A method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning

Abstract

The present invention belongs to the field of rust inhibitor performance prediction, specifically relating to a method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning. First, the active groups and functional groups in the rust inhibitor molecular structure are scientifically classified and data collected. Indicator data highly relevant to the rust inhibitor, such as immersion time, rust inhibitor content, transfer resistance, and rust inhibition efficiency, are collected to form a raw data feature set. Secondly, the collected raw data is preprocessed. Then, based on importance ranking, key functional groups and environmental condition characteristics that affect rust inhibition efficiency are identified. Finally, a machine learning regression model is trained using the training set, and the model performance is evaluated using the error index of the test set. This method, which can predict the rust inhibition efficiency under different rust inhibitor molecular structures, has the advantages of high prediction accuracy, strong interpretability, and good scalability, providing efficient technical support for the research, development, screening, and application of steel bar rust inhibitors.

Description

Technical Field

[0001] The present invention belongs to the technical field of rust inhibitor performance prediction, and in particular relates to a method for predicting steel bar rust inhibitor performance based on functional group classification and machine learning. Background Art

[0002] Reinforced concrete, due to its superior mechanical properties and cost-effectiveness, is widely used in construction, transportation, marine engineering, and other fields. However, in environments subject to chloride attack, carbonation, or other multi-factorial corrosive conditions, the steel bars in concrete are highly susceptible to corrosion, causing cracks in the concrete and even structural damage. Currently, there are two approaches to preventing steel rust: one is to enhance the inherent protective capabilities of concrete, such as the use of high-performance concrete; the other is "additional measures," such as electrochemical desalination, film coating protection, cathodic protection, and the addition of steel rust inhibitors. Steel rust inhibitors are economical, practical, and easy to use, making them widely used in steel rust prevention. There are many types of rust inhibitors, each operating on different principles. Their working principles are closely related to the active functional groups they contain, with the core difference being the different mechanisms by which these active functional groups form a protective film or undergo complex reactions on the metal surface. Existing research on rust inhibitor design often relies on empirical mixes or small-scale experimental screening, making it difficult to timely and systematically compare key indicators such as rust inhibition efficiency across a variety of mixes.

[0003] With the rapid development of big data and machine learning technologies, the materials field has begun to explore the use of data-driven methods to predict and optimize material properties. However, the current design of multi-functional groups for steel bar rust inhibitors still lacks mature and effective data accumulation and modeling methods. In addition, the various active functional groups vary greatly in terms of dimension, type, and number. Without systematic data classification and feature extraction rules, machine learning models often find it difficult to obtain high-precision predictions. Based on this, it is necessary to propose a technical solution that can not only effectively classify and encode the characteristics of the active functional groups of rust inhibitors, but also use machine learning to fully explore the relationship between the type of functional groups of steel bar rust inhibitors and rust inhibition efficiency, in order to reduce R&D costs and accelerate material performance evaluation and optimization design. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning. By systematically classifying and encoding active functional groups and combining multidimensional indicator data (such as immersion time, rust inhibitor content, transfer resistance, rust inhibition efficiency, etc.), a machine learning model is used to achieve rapid and accurate prediction of the performance of steel bar rust inhibitors, overcoming the problems of time-consuming, experience-dependent, and insufficient mechanism analysis in the existing steel bar rust inhibitor formula screening and performance evaluation.

[0005] The present invention provides the following technical solutions:

[0006] A method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning includes the following steps:

[0007] (1) Functional group classification and data collection:

[0008] The active groups in the molecular structure of steel bar rust inhibitors were systematically classified according to different functional group types. The functional group types and quantities of each rust inhibitor sample were collected, and the corresponding immersion time, rust inhibitor content, transfer resistance, and rust inhibition efficiency data were extracted to form the original data feature set.

[0009] (2) Data preprocessing:

[0010] The collected raw data is pre-processed by removing all zero features, eliminating outliers, and standardizing to convert the functional group classification information and quantity into feature parameters that can be identified by machine learning;

[0011] (3) Feature analysis:

[0012] Conduct feature importance analysis to identify key functional groups and environmental conditions that affect rust inhibition efficiency and construct a simplified model;

[0013] (4) Machine learning model training and evaluation:

[0014] The machine learning regression model was trained using the training set, and the model performance was evaluated using the error index of the test set to obtain a prediction model that can predict the rust inhibition efficiency under different rust inhibitor molecular structures;

[0015] (5) Prediction of steel bar rust inhibitor performance:

[0016] For the steel bar rust inhibitor to be predicted, its characteristics are preprocessed and then input into the prediction model established in step (4), and the corresponding prediction results are output.

[0017] Furthermore, in step (1), the active groups are divided into eight categories, namely: amino groups, aromatic amines, alcohols, alcoholamines, nitrogen-containing heterocycles, carboxylic acids, phosphates, esters, and fatty acid esters;

[0018] There are twenty types of functional groups, namely: hydroxyl, carboxyl, amino, amide, imino, nitro, ether, ester, carbonyl, thioketone, cyano, imidazoline, imidazole, triazole, pyridyl, thiol, sulfonic acid, phosphate, halogen atom, and phenyl.

[0019] Furthermore, in step (2), the feature columns that are all 0 or seriously missing in the original data feature set are deleted, individual extreme outliers are excluded, and invalid features are removed; the classification information of the active groups and functional groups is numerically encoded; and the continuous numerical features are standardized so that the mean is 0 and the standard deviation is 1.

[0020] Furthermore, in step (3), the feature importance is sorted by the CatBoost model, and the nonlinear correlation between each active group and functional group and between each other and the rust inhibition efficiency index is evaluated by correlation heat map analysis; based on the feature importance sorting and correlation analysis results, the top 12 most influential features are retained to participate in subsequent machine learning training and prediction tasks.

[0021] Furthermore, in step (3), the steel bar rust inhibitor performance prediction method based on functional group classification and machine learning also performs PCA feature dimensionality reduction analysis and interpretability analysis.

[0022] Furthermore, in step (3), the steel bar rust inhibitor performance prediction method based on functional group classification and machine learning also uses a three-dimensional scatter plot to preliminarily observe the sample distribution of different functional group combinations. In the three-dimensional scatter plot, a 1% dynamic adjustment random fine-tuning offset is used according to the axis range to avoid excessive overlap of sample data points of multiple functional groups.

[0023] Furthermore, in step (4), the data is divided into a training set and a test set in a certain ratio, and the training set is used to model a CatBoost machine learning regression model suitable for tabular data and nonlinear features. The model is trained and verified, and the network model hyperparameters are adjusted according to the error index of the test set. The model configuration is further optimized using cross-validation or grid search methods to obtain a prediction model that can predict the rust inhibition efficiency under different rust inhibitor molecular structures.

[0024] Furthermore, in step (4), the performance of the machine learning model is intuitively evaluated by drawing a regression fitting graph of the true value and the predicted value and a comparison graph of the test set prediction results; and the error indicators mean square error, root mean square error, mean absolute error, mean absolute percentage error and determination coefficient are combined to evaluate the fitting ability of the prediction model for the rust inhibition efficiency under different rust inhibitor molecular structures.

[0025] The principle of the present invention is that the present invention establishes a data set based on the classification rules of the active functional groups of the rust inhibitor, combines the machine learning method to carry out regression training on the rust inhibition efficiency, and realizes the accurate prediction of the rust inhibition performance of the steel bar rust inhibitor.

[0026] Compared with the prior art, the present invention has the following significant advantages:

[0027] (1) The present invention groups active functional groups according to the proposed classification rules, with a clear and reasonable classification hierarchy, which facilitates data collation and modeling analysis. Combined with machine learning algorithms, it can capture the complex nonlinear relationship between multiple functional groups and rust inhibition efficiency, and the prediction accuracy is significantly better than traditional empirical methods or single-factor experiments;

[0028] (2) When new rust inhibitors or other active groups are introduced, feature extraction and model update can be performed according to the same classification principle, which is scalable. Using highly interpretable visualization tools such as SHAP, the contribution of each functional group category to rust inhibition efficiency can be analyzed, providing R&D personnel with intuitive mechanism analysis and improvement directions;

[0029] (3) In engineering applications, a large number of candidate mixes can be quickly screened virtually, reducing repeated experiments and lowering the research and development costs of steel bar rust inhibitors. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings and the accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. Among them:

[0031] Figure 1 Schematic diagram of the classification of active functional groups of the present invention;

[0032] Figure 2 Schematic diagram of a three-dimensional scatter plot of functional group data distribution of the present invention;

[0033] Figure 3 This is a three-dimensional PCA analysis diagram of feature dimensionality reduction of the present invention;

[0034] Figure 4 It is the feature correlation matrix heat map of the present invention;

[0035] Figure 5 This is a graph ranking the importance of features of the present invention;

[0036] Figure 6 This is the SHAP summary diagram of the present invention;

[0037] Figure 7 It is the regression fitting diagram of the true value and the predicted value of the present invention;

[0038] Figure 8 This is a comparison chart of the test set prediction results of the present invention. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0040] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. In this embodiment, 330 groups of steel bar rust inhibitor sample information in the prior art were collected, and the molecular structure of each rust inhibitor contained at least one active group.

[0041] A method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning includes the following steps:

[0042] (1) Functional group classification and data collection:

[0043] According to the classification rules of active functional groups in the molecular structure of steel bar rust inhibitor, the number was recorded, and the immersion time (h), rust inhibitor content (%), rust inhibition efficiency η (%) and transfer resistance Rct (kΩ·cm 2 ) and other data to form the original data feature set; the original data information summary of 330 groups of steel bar rust inhibitor sample data is shown in Table 1;

[0044] Table 1 Summary of raw data of 330 groups of steel bar rust inhibitor samples

[0045]

[0046]

[0047] In Table 1, the mean, standard deviation, and five-number summary are used to describe the original data set of the steel bar rust inhibitor sample data; the minimum value, 0.25 (first quartile), 0.5 (second quartile), 0.75 (third quartile), and maximum value in the title row of Table 1 are the five key values ​​summarized by the five-number summary. They are important components of descriptive statistics and help to quickly understand the characteristics of the data set.

[0048] As shown in Table 1, Figure 1 As shown in the figure, the active functional group classification rule is to divide each active group into eight categories and record the number of each functional group type; the eight categories of active groups are: 1-amino group, 2-aromatic amine group, 3-alcohol group, 4-alcohol amine group, 5-nitrogen-containing heterocyclic group (such as pyridine, imidazole, etc.), 6-carboxylic acid group, 7-phosphate group, 8-ester group and fatty acid ester; there are 20 types of functional groups, namely: hydroxyl (-OH), carboxyl (-COOH), amino group (-NH2), amide group (-CONH2), imino group (=NH), nitro group (-NO2), ether group (-O-), ester group (-COOR), carbonyl group (-C=O), thiol group, cyano group (-CN), imidazoline group, imidazole, triazole, pyridyl group (-C5H5N), thiol group (-SH), sulfonic acid group (-SO3H), phosphate group (-PO4), halogen atom (-X), and phenyl group. Figure 1 The numbers in the table represent the number of functional groups in the eight types of active groups.

[0049] (2) Data preprocessing:

[0050] The collected raw data is pre-processed by removing all zero features, eliminating outliers, and standardizing to convert the functional group classification information and quantity into feature parameters that can be identified by machine learning;

[0051] Columns that are zero across all samples are removed to eliminate extreme outliers caused by individual statistical errors. Continuous features such as "immersion time," "rust inhibitor content," and "transfer resistance" are normalized to a mean of 0 and a standard deviation of 1 to eliminate the impact of dimensional differences on model training. Different functional group categories are numerically encoded and incorporated into the machine learning feature matrix X (the encoded functional group categories will be included as part of the feature matrix X). The target value of the rust inhibition efficiency η (%) is organized into a vector y to prepare for subsequent model training and prediction. For example, in Table 1, the columns that are zero across all samples include nitro (-NO2) and thiol (-SH) groups and should be removed. The 3σ criterion indicates that columns such as "immersion time" and "rust inhibition efficiency" contain extreme outliers caused by individual statistical errors. Numerical encoding is performed based on the eight active groups, for example, "amino" is considered feature 1, "aromatic amine" is considered feature 2, and so on, with the corresponding quantities also included in the feature matrix. Figure 1 The first column is the 8 types of active groups after numerical coding. Figure 1 This is just an example of the classification rule for active groups. In other embodiments, other classification rules may be used to classify and numerically encode active groups.

[0052] (3) Feature analysis:

[0053] Based on PCA (principal component analysis) feature dimensionality reduction analysis, feature correlation analysis, importance ranking and interpretability analysis, the key functional groups and environmental condition characteristics that affect rust inhibition efficiency are identified;

[0054] Use the three-dimensional scatter plot (immersion time, rust inhibitor content, rust inhibition efficiency) to preliminarily observe the distribution of samples with different functional group combinations. Figure 2 is a three-dimensional scatter plot of the functional group data distribution, Figure 2 In the , a 1% dynamic adjustment random fine-tuning (jitter) offset is used according to the axis range to avoid excessive overlap of sample data points of multiple functional groups; Figure 2 It can be seen that the functional group data are mostly distributed in the area of ​​low "rust inhibitor content", short "immersion time" and high "rust inhibition efficiency", indicating that most steel bar rust inhibitors have high efficiency despite small dosage. Their functional groups can quickly generate protective films or react in a short period of time, and can play a greater role in rust inhibition. Among them, the data points of the functional groups "hydroxyl" and "amino" are widely scattered, with both high efficiency and low efficiency. This may indicate that the functional group itself is not the only dominant factor, and a stable rust inhibition effect can only be achieved in combination with "rust inhibitor content", "immersion time" or other functional group conditions. The functional groups "hydroxyl", "carboxyl" and "amino" easily form clusters, indicating that they have similar effects and a certain synergistic effect.

[0055] like Figure 3As shown in the figure, PCA (n_components=3) is performed on the feature matrix to reduce the dimension, generate three-dimensional principal component coordinates, and use the rust inhibition efficiency value as the color mapping to intuitively understand the distribution characteristics of the sample in the principal component coordinate system from a macro level. Figure 3 It can be found that the data point cloud has a large dispersion and a wide range of colors, indicating that the samples have significant differences in the main feature dimensions. The difference in rust inhibition efficiency may be due to the influence of multiple factors.

[0056] The characteristic correlation analysis is presented through the correlation heat map to evaluate the nonlinear correlation between each functional group and the rust inhibition efficiency index. Figure 4 The correlation between the sample data features in Table 1 is shown. For example, Figure 4 It can be seen that "thioketone" has a high positive correlation with "amino group", "imino group" and "ether group" functional groups, indicating that these functional groups may often exist at the same time; "sulfonic acid group" is negatively correlated with "rust inhibition efficiency", indicating that the two may have an inverse relationship;

[0057] Extract the feature importance ranking based on the CatBoost model to identify which key functional groups and environmental characteristics most significantly affect the rust inhibition efficiency. The results are as follows: Figure 5 As shown, the importance of each feature is: rust inhibitor content (%) > immersion time (h) > transfer resistance Rct (kΩ·cm 2 )>carboxyl (-COOH)>amino (-NH2)>hydroxyl (-OH)>phenyl>phosphate (-PO4)>imino (=NH)>imidazoline>thioketone>imidazole>carbonyl (-C=O)>amide (-CONH2)>sulfonic acid (-SO3H)>ether (-O-)>halogen atom (-X)>pyridyl (-C5H5N)>triazole>cyano (-CN)>ester (-COOR); the results show that the active functional groups such as carboxyl (-COOH), amino (-NH2), hydroxyl (-OH) and phenyl have the greatest contribution to the rust inhibition efficiency;

[0058] like Figure 6 As shown in the figure, SHAP is also used to draw an interactive dependency diagram to observe the positive / negative impact of different features on the model prediction output, providing R&D personnel with an interpretable mechanism analysis and a basis for rust inhibitor design to guide subsequent design; SHAP is the abbreviation of "SHapley Additive exPlanations", which is a method for explaining machine learning model predictions. Figure 6As shown in the figure, features such as "rust inhibitor content", "transfer resistance", "carboxyl group", "immersion time", "amino group", "hydroxyl group", and "phenyl group" are located at the top, indicating that they have a greater impact on the model output (prediction of rust inhibition efficiency); among them, the "rust inhibitor content" feature has both low-value (blue) points distributed in the positive SHAP area and some high-value (red) points distributed in the negative SHAP area on the same row. The scattered distribution of points is relatively dispersed, indicating that the relationship between this feature and the target value is not a simple linear monotonic relationship and may also be affected by other features;

[0059] To reduce feature dimensionality, we sorted the results by feature importance, retaining only the top 12 most important features for subsequent machine learning training and prediction tasks. This reduced redundant features to build a simplified model and improve model prediction speed and accuracy. PCA analysis, interpretability analysis, correlation analysis, and 3D scatter plots all analyze features to understand the influencing patterns of key features, assist in feature screening, and aid in rust inhibitor design.

[0060] (4) Machine learning model training and evaluation:

[0061] The machine learning regression model was trained using the training set, and the model performance was evaluated using the error index of the test set to obtain a prediction model that can predict the rust inhibition efficiency under different rust inhibitor molecular structures;

[0062] The processed data is divided into training set and test set according to 80%:20%, and the CatBoost machine learning regression model suitable for tabular data and nonlinear features is built using the training set, and the test set error indicators (MSE, MAE, R 2 , MAPE) to adjust the network model hyperparameters (such as tree depth, learning rate, number of iterations, etc.), and use cross-validation or grid search methods to further optimize the model configuration. Finally, a prediction model (regression prediction model) that can predict the rust inhibition efficiency under different rust inhibitor molecular structures is obtained: depth = 8, learning_rate = 0.05, iterations = 1000, l2_leaf_reg = 10, random_state = 42, verbose = 0 (tree depth is 8, learning rate is 0.05, iterations are 1000, L2 leaf node regularization term is 10, random state is 42, and detailed information is not output during the training process);

[0063] Grid Search and 5-fold cross-validation were used to further tune model hyperparameters, iterating multiple times on the training set and evaluating on the test set;

[0064] By drawing the regression fitting graph of the true value and the predicted value and the comparison graph of the test set prediction results, the performance of the machine learning model can be evaluated intuitively. Figure 7 , the test set prediction results are compared in Figure 8 The error evaluation indicators are mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R 2 );

[0065] From the training set and test set error results in Table 2, we can see that R 2 Both are greater than 0.90, indicating that the established machine learning model has a good fitting ability for the rust inhibition efficiency and is suitable for carrying out regression training and prediction tasks of steel bar rust inhibitor performance based on functional group classification.

[0066] Table 2 Error comparison between training set and test set

[0067] Error index MSE RMSE MAE MAPE <![CDATA[R 2 <!-- 6 -->]]> training set 1.57264654 1.254052 0.964139 0.037705 0.998414 Test set 200.597024 14.16323 7.692063 9.13E+13 0.90132

[0068] (5) Prediction of steel bar rust inhibitor performance:

[0069] For the steel bar rust inhibitor to be predicted, the sample features of the steel bar rust inhibitor are preprocessed according to step (2) and then input into the trained regression prediction model to output the corresponding prediction results.

[0070] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are within the scope of protection of the pending claims of the present invention.

Claims

1. A method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning, characterized in that: The following steps are involved: (1) Functional group classification and data collection: The active groups in the molecular structure of steel bar rust inhibitors were systematically classified according to different functional group types. The functional group types and quantities of each rust inhibitor sample were collected, and the corresponding immersion time, rust inhibitor content, transfer resistance, and rust inhibition efficiency data were extracted to form the original data feature set. (2) Data preprocessing: The collected raw data is pre-processed by removing all zero features, eliminating outliers, and standardizing to convert the functional group classification information and quantity into feature parameters that can be identified by machine learning; (3) Feature analysis: Conduct feature importance analysis to identify key functional groups and environmental conditions that affect rust inhibition efficiency and construct a simplified model; (4) Machine learning model training and evaluation: The machine learning regression model was trained using the training set, and the model performance was evaluated using the error index of the test set to obtain a prediction model that can predict the rust inhibition efficiency under different rust inhibitor molecular structures; (5) Prediction of steel bar rust inhibitor performance: For the steel bar rust inhibitor to be predicted, its characteristics are preprocessed and then input into the prediction model established in step (4), and the corresponding prediction results are output.

2. The method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning according to claim 1, wherein: In step (1), the active groups are divided into eight categories, namely: amino groups, aromatic amines, alcohols, alcoholamines, nitrogen-containing heterocycles, carboxylic acids, phosphates, esters and fatty acid esters; There are twenty types of functional groups, namely: hydroxyl, carboxyl, amino, amide, imino, nitro, ether, ester, carbonyl, thioketone, cyano, imidazoline, imidazole, triazole, pyridyl, thiol, sulfonic acid, phosphate, halogen atom, and phenyl.

3. The method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning according to claim 1, wherein: In step (2), the feature columns that are all 0 or seriously missing in the original data feature set are deleted, individual extreme outliers are excluded, and invalid features are removed; the classification information of the active groups and functional groups is numerically encoded; and the continuous numerical features are standardized so that the mean is 0 and the standard deviation is 1.

4. The method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning according to claim 1, wherein: In step (3), the feature importance is ranked by the CatBoost model, and the nonlinear correlation between each active group and functional group and between each other and the rust inhibition efficiency index is evaluated by correlation heat map analysis; Based on the feature importance ranking and correlation analysis results, the top 12 most influential features are retained to participate in subsequent machine learning training and prediction tasks.

5. The method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning according to claim 1, wherein: In step (3), feature correlation analysis, PCA feature dimensionality reduction analysis and interpretability analysis were also performed.

6. The method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning according to claim 1, wherein: In step (3), a three-dimensional scatter plot is also used to preliminarily observe the sample distribution of different functional group combinations. In the three-dimensional scatter plot, a 1% dynamic adjustment random fine-tuning offset is used according to the axis range to avoid excessive overlap of sample data points of multiple functional groups.

7. The method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning according to claim 1, wherein: In step (4), the data is divided into a training set and a test set in a certain ratio, and a CatBoost machine learning regression model suitable for tabular data and nonlinear features is modeled using the training set. The model is trained and verified, and the network model hyperparameters are adjusted according to the error index of the test set. The model configuration is further optimized using cross-validation or grid search methods to obtain a prediction model that can predict the rust inhibition efficiency under different rust inhibitor molecular structures.

8. The method for predicting the performance of steel bar rust inhibitors based on functional group classification and machine learning according to claim 1, wherein: In step (4), the performance of the machine learning model is intuitively evaluated by drawing a regression fitting graph of the true value and the predicted value and a comparison graph of the test set prediction results; and the error indicators mean square error, root mean square error, mean absolute error, mean absolute percentage error and determination coefficient are combined to evaluate the fitting ability of the prediction model for the rust inhibition efficiency under different rust inhibitor molecular structures.

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