency. Throughout the curing process, the relative humidity fluctuation of the surface was controlled within 6.8%, meeting the constraint requirement of less than 8%.

[0116] An 800mm wide expansion reinforcement strip was installed in the construction joint area with high stress concentration. The concrete within the expansion reinforcement strip contained 8% (8% of the total cementitious material) of an expansion agent, which was a calcium sulfoaluminate-based material. The technical team prepared concrete specimens with expansion agent dosages of 6%, 8%, and 10% for restricted expansion rate testing. The specimens were 100mm × 100mm × 300mm in size and cured in a restricted expansion rate testing device. The test results showed that with a dosage of 6%, the 7-day restricted expansion rate was 0.018%, indicating insufficient shrinkage compensation; with a dosage of 10%, the 7-day restricted expansion rate was 0.042%, indicating excessive expansion and the induction of new stress; with a dosage of 8%, the 7-day restricted expansion rate was 0.030%, and the 28-day restricted expansion rate was 0.032%, which is consistent with the concrete shrinkage strain of 280mm × 100mm × 100mm. Basic matching. A 15mm width space is reserved on both sides of the expansion reinforcement strip for shrinkage deformation. Based on the total shrinkage deformation of a 30m span cell unit, the concrete shrinkage strain is calculated to be 280 × With a constraint release coefficient of 0.45 and an effective shrinkage deformation of 12.6 mm, and considering construction deviations and a safety factor of 1.2, the reserved space width is determined to be 15 mm. The expansion reinforcement strip will be poured after the adjacent cell unit concrete has cured for 14 days. Strict quality control will be implemented during the pouring process to avoid cold joint issues. Figure 4 As shown.

[0117] A phase-field simulation analysis system for crack propagation paths was established, introducing phase-field variables to characterize the crack state evolution process. The phase-field variables are functions of spatial coordinates and time, ranging from 0 to 1. A value of 1 indicates the material is in a complete, undamaged state; a value of 0 indicates the material has fully cracked; and values ​​between 0 and 1 indicate a transitional damage state. A regularization length scale parameter of 8 mm was set. This parameter controls the spatial diffusion width of the phase-field variables, physically characterizing the size of the crack process zone. The technical team conducted parameter sensitivity analysis. When the regularization length scale parameter is 8 mm, the deviation between the simulated crack path and the experimentally observed path is less than 5 mm, the prediction error of the crack propagation rate is less than 10%, and the computation time meets engineering application requirements. Finite element discretization was performed using an implicit Euler time integration scheme and quadrilateral isoparametric elements, with a mesh element size of 2 mm and a time step of 0.1 h. The complete process of crack propagation from the initial defect within 30 hours after pouring was simulated, with input concrete elastic modulus of 30 GPa, Poisson's ratio of 0.2, tensile strength of 3.2 MPa, and fracture energy of 120 kJ / m³. The material parameters, as well as the measured temperature field and shrinkage strain field data, were analyzed. Simulation results showed that in the first batch of seven cell units, three cell units exhibited crack propagation rates exceeding 300°C within 18 to 24 hours after casting. The warning signal was received. The technical team immediately increased the surface spraying frequency in the corresponding areas of these three compartments from once every 15 minutes to once every 8 minutes, and reduced the surface wind speed to 0.5. The following results show that after implementing enhanced maintenance measures, the crack propagation rate rapidly decreased to 1.5%. The cracks stopped expanding after reaching 30mm to 45mm, without forming a through crack. Figure 5 As shown.

[0118] Twenty-eight days after construction, the technical team conducted a comprehensive inspection of the floor. Surface flatness was tested using a three-meter straightedge method, measuring every 100 square meters. Five points were tested, totaling 600 points. Test results showed a flatness deviation ranging from 0.8mm to 1.9mm, with an average of 1.4mm, meeting the design requirement of within 2mm. The pressure uniformity of the air flotation system was tested using a pressure sensor array, simultaneously measuring the outlet pressure of the air flotation orifices at 120 measuring points. Test results showed that the ratio of the pressure standard deviation to the average pressure was 4.1%, meeting the design requirement of less than 5%. Crack detection used a combination of fluorescent penetrant and ultrasonic testing methods, identifying 13 microcracks on the entire floor surface. The crack lengths ranged from 28mm to 52mm, and the crack widths ranged from 0.05mm to 0.12mm. All were non-penetrating surface microcracks and did not affect the floor's functionality. Load-bearing capacity testing used a distributed loading method, applying 50... A uniformly distributed load was applied and unloaded after 24 hours. No visible cracks or deformations appeared on the floor during the test, and the bearing capacity met the design requirements.

[0119] This invention achieves synergistic optimization of the air-float hole layout by maximizing the uniformity of load bearing capacity and minimizing the number of holes through multi-objective topology optimization. Compared with the traditional uniform hole distribution method, the optimized layout increases the hole density in areas with high load bearing capacity requirements and reduces the number of holes in edge areas with low stress levels, thereby significantly reducing construction costs while ensuring uniform load bearing capacity. A collaborative game optimization model establishes a two-layer optimization structure of cooling water circulation temperature control and spray curing. Information interaction between the two optimization models is achieved by using surface temperature reduction as a coupling term. Compared with the traditional method of optimizing cooling or curing parameters separately, collaborative optimization can simultaneously consider the surface curing effect during cooling and the internal cooling requirements during curing, achieving global optimization of temperature field uniformity and surface humidity maintenance. The skip-pour pouring sequence involves pouring adjacent cell units in batches at certain time intervals, allowing the shrinkage deformation of the first poured cell unit to be partially released before the subsequent poured cell unit, thereby reducing the constraint stress at the construction joint. Compared with continuous pouring, this significantly reduces the stress concentration factor. The phase field simulation analysis system characterizes discrete crack interfaces through continuous phase field variables, avoiding the difficulty of tracking crack tips in traditional fracture mechanics. It can simulate the simultaneous propagation of multiple cracks and complex bifurcation of crack paths, providing reliable theoretical support for crack risk early warning during construction.

[0120] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method thereof, characterized in that, include: A multi-objective topology optimization model for the air-float hole layout was established, and an improved NSGA-III algorithm was used for iterative optimization to obtain the air-float hole layout scheme. A three-dimensional coordinate database was established using an RTK-GPS base station and a laser total station, and an automatic drilling operation was performed by a six-degree-of-freedom robotic arm. The floor was divided into cell units, and a skip-cell pouring sequence was adopted. HDPE cooling pipe network was pre-embedded simultaneously when the first batch of cell unit concrete was poured. A collaborative game optimization model for cooling water circulation temperature control and spray curing was established to obtain the collaborative optimization parameter combination. After the concrete was poured, the temperature monitoring system was started, and cooling water was introduced when the internal and external temperature difference reached the threshold. An infrared temperature and humidity sensor array was deployed to monitor the surface microenvironment and the intelligent spray curing system was started. An expansion reinforcement zone was set in the construction joint area. A phase field simulation analysis system for crack propagation path was established, and the curing parameters were adjusted when the simulated crack propagation rate exceeded the threshold.

2. The method according to claim 1, characterized in that, The establishment of the multi-objective topology optimization model for the air-bearing hole layout specifically involves taking the air-bearing hole position as a design variable and setting objective functions for maximizing load uniformity, minimizing the number of holes, and optimizing edge distance.

3. The method according to claim 2, characterized in that, The temperature monitoring system collects internal temperature data in real time through a pre-embedded thermocouple sensor array, controls the cooling rate within a set range, and simultaneously covers the surface with an insulation blanket to maintain the internal and external temperature difference less than a set value.

4. The method according to claim 3, characterized in that, The selection of the air flotation hole layout scheme is specifically carried out by screening out the solution set with pressure non-uniformity less than a set value from multiple non-dominated solutions at the Pareto front, and then selecting the scheme with the fewest holes from the solution set that meets the pressure uniformity requirement.

5. The method according to claim 4, characterized in that, The drilling control of the six-degree-of-freedom robotic arm is specifically achieved through real-time pose feedback via a vision system and a laser rangefinder. Three-point positioning calibration is performed before drilling, and the force and vibration of the drill bit are monitored in real time during the drilling process.

6. The method according to claim 5, characterized in that, After drilling is completed, the hole position coordinates are verified by three-dimensional scanning. For holes with deviations exceeding the set value, a combination of hole enlargement and filling is used for correction.

7. The method according to claim 6, characterized in that, The HDPE cooling pipe network is arranged in a double-layer serpentine pattern. The distances between the upper pipe and the concrete surface and between the lower pipe and the bottom are determined by finite element heat conduction simulation analysis.

8. The method according to claim 7, characterized in that, The determination of the skip-pouring sequence is specifically achieved by using finite element structural analysis software to establish a floor shrinkage stress model, simulating the stress distribution under different pouring sequences, and selecting the scheme that minimizes the stress concentration coefficient at the construction joint.

9. The method according to claim 8, characterized in that, The collaborative game optimization model is a two-layer optimization structure. The upper-layer cooling optimization model determines the circulating water flow rate, circulating water temperature, and cooling duration, while the lower-layer maintenance optimization model determines the spray frequency, droplet size, and spray duration.

10. The method according to claim 9, characterized in that, The upper cooling optimization model and the lower maintenance optimization model interact with each other by using the surface temperature drop as a coupling term, and are solved iteratively using the Stackelberg game framework.

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