Simulation method, system and equipment for bending performance of concrete laminated slab

Abstract

The invention relates to a simulation method, system and equipment for the bending performance of a concrete laminated slab, and belongs to the technical field of concrete construction.The simulation method comprises the steps that a target laminated slab meeting the optimal limestone powder mixing amount interval is screened from different-formula composite limestone powder concrete laminated slabs; constructing a laminated slab model according to the concrete data corresponding to the target laminated slab, and verifying the reliability; after the verification is passed, carrying out stress analysis on the test piece plate in the laminated slab model to obtain a first simulation value of theoretical cracking bending moment and a second simulation value of normal section flexural bearing capacity; calculating a first standard value of the theoretical cracking bending moment of the test piece plate and a second standard value of the normal section flexural capacity according to the concrete current standard; calculating the deviation ratio of the simulation value and the standard value; and when the deviation ratio is greater than a set value, correcting the parameters of the laminated slab model. The method has the beneficial effect of quickly analyzing the bending performance of the laminated slab.

Description

Technical Field

[0001] This application relates to the technical field of concrete construction, and in particular to a method, system and equipment for simulating the bending performance of concrete composite slabs. Background Technology

[0002] With the continuous development of the construction industry, prefabricated buildings have gradually become a development trend in the construction field due to their advantages such as fast construction speed, controllable quality, energy saving, and environmental protection. Composite slabs, as an important component in prefabricated buildings, are composed of precast slabs and cast-in-place reinforced concrete layers. They combine the advantages of cast-in-place and precast structures, ensuring structural integrity while meeting the requirements of industrialization progress. Furthermore, they save a significant amount of formwork erection and dismantling, reducing construction costs, and are widely used in various types of buildings, including residential, public, and industrial plants. The bending performance of composite slabs directly affects the safety and reliability of building structures; therefore, research on their bending performance is crucial.

[0003] Currently, research on the flexural performance of reinforced concrete slabs typically employs experimental methods. Experimental research involves fabricating actual composite slab specimens, applying different loads, and observing and recording data such as deformation, stress distribution, and failure modes to analyze the flexural performance of the composite slab. While this method can directly reflect the performance of the composite slab under actual stress conditions, the experimental process is time-consuming, labor-intensive, and costly, and it is difficult to comprehensively study all possible working conditions. Summary of the Invention

[0004] To quickly analyze the flexural performance of composite slabs, this application provides a method, system, and equipment for simulating the flexural performance of concrete composite slabs.

[0005] In a first aspect, this application provides a method for simulating the flexural performance of composite concrete slabs, employing the following technical solution: A method for simulating the flexural properties of composite concrete slabs, comprising: Select target composite slabs that meet the optimal limestone powder dosage range from composite limestone powder concrete composite slabs with different formulations; A composite slab model is constructed based on the concrete data corresponding to the target composite slab, and the reliability of the model is verified. After the verification was passed, the stress analysis of the specimen plate was carried out in the composite plate model to obtain the first simulated value of the theoretical cracking bending moment and the second simulated value of the bending bearing capacity of the normal section. The theoretical cracking moment and the second specification value of the flexural capacity of the specimen slab were calculated according to the current concrete specifications. Calculate the first deviation rate between the first simulated value and the first standard value, and the second deviation rate between the second simulated value and the second standard value; When the first deviation rate is greater than the first set value or the second deviation rate is greater than the second set value, the parameters of the composite plate model are corrected.

[0006] By adopting the above technical solution, target composite slabs that meet the optimal limestone powder dosage range are selected from composite limestone powder concrete composite slabs with different formulations. This ensures that subsequent simulations are based on composite slabs with superior performance, laying the foundation for accurate simulation of flexural performance. A composite slab model is constructed based on the concrete data corresponding to the target composite slab, and its reliability is verified, ensuring that the model can truly reflect the actual situation of the target composite slab. In the stress analysis after successful verification, the first simulated value of the theoretical cracking moment and the second simulated value of the flexural bearing capacity of the normal section are obtained. The simulated values ​​are compared with the standard values ​​calculated according to the current concrete specifications, and the first and second deviation rates are calculated. This intuitively reflects the difference between the simulation results and the standard requirements, providing a basis for judging the accuracy of the simulation model. When the deviation rate exceeds the set value, the composite slab model parameters are corrected, continuously optimizing the model to better conform to the actual situation and improve the accuracy and reliability of the simulation results. Through this iterative correction, the simulation method can better predict the flexural performance of concrete composite slabs, providing a more scientific and accurate reference for actual engineering design and construction, and helping to ensure the safety and stability of concrete composite slabs in practical applications.

[0007] Optionally, the steps for screening target composite slabs that meet the optimal limestone powder dosage range from composite limestone powder concrete composite slabs with different formulations include: Composite limestone powder concrete specimens with different admixture ratios were prepared, wherein the mass percentages of limestone powder replacing cement were x, fly ash y, and slag powder z, and x+y+z≤40%. Several sets of precast-cast-in-place integrated composite slab components and corresponding cast-in-place comparison slabs are formed by casting, with each slab having the same geometric dimensions and the same reinforcement ratio; Static monotonic loads were applied to all specimens until failure, and crack development images, strain gauge data, and displacement gauge readings were acquired simultaneously. The test parameters of each specimen were analyzed, including initial cracking load, ultimate bearing capacity, maximum deflection, crack width evolution law, and failure mode type. The differences in test indicators between composite slab components and cast-in-place control slabs were compared to determine the optimal range of limestone powder dosage.

[0008] By adopting the above technical solution and preparing specimens with various admixture ratios while strictly controlling the total amount of mineral admixtures, the synergistic effect of limestone powder, fly ash, and slag powder can be comprehensively investigated while ensuring the basic performance of concrete. This avoids the limitations of single-variable analysis and provides rich sample data to support subsequent screening. Applying static monotonic loads to all specimens until failure and simultaneously acquiring crack development images, strain gauge data, and displacement gauge readings allows for a multi-dimensional data acquisition method that fully records the mechanical behavior of the component from initial stress to final failure. This not only obtains key strength indicators such as initial cracking load and ultimate bearing capacity but also allows for in-depth analysis of ductility and deformation performance parameters such as maximum deflection, crack width evolution, and failure mode type, providing comprehensive technical basis for evaluating the stress performance of composite slabs. Furthermore, by analyzing the test indicators of each specimen and comparing them with the cast-in-place control slab, the performance of the composite slab under different limestone powder dosages can be clearly identified. This allows for the precise selection of the optimal dosage range that meets both structural safety and functional requirements while fully leveraging the "synergistic effect" of limestone powder. This can effectively guide the mix design of composite limestone powder concrete slabs in engineering practice, reducing cement usage and carbon emissions while ensuring or even improving the mechanical properties and durability of the composite slab, thus achieving a balance between economic and environmental benefits.

[0009] Optionally, the steps of constructing a composite slab model based on the concrete data corresponding to the target composite slab and verifying the reliability of the model include: A three-dimensional finite element model of the composite slab was established, and constitutive relations were set based on the measured material parameters of the target composite slab. The concrete adopted a damage-plastic model, and the steel reinforcement adopted an ideal elastic-plastic model. Equivalent boundary conditions and a graded loading regime were applied to simulate the entire process from static monotonic loading to failure, with the loading regime consistent with that of the experiment; Simulated load-deflection curves, crack distribution patterns, ultimate bearing capacity data, and strain distribution data at mid-span section were extracted. The simulated data were compared and verified with the measured data of the corresponding experimental specimens. The verification indicators included the fit of the load-deflection curves, the relative error of the ultimate bearing capacity, the crack propagation path, the consistency of the failure mode, and the matching degree of the strain distribution law.

[0010] Optionally, the steps for performing stress analysis in the composite slab model include: Layered shell elements are used to simulate the collaborative work of concrete and steel reinforcement, and a friction-bond coupling contact model is set at the interface between the precast layer and the cast-in-place layer. Four-point bending loads are applied, and the nonlinear equilibrium equations are solved using the Newton-Raphson iterative method. Extract the moment-curvature relationship curve of the mid-span section, and use the moment when the concrete stress in the tension zone of the section reaches the tensile strength as the first simulated value of the theoretical cracking moment. The failure criteria are the crushing of concrete in the compression zone or the yielding of tensile steel bars, and the bending moment corresponding to the peak load point is taken as the second simulated value of the flexural bearing capacity of the normal section.

[0011] Optionally, the step of correcting the parameters of the composite plate model includes: Based on the deviation rate analysis results, the model parameters that have a significant impact on cracking moment and flexural bearing capacity are identified. Quantify the contribution of each model parameter to the simulation results; The model parameters are adjusted step by step according to the contribution ranking. After each correction, the deviation rate between the simulated value and the standard value is recalculated until the corresponding set value is met. Record the final optimized parameter set and build a parameter database for use by similar composite plate models.

[0012] By adopting the above technical solution, deviation rate analysis can be used to identify model parameters that significantly affect cracking moment and flexural bearing capacity. This allows for targeted model improvement, significantly enhancing the accuracy of the model's simulation of the flexural performance of composite slabs. Recording the final optimized parameter set and constructing a parameter database for use by similar composite slab models greatly improves work efficiency and the model's versatility.

[0013] Optionally, the simulation method further includes: Temperature and humidity field coupling is introduced into the composite slab model to simulate the influence of environmental factors on the long-term bending performance of the composite slab; fatigue cumulative damage theory is used to predict the crack propagation law and residual bearing capacity decay trend of the composite slab under repeated loading, and fatigue life curves are plotted. Based on the results of life cycle cost analysis and performance simulation, a mix design that meets safety requirements, has the lowest cost, and the lowest carbon emissions is output.

[0014] By adopting the above technical solutions and introducing the coupling effect of temperature and humidity fields, the performance changes of composite slabs under different environmental conditions can be predicted more accurately. This provides a reliable basis for the durability design of building structures, helps avoid premature structural damage caused by environmental factors, extends the service life of composite slabs, and reduces maintenance costs. The fatigue life curve visually displays the fatigue life of composite slabs under different load conditions, helping engineers to rationally design the service life and maintenance cycle of structures, prevent safety accidents caused by fatigue failure, and improve the safety of building structures. By combining the results of life-cycle cost analysis with performance simulation, the material ratio of composite slabs can be optimized while meeting safety requirements, reducing production costs and maintenance costs during use, while also reducing carbon emissions, aligning with the concept of sustainable development.

[0015] Optionally, the simulation method further includes: Real-time monitoring of strain field distribution and crack dynamic evolution of composite slabs under load during service phase; Construct a feedback mechanism based on monitoring data to automatically calibrate key parameters in the composite slab model; The monitoring data is analyzed to generate a health status assessment report for the composite slab and to output maintenance strategy recommendations for specific environmental conditions.

[0016] By adopting the above technical solutions and through real-time monitoring, abnormal deformation and crack propagation of the composite slab can be detected in a timely manner, providing early warning of potential safety hazards, preventing serious structural damage, and ensuring the safety of personnel and property. The monitoring data reflects the actual performance of the composite slab in practical use. Through a feedback mechanism, this data is used to automatically calibrate model parameters, making the model more closely reflect actual conditions. Analyzing the monitoring data generates a composite slab health status assessment report and outputs maintenance strategy recommendations for specific environmental conditions, contributing to the refined management and maintenance of the composite slab.

[0017] Secondly, this application provides a simulation system for the flexural performance of composite concrete slabs, employing the following technical solution: A simulation system for the flexural performance of composite concrete slabs, comprising: The composite slab screening module is used to screen target composite slabs that meet the optimal limestone powder dosage range from composite limestone powder concrete composite slabs with different formulations. The model building and verification module is used to build a composite slab model based on the concrete data corresponding to the target composite slab, and to verify the reliability of the model. The simulation module is used to perform stress analysis on the specimen plate in the composite plate model after the verification is passed, and to obtain the first simulated value of the theoretical cracking bending moment and the second simulated value of the bending bearing capacity of the normal section. The data processing module is used to calculate the first standard value of the theoretical cracking bending moment and the second standard value of the flexural bearing capacity of the specimen plate according to the current concrete specifications; and to calculate the first deviation rate between the first simulated value and the first standard value and the second deviation rate between the second simulated value and the second standard value. The feedback adjustment module is used to correct the parameters of the composite plate model when the first deviation rate is greater than the first set value or the second deviation rate is greater than the second set value.

[0018] Thirdly, this application provides a computer device that adopts the following technical solution: A computer device includes a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the simulation method for the flexural performance of concrete composite slabs as described in the first aspect.

[0019] In summary, this application includes at least one of the following beneficial technical effects: Selecting target composite slabs from different limestone powder concrete composite slab formulations that meet the optimal limestone powder dosage range ensures that subsequent simulations are based on slabs with superior performance, laying the foundation for accurate flexural performance simulation. A composite slab model is constructed based on the concrete data corresponding to the target slab, and its reliability is verified, ensuring that the model accurately reflects the actual situation of the target composite slab. In the stress analysis after successful verification, the first simulated value of the theoretical cracking moment and the second simulated value of the flexural bearing capacity of the normal section are obtained. The simulated values ​​are compared with the standard values ​​calculated according to current concrete specifications, and the first and second deviation rates are calculated. This intuitively reflects the difference between the simulation results and the standard requirements, providing a basis for judging the accuracy of the simulation model. When the deviation rate exceeds the set value, the composite slab model parameters are corrected, continuously optimizing the model to better reflect the actual situation and improve the accuracy and reliability of the simulation results. Through this iterative correction, the simulation method can better predict the flexural performance of concrete composite slabs, providing a more scientific and accurate reference for actual engineering design and construction, and helping to ensure the safety and stability of concrete composite slabs in practical applications. Attached Figure Description

[0020] Figure 1 This is a first flowchart of an embodiment of the method of this application; Figure 2 This is a second flowchart of an embodiment of the method of this application; Figure 3 This is a third flowchart of an embodiment of the method of this application; Figure 4 This is the fourth flowchart of an embodiment of the method of this application; Figure 5 This is the fifth flowchart of an embodiment of the method of this application; Figure 6 This is the sixth flowchart of an embodiment of the method of this application; Figure 7 This is the seventh flowchart of an embodiment of the method of this application. Detailed Implementation

[0021] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-7 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0022] The first embodiment of this application discloses a method for simulating the flexural performance of composite concrete slabs. (Refer to...) Figure 1 The simulation method may include S110-S160: S110, screening target composite slabs that meet the optimal limestone powder dosage range from composite limestone powder concrete composite slabs with different formulations. S120: Construct a composite slab model based on the concrete data corresponding to the target composite slab, and verify the reliability of the model; S130, after verification, stress analysis was performed on the specimen plate in the composite plate model to obtain the first simulated value of the theoretical cracking bending moment and the second simulated value of the bending bearing capacity of the normal section. S140, the first specification value of the theoretical cracking moment and the second specification value of the flexural capacity of the specimen plate are calculated according to the current concrete specifications. S150, calculate the first deviation rate between the first simulated value and the first standard value, and the second deviation rate between the second simulated value and the second standard value; S160, when the first deviation rate is greater than the first set value or the second deviation rate is greater than the second set value, correct the parameters of the composite plate model.

[0023] Reference Figure 2 S110, the steps for screening target composite slabs that meet the optimal limestone powder dosage range from composite limestone powder concrete composite slabs with different formulations include S210-S250: S210, prepare composite limestone powder concrete specimens with different admixture ratios, wherein the mass percentage of limestone powder replacing cement is x, fly ash is y, and slag powder is z, and x+y+z≤40%; S220, cast to form several sets of precast-cast-in-place integrated composite slab components and corresponding cast-in-place comparison slabs, with each slab having the same geometric dimensions and the same reinforcement ratio; S230: Apply static monotonic load to all specimens until failure, and simultaneously acquire crack development images, strain gauge data, and displacement gauge readings. S240, analyze the test indicators of each specimen, including initial cracking load, ultimate bearing capacity, maximum deflection, crack width evolution law and failure mode type; S250 compares the differences in test indicators between composite slab components and cast-in-place control slabs to determine the optimal range of limestone powder dosage.

[0024] Specifically, to implement this simulation method, the preparation of material specimens begins. Specifically, the mass percentage of limestone powder replacing cement can be selected as x: 5%, 10%, 15%, and 20%; fly ash as y: 5%, 10%, and 15%; and slag powder as z: 5%, 10%, and 15%, ensuring that x+y+z≤40%. At least 5 groups of composite limestone powder concrete with different combinations are designed according to this dosage ratio. For each group, 3 cubic specimens of 150mm×150mm×150mm and prism specimens of 150mm×150mm×300mm are prepared. At the same time, standard curing specimens of concrete with the same mix ratio as the composite slab are made to test its mechanical property parameters.

[0025] The composite slab components were cast. The precast layer was designed to be 60mm thick and the cast-in-place layer to be 40mm thick, with uniform geometric dimensions of 2400mm×1200mm×100mm. HRB400 grade steel bars were used for the bottom reinforcement, and the reinforcement ratio was controlled between 0.8% and 1.2%. Five sets of precast-cast-in-place integrated composite slab components were fabricated, with three specimens in each set. At the same time, cast-in-place control slabs of the same size and reinforcement were cast as a reference. During the casting process, strain gauges were pre-embedded at the mid-span and support positions, and displacement gauges were placed at the bottom of the slab. The interface between the precast layer and the cast-in-place layer was roughened manually and coated with an interface agent to simulate actual construction conditions.

[0026] When performing static monotonic loading tests on all specimens, a 500kN hydraulic servo universal testing machine was used to apply four-point bending loads at a loading rate of 0.5kN / s. A high-definition industrial camera (resolution not less than 20 million pixels) was used to acquire crack development images at a frequency of 10 seconds / frame. Strain gauge data was simultaneously acquired through a DH3816 static strain testing system with a sampling frequency of 10Hz. Displacement gauge data was recorded in real time through a data acquisition instrument until the specimen failed. During the test, the initial load value at crack initiation, the location and number of cracks, the maximum deflection corresponding to the ultimate load, and the final failure mode, such as bending failure, shear failure, or interface peeling failure, were recorded in detail.

[0027] The test indicators of each specimen were analyzed. Crack images were processed using image recognition technology (such as OpenCV algorithm) to extract the evolution law of crack width with load. The initial cracking load, ultimate bearing capacity, maximum deflection and failure mode of the composite slab under different admixture combinations were compared. The test indicators of the composite slab and the cast-in-place comparison slab were normalized. The ultimate bearing capacity improvement rate and deflection control coefficient were used as evaluation indicators. The optimal range of limestone powder admixture was determined by range analysis. For example, when x = 10%, y = 15%, and z = 10%, the composite slab has the best comprehensive performance.

[0028] Reference Figure 3S120, the steps of constructing a composite slab model based on the concrete data corresponding to the target composite slab and verifying the reliability of the model include S310-S340: S310, Establish a three-dimensional finite element model of the composite slab, and set the constitutive relation based on the measured material parameters of the target composite slab, wherein the concrete adopts the damage plasticity model and the steel reinforcement adopts the ideal elastic-plastic model; S320, apply equivalent boundary conditions and a graded loading regime to simulate the entire process from static monotonic loading to failure, with the loading regime consistent with the experiment; S330 extracts simulated load-deflection curves, crack distribution patterns, ultimate bearing capacity data, and strain distribution data at mid-span section. S340 compares and verifies the simulated data with the measured data of the corresponding experimental specimens. The verification indicators include the fit of the load-deflection curve, the relative error of the ultimate bearing capacity, the crack propagation path, the consistency of the failure mode, and the matching degree of the strain distribution law.

[0029] Specifically, ANSYS or ABAQUS finite element software was used to establish a three-dimensional finite element model of the composite slab. Based on the measured concrete cube compressive strength, elastic modulus, Poisson's ratio, and steel reinforcement yield strength and elastic modulus, parameters for the concrete damage plasticity model were set, such as an expansion angle of 30°, a flow potential eccentricity of 0.1, a biaxial compressive strength to uniaxial compressive strength ratio of 1.16, and a damage factor. An ideal elastoplastic model was used for the steel reinforcement. In the model, C3D8R solid elements were used for the concrete elements, and T3D2 truss elements were used for the steel reinforcement. The steel reinforcement and concrete worked together through an embedded method.

[0030] When applying equivalent boundary conditions, four-point bending loading is simulated. Fixed hinge supports and rolling hinge supports are used at the supports. The loading regime is consistent with the experiment, that is, 0.1 times the estimated ultimate load is applied for preloading, and after eliminating the gap, the load is applied in 0.5kN increments, with each load held for 30 seconds, until the load drops to 85% of the ultimate load and then loading is stopped.

[0031] During the simulation, the load-deflection curve (comparing the yield plateau and descending segments of the experimental curve), Mises stress cloud map (identifying crack initiation locations), and mid-span section strain distribution (verifying the plane section assumption) were extracted through the post-processing module. Quantitative indicators were used to evaluate the consistency: the correlation coefficient of the load-deflection curve ≥ 0.95, the relative error of the ultimate bearing capacity ≤ 5%, the crack distribution pattern was calculated to have a similarity of ≥ 85% using image processing software, and the strain distribution law was assessed using a root mean square error (RMSE) ≤ 10με to ensure the reliability of the simulation model.

[0032] Reference Figure 4The steps for stress analysis in the composite slab model in S130 include S410-S440: S410 uses layered shell elements to simulate the collaborative work of concrete and steel reinforcement, and sets a friction-bond coupling contact model at the interface between the precast layer and the cast-in-place layer. S420, apply four-point bending loads, and solve the nonlinear equilibrium equations using the Newton-Raphson iterative method; S430, extract the moment-curvature relationship curve of the mid-span section, and take the moment when the concrete stress in the tension zone of the section reaches the tensile strength as the first simulated value of the theoretical cracking moment. S440 uses the crushing of concrete in the compression zone or the yielding of tensile steel bars as the failure criteria, and takes the bending moment corresponding to the peak load point as the second simulated value of the flexural bearing capacity of the normal section.

[0033] Specifically, after successful verification, the model settings were optimized. Layered shell elements (such as S4R shell elements) were used to simulate the collaborative work between concrete and steel reinforcement. The precast and cast-in-place layers were divided into different shell element layers, and the steel reinforcement layer was equivalent to a membrane element based on the actual reinforcement ratio. A friction-bond coupling contact model was set at the interface between the precast and cast-in-place layers. In the contact properties, the normal behavior adopted "hard contact", and the tangential behavior adopted the Coulomb friction model (friction coefficient was set to 0.6). The bond stiffness was also defined (e.g., normal bond stiffness 10). 5 N / m 3 Tangential bond stiffness 5×10 4 N / m 3 To simulate the bond-slip properties of the interface, four-point bending loads were applied, and the nonlinear equilibrium equations were solved using the Newton-Raphson iterative method. The convergence criteria for the iteration were set as follows: the convergence tolerances for force and displacement were 1 × 10⁻⁶. -5 and 5×10 -4 The moment-curvature relationship curve of the mid-span section is extracted, and the theoretical cracking moment (first simulated value) is determined based on the characteristic points of the curve. The moment value corresponding to the tensile strength of the concrete in the tensile zone of the section is taken as the failure criterion, with the crushing of the concrete in the compression zone (damage factor reaches 0.9) or the yielding of the tensile steel reinforcement (stress reaches yield strength). The moment corresponding to the peak load point is taken as the flexural bearing capacity of the normal section (second simulated value).

[0034] The theoretical cracking moment (first code value) and the flexural capacity of the normal section (second code value) of the specimen slab are calculated according to the current "Code for Design of Concrete Structures". The cracking moment is calculated using the formula Mcr = γ. m f tk W0 is calculated, γ m f is the plastic coefficient of section modulus. tk W0 represents the standard value of the axial tensile strength of concrete, and W0 is the elastic section modulus of the tension zone of the equivalent section. The flexural capacity of the normal section is calculated using the formula M = α1f for a properly reinforced beam.c Calculate bx(h0-x / 2), where α1 is the coefficient, and f c Here, b is the design value for the axial compressive strength of concrete, x is the cross-sectional width, and h0 is the effective height of the cross-section. Calculate the first deviation rate between the first simulated value and the first standard value, and the second deviation rate between the second simulated value and the second standard value, for example, deviation rate = |simulated value - standard value| / standard value × 100%.

[0035] Reference Figure 5 In S160, the steps for correcting the parameters of the composite plate model include S510-S540: S510, based on the deviation rate analysis results, identifies the model parameters that have a significant impact on cracking moment and flexural bearing capacity; S520 quantifies the contribution of each model parameter to the simulation results; S530, adjust the model parameters step by step according to the contribution ranking, and recalculate the deviation rate between the simulated value and the standard value after each correction until the corresponding set value is met; S540 records the final optimized parameter set and builds a parameter database for use by similar composite plate models.

[0036] Specifically, when the first deviation rate is greater than the first set value, such as 5%, or the second deviation rate is greater than the second set value, such as 8%, the influence of model parameters on the simulation results is analyzed by the control variable method. The model parameters include the expansion angle, interfacial friction coefficient, and bond strength between steel reinforcement and concrete in the concrete damage plasticity model.

[0037] Sensitivity analysis methods (such as the Sobol method or Morris screening method) were used to quantify the contribution of each model parameter. The model parameters were then gradually adjusted by ranking them from highest to lowest contribution (e.g., interfacial bond stiffness > concrete expansion angle > steel yield strength). After each adjustment, the deviation rate between the simulated value and the specification value was recalculated until the set values ​​were met (first deviation rate ≤ 5%, second deviation rate ≤ 8%). The final optimized parameter set was recorded (e.g., the interfacial normal bond stiffness was adjusted to 1.2 × 10⁻⁶). 5 N / m 3 (The expansion angle is adjusted to 35°), and the parameter database is built and stored in the SQL Server database for direct querying when calling similar composite slab models.

[0038] Reference Figure 6 The simulation method also includes S610-S630: S610 introduces the temperature field-humidity field coupling effect into the composite slab model to simulate the influence of environmental factors on the long-term bending performance of the composite slab. S620, the fatigue cumulative damage theory is used to predict the crack propagation law and the residual bearing capacity decay trend of the composite slab under repeated load, and the fatigue life curve is plotted. S630 combines full life cycle cost analysis and performance simulation results to output a mix design that meets safety requirements, has the lowest cost, and the lowest carbon emissions.

[0039] Specifically, a coupled analysis of the temperature field and humidity field is introduced into the finite element model, using the heat conduction equation. And humidity diffusion equation Set the ambient temperature-humidity history (e.g., -20℃ to 60℃ cycle, humidity 40% to 90%), taking into account the drying shrinkage and wet expansion effects of concrete (drying shrinkage coefficient 300×10). -6 ) and temperature stress (linear expansion coefficient 1×10 -5 / ℃), simulating 50 years of long-term deflection growth and cross-sectional stress redistribution.

[0040] For fatigue performance simulation, based on Miner's linear cumulative damage theory, repetitive loads (such as vehicle loads) are simplified into sinusoidal cyclic loads. The load amplitude is taken as 0.3 to 0.8 times the ultimate bearing capacity, and the frequency is 1 Hz. The number of load cycles is counted by the rainflow counting method, and the crack length propagation law under different number of cycles is simulated (using the Paris formula da / dN=C(ΔK)). m , where ΔK is the stress intensity factor amplitude, and C and m are material constants), predict the decay trend of the remaining bearing capacity, plot the fatigue life curve (SN curve), and determine the fatigue safety factor of the composite plate within its design service life.

[0041] When combining life cycle cost analysis, market prices, transportation costs, construction costs, and maintenance costs of materials (cement, limestone powder, fly ash, slag powder, steel bars, etc.) are collected. The net present value (NPV) method is used to calculate the life cycle cost of different mix proportion schemes. At the same time, the carbon emissions of each scheme are calculated according to the "Building Carbon Emission Calculation Standard". A multi-objective optimization algorithm (such as the NSGA-II algorithm) is used to balance safety performance, cost, and carbon emissions, and output the mix proportion scheme that meets safety requirements (ultimate bearing capacity ≥ 1.2 times the design value), has the lowest cost, and has the lowest carbon emissions.

[0042] Reference Figure 7 The simulation method also includes S710-S730: S710, real-time monitoring of strain field distribution and crack dynamic evolution of composite slabs under load during service phase; S720, constructs a feedback mechanism based on monitoring data to automatically calibrate key parameters in the composite plate model; The S730 analyzes monitoring data, generates a health status assessment report for the composite slab, and outputs maintenance strategy recommendations for specific environmental conditions.

[0043] Specifically, in terms of monitoring during the usage phase, fiber optic grating (FBG) sensors are deployed at the mid-span, 1 / 4-span, and support locations of the composite slab to monitor the strain field distribution. The sensors are surface-mounted or embedded, with a sampling frequency of 5 minutes per time. At the same time, crack monitoring instruments (such as vibrating wire crack gauges) are installed in key areas on the bottom of the slab to dynamically record changes in crack width. The data is uploaded to the cloud platform in real time via a wireless transmission module (such as LoRa or NB-IoT).

[0044] A feedback mechanism based on monitoring data is constructed, and machine learning algorithms (such as BP neural networks) are used to establish a mapping relationship between monitoring data and key model parameters. Monitoring data includes strain, crack width, etc., while key model parameters include interfacial bond stiffness, concrete elastic modulus, etc.

[0045] When the deviation between the monitored values ​​and the model predictions exceeds 10%, key parameters in the model are automatically calibrated, such as reducing the interfacial bond stiffness to reflect the actual degradation. Trend analysis (e.g., using exponential smoothing to predict strain growth trends) and anomaly detection (e.g., using the 3σ criterion to identify abrupt changes) are performed on the monitoring data to generate a composite slab health status assessment report. The assessment indicators in the report include the current load-bearing capacity reserve coefficient, crack development level (e.g., minor, moderate, severe), and interfacial integrity. Maintenance strategy recommendations are also proposed for specific environmental conditions (e.g., high-temperature and high-humidity areas) (e.g., applying protective coatings, periodic grouting to repair cracks, or reinforcement treatment).

[0046] Based on the above method embodiments, the second embodiment of this application discloses a simulation system for the flexural performance of concrete composite slabs. The simulation system for the flexural performance of concrete composite slabs in this embodiment can implement any of the above-described methods for simulating the flexural performance of concrete composite slabs, and the specific working process of each module in the simulation system for the flexural performance of concrete composite slabs can be referred to the corresponding process in the above method embodiments.

[0047] For ease of understanding, an example is given below: A simulation system for the flexural performance of composite concrete slabs includes: The composite slab screening module is used to screen target composite slabs that meet the optimal limestone powder dosage range from composite limestone powder concrete composite slabs with different formulations. The model building and verification module is used to build a composite slab model based on the concrete data corresponding to the target composite slab and to verify the reliability of the model. The simulation module is used to perform stress analysis on the specimen plate in the composite plate model after the verification is passed, and to obtain the first simulated value of the theoretical cracking bending moment and the second simulated value of the bending bearing capacity of the normal section. The data processing module is used to calculate the first standard value of the theoretical cracking bending moment and the second standard value of the flexural bearing capacity of the normal section of the specimen plate according to the current concrete specifications; and to calculate the first deviation rate between the first simulated value and the first standard value and the second deviation rate between the second simulated value and the second standard value. The feedback adjustment module is used to correct the parameters of the composite plate model when the first deviation rate is greater than the first set value or the second deviation rate is greater than the second set value.

[0048] The third embodiment of this application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement a method for simulating the bending performance of concrete composite slabs.

[0049] The memory can communicate with the processor via a communication bus, which can be an address bus, a data bus, a control bus, etc.

[0050] Additionally, the memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device.

[0051] Furthermore, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0052] In this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0053] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce a good effect.

[0054] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for simulating the flexural performance of a concrete composite slab, characterized in that, The method comprises the following steps: selecting a target composite slab of limestone powder concrete from different composite slabs of limestone powder concrete according to an optimal limestone powder content range; constructing a composite slab model according to concrete data corresponding to the target composite slab and verifying the reliability of the model; after verification, performing stress analysis on the test specimen in the composite slab model to obtain a first simulated value of the theoretical cracking moment and a second simulated value of the normal section flexural bearing capacity; calculating a first standard value of the theoretical cracking moment and a second standard value of the normal section flexural bearing capacity of the test specimen according to the current concrete specification; calculating a first deviation rate of the first simulated value and the first standard value and a second deviation rate of the second simulated value and the second standard value; when the first deviation rate is greater than a first set value or the second deviation rate is greater than a second set value, modifying the parameters of the composite slab model.

2. The method for simulating the flexural performance of a concrete composite slab according to claim 1, wherein, The step of selecting a target composite slab of limestone powder concrete from different composite slabs of limestone powder concrete according to an optimal limestone powder content range comprises: preparing a plurality of composite limestone powder concrete specimens with different proportions of limestone powder, wherein the mass percentage of limestone powder replacing cement is x, fly ash is y, and slag powder is z, and x+y+z≤40%; casting a plurality of groups of precast-in-situ integrated composite slab components and corresponding in-situ comparison slabs, the geometric dimensions of each slab being consistent and the reinforcement ratios being the same; applying static monotonic load to each specimen until failure, and synchronously collecting crack development images, strain gauge data, and displacement meter readings; analyzing the test indexes of each specimen, including the initial cracking load, the ultimate bearing capacity, the maximum deflection, the crack width evolution law, and the failure mode type; comparing the differences in the test indexes between the composite slab components and the in-situ comparison slabs to determine the optimal limestone powder content range.

3. The method for simulating the flexural performance of a concrete composite slab according to claim 1, wherein, The step of constructing a composite slab model according to the concrete data corresponding to the target composite slab and verifying the reliability of the model comprises: establishing a three-dimensional finite element model of the composite slab, setting the constitutive relationship based on the measured material parameters of the target composite slab, wherein the concrete adopts a damage plasticity model and the steel bar adopts an ideal elastic-plastic model; applying equivalent boundary conditions and a staged loading system to simulate the whole process of static monotonic loading to failure, and the loading system is consistent with the experiment; extracting simulated data of the load-deflection curve, the crack distribution mode, the ultimate bearing capacity data, and the strain distribution of the mid-span section; comparing and verifying the simulated data with the measured data of the corresponding experimental specimen, and the verification indexes include the load-deflection curve fitting degree, the relative error of the ultimate bearing capacity, the crack development path, the failure shape consistency, and the strain distribution law matching degree.

4. The method for simulating the flexural performance of a concrete composite slab according to claim 1, wherein, The step of performing stress analysis in the composite slab model comprises: simulating the cooperative work of concrete and steel bars by using layered shell elements, and setting a friction-bonding coupled contact model at the interface between the precast layer and the cast-in-place layer; applying a four-point bending load and solving the nonlinear equilibrium equation by the Newton-Raphson iteration method; extracting the mid-span section moment-curvature relationship curve, and taking the moment when the tensile stress of the section reaches the tensile strength as the first simulated value of the theoretical cracking moment. Take the concrete crushing in the compression zone or the steel bar yielding in the tension zone as the failure criterion, and take the bending moment corresponding to the peak load as the second simulation value of the flexural capacity of the normal section.

5. The method for simulating the flexural performance of a concrete composite slab according to claim 1, wherein, The step of modifying the model parameters of the composite slab includes: Based on the deviation rate analysis results, locate the model parameters that significantly affect the cracking moment and flexural capacity; Quantify the contribution of each model parameter to the simulation results; Sort and gradually adjust the model parameters according to the contribution, recalculate the deviation rate of the simulation value and the specification value after each modification, until the corresponding set value is met; Record the final optimized parameter set and construct a parameter database for similar composite slab models to call.

6. The method for simulating the flexural performance of a concrete composite slab according to claim 1, wherein, The simulation method further includes: Introduce temperature field-humidity field coupling in the composite slab model to simulate the influence of environmental factors on the long-term flexural performance of the composite slab; Use the fatigue cumulative damage theory to predict the crack propagation law and residual capacity decay trend of the composite slab under repeated loads, and draw the fatigue life curve; Combine the life cycle cost analysis and performance simulation results to output the matching scheme that meets the safety requirements, has the lowest cost, and has the lowest carbon emissions.

7. The method for simulating the flexural performance of a concrete composite slab according to claim 1, wherein, The simulation method further includes: Real-time monitoring of the strain field distribution and crack dynamic evolution of the composite slab under the action of the load in the use stage; Construct a feedback mechanism based on the monitoring data to automatically calibrate the key parameters in the composite slab model; Analyze the monitoring data to generate a composite slab health status evaluation report and output maintenance strategy suggestions for specific environmental conditions.

8. A simulation system for flexural performance of a concrete composite slab, characterized by, Performing the simulation method for the flexural performance of a concrete composite slab according to any one of claims 1-7, comprising: A composite slab screening module for screening target composite slabs that meet the optimal limestone powder content interval from different formula composite limestone powder concrete composite slabs; A model construction and verification module for constructing a composite slab model according to the concrete data corresponding to the target composite slab and verifying the reliability of the model; A simulation module for performing stress analysis on the test specimen plate in the composite slab model after verification to obtain a first simulation value of the theoretical cracking moment and a second simulation value of the flexural capacity of the normal section; A data processing module for calculating a first specification value of the theoretical cracking moment and a second specification value of the flexural capacity of the normal section of the test specimen plate according to the current concrete specification; and calculating a first deviation rate of the first simulation value and the first specification value and a second deviation rate of the second simulation value and the second specification value; A feedback adjustment module for modifying the model parameters of the composite slab when the first deviation rate is greater than a first set value or the second deviation rate is greater than a second set value.

9. A computer device, comprising: A computer program product comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the simulation method for the flexural performance of a concrete composite slab according to any one of claims 1-7.

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