A method for predicting chloride ion diffusion in a concrete structure of port infrastructure

Abstract

The present invention discloses a method for predicting chloride ion diffusion in the concrete structure of port infrastructure, which relates to the technical field of chloride ion diffusion prediction. The present invention aims to update a series of data-driven methods for the rationality of existing initial condition settings, especially to perform a series of analyses on the initial chloride ion concentration in the existing initial conditions to obtain the final initial chloride ion concentration. This process particularly involves compensation processing. Specifically, it particularly involves the compensation processing of the initial chloride ion concentration. After performing two compensation processes, the initial and final chloride ion concentration compensation values are obtained respectively, and based on the final chloride ion concentration compensation value, the final initial chloride ion concentration can be effectively obtained. Through the above compensation analysis method, the accuracy, scientificity and rationality of the initial condition setting can be effectively improved, thereby improving the credibility of the subsequent simulation results, and further reducing the deviation between the prediction result and the actual situation to the greatest extent.

Description

Technical Field

[0001] The present invention relates to the technical field of chloride ion diffusion prediction, and particularly to a method for predicting chloride ion diffusion in the concrete structure of port infrastructure. Background Art

[0002] Port infrastructure such as docks and breakwaters is exposed to the marine environment rich in chlorides for a long time. Chloride ion penetration has become one of the main factors affecting its durability. In order to effectively manage and extend the service life of these important assets, a variety of methods have been developed to predict the diffusion process of chloride ions in concrete structures. Among them, numerical calculation and analysis have become a widely used technical means.

[0003] Specifically, with the progress of computer technology and numerical methods, numerical calculation and analysis have become an important tool for predicting chloride ion diffusion. This method mainly includes finite element analysis, finite difference method, etc. Numerical calculation and analysis can more accurately simulate complex geometries and boundary conditions, as well as the inhomogeneity and anisotropy of concrete materials. It can also consider the influence of environmental factors such as temperature and humidity changes on chloride ion diffusion. By setting appropriate initial conditions and boundary conditions, combined with specific parameters of concrete materials such as diffusion coefficient and porosity, the numerical model can provide a detailed chloride ion concentration distribution map, thus helping engineers evaluate the durability of concrete structures.

[0004] Although numerical calculation and analysis bring many advantages to chloride ion diffusion prediction, it still faces a series of problems and challenges in practical applications:

[0005] In the prior art, the setting of initial conditions is often only set after simple testing of existing numerical values. Specifically, it especially includes the estimation of the initial chloride ion concentration in concrete. This estimation is usually based on the results of experience or standard tests. For example, it is determined through laboratory tests, such as measuring the chloride ion concentration in fresh concrete or concrete samples in the initial curing period using titration, potentiometric titration or ion chromatography. However, such a setting method lacks a further detailed calculation and analysis process, which poses a challenge to the scientificity and accuracy of the initial condition setting. The accuracy of the initial conditions directly affects the credibility of the subsequent simulation results. Therefore, when the initial conditions are set unreasonably, it is very easy to lead to a large deviation between the prediction results and the actual situation.

[0006] Therefore, there is an urgent need for a technical solution for a method for predicting chloride ion diffusion in the concrete structure of port infrastructure in the prior art. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention provides a method for predicting chloride ion diffusion in the concrete structure of port infrastructure, which specifically includes the following steps:

[0008] Step S1: Set the initial conditions and boundary conditions of the numerical model, and obtain the parameters of the concrete material;

[0009] Step S2: Analyze the initial chloride ion concentration in the initial conditions to obtain the final initial chloride ion concentration;

[0010] Step S2a: Perform compensation processing on the initial chloride ion concentration in the initial conditions to obtain the initial chloride ion concentration compensation value;

[0011] Step S2a1: Obtain the chloride ion concentrations in at least two groups of concrete samples through laboratory testing methods, and take the average value as the first initial chloride ion concentration;

[0012] Step S2a2: Measure and obtain the chloride ion concentrations in at least two groups of concrete samples through sensors, and take the average value as the second initial chloride ion concentration;

[0013] Step S2a3: Obtain the predicted chloride ion concentration in the concrete sample through a preset prediction model;

[0014] Step S2a31: Repeatedly execute Step S2a1 to Step S2a2 to obtain at least N groups of the first initial chloride ion concentration and the second initial chloride ion concentration;

[0015] Step S2a32: Construct a data set with at least N groups of the first initial chloride ion concentration, at least N groups of the second initial chloride ion concentration, and the parameters of the concrete material, and perform preprocessing on the data set;

[0016] Step S2a33: Divide the preprocessed data set according to a preset ratio, and obtain any part of the divided data set as the training set;

[0017] Step S2a34: Construct a prediction model, and use the training set to train the prediction model to obtain a trained prediction model;

[0018] The prediction model uses a neural network model;

[0019] Step S2a35: Determine the chloride ion concentration to be input, and input the chloride ion concentration to be input into the trained prediction model to obtain the predicted chloride ion concentration in the concrete sample;

[0020] Step S2a4: Obtain the initial chloride ion concentration compensation value based on the first initial chloride ion concentration, the second initial chloride ion concentration, and the predicted chloride ion concentration;

[0021] Step S2a41: Obtain the first difference between the second initial chloride ion concentration and the first initial chloride ion concentration;

[0022] Step S2a42: Obtain the second difference between the predicted chloride ion concentration and the first initial chloride ion concentration;

[0023] Step S2a43: Obtain the third difference between the predicted chloride ion concentration and the second initial chloride ion concentration;

[0024] Step S2a44: Obtain the initial chloride ion concentration compensation value according to the first difference, the second difference and the third difference;

[0025] Among them, the calculation formula for obtaining the initial chloride ion concentration compensation value is:

[0026]

[0027] Among them, represents the initial chloride ion concentration compensation value; represents the first difference between the second initial chloride ion concentration and the first initial chloride ion concentration; represents the second difference between the predicted chloride ion concentration and the first initial chloride ion concentration; represents the third difference between the predicted chloride ion concentration and the second initial chloride ion concentration;

[0028] Step S2b: Perform compensation processing on the initial chloride ion concentration compensation value to obtain the final chloride ion concentration compensation value;

[0029] Step S2b1: Repeat Step S2a to obtain at least three groups of initial chloride ion concentration compensation values;

[0030] Step S2b2: Obtain the absolute value of the difference between each group of initial chloride ion concentration compensation values;

[0031] Step S2b3: Combine the absolute value of the difference with the initial chloride ion concentration compensation value to obtain the final chloride ion concentration compensation value;

[0032] Step S2c: Obtain the final initial chloride ion concentration based on the final chloride ion concentration compensation value;

[0033] Combine the final chloride ion concentration compensation value with the first initial chloride ion concentration to obtain the final initial chloride ion concentration;

[0034] Step S3: Update the initial conditions of the numerical model according to the final initial chloride ion concentration, and input the boundary conditions of the numerical model, the updated initial conditions of the numerical model and the parameters of the concrete material into the preset numerical model to obtain the chloride ion concentration diffusion distribution map;

[0035] Step S4: Evaluate the durability of the concrete structure according to the chloride ion concentration diffusion distribution map.

[0036] The embodiments of the present invention have the following technical effects:

[0037] The present invention aims to update a series of data-driven methods for the rationality of existing initial condition settings, especially to perform a series of analyses on the initial chloride concentration in the existing initial conditions to obtain the final initial chloride concentration. This process particularly involves compensation processing. Specifically, it especially involves the compensation processing of the initial chloride concentration. After performing two compensation processes, the initial and final chloride concentration compensation values are obtained respectively, and based on the final chloride concentration compensation value, the final initial chloride concentration is effectively obtained. Through the above compensation analysis method, the accuracy, scientificity, and rationality of the initial condition setting can be effectively improved, so as to improve the simulation results of the subsequent numerical model, that is, the credibility of the chloride concentration diffusion distribution map, and further the deviation between the prediction result and the actual situation can be minimized to the greatest extent. Brief Description of the Drawings

[0038] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is a flowchart of a method for predicting chloride diffusion in a concrete structure of port infrastructure provided by an embodiment of the present invention. Detailed Embodiments

[0040] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present invention.

[0041] Embodiment 1: As Figure 1 shown, the present invention provides a method for predicting chloride diffusion in a concrete structure of port infrastructure, including the following steps:

[0042] Step S1: Set the initial conditions and boundary conditions of the numerical model, and obtain the parameters of the concrete material.

[0043] Initial conditions refer to the description of the system state at the start time (t = 0) of numerical simulation. For chloride ion diffusion prediction, the initial conditions mainly include the initial chloride ion concentration in concrete, the initial moisture saturation of concrete, and the initial temperature. Among them, the acquisition method of the initial chloride ion concentration in concrete includes determining it through laboratory tests, such as measuring the chloride ion content in fresh concrete or concrete samples in the early curing stage using titration, potentiometric titration, or ion chromatography. The acquisition method of the initial moisture saturation of concrete includes indirectly estimating the moisture content through resistivity tests. The acquisition method of the initial temperature includes setting it based on the climate data at the construction site. By installing temperature sensors at different positions at the construction site and recording the temperature changes over a period of time, a reasonable average initial temperature can be determined.

[0044] Boundary conditions are the conditions imposed on the model boundaries during the simulation process, which reflect the influence of the external environment on the concrete structure. For chloride ion diffusion prediction, common boundary conditions include surface chloride ion concentration, moisture evaporation rate, and temperature gradient. Among them, the acquisition method of the surface chloride ion concentration includes regularly collecting samples on the surface of the concrete structure and measuring the surface chloride ion concentration through chemical analysis methods such as potentiometric titration. The acquisition method of the moisture evaporation rate includes using meteorological data and evaporation models to estimate. The acquisition method of the temperature gradient includes calculating the temperature distribution inside the concrete using the air temperature data provided by the weather station in combination with the heat conduction model.

[0045] Step S2: Analyze the initial chloride ion concentration in the initial conditions to obtain the final initial chloride ion concentration, which specifically includes the following steps:

[0046] Step S2a: Perform compensation processing on the initial chloride ion concentration in the initial conditions to obtain the initial chloride ion concentration compensation value. The specific process of how to obtain the initial chloride ion concentration compensation value includes the following steps:

[0047] Step S2a1: Obtain the chloride ion concentration in at least two groups of concrete samples through laboratory test methods and take the average value as the first initial chloride ion concentration.

[0048] Step S2a2: Obtain the chloride ion concentration in at least two groups of concrete samples through sensor measurement and take the average value as the second initial chloride ion concentration.

[0049] It should be noted that when measuring the chloride ion concentration in a concrete sample through a sensor, the chloride ion concentration in the concrete sample is mainly determined by sampling and analyzing on-site with an optical fiber sensor. Further, the optical fiber sensor is specifically a chloride ion optical fiber sensor based on detecting the refractive index of the environment, and its principle is as follows: when the refractive index of the external medium changes, the central wavelength of the long-period fiber grating will shift, and when the chloride ion concentration changes, it will cause a change in the refractive index in the solution. Therefore, by observing and recording the change in the central wavelength of the long-period grating, the chloride ion concentration can be detected. Since the optical fiber sensor is extremely sensitive to temperature, when using an optical fiber sensor-based chloride ion concentration sensor in actual engineering, a temperature compensation sensor must be equipped.

[0050] Step S2a3: Obtain the predicted chloride ion concentration in the concrete sample through a preset prediction model, which specifically includes the following steps:

[0051] Step S2a31: Repeatedly execute Step S2a1 to Step S2a2 to obtain at least N groups of first initial chloride ion concentrations and second initial chloride ion concentrations;

[0052] It should be noted that the purpose of Step S2a31 is to collect data of multiple concrete samples with known chloride ion concentrations to ensure the generalization ability of the model. The data sources include the laboratory test data in Step S2a1 and the on-site monitoring data in Step S2a2.

[0053] Step S2a32: Construct the at least N groups of first initial chloride ion concentrations, the at least N groups of second initial chloride ion concentrations, and the parameters of the concrete material into a data set, and perform preprocessing on the data set.

[0054] During the preprocessing process, first remove outliers and missing values to ensure the integrity and accuracy of the data. Subsequently, the data should be standardized so that all features have the same scale, avoiding some features dominating the model due to large numerical ranges. Then perform feature extraction, and preferentially select features related to the chloride ion concentration, such as the diffusion coefficient, porosity, moisture saturation, temperature of the concrete material, and the first initial chloride ion concentration and the second initial chloride ion concentration, etc.

[0055] Step S2a33: Divide the preprocessed data set according to a preset ratio, and obtain any part of the divided data set as the training set.

[0056] It should be noted that the preset ratio is usually 8 / 2, or 7 / 3. Among them, 8 parts can be selected as the training set, and the remaining 2 parts correspond to the validation set, which is used to verify the accuracy of the trained model. The basis for dividing the preset ratio is that, generally, when the preset ratio is 8 / 2, 80% of the data is used for training, which can provide enough samples for the model to learn, and the remaining 20% of the data is used for validation, which can effectively evaluate the generalization ability of the model. This is a division basis based on practice.

[0057] Step S2a34: Construct a prediction model and train the prediction model using the training set to obtain a trained prediction model.

[0058] In terms of the selection of the prediction model, a neural network model is preferably selected.

[0059] Step S2a35: Determine the chloride ion concentration to be input and input the chloride ion concentration to be input into the trained prediction model to obtain the predicted chloride ion concentration in the concrete sample.

[0060] When determining the chloride ion concentration to be input, it is preferably considered to obtain the chloride ion concentration by laboratory testing method and sensor measurement, and use it as the chloride ion concentration to be input.

[0061] Step S2a4: Obtain the initial chloride ion concentration compensation value based on the first initial chloride ion concentration, the second initial chloride ion concentration, and the predicted chloride ion concentration, which specifically includes the following steps:

[0062] Step S2a41: Obtain the first difference between the second initial chloride ion concentration and the first initial chloride ion concentration.

[0063] Step S2a42: Obtain the second difference between the predicted chloride ion concentration and the first initial chloride ion concentration.

[0064] Step S2a43: Obtain the third difference between the predicted chloride ion concentration and the second initial chloride ion concentration.

[0065] Step S2a44: Obtain the initial chloride ion concentration compensation value according to the first difference, the second difference, and the third difference.

[0066] Among them, the calculation formula for obtaining the initial chloride ion concentration compensation value is:

[0067]

[0068] Among them, represents the initial chloride ion concentration compensation value; represents the first difference; represents the second difference; represents the third difference.

[0069] It should be noted that the method of obtaining the initial chloride ion concentration compensation value through the first, second, and third differences above can more comprehensively reflect the change trend of the chloride ion concentration, and can reduce the fluctuations caused by measurement errors or environmental changes, making the results more stable and reliable; at the same time, based on statistical principles, the average value of multiple differences can reduce the influence of random errors and improve the statistical reliability of the results. According to the central limit theorem, the average value of multiple independent samples will tend to a normal distribution, thereby improving the stability and accuracy of obtaining the initial chloride ion concentration compensation value.

[0070] Step S2b: Perform compensation processing on the initial chloride ion concentration compensation value to obtain the final chloride ion concentration compensation value, which specifically includes the following steps:

[0071] Step S2b1: Repeat Step S2a to obtain at least three groups of initial chloride ion concentration compensation values.

[0072] Step S2b2: Obtain the absolute value of the difference between each group of initial chloride ion concentration compensation values.

[0073] Step S2b3: Combine the absolute value of the difference with the initial chloride ion concentration compensation value to obtain the final chloride ion concentration compensation value.

[0074] It should be further noted that by combining the absolute value of the difference with the initial chloride ion concentration compensation value, the fluctuations caused by measurement errors or environmental changes can be further reduced, enabling the acquisition of the final chloride ion concentration compensation value to be more stable and more accurate.

[0075] Step S2c: Obtain the final initial chloride ion concentration based on the final chloride ion concentration compensation value.

[0076] Combine the final chloride ion concentration compensation value with the first initial chloride ion concentration to obtain the final initial chloride ion concentration.

[0077] It should be noted that the method of combining the final chloride ion concentration compensation value with the first initial chloride ion concentration can correct the deviation caused by measurement errors or environmental changes, making the final initial chloride ion concentration closer to the true value.

[0078] Step S3: Update the initial conditions of the numerical model according to the final initial chloride ion concentration, and input the boundary conditions of the numerical model, the updated initial conditions of the numerical model, and the parameters of the concrete material into the preset numerical model to obtain the chloride ion concentration diffusion distribution map.

[0079] In terms of the selection of numerical models, the finite element analysis or the finite difference method can be selected; the input parameters should include the updated initial conditions, boundary conditions, and parameters of the concrete material. The updated initial conditions should include the final initial chloride ion concentration, the initial moisture saturation of the concrete, and the initial temperature; the boundary conditions should include the surface chloride ion concentration, the moisture evaporation rate, and the temperature gradient; the parameters of the concrete material should include the diffusion coefficient, porosity, and moisture saturation, etc.

[0080] Subsequently, set the simulation parameters: set parameters such as the simulation time step and mesh division; then start the numerical model to perform the simulation calculation of chloride ion diffusion; finally, after the numerical model runs to completion, output the chloride ion concentration diffusion distribution map, which shows the concentration distribution of chloride ions in the concrete structure, including the chloride ion concentrations at different positions and time points.

[0081] Step S4: Evaluate the durability of the concrete structure based on the chloride ion concentration diffusion distribution map.

[0082] Analyze the chloride ion concentration diffusion distribution map, determine the diffusion path and speed of chloride ions in the concrete, evaluate the corrosion risk of chloride ions to the internal steel bars in the concrete, and further evaluate the durability of the concrete structure.

[0083] It should be noted that the terms used in the present invention are only for describing specific embodiments and do not limit the scope of the present application. As shown in the specification of the present invention, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. The term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, or device including the said element.

[0084] It should also be noted that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. Unless otherwise clearly specified and defined, terms such as "installed", "connected", "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting chloride ion diffusion in concrete structures of port infrastructure, characterized in that: The following steps are involved: Step S1, setting the initial conditions and boundary conditions of the numerical model, and obtaining the parameters of the concrete material; Step S2, analyzing the initial concentration of chloride ions in the initial conditions to obtain a final initial concentration of chloride ions; Step S2a, performing compensation processing on the initial concentration of chloride ions in the initial condition to obtain a compensation value of the initial chloride ion concentration; Step S2a1, obtaining chloride ion concentrations in at least two groups of concrete samples by a laboratory test method, and taking the average as a first initial chloride ion concentration; Step S2a2, obtaining chloride ion concentrations in at least two groups of concrete samples by measuring with a sensor, and taking an average value as a second initial chloride ion concentration; Step S2a3, obtaining the predicted chloride ion concentration in the concrete sample through a preset prediction model; specifically comprising: Step S2a31, repeating step S2a1 to step S2a2 to obtain at least N groups of first initial chloride ion concentrations and second initial chloride ion concentrations; Step S2a32, constructing at least N groups of first initial chloride ion concentrations, at least N groups of second initial chloride ion concentrations and parameters of concrete materials into a data set, and performing preprocessing on the data set; Step S2a33, dividing the preprocessed data set according to a preset ratio, and obtaining any part of the divided data set as a training set; Step S2a34, constructing a prediction model, and using the training set to train the prediction model to obtain a trained prediction model; Step S2a35, determining the chloride ion concentration to be input, and inputting the chloride ion concentration to be input into the trained prediction model to obtain the predicted chloride ion concentration in the concrete sample; Step S2a4, obtaining an initial chloride ion concentration compensation value according to the first initial chloride ion concentration, the second initial chloride ion concentration and the predicted chloride ion concentration; Step S2b, performing compensation processing on the initial chloride ion concentration compensation value to obtain a final chloride ion concentration compensation value; Step S2b1, repeating step S2a to obtain at least three sets of initial chloride ion concentration compensation values; Step S2b2, obtaining the absolute value of the difference between each group of initial chloride ion concentration compensation values; Step S2b3, combining the absolute value of the difference with the initial chloride ion concentration compensation value to obtain a final chloride ion concentration compensation value; Step S2c, obtaining a final chloride ion initial concentration according to the final chloride ion concentration compensation value; Step S3, updating the initial conditions of the numerical model according to the final initial chloride ion concentration, and inputting the boundary conditions of the numerical model, the updated initial conditions of the numerical model and the parameters of the concrete material into the preset numerical model to obtain a chloride ion concentration diffusion distribution map; Step S4: evaluating the durability of the concrete structure according to the chloride ion concentration diffusion distribution diagram.

2. The method for predicting chloride ion diffusion in a port infrastructure concrete structure according to claim 1, characterized in that: The step S2c, according to the final chloride ion concentration compensation value, obtains the final chloride ion initial concentration, specifically: The final chloride ion concentration compensation value is combined with the first initial chloride ion concentration to obtain a final chloride ion initial concentration.

3. The method for predicting chloride ion diffusion in a port infrastructure concrete structure according to claim 1, characterized in that: The step S2a4, obtaining the initial chloride ion concentration compensation value according to the first initial chloride ion concentration, the second initial chloride ion concentration and the predicted chloride ion concentration, specifically comprises: Step S2a41, obtaining a first difference between the second initial chloride ion concentration and the first initial chloride ion concentration; Step S2a42, obtaining a second difference between the predicted chloride ion concentration and the first initial chloride ion concentration; Step S2a43, obtaining a third difference between the predicted chloride ion concentration and the second initial chloride ion concentration; Step S2a44, obtaining an initial chloride ion concentration compensation value according to the first difference, the second difference and the third difference.

4. The method for predicting chloride ion diffusion in a port infrastructure concrete structure according to claim 3, characterized in that: The calculation formula for obtaining the initial chloride ion concentration compensation value is: ； in, Represents the initial chloride ion concentration compensation value; represents a first difference between the second initial chloride ion concentration and the first initial chloride ion concentration; a second difference representing the predicted chloride ion concentration and the first initial chloride ion concentration; Represents a third difference between the predicted chloride ion concentration and the second initial chloride ion concentration.

5. A method for predicting chloride ion diffusion in a port infrastructure concrete structure according to any one of claims 1 to 4, characterized in that: The prediction model adopts a neural network model.

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