Function gradient concrete interface layer chloride ion diffusion coefficient inversion method and device

Abstract

According to the functional gradient concrete interface layer chloride ion diffusion coefficient inversion method and device, a functional gradient concrete chloride ion diffusion physical model is constructed, chloride ion diffusion coefficients of an external hydrophobic layer and an internal structure layer and an inversion target calibration value are initialized, and a foundation is laid for the inversion process. Then, setting an initial search space and error tolerance of an interface layer chloride ion diffusion coefficient as convergence conditions, adopting a numerical search algorithm to calculate an intermediate value of the search space as a guess value, performing erosion diffusion simulation based on the value, and judging convergence through error comparison; and if not, iteratively adjusting the search space and the guess value until an error requirement is met, and then outputting an inversion result. According to the method, accurate and efficient inversion of the diffusion coefficient of the chloride ions of the interface layer is realized, the difficulty of direct measurement is overcome, the accuracy and the reliability of determination of the diffusion coefficient of the chloride ions of the concrete interface layer are remarkably improved, and meanwhile, the experiment cost and the time overhead are reduced.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of durability of civil engineering materials and computational science, and in particular to a method for inverting the chloride ion diffusion coefficient of functionally graded concrete interface layer, a device for inverting the chloride ion diffusion coefficient of functionally graded concrete interface layer, an electronic device, and a computer-readable medium. Background Technology

[0002] Functionally graded concrete (FJC) is a novel type of composite concrete that employs a layered design and is composed of materials with different performance characteristics. It typically consists of an external functional layer and an internal structural layer. The functional layer, achieved through the addition of novel active admixtures, functional additives, or special processing techniques, possesses excellent resistance to seawater corrosion and serves as the first line of defense against harmful external media. The internal structural layer primarily bears the load-bearing function of the structure. This design, which separates and efficiently combines the functions of "protection" and "load-bearing," effectively improves the durability of concrete structures and reduces operating costs, and has been widely applied in marine engineering and transportation infrastructure.

[0003] For functionally graded concrete structures, although the external functional and structural layers have excellent impermeability, the interface layer between the functional and structural layers can still become a weak point for chloride ion attack. This interface layer is a special region formed by the interaction of two different types of concrete during construction and hardening, and its porosity and permeability are significantly greater than those of the functional and structural layers. Therefore, the chloride ion diffusion coefficient of the interface layer of functionally graded concrete is a key factor in evaluating its durability.

[0004] However, accurately determining the chloride ion diffusion coefficient of the functionally graded concrete (FPC) interface layer remains a major challenge. Currently, researchers often use seawater immersion, grinding, and titration to determine the chloride ion diffusion coefficient of the interface layer. However, the actual thickness of the interface transition layer is only at the micrometer level, while the precision of the grinding operation and the drill bit size are at the millimeter level. Therefore, powder samples collected near the interface layer are mixtures of the external functional structural layer, the interface layer, and the internal structural layer, and using them to characterize the ion transport performance at the interface may introduce errors. With the development of microscopic characterization techniques, some scholars have used energy dispersive spectroscopy (EDS) and electron probe microanalysis to evaluate the chloride ion erosion resistance of the FPC interface layer. However, these methods are not only complex to sample but also expensive, making them unsuitable for practical engineering applications.

[0005] In summary, conventional seawater immersion and grinding methods for determining the chloride ion erosion resistance of the interface layer in functionally graded concrete (FJC) suffer from significant errors. Meanwhile, microscopic characterization techniques such as energy dispersive spectroscopy (EDS) and electron probe microanalysis are complex to sample, costly, and difficult to implement widely. This has resulted in a long-standing reliance on estimations or ambiguity in understanding this crucial parameter, severely hindering the refined design and evaluation of the durability of FJC structural materials and impacting their widespread application. Therefore, there is an urgent need to develop an efficient and accurate method for evaluating the durability performance of the interface layer in FJC. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed to provide a method for inverting the chloride ion diffusion coefficient of a functionally graded concrete interface layer, a corresponding device for inverting the chloride ion diffusion coefficient of a functionally graded concrete interface layer, an electronic device, and a computer-readable medium to overcome or at least partially solve the above problems.

[0007] This invention discloses a method for inverting the chloride ion diffusion coefficient of the interface layer in functionally graded concrete, the method comprising: Constructing a physical model of chloride ion diffusion in functionally graded concrete; Obtain and set the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model, as well as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model. Set the initial search space for the chloride ion diffusion coefficient of the interface layer as the inversion parameter and the error tolerance as the convergence criterion. Based on the numerical search algorithm, the median value of the current search space is calculated as a guess of the chloride ion diffusion coefficient of the interface layer. The erosion diffusion of chloride ions in functionally graded concrete is simulated based on the guessed value of the chloride ion diffusion coefficient in the interface layer. The convergence is judged based on the error tolerance. If the convergence is not achieved, the process is iterated. If the convergence is achieved, the current guessed value is output as the inversion result of the chloride ion diffusion coefficient in the interface layer as the inversion parameter.

[0008] Optionally, the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model are obtained and set, including: The chloride ion diffusion coefficients of the external hydrophobic layer concrete specimen and the internal structural layer concrete specimen, respectively, were obtained by the rapid chloride ion migration coefficient method. The chloride ion diffusion coefficients of the external hydrophobic layer concrete specimen and the internal structural layer concrete specimen were set as the chloride ion diffusion coefficients of the external hydrophobic layer and the internal structural layer of the functionally graded concrete chloride ion diffusion physical model, respectively.

[0009] Optionally, the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model are obtained and set, including: The apparent chloride ion diffusion coefficient of functionally graded concrete as a whole, determined by seawater immersion grinding titration, was obtained, and the apparent chloride ion diffusion coefficient of the functionally graded concrete specimen as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model was set.

[0010] Optionally, based on the guessed value of the chloride ion diffusion coefficient of the interface layer, an erosion diffusion simulation of the functionally graded concrete chloride ion diffusion physical model is performed, and convergence is judged based on the error tolerance. If convergence fails, iterative iteration is performed; if convergence occurs, the current guessed value is output as the inversion result of the chloride ion diffusion coefficient of the interface layer, including: Based on the guessed value of the chloride ion diffusion coefficient of the interface layer, the physical model of chloride ion diffusion in functionally graded concrete is used to simulate erosion diffusion, and the current equivalent diffusion coefficient of the model is calculated after each simulation. Calculate the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, and determine whether the deviation is less than the error tolerance. If the deviation is less than the error tolerance, the current guess value is output as the inversion result of the chloride ion diffusion coefficient of the interface layer inversion parameter. If the deviation is not less than the error tolerance, the search space of the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm based on the deviation between the current equivalent diffusion coefficient of the model and the inversion target calibration value. Based on the updated search space, the guessed value of the chloride ion diffusion coefficient of the interface layer is recalculated using the bisection algorithm. Based on the updated guessed value, the erosion diffusion simulation and convergence judgment are continued until the convergence condition is met and the inversion result of the chloride ion diffusion coefficient of the interface layer is output.

[0011] Optionally, based on the guessed value of the chloride ion diffusion coefficient at the interface layer, an erosion diffusion simulation is performed on the physical model of chloride ion diffusion in functionally graded concrete, and the current equivalent diffusion coefficient of the model is calculated after each simulation, including: Erosion diffusion simulation of functionally graded concrete (FPC) chloride ion diffusion physical model was performed based on the guessed value of the chloride ion diffusion coefficient at the interface layer. After each simulation, the chloride ion concentration at different depths of the FPC chloride ion diffusion physical model was extracted. Based on the chloride ion concentration at different depths of the FPC chloride ion diffusion physical model, the current equivalent diffusion coefficient of the model was calculated using Fick's second law.

[0012] Optionally, the lower limit of the initial search space for the chloride ion diffusion coefficient of the interface layer, which is used as an inversion parameter, is the chloride ion diffusion coefficient of the internal structural layer, and the upper limit is a preset multiple of the chloride ion diffusion coefficient of the internal structural layer.

[0013] Optionally, based on the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, a bisection algorithm is used to update the search space of the chloride ion diffusion coefficient of the interface layer, including: Based on the sign of the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, the search space of the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm. If the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value is positive, then the upper limit of the search space for the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm. If the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value is negative, then the lower limit of the search space for the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm.

[0014] This invention also discloses a device for inverting the chloride ion diffusion coefficient of functionally graded concrete interface layer, the device comprising: The model building module is used to build a physical model of chloride ion diffusion in functionally graded concrete. The basic parameter setting module is used to obtain and set the chloride ion diffusion coefficients of the external hydrophobic layer and the internal structural layer of the functionally graded concrete chloride ion diffusion physical model, as well as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model. The inversion parameter setting module is used to set the initial search space for the chloride ion diffusion coefficient of the interface layer, which is used as an inversion parameter, and the error tolerance, which is used as a convergence criterion. The module for calculating the estimated value of chloride ion diffusion coefficient in the interface layer is used to calculate the intermediate value of the current search space as the estimated value of chloride ion diffusion coefficient in the interface layer based on the numerical search algorithm. The interface layer chloride ion diffusion coefficient inversion determination module is used to simulate the erosion diffusion of the functionally graded concrete chloride ion diffusion physical model based on the guessed value of the interface layer chloride ion diffusion coefficient, and to judge convergence based on the error tolerance. If convergence is not achieved, it is performed in a loop. If convergence is achieved, the current guessed value is output as the inversion result of the interface layer chloride ion diffusion coefficient as the inversion parameter.

[0015] The present invention also discloses an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the method for inverting the chloride ion diffusion coefficient of the functionally graded concrete interface layer as described in this invention.

[0016] The present invention also discloses a computer-readable medium storing instructions that, when executed by one or more processors, cause the processors to perform the chloride ion diffusion coefficient inversion method for functionally graded concrete interface layers as described in the present invention.

[0017] This invention has the following advantages: The present invention discloses a method for inverting the chloride ion diffusion coefficient of functionally graded concrete (FJC) interface layers. This method establishes a physical model of chloride ion diffusion in FJC, initializes the chloride ion diffusion coefficients of the external hydrophobic layer and the internal structural layer, and sets the target calibration value for inversion, thus laying the foundation for the inversion process. Subsequently, an initial search space and error tolerance for the chloride ion diffusion coefficient of the interface layer are set as convergence conditions. A numerical search algorithm is used to calculate the median value of the search space as a guess, and erosion diffusion simulation is performed based on this value. Convergence is judged by error comparison. If convergence is not achieved, the search space and guess value are iteratively adjusted until the error requirement is met, at which point the inversion result is output. This method achieves accurate and efficient inversion of the chloride ion diffusion coefficient of the interface layer, overcomes the difficulties of direct measurement, significantly improves the accuracy and reliability of measuring the chloride ion diffusion coefficient of concrete interface layers, and reduces experimental costs and time. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of a method for inverting the chloride ion diffusion coefficient of a functionally graded concrete interface layer according to an embodiment of the present invention. Figure 2 This is a boundary diagram of the physical model of chloride ion diffusion in functionally graded concrete provided in this embodiment of the invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Reference Figure 1 The diagram illustrates a flowchart of the steps involved in retrieving the chloride ion diffusion coefficient of a functionally graded concrete interface layer, as provided in an embodiment of the present invention. Specifically, the method may include the following steps: Step 101: Construct a physical model for chloride ion diffusion in functionally graded concrete; Step 102: Obtain and set the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model, as well as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model. Step 103: Set the initial search space for the chloride ion diffusion coefficient of the interface layer as the inversion parameter and the error tolerance as the convergence criterion. Step 104: Calculate the median value of the current search space as a guess of the chloride ion diffusion coefficient of the interface layer according to the numerical search algorithm. Step 105: Based on the guessed value of the chloride ion diffusion coefficient of the interface layer, the physical model of chloride ion diffusion in functionally graded concrete is used to simulate erosion diffusion. The convergence is judged based on the error tolerance. If it does not converge, it is iterated in a loop. If it converges, the current guessed value is output as the inversion result of the chloride ion diffusion coefficient of the interface layer as the inversion parameter.

[0021] In an optional embodiment of the present invention, obtaining and setting the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model includes: The chloride ion diffusion coefficients of the external hydrophobic layer concrete specimen and the internal structural layer concrete specimen, respectively, were obtained by the rapid chloride ion migration coefficient method. The chloride ion diffusion coefficients of the external hydrophobic layer concrete specimen and the internal structural layer concrete specimen were set as the chloride ion diffusion coefficients of the external hydrophobic layer and the internal structural layer of the functionally graded concrete chloride ion diffusion physical model, respectively.

[0022] In an optional embodiment of the present invention, obtaining and setting the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model includes: The apparent chloride ion diffusion coefficient of functionally graded concrete as a whole, determined by seawater immersion grinding titration, was obtained, and the apparent chloride ion diffusion coefficient of the functionally graded concrete specimen as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model was set.

[0023] In an optional embodiment of the present invention, an erosion diffusion simulation of a functionally graded concrete chloride ion diffusion physical model is performed based on a guessed value of the chloride ion diffusion coefficient of the interface layer. Convergence is determined based on error tolerance. If convergence fails, iterative iterations are performed. If convergence occurs, the current guessed value is output as the inversion result of the chloride ion diffusion coefficient of the interface layer, including: Based on the guessed value of the chloride ion diffusion coefficient of the interface layer, the physical model of chloride ion diffusion in functionally graded concrete is used to simulate erosion diffusion, and the current equivalent diffusion coefficient of the model is calculated after each simulation. Calculate the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, and determine whether the deviation is less than the error tolerance. If the deviation is less than the error tolerance, the current guess value is output as the inversion result of the chloride ion diffusion coefficient of the interface layer inversion parameter. If the deviation is not less than the error tolerance, the search space of the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm based on the deviation between the current equivalent diffusion coefficient of the model and the inversion target calibration value. Based on the updated search space, the guessed value of the chloride ion diffusion coefficient of the interface layer is recalculated using the bisection algorithm. Based on the updated guessed value, the erosion diffusion simulation and convergence judgment are continued until the convergence condition is met and the inversion result of the chloride ion diffusion coefficient of the interface layer is output.

[0024] In an optional embodiment of the present invention, an erosion diffusion simulation of a functionally graded concrete chloride ion diffusion physical model is performed based on a guessed value of the chloride ion diffusion coefficient at the interface layer, and the current equivalent diffusion coefficient of the model is calculated after each simulation, including: Erosion diffusion simulation of functionally graded concrete (FPC) chloride ion diffusion physical model was performed based on the guessed value of the chloride ion diffusion coefficient at the interface layer. After each simulation, the chloride ion concentration at different depths of the FPC chloride ion diffusion physical model was extracted. Based on the chloride ion concentration at different depths of the FPC chloride ion diffusion physical model, the current equivalent diffusion coefficient of the model was calculated using Fick's second law.

[0025] In an optional embodiment of the present invention, the lower limit of the initial search space for the chloride ion diffusion coefficient of the interface layer, which is used as an inversion parameter, is the chloride ion diffusion coefficient of the internal structural layer, and the upper limit is a preset multiple of the chloride ion diffusion coefficient of the internal structural layer.

[0026] In an optional embodiment of the present invention, the search space for the chloride ion diffusion coefficient of the interface layer is updated using a bisection algorithm based on the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, including: Based on the sign of the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, the search space of the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm. If the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value is positive, then the upper limit of the search space for the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm. If the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value is negative, then the lower limit of the search space for the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm.

[0027] The purpose of this invention is to overcome the problem that existing experimental methods cannot accurately and efficiently determine the chloride ion diffusion coefficient of the interface layer in functionally graded concrete, and to propose a method for inverting the chloride ion diffusion coefficient of the interface layer based on macroscopic experimental calibration, microscopic model simulation, and automatic algorithm optimization.

[0028] To achieve the above objectives, the automated inversion method for determining the chloride ion diffusion coefficient of the interface layer in functionally graded concrete according to the present invention comprises the following parts: 1. Computational Model: A physical model of chloride ion diffusion built in finite element analysis software (such as COMSOL Multiphysics) that reflects the actual structure of functionally graded concrete (including functional layers, interface layers, and structural layers). The diffusion coefficient of the interface layer to be identified is defined as a variable that can be externally accessed. D itz .

[0029] 2. Script Control End: A control script that runs in a programming language environment such as Python. This script is responsible for communication with the COMSOL computational model, the logic of the numerical search algorithm (bisection method), the comparison of simulation results with experimental data, and error assessment.

[0030] 3. Communication Interface: An application programming interface (API), such as COMSOL LiveLink for Python, connects the Python script with the COMSOL calculation model and is responsible for bidirectional transmission of parameter setting instructions and calculation result data.

[0031] The specific steps for retrieving the chloride ion diffusion coefficient at the interface layer are as follows: S1: Macroscopic Experiments and Target Value Acquisition Specimens of external hydrophobic layer, internal structural layer, and functionally graded concrete were prepared separately. The chloride ion diffusion coefficient of the external hydrophobic layer and the internal structural layer concrete was determined by the rapid chloride ion migration coefficient method (RCM). The functionally graded concrete was immersed in a high-concentration artificial seawater environment. After chloride ions penetrated the external hydrophobic layer, passed through the interface layer, and entered the internal functional layer, the concrete specimens were removed and subjected to grinding titration tests. The chloride ion erosion was calculated using Fick's second law. t The chloride ion diffusion coefficient of functionally graded concrete was determined and set as the target value. D exp .

[0032] S2: Establishment of the computational model A geometric model of functionally graded concrete (FJC) was created in COMSOL, comprising a functional layer, a structural layer, and an interface layer. The chloride ion diffusion coefficients of the functional and structural layers were set to experimental values ​​measured by RCM, while the chloride ion diffusion coefficient of the interface layer was set using a Python script. The model boundaries were set according to the concentration of artificial seawater used in the experiment. The geometric and model boundary settings are as follows: Figure 2 As shown.

[0033] S3: Python script settings In the Python control script, the target value of the chloride ion diffusion coefficient of the functionally graded concrete interface layer is set in step S1. Based on extensive previous research, the inversion parameters are set. Ditz Initial search space[ D a , D b ],in D a Set as the chloride ion diffusion coefficient of the structural layer. D b Set to 60 times the chloride ion diffusion coefficient of the structural layer. Set the error tolerance required for convergence judgment. ε, The functional gradient concrete calculation model from step S2 is started and loaded via the communication interface.

[0034] S4: Execute the automated iterative solution loop The system enters a fully automated closed-loop iteration until the results converge. The specific process is as follows: (a) Calculate the guess value: The script calculates the median value of the current search interval based on the binary search algorithm. D mid =( D a + D b ) / 2, used as the guessed parameter for this iteration. (b) Parameter Update and Model Solving: The script updates the parameters in the COMSOL model via a communication interface. D itz The value is automatically updated to D mid Subsequently, an instruction was issued commanding COMSOL to perform an erosion operation with a time interval of [time value missing]. t Diffusion simulation.

[0035] (c) Result Extraction and Bias Calculation: After the simulation, the script automatically extracts the chloride ion concentration at different depths of the functionally graded concrete at erosion time t from COMSOL, and calculates the equivalent diffusion coefficient of the model at this time using Fick's second law. D sim And calculate its difference from the target value. D itz deviation f = D sim -D itz 。

[0036] (d) Interval update and convergence judgment: The script updates based on the deviation. f The sign of the positive or negative sign is used to update the search interval according to the binary search logic. D a or D bThe value of ... f Is the error tolerance already below the preset limit? ε If the condition is not met, the loop returns; if the condition is met, the loop exits.

[0037] S5: Output Results After the iteration loop terminates, the last one used D mid The value is considered to be the effective chloride ion diffusion coefficient of the functionally graded concrete interface layer, which is the closest to the actual situation obtained through inversion. The system archives or outputs this final result.

[0038] The beneficial effects of this invention are as follows: 1. Accuracy This invention eliminates the need for grinding and titrating the micron-level interface layer. Instead, it uses reliable and easily measurable macroscopic experimental data for reverse calculation, thus completely solving the problem of inaccurate results caused by sampling errors in traditional physical methods.

[0039] 2. Economic practicality This invention does not require expensive microscopic characterization equipment; it can be achieved using only conventional macroscopic experiments and general-purpose computing software. While ensuring the accuracy of the results, it greatly reduces testing costs and complexity, and has the potential to be promoted in practical engineering.

[0040] 3. High efficiency The process of manually adjusting parameters repeatedly, which used to take days or even weeks, has been transformed into an automated calculation task that can be set up and requires no manual intervention, greatly improving R&D efficiency.

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, a specific example will be used below to clearly and completely describe the technical solutions of this invention.

[0042] S1: Macroscopic Experiments and Target Value Acquisition First, RCM tests were conducted on the separately prepared structural and functional concrete specimens to obtain the following basic parameters: External functional layer diffusion coefficient: D func =1.8×10 -12 m 2 / s.

[0043] Diffusion coefficient of internal structural layers: D struc =9.0×10 -12 m 2 / s.

[0044] Then, a functionally graded concrete specimen made of the two materials mentioned above was immersed in artificial seawater. t =90 days.

[0045] After soaking, the specimens were removed, ground, and chemically titrated. The obtained chloride ion concentration profile data were fitted using the analytical solution of Fick's second law to calculate the overall apparent diffusion coefficient of the functionally graded concrete. This coefficient was then used as the sole target calibration value for this inversion. D exp =1.50×10 -11 m 2 / s.

[0046] S2: Establishment of the computational model In COMSOL software, a two-dimensional axisymmetric geometric model corresponding to the specimen is created, including a functional layer, an interface layer (set to a thickness of 0.1 mm), and a structural layer. The rare matter transport physics field interface under the chemical substance transport module is selected. The material properties of each region are set as follows: Functional layer diffusion coefficient: set to the experimental value of 1.8 × 10⁻⁶ -12 m 2 / s.

[0047] Structural layer diffusion coefficient: set to the experimental value of 9.0 × 10⁻⁶. -12 m 2 / s.

[0048] Interface layer diffusion coefficient: defined in COMSOL global definition - parameters as follows D itz Its value will be dynamically controlled by the Python script during iteration.

[0049] The boundary conditions of the model are set to a fixed chloride ion concentration consistent with the artificial seawater environment.

[0050] S3: Python Script Setup and System Initialization Write a Python script to perform the following initialization operations: Load target calibration values: D exp =1.50×10 -11 m 2 / s.

[0051] Set the initial search range [ D a ,D b Strictly follow the preset rules: lower limit D a = Dstruc =9.0×10 -12 m 2 / s.

[0052] upper limit D b = 60× D struc =5.4×10 -10 m 2 / s.

[0053] Set the error tolerance required for convergence judgment ε = 10%.

[0054] The script is launched via the LiveLink for Python interface and loads the computational model in COMSOL.

[0055] S4: Execute the automated iterative solution loop After system startup, it automatically enters the bisection method closed-loop iteration. Parameter updates, model simulation, result extraction, and judgment during the iteration process are all executed automatically by the program. The detailed process record is shown in the table below: Table 1. Iterative process of model calculation

[0056] S5: Output Results After completing the 8th iteration, the system... D mid = 5.26×10 -11 m 2 Running the simulation with / s as the parameter yields the results. D sim =1.63×10 -11 m 2 / s. At this point, the relative error is calculated to be approximately 8.7%. This value is less than the preset 10% error tolerance ε for the first time, the system determines that the result has converged, and automatically terminates the iteration loop.

[0057] Finally, the system outputs the identification results of this inversion: the effective chloride ion diffusion coefficient of the interface layer of the functionally graded concrete specimen. D itz ≈5.26×10 -11 m 2 / s.

[0058] This example fully demonstrates how the method of the present invention can accurately and efficiently obtain the critical chloride ion diffusion coefficient of the interface layer through a fully automated, algorithm-driven process in a more realistic, stepwise convergence process.

[0059] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0060] The device for inverting the chloride ion diffusion coefficient of the functionally graded concrete interface layer provided in this embodiment of the invention may specifically include the following modules: The model building module is used to build a physical model of chloride ion diffusion in functionally graded concrete. The basic parameter setting module is used to obtain and set the chloride ion diffusion coefficients of the external hydrophobic layer and the internal structural layer of the functionally graded concrete chloride ion diffusion physical model, as well as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model. The inversion parameter setting module is used to set the initial search space for the chloride ion diffusion coefficient of the interface layer, which is used as an inversion parameter, and the error tolerance, which is used as a convergence criterion. The module for calculating the estimated value of chloride ion diffusion coefficient in the interface layer is used to calculate the intermediate value of the current search space as the estimated value of chloride ion diffusion coefficient in the interface layer based on the numerical search algorithm. The interface layer chloride ion diffusion coefficient inversion determination module is used to simulate the erosion diffusion of the functionally graded concrete chloride ion diffusion physical model based on the guessed value of the interface layer chloride ion diffusion coefficient, and to judge convergence based on the error tolerance. If convergence is not achieved, it is performed in a loop. If convergence is achieved, the current guessed value is output as the inversion result of the interface layer chloride ion diffusion coefficient as the inversion parameter.

[0061] In an optional embodiment of the present invention, the basic parameter setting module is further configured to: The chloride ion diffusion coefficients of the external hydrophobic layer concrete specimen and the internal structural layer concrete specimen, respectively, were obtained by the rapid chloride ion migration coefficient method. The chloride ion diffusion coefficients of the external hydrophobic layer concrete specimen and the internal structural layer concrete specimen were set as the chloride ion diffusion coefficients of the external hydrophobic layer and the internal structural layer of the functionally graded concrete chloride ion diffusion physical model, respectively.

[0062] In an optional embodiment of the present invention, the basic parameter setting module is further configured to: The apparent chloride ion diffusion coefficient of functionally graded concrete as a whole, determined by seawater immersion grinding titration, was obtained, and the apparent chloride ion diffusion coefficient of the functionally graded concrete specimen as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model was set.

[0063] In an optional embodiment of the present invention, the interface layer chloride ion diffusion coefficient inversion determination module is further configured to: Based on the guessed value of the chloride ion diffusion coefficient of the interface layer, the physical model of chloride ion diffusion in functionally graded concrete is used to simulate erosion diffusion, and the current equivalent diffusion coefficient of the model is calculated after each simulation. Calculate the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, and determine whether the deviation is less than the error tolerance. If the deviation is less than the error tolerance, the current guess value is output as the inversion result of the chloride ion diffusion coefficient of the interface layer inversion parameter. If the deviation is not less than the error tolerance, the search space of the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm based on the deviation between the current equivalent diffusion coefficient of the model and the inversion target calibration value. Based on the updated search space, the guessed value of the chloride ion diffusion coefficient of the interface layer is recalculated using the bisection algorithm. Based on the updated guessed value, the erosion diffusion simulation and convergence judgment are continued until the convergence condition is met and the inversion result of the chloride ion diffusion coefficient of the interface layer is output.

[0064] In an optional embodiment of the present invention, the interface layer chloride ion diffusion coefficient inversion determination module is further configured to: Erosion diffusion simulation of functionally graded concrete (FPC) chloride ion diffusion physical model was performed based on the guessed value of the chloride ion diffusion coefficient at the interface layer. After each simulation, the chloride ion concentration at different depths of the FPC chloride ion diffusion physical model was extracted. Based on the chloride ion concentration at different depths of the FPC chloride ion diffusion physical model, the current equivalent diffusion coefficient of the model was calculated using Fick's second law.

[0065] In an optional embodiment of the present invention, the lower limit of the initial search space for the chloride ion diffusion coefficient of the interface layer, which is used as an inversion parameter, is the chloride ion diffusion coefficient of the internal structural layer, and the upper limit is a preset multiple of the chloride ion diffusion coefficient of the internal structural layer.

[0066] In an optional embodiment of the present invention, the interface layer chloride ion diffusion coefficient inversion determination module is further configured to: Based on the sign of the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, the search space of the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm. If the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value is positive, then the upper limit of the search space for the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm. If the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value is negative, then the lower limit of the search space for the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm.

[0067] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0068] In addition, embodiments of the present invention also provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. Memory, used to store computer programs; When the processor executes the program stored in the memory, it implements the method for inverting the chloride ion diffusion coefficient of the functionally graded concrete interface layer as described in the above embodiments.

[0069] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0070] The communication interface is used for communication between the aforementioned terminal and other devices.

[0071] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0072] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0073] In another embodiment of the present invention, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the method for inverting the chloride ion diffusion coefficient of the functionally graded concrete interface layer as described in the above embodiments.

[0074] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the method for inverting the chloride ion diffusion coefficient of the functionally graded concrete interface layer described in the above embodiments.

[0075] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0076] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0077] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for inverting the chloride ion diffusion coefficient of the interface layer in functionally graded concrete, characterized in that, The method includes: Constructing a physical model of chloride ion diffusion in functionally graded concrete; Obtain and set the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model, as well as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model. Set the initial search space for the chloride ion diffusion coefficient of the interface layer as the inversion parameter and the error tolerance as the convergence criterion. Based on the numerical search algorithm, the median value of the current search space is calculated as a guess of the chloride ion diffusion coefficient of the interface layer. The erosion diffusion of chloride ions in functionally graded concrete is simulated based on the guessed value of the chloride ion diffusion coefficient in the interface layer. The convergence is judged based on the error tolerance. If the convergence is not achieved, the process is iterated. If the convergence is achieved, the current guessed value is output as the inversion result of the chloride ion diffusion coefficient in the interface layer as the inversion parameter.

2. The method according to claim 1, characterized in that, Obtain and set the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model, including: The chloride ion diffusion coefficients of the external hydrophobic layer concrete specimen and the internal structural layer concrete specimen, respectively, were obtained by the rapid chloride ion migration coefficient method. The chloride ion diffusion coefficients of the external hydrophobic layer concrete specimen and the internal structural layer concrete specimen were set as the chloride ion diffusion coefficients of the external hydrophobic layer and the internal structural layer of the functionally graded concrete chloride ion diffusion physical model, respectively.

3. The method according to claim 1, characterized in that, Obtain and set the chloride ion diffusion coefficients of the outer hydrophobic layer and the inner structural layer of the functionally graded concrete chloride ion diffusion physical model, including: The apparent chloride ion diffusion coefficient of functionally graded concrete as a whole, determined by seawater immersion grinding titration, was obtained, and the apparent chloride ion diffusion coefficient of the functionally graded concrete specimen as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model was set.

4. The method according to claim 1, characterized in that, The erosion diffusion of chloride ions in functionally graded concrete is simulated using a physical model based on the guessed value of the chloride ion diffusion coefficient in the interface layer. Convergence is determined based on error tolerance. If convergence fails, iterative iterations are performed. If convergence occurs, the current guessed value is output as the inversion result of the chloride ion diffusion coefficient in the interface layer, including: Based on the guessed value of the chloride ion diffusion coefficient of the interface layer, the physical model of chloride ion diffusion in functionally graded concrete is used to simulate erosion diffusion, and the current equivalent diffusion coefficient of the model is calculated after each simulation. Calculate the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, and determine whether the deviation is less than the error tolerance. If the deviation is less than the error tolerance, the current guess value is output as the inversion result of the chloride ion diffusion coefficient of the interface layer inversion parameter. If the deviation is not less than the error tolerance, the search space of the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm based on the deviation between the current equivalent diffusion coefficient of the model and the inversion target calibration value. Based on the updated search space, the guessed value of the chloride ion diffusion coefficient of the interface layer is recalculated using the bisection algorithm. Based on the updated guessed value, the erosion diffusion simulation and convergence judgment are continued until the convergence condition is met and the inversion result of the chloride ion diffusion coefficient of the interface layer is output.

5. The method according to claim 4, characterized in that, Based on the guessed value of the chloride ion diffusion coefficient at the interface layer, an erosion diffusion simulation of the chloride ion diffusion physical model of functionally graded concrete is performed, and the current equivalent diffusion coefficient of the model is calculated after each simulation, including: Erosion diffusion simulation of functionally graded concrete (FPC) chloride ion diffusion physical model was performed based on the guessed value of the chloride ion diffusion coefficient at the interface layer. After each simulation, the chloride ion concentration at different depths of the FPC chloride ion diffusion physical model was extracted. Based on the chloride ion concentration at different depths of the FPC chloride ion diffusion physical model, the current equivalent diffusion coefficient of the model was calculated using Fick's second law.

6. The method according to claim 1, characterized in that, The lower limit of the initial search space for the chloride ion diffusion coefficient of the interface layer, which is used as an inversion parameter, is the chloride ion diffusion coefficient of the internal structural layer, and the upper limit is a preset multiple of the chloride ion diffusion coefficient of the internal structural layer.

7. The method according to claim 4, characterized in that, Based on the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, a bisection algorithm is used to update the search space of the chloride ion diffusion coefficient of the interface layer, including: Based on the sign of the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value, the search space of the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm. If the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value is positive, then the upper limit of the search space for the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm. If the deviation between the current equivalent diffusion coefficient of the model and the inverted target calibration value is negative, then the lower limit of the search space for the chloride ion diffusion coefficient of the interface layer is updated using the bisection algorithm.

8. A device for inverting the chloride ion diffusion coefficient of a functionally graded concrete interface layer, characterized in that, The device includes: The model building module is used to build a physical model of chloride ion diffusion in functionally graded concrete. The basic parameter setting module is used to obtain and set the chloride ion diffusion coefficients of the external hydrophobic layer and the internal structural layer of the functionally graded concrete chloride ion diffusion physical model, as well as the inversion target calibration value of the functionally graded concrete chloride ion diffusion physical model. The inversion parameter setting module is used to set the initial search space for the chloride ion diffusion coefficient of the interface layer, which is used as an inversion parameter, and the error tolerance, which is used as a convergence criterion. The module for calculating the estimated value of chloride ion diffusion coefficient in the interface layer is used to calculate the intermediate value of the current search space as the estimated value of chloride ion diffusion coefficient in the interface layer based on the numerical search algorithm. The interface layer chloride ion diffusion coefficient inversion determination module is used to simulate the erosion diffusion of the functionally graded concrete chloride ion diffusion physical model based on the guessed value of the interface layer chloride ion diffusion coefficient, and to judge convergence based on the error tolerance. If convergence is not achieved, it is performed in a loop. If convergence is achieved, the current guessed value is output as the inversion result of the interface layer chloride ion diffusion coefficient as the inversion parameter.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the method for inverting the chloride ion diffusion coefficient of the functionally graded concrete interface layer as described in any one of claims 1-7.

10. A computer-readable medium, characterized in that, It stores instructions that, when executed by one or more processors, cause the processors to perform the chloride ion diffusion coefficient inversion method for functionally graded concrete interface layers as described in any one of claims 1-7.

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