Thin-walled lightweight concrete detection method and device

Abstract

The invention relates to the technical field of concrete detection, and discloses a thin-wall type lightweight concrete detection method and device, and the method comprises the following steps: collecting a thin-wall type lightweight concrete sample to be detected from a site or a laboratory, preprocessing the thin-wall type lightweight concrete sample, and constructing a multi-physics field coupling model and a coupling model of the thin-wall type lightweight concrete; arranging at least one sensor on the thin-walled lightweight concrete sample to collect physical field data of mechanics, temperature, humidity and permeability; determining an actual performance value of the thin-walled lightweight concrete based on the acquired physical field data; and based on a result after optimization algorithm processing, carrying out analogue simulation on the performance of the thin-walled lightweight concrete, and generating a detection report. By introducing a multi-physics field modeling module, a coupling model of mechanics, heat, humidity and a permeation field is constructed, comprehensive analysis of complex environmental response of concrete is realized, and a more real and comprehensive performance evaluation effect is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete detection, and specifically to a detection method and device for thin-walled lightweight concrete. Background Art

[0002] Thin-walled concrete is a structural material with a relatively thin wall thickness formed with cement as the matrix and by optimizing the proportioning or adding lightweight materials (such as foaming agents, fibers). It has the characteristics of lightweight, high strength, and high durability. During the construction process, aerated concrete thin-wall panels are usually used to replace traditional brick walls to achieve the purpose of building energy conservation.

[0003] When detecting the structural strength of thin-walled concrete, due to the very low thickness of thin-walled concrete, when sampling, in order to avoid affecting the overall strength of the concrete project, the sampled specimens usually need to be controlled within 3 - 5 cm. During the detection process, due to the relatively thin core specimens of concrete, when conducting compressive strength, impermeability, and durability tests, traditional concrete detection equipment is prone to large errors. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a detection method and device for thin-walled lightweight concrete, which solves the problem that the prior art lacks a modeling method that can comprehensively consider the mutual coupling effects of multiple physical fields such as the mechanical field, thermal field, humidity field, and penetration field, resulting in a large deviation in the performance evaluation results of concrete in complex environments and making it difficult to meet the high-reliability detection requirements of engineering.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A detection method for thin-walled lightweight concrete includes the following steps: First, collect samples of thin-walled lightweight concrete to be detected from the site or laboratory and perform pretreatment on them; After pretreatment, construct a multi-physical field coupling model for thin-walled lightweight concrete, and the coupling model includes the interaction of the mechanical field, thermal field, humidity field, and penetration field; Arrange at least one type of sensor on the thin-walled lightweight concrete sample to collect physical field data of mechanics, temperature, humidity, and permeability; Based on the collected physical field data, use a multi-objective optimization algorithm to correct the detection errors caused by the multi-physical field coupling effect, so as to determine the actual performance value of the thin-walled lightweight concrete; Based on the results processed by the optimization algorithm, perform a simulation on the performance of the thin-walled lightweight concrete and generate a detection report.

[0006] Preferably, the mechanical field modeling includes establishing the stress-strain relationship of concrete through nonlinear elastic theory and considering the influence of temperature and humidity environmental factors on the mechanical properties of concrete.

[0007] Preferably, the coupled modeling of the multi-physical field model is achieved by coupling the heat conduction equation and the humidity diffusion equation, taking into account the dual diffusion effects of moisture and heat in concrete.

[0008] Preferably, the seepage field modeling describes the permeability using Darcy's law and the Navier-Stokes equation.

[0009] Preferably, the sensors are arranged at different positions of the thin-walled lightweight concrete samples, and the sensors include: A temperature sensor for real-time acquisition of the internal temperature change of the thin-walled lightweight concrete; A humidity sensor for real-time monitoring of the internal humidity change of the thin-walled lightweight concrete; A stress sensor for real-time measurement of the internal stress change of the thin-walled lightweight concrete; A seepage sensor for detecting the permeability of the thin-walled lightweight concrete and the rate of moisture flow; The sensors transmit data to the data processing unit through wireless transmission or wired connection.

[0010] Preferably, the multi-objective optimization algorithm includes the particle swarm optimization algorithm and the multi-objective genetic algorithm, which are used to minimize the performance index errors of the concrete compressive strength and impermeability. The optimization steps include: Using the particle swarm optimization algorithm to adjust the model parameters and reduce the difference between the model and the experimental data; Using the multi-objective genetic algorithm to process multiple detection targets, balancing the minimization of measurement error and the balance of measurement accuracy and efficiency; during the optimization process, determine the best parameter combination to obtain the minimum error and the fastest detection speed.

[0011] Preferably, the objective function of the optimization algorithm includes: Minimization of compressive strength error: Minimize the absolute error between the actual compressive strength and the predicted compressive strength; Minimization of impermeability error: Minimize the absolute error between the actual seepage flow and the predicted seepage flow; Balance of measurement accuracy and efficiency: Minimize the detection time required while ensuring the detection accuracy to improve the detection efficiency.

[0012] Preferably, the simulation uses finite element analysis and computational fluid dynamics models for multi-physical field coupling simulation. During the simulation process, the influence of environmental changes on the concrete performance is considered, and the stress distribution, temperature and humidity changes, and permeability performance of the concrete under different environmental conditions are obtained through simulation calculations, so as to verify the reliability of the optimized detection scheme.

[0013] Preferably, the test report includes the following content: Detailed analysis of the compressive strength, impermeability, stress-strain curve, and data on temperature and humidity changes of the concrete; Based on the optimized test results, provide a quality assessment of the thin-walled lightweight concrete and put forward targeted quality control suggestions; provide an analysis of the error range and accuracy of the test method to support subsequent engineering applications and quality control.

[0014] A thin-walled lightweight concrete testing device, comprising: A sample collection module for collecting samples of thin-walled lightweight concrete to be tested and preprocessing them; A physical field modeling module for constructing a multi-physical field coupling model of thin-walled lightweight concrete, including at least the interaction of the mechanical field, thermal field, humidity field, and penetration field; A data collection module for arranging sensors on the thin-walled lightweight concrete sample and collecting physical field data of mechanics, temperature, humidity, and permeability; An optimization calculation module for correcting the test error using a multi-objective optimization algorithm based on the collected data and determining the true performance value of the thin-walled lightweight concrete; A simulation module for performing multi-physical field simulation based on the results processed by the optimization algorithm; A report generation module for generating a performance test report of the thin-walled lightweight concrete and providing quality control suggestions.

[0015] The present invention provides a method and device for testing thin-walled lightweight concrete. It has the following beneficial effects: 1. By introducing a multi-physical field modeling module, the present invention constructs a coupling model of mechanics, heat, humidity, and penetration fields, realizes a comprehensive analysis of the complex environmental response of concrete, and obtains a more real and comprehensive performance evaluation effect.

[0016] 2. By using a multi-objective optimization algorithm in the optimization calculation module to correct the test error, the present invention realizes the accurate correction of test data and obtains a high-precision identification effect of the true performance of the concrete.

[0017] 3. By arranging multiple types of sensors in the data collection module and collecting physical field data in real time, the present invention realizes the dynamic monitoring of the concrete state and obtains a more reliable and timely performance feedback effect.

[0018] 4. By integrating the analysis results in the report generation module and giving quality control suggestions, the present invention realizes an integrated process from testing to decision-making and obtains the effect of improving the efficiency of engineering quality management. Description of the Drawings

[0019] Figure 1It is the method flow chart of a detection method for a thin-walled lightweight concrete of the present invention; Figure 2 It is the structural block diagram of a detection device for a thin-walled lightweight concrete of the present invention. Specific embodiments

[0020] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the scope of protection of the present invention.

[0021] Please refer to the attached Figure 1 , an embodiment of the present invention provides a detection method for thin-walled lightweight concrete, including the following steps: first, collect samples of thin-walled lightweight concrete to be detected from the site or laboratory and preprocess them; After preprocessing, a multi-physical field coupling model of the thin-walled lightweight concrete is constructed, and the coupling model includes the interaction of the mechanical field, thermal field, humidity field and seepage field; Arrange at least one sensor on the thin-walled lightweight concrete sample to collect physical field data of mechanics, temperature, humidity and permeability; Based on the collected physical field data, a multi-objective optimization algorithm is used to correct the detection error caused by the multi-physical field coupling effect, so as to determine the actual performance value of the thin-walled lightweight concrete; Based on the results processed by the optimization algorithm, the performance of the thin-walled lightweight concrete is simulated and a detection report is generated.

[0022] The mechanical field modeling includes establishing the stress-strain relationship of concrete through the nonlinear elastic theory and considering the influence of temperature and humidity environmental factors on the mechanical properties of concrete.

[0023] The coupling modeling of the multi-physical field coupling model is coupled through the heat conduction equation and the humidity diffusion equation, considering the double diffusion effect of moisture and heat in the concrete.

[0024] Specifically, the nonlinear elasticity theory takes into account that when a material is subjected to external forces, the relationship between its stress and strain is not linear but gradually changes as the loading level increases. Through this theory, the deformation process of concrete under stress can be more accurately reflected. This process is represented by a stress-strain curve. In the initial loading stage of concrete materials, concrete may exhibit approximate linearity, but as the stress further increases, the stress-strain relationship of concrete will tend to be nonlinear. Temperature and humidity are two key environmental factors. Generally, changes in temperature will cause expansion or contraction inside the concrete, thereby affecting its mechanical properties. Changes in humidity directly affect the moisture content of the concrete, and further affect its internal structure and strength characteristics. Therefore, the present invention introduces correction factors for temperature and humidity into the stress-strain relationship model, and uses the following formula to represent the influence of temperature and humidity on the mechanical properties of concrete: σ = E∈(1 + αT + βH); Where, σ is the stress of the concrete, ∈ is the strain, E is the elastic modulus, α is the temperature expansion coefficient, β is the humidity coefficient, T is the temperature, and H is the humidity. α and β in the formula are constants determined by experimental data and material properties, and the specific values will be adjusted according to different types of concrete; A multi-dimensional analysis of environmental factors is carried out. For example, in some embodiments, considering the influence of temperature changes on the internal microstructure of concrete, a mathematical model combining the nonlinear elasticity theory and the thermal expansion effect is further derived, and the mechanical behavior of concrete under different temperature and humidity conditions is simulated using finite element analysis (FEA). In an environment with a higher temperature, the concrete will expand and may cause the formation of microcracks, while when the humidity is higher, the evaporation and condensation of water will affect the strength and stability of the concrete. For these influences, the present invention proposes a correction factor to enable the stress-strain relationship to effectively adapt to different environmental changes. The establishment of the mechanical field also includes the dynamic response analysis of thin-walled lightweight concrete. When subjected to external loads, the dynamic response of concrete is closely related to its mechanical properties. For this reason, the present invention adopts a dynamic strain analysis method to simulate the performance changes of concrete under actual use conditions. On the basis of the above mechanical field modeling, the present invention further applies a multi-objective optimization algorithm to correct the errors caused by temperature, humidity and other environmental factors by adjusting the key parameters in the model. Especially in the process of collecting experimental data, the present invention adopts the particle swarm optimization algorithm (PSO) and the multi-objective genetic algorithm (MOGA) to optimize the output results of the model. The optimization objectives include minimizing the compressive strength error, permeability error, etc., and ensuring the real-time nature of data processing.

[0025] The permeability field modeling uses Darcy's law and the Navier-Stokes equation to describe the permeability.

[0026] Specifically, first, Darcy's law is used to describe the seepage behavior of fluids in porous media. In concrete, the flow of water is usually affected by factors such as porosity, pore connectivity, and the compactness of concrete. Darcy's law is described by the following formula: where Q is the seepage flow rate, with the unit of volume flow rate per unit time; k is the permeability coefficient, which is a characteristic parameter of the concrete material; is the pressure gradient, representing the pressure difference of the fluid. In concrete, the pressure gradient is closely related to factors such as the distribution and connectivity of pores. The permeability coefficient k varies with the compactness of the concrete and the pore structure; Due to the complexity of fluid flow in concrete, especially under high-permeability conditions, the Navier-Stokes equation is also adopted in the present invention to describe the seepage field. The Navier-Stokes equation is mainly used to describe the flow behavior of viscous fluids, taking into account the effects of fluid inertia, pressure, viscosity, and external forces. The Navier-Stokes equation is shown as follows: where ρ is the density of the fluid, v is the fluid velocity, μ is the viscosity of the fluid, P is the pressure, and f is the volume force (such as gravity), etc. This equation can describe the action of various physical forces on the fluid when it flows in concrete. By solving this equation, the process of water flow inside the concrete can be better simulated. The Navier-Stokes equation takes into account the flow characteristics of water in pores, the connectivity between pores, and the influence of frictional force on the concrete surface. These factors play an important role in permeability detection. Especially in a high-humidity or long-term water immersion environment, the permeability performance of concrete will be more significantly affected; The seepage field modeling not only considers the seepage flow rate, but also makes adjustments in combination with the microstructural characteristics of concrete. For example, in thin-walled lightweight concrete, due to its relatively high porosity and relatively low density, the flow path of water may be more complex. Therefore, in addition to the basic Darcy's law and Navier-Stokes equations, the present invention further considers the non-uniform flow problem in porous media. Using numerical simulation methods, in-depth research has been carried out on the seepage behavior under different porosities and different pore morphologies. Through finite element analysis (FEA) and computational fluid dynamics (CFD) methods, it is possible to simulate how water flows in a complex pore structure and how it interacts with the surface of concrete, so as to more accurately predict the seepage performance. The seepage field model is corrected through a multi-objective optimization algorithm, and the permeability coefficient and hydrodynamic parameters are adjusted to improve the accuracy of model prediction. During the experiment, the optimization algorithm automatically adjusts the parameters in the permeability model to make the actual seepage flow rate closer to the theoretical predicted value. The goal of the optimization algorithm is to minimize the permeability error and ensure the reliability and stability of the detection data.

[0027] The sensors are arranged at different positions of the thin-walled lightweight concrete sample, and the sensors include: A temperature sensor for real-time collection of the internal temperature change of the thin-walled lightweight concrete; A humidity sensor for real-time monitoring of the internal humidity change of the thin-walled lightweight concrete; A stress sensor for real-time measurement of the internal stress change of the thin-walled lightweight concrete; A seepage sensor for detecting the permeability of the thin-walled lightweight concrete and the rate of water flow; The sensors transmit data to the data processing unit through wireless transmission or wired connection.

[0028] Specifically, a combination of multiple sensors is adopted to monitor the physical property changes of thin-walled lightweight concrete under different environmental conditions in real time. By arranging multiple sensors such as temperature, humidity, stress, and permeability sensors, the present invention can collect data in real time during the detection process of concrete and transmit the data to the data processing unit wirelessly or by wire. This multi-sensor data acquisition method can not only comprehensively monitor the internal changes of concrete but also effectively evaluate its performance under different environmental conditions. The sensor arrangement scheme of the present invention aims to accurately record the changes in temperature, humidity, stress, and permeability inside the concrete by collecting data at multiple points. The data is integrated into a unified processing system and, after analysis and processing, can reflect the actual state of the concrete in real time, thereby helping engineers evaluate the quality and service life of the concrete; by arranging temperature, humidity, stress, and permeability sensors, the present invention can comprehensively monitor the physical property changes of concrete, especially its performance under complex environmental conditions. Real-time data collection and analysis not only improve the detection accuracy but also make the monitoring process more efficient. When the external temperature of the concrete changes greatly, the temperature sensor can promptly capture the temperature change. Combining the data of the humidity sensor, the dry-wet state change of the concrete can be understood in real time, and then it can be judged whether the concrete may be damaged due to cracking or swelling. Through the stress sensor, the stress concentration inside the concrete can be pre-warned in advance to avoid damage caused by overloading.

[0029] The multi-objective optimization algorithm includes the particle swarm optimization algorithm and the multi-objective genetic algorithm, which are used to minimize the performance index errors of the compressive strength and impermeability of concrete. The optimization steps include: Using the particle swarm optimization algorithm to adjust the model parameters and reduce the difference between the model and the experimental data; Using the multi-objective genetic algorithm to process multiple detection targets and balance the minimization of measurement error and the balance of measurement accuracy and efficiency; during the optimization process, determine the best parameter combination to obtain the minimum error and the fastest detection speed.

[0030] Specifically, the multi-objective optimization algorithm determines the optimal parameter combination by optimizing multiple parameters involved in the concrete detection process to achieve the minimum error and the fastest detection speed. The core of the optimization process is to minimize the possible errors in the experimental process while ensuring the shortest detection time, thereby improving the overall detection efficiency and accuracy. There is a certain contradiction between the error in the detection process and the detection speed. Generally, increasing the number of sampling points or improving the measurement accuracy will increase the detection time; conversely, if the number of sampling points is reduced or the accuracy is lowered, it may lead to an increase in errors. Therefore, through the multi-objective optimization algorithm, the present invention comprehensively considers the requirements of both detection error and detection speed, determines the optimal parameter combination to achieve the optimization effect, and combines the particle swarm optimization algorithm (PSO) and the multi-objective genetic algorithm (MOGA) to optimize the parameters. The particle swarm optimization algorithm can efficiently search for the optimal solution by simulating the foraging process of bird flocks. The multi-objective genetic algorithm, on the other hand, simulates the process of natural selection and considers multiple optimization objectives simultaneously, so that the final optimization scheme achieves a balance between error and speed; During the optimization process, the particle swarm optimization algorithm and the genetic algorithm will continuously adjust the parameters. In each iteration, the algorithm evaluates the quality of the current solution according to the objective function, selects the optimal solution and updates it. Through multiple iterations, an optimal parameter combination that can both minimize the error and ensure the detection speed is finally found. Through this optimization algorithm, the present invention can significantly improve the efficiency and accuracy of the thin-walled lightweight concrete detection process. Under the action of multi-objective optimization, the detection error is effectively controlled, and at the same time, the detection time is greatly shortened. Especially in large-scale projects, a large amount of detection costs and time can be saved.

[0031] The objective function of the optimization algorithm includes: Minimization of compressive strength error: Minimize the absolute error between the actual compressive strength and the predicted compressive strength; Minimization of impermeability error: Minimize the absolute error between the actual seepage flow rate and the predicted seepage flow rate; Balance between measurement accuracy and efficiency: Minimize the detection time required on the premise of ensuring the detection accuracy to improve the detection efficiency.

[0032] Specifically, during the detection process, the compressive strength is an important indicator to measure the performance of concrete. In order to minimize the difference between the predicted compressive strength and the actual compressive strength, the present invention designs an error minimization objective function to measure the difference between the two. Specifically, the objective function of the compressive strength error can be expressed as: where σ pred,i represents the predicted compressive strength of the i-th experiment, and σ real,i$R_i$ represents the compressive strength actually measured in the $i$-th experiment, and $N$ represents the number of experiments. By minimizing this objective function, the error between the predicted value and the actual value can be effectively reduced, and the accuracy of compressive strength detection can be improved. The impermeability of concrete refers to its ability to resist water penetration. To more accurately evaluate the impermeability of concrete, the present invention defines an objective function for minimizing the impermeability error. This function is used to measure the error between the predicted seepage flow rate and the actual seepage flow rate. The objective function can be expressed as: where $Q$ pred,i represents the predicted seepage flow rate in the $i$-th experiment, and $Q$ real,i represents the actually measured seepage flow rate in the $i$-th experiment. By minimizing this objective function, the difference between the predicted value and the actual value can be reduced, thereby improving the accuracy of impermeability detection. During the detection process, there is usually a certain trade-off relationship between accuracy and efficiency. To improve the detection efficiency, the present invention adjusts the detection parameters to balance the accuracy and time of detection. For this purpose, the present invention defines a comprehensive objective function that takes into account the time required for detection and the measurement accuracy. The objective function can be expressed as: where $T$ total represents the total time required to complete all detection tasks, $E$ pred,i and $E$ real,i represent the predicted error and the actual error in the $i$-th experiment respectively, and $\alpha$ and $\beta$ are weight factors for balancing accuracy and time. By minimizing this objective function, the detection speed can be maximized on the premise of ensuring sufficient accuracy.

[0033] The simulation is carried out using finite element analysis and computational fluid dynamics models for multi-physics field coupling simulation. During the simulation process, the influence of environmental changes on the performance of concrete is considered. Through simulation calculations, the stress distribution, temperature and humidity changes, and permeability performance of concrete under different environmental conditions are obtained, so as to verify the reliability of the optimized detection scheme.

[0034] Specifically, two main physical field simulation methods are adopted in the simulation: finite element analysis and computational fluid dynamics. Finite element analysis is mainly used to simulate the stress and strain distribution of concrete under external loading, and computational fluid dynamics is used to simulate the temperature and humidity changes and permeability performance of concrete under different environments. Finite element analysis divides the concrete material into multiple small elements (elements) and calculates its deformation and stress distribution under external stress. Through this method, the mechanical behavior of concrete under different loading conditions can be effectively simulated, and the distribution and change of internal stress can be analyzed. This process can help optimize a key point in the scheme verification - the prediction accuracy of compressive strength in detection; The computational fluid dynamics model is used to simulate the interaction between the fluid and concrete when the concrete is exposed to different environmental conditions (such as temperature, humidity, air flow, etc.). Through CFD simulation, the temperature field, humidity field, and the penetration and flow of the fluid on the concrete surface can be obtained, so as to evaluate the permeability of the concrete. In the permeability simulation, CFD simulation can effectively display the diffusion and penetration processes of fluids such as water and gas inside the concrete, and evaluate its permeability and impermeability. By simulating the temperature and humidity changes under different environmental conditions, the influence on the concrete penetration flow can be analyzed, and then accurate parameter data support can be provided for optimizing the detection scheme; To achieve the coupled simulation of multiple physical fields, it is first necessary to model the concrete and its environment. First, a mechanical model of the concrete is constructed through finite element analysis, including parameters such as the elastic modulus, Poisson's ratio, and compressive strength of the material; then, a change model of environmental factors (such as temperature, humidity, air flow, etc.) is established through the computational fluid dynamics model. The coupling relationship between these models is calculated and solved through multi-physics simulation software. Specifically, the finite element analysis and the computational fluid dynamics model will share the same boundary conditions and initial conditions, so as to transmit information in real time during the simulation process and achieve the coupled calculation of the temperature and humidity field, stress field, and penetration field; Through finite element analysis, the stress distribution of the concrete under different environmental conditions is simulated, and the prediction accuracy of the optimization scheme under different environments is further analyzed. The simulation results are compared with the actual measurement results, so as to verify that the optimization scheme can effectively improve the detection speed while ensuring the detection accuracy. The penetration flow is simulated through the computational fluid dynamics model, and the permeability performance of the concrete under different environmental conditions is analyzed. The simulation results show that the optimized scheme can effectively reduce the error of the penetration flow and ensure the balance between the detection accuracy and efficiency.

[0035] The test report includes the following content: Detailed analysis of the compressive strength, impermeability, stress-strain curve, and temperature and humidity change data of the concrete; According to the optimized test results, give the quality evaluation of the thin-walled lightweight concrete and put forward targeted quality control suggestions; provide the error range and accuracy analysis of the test method to support subsequent engineering applications and quality control.

[0036] Specifically, based on the experimental data and finite element analysis results, the report lists the specific values of the compressive strength, compares them with the predicted values, analyzes the errors and provides an evaluation. Using the computational fluid dynamics model, the report analyzes the influence of temperature and humidity changes on the concrete permeability performance, provides detailed seepage flow rate data. Through the stress-strain curve analysis, the report evaluates the mechanical properties of the concrete, such as the elastic modulus and yield point, and verifies the accuracy of the optimized detection scheme. Through the experimental and simulation data, the report evaluates the influence of environmental factors on the concrete performance and analyzes the effects of temperature and humidity on the compressive strength and impermeability.

[0037] Please refer to the appendix Figure 2 , a thin-walled lightweight concrete detection device, comprising: A sample collection module for collecting thin-walled lightweight concrete samples to be detected and preprocessing them; A physical field modeling module for constructing a multi-physical field coupling model of thin-walled lightweight concrete, including at least the interaction of the mechanical field, thermal field, humidity field and seepage field; A data collection module for arranging sensors on the thin-walled lightweight concrete samples and collecting physical field data of mechanics, temperature, humidity and permeability; An optimization calculation module for correcting the detection error using a multi-objective optimization algorithm based on the collected data and determining the true performance value of the thin-walled lightweight concrete; A simulation module for performing multi-physical field simulation based on the results processed by the optimization algorithm; A report generation module for generating a performance detection report of the thin-walled lightweight concrete and providing quality control suggestions.

[0038] Specifically, when sampling with the sample collection module, it is necessary to ensure that there are no obvious defects on the sample surface. The sample is cut, polished and subjected to standard curing treatment to meet the requirements of the detection standard. The humidity of the sample can be maintained by spray curing to avoid cracking; The physical field modeling module constructs a multi-physical field coupling model of the concrete sample, uses the finite element method (FEM) to establish the stress-strain field, and adopts computational fluid dynamics (CFD) to simulate the temperature and humidity diffusion and seepage flow. The model parameters are derived from actual tests or standard databases to ensure accurate modeling; The data collection module is responsible for arranging various sensors on the sample surface, including strain gauges, temperature and humidity sensors and micro flow meters. During the detection process, it continuously collects data of mechanics, temperature, humidity and permeability. The sensor arrangement can be optimized according to the stress concentration area to improve the data representativeness. The data collection period and sampling frequency are dynamically adjusted according to the actual detection accuracy requirements; Optimization calculation module, based on the collected data, uses a multi-objective optimization algorithm to correct the detection error. It preferentially uses the Particle Swarm Optimization (PSO) combined with the Multi-Objective Genetic Algorithm (MOGA) for global optimization. The optimization objectives include the comprehensive minimization of the compressive strength error, the seepage flow error, and the detection time. The error correction formula is: min(ω1×E 压 +ω2×E 渗 +ω3×T); where E 压为抗压强度误差 , E 渗为渗透流量误差 , and T is the total detection time, and ω1, ω2, and ω3 are weight coefficients. The parameter definitions are clear and the formula is complete; The simulation module conducts multi-physical field simulations based on the optimized detection results. The simulation process takes into account environmental changes, such as the effects of factors like temperature fluctuations, humidity changes, and load changes on the performance of concrete. Actual construction environment parameters can be introduced into the simulation to improve the engineering adaptability of the simulation results. The simulation results are used to verify the stability and reliability of the optimized performance values; The report generation module automatically generates a detection report. The content includes the compressive strength, impermeability, stress-strain curve of the concrete, and the analysis of temperature and humidity changes. The report also provides an assessment of the concrete quality grade based on the detection results and puts forward targeted quality control suggestions. The report is accompanied by the error range, accuracy analysis, and optimization suggestions of the detection method to support subsequent construction and material applications.

[0039] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting thin-walled lightweight concrete, characterized in that It includes the following steps: First, collect thin-walled lightweight concrete samples to be detected from the site or laboratory and perform preprocessing on them; After preprocessing, construct a multi-physical field coupling model of thin-walled lightweight concrete, and the coupling model includes the interaction of mechanical field, thermal field, humidity field and seepage field; Arrange at least one type of sensor on the thin-walled lightweight concrete sample to collect physical field data of mechanics, temperature, humidity and permeability; Based on the collected physical field data, use a multi-objective optimization algorithm to correct the detection error caused by the multi-physical field coupling effect, so as to determine the actual performance value of thin-walled lightweight concrete; Based on the results processed by the optimization algorithm, simulate the performance of thin-walled lightweight concrete and generate a test report.

2. The thin-walled lightweight concrete detection method according to claim 1, wherein: The mechanical field modeling includes establishing the stress-strain relationship of concrete through non-linear elastic theory and considering the influence of temperature and humidity environmental factors on the mechanical properties of concrete.

3. A method for detecting a thin-walled lightweight concrete according to claim 1, characterized in that: The coupling modeling of the multi-physical field coupling model is coupled through the heat conduction equation and the humidity diffusion equation, considering the dual diffusion effect of moisture and heat in concrete.

4. A method for detecting a thin-walled lightweight concrete according to claim 1, characterized in that: The seepage field modeling uses Darcy's law and the Navier-Stokes equation to describe the permeability.

5. A method for detecting a thin-walled lightweight concrete according to claim 1, characterized in that: The sensors are arranged at different positions of the thin-walled lightweight concrete sample, and the sensors include: Temperature sensors, used to collect the internal temperature change of thin-walled lightweight concrete in real time; Humidity sensors, used to monitor the internal humidity change of thin-walled lightweight concrete in real time; Stress sensors, used to measure the internal stress change of thin-walled lightweight concrete in real time; Seepage sensors, used to detect the permeability of thin-walled lightweight concrete and the rate of water flow; The sensors transmit data to the data processing unit through wireless transmission or wired connection.

6. The thin-walled lightweight concrete detection method according to claim 1, wherein: The multi-objective optimization algorithm includes the particle swarm optimization algorithm and the multi-objective genetic algorithm, which are used to minimize the performance index errors of concrete compressive strength and impermeability. The optimization steps include: Use the particle swarm optimization algorithm to adjust the model parameters and reduce the difference between the model and the experimental data; Use the multi-objective genetic algorithm to process multiple detection targets and balance the minimization of measurement error and the balance of measurement accuracy and efficiency; During the optimization process, determine the best parameter combination to obtain the minimum error and the fastest detection speed.

7. A method for detecting a thin-walled lightweight concrete according to claim 1, characterized in that: The objective functions of the optimization algorithm include: Minimization of compressive strength error: Minimize the absolute error between the actual compressive strength and the predicted compressive strength; Minimization of impermeability error: Minimize the absolute error between the actual seepage flow and the predicted seepage flow; Balance of measurement accuracy and efficiency: Minimize the time required for detection under the premise of ensuring detection accuracy to improve detection efficiency.

8. A method for detecting a thin-walled lightweight concrete according to claim 1, characterized in that: The simulation uses finite element analysis and computational fluid dynamics models to perform multi-physical field coupling simulation. During the simulation process, consider the influence of environmental changes on the performance of concrete, and obtain the stress distribution, temperature and humidity changes and permeability performance of concrete under different environmental conditions through simulation calculation, so as to verify the reliability of the optimized detection scheme.

9. A method for detecting a thin-walled lightweight concrete according to claim 1, characterized in that: The test report includes the following content: Detailed analysis of the data of the compressive strength, impermeability, stress-strain curve, temperature and humidity changes of concrete; Based on the optimized detection results, give the quality assessment of thin-walled lightweight concrete and put forward targeted quality control suggestions; Provide the error range and accuracy analysis of the detection method to support subsequent engineering applications and quality control.

10. A thin-walled lightweight concrete detection device, which is applied to the thin-walled lightweight concrete detection method described in any one of claims 1-9, and is characterized in that, Including: A sample collection module for collecting samples of thin-walled lightweight concrete to be detected and preprocessing them; A physical field modeling module for constructing a multi-physical field coupling model of thin-walled lightweight concrete, including at least the interaction of mechanical field, thermal field, humidity field and permeability field; A data collection module for arranging sensors on the thin-walled lightweight concrete sample and collecting physical field data of mechanics, temperature, humidity and permeability; An optimization calculation module for correcting the detection error by using a multi-objective optimization algorithm based on the collected data and determining the true performance value of thin-walled lightweight concrete; A simulation module for performing multi-physical field simulation based on the results processed by the optimization algorithm; A report generation module for generating a performance detection report of thin-walled lightweight concrete and providing quality control suggestions.

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