A freeze-thaw damage prediction method and system for high-ductility cement-based composite materials integrating surface multi-scale structure identification

Abstract

The present invention provides a method and system for predicting freeze-thaw damage of high-ductility cementitious composite materials that integrates surface multi-scale structural recognition, comprising the following steps: S1, obtaining surface damage information of ECC after freeze-thaw cycles; S2, constructing an optimized ECC freeze-thaw damage strength prediction model based on machine learning methods; S3, determining the surface damage coefficient of the ECC using the prediction model described in step S2 based on the parameters obtained in step S1; and S4, predicting the damage strength of the ECC after freeze-thaw based on the surface damage coefficient. The present invention proposes an ECC freeze-thaw damage prediction model based on surface recognition, which can infer the damage strength of the material by analyzing surface damage photographic information. The model uses image feature extraction and machine learning algorithms to establish a direct correlation between image information such as fiber exposure and surface spalling and damage strength, thereby achieving efficient and accurate prediction of ECC material performance after freeze-thaw damage.

Description

Technical Field

[0001] The present invention relates to the technical field of ECC material damage prediction, and in particular to a freeze-thaw damage prediction method and system for high-ductility cement-based composite materials integrating surface multi-scale structure recognition. Background Art

[0002] Due to its excellent crack resistance, high-ductility cementitious composites (ECC) are widely used in areas with extremely high durability requirements, such as bridges, roads, and marine structures. However, in cold environments, due to the high content of auxiliary cementitious materials, porous structure, and hydrophilic nature of ECC, mechanical property degradation still occurs, but the manifestation is quite different from that of ordinary concrete. Specifically, 1. Ordinary concrete usually shows single cracks or large-scale surface spalling during freeze-thaw cycles, and the crack width is relatively large. However, after freeze-thaw cycles, ECC shows the expansion of micro-cracks, but the cracks remain small and dispersed (usually less than 100 microns in width) and do not develop into large-scale fractures. 2. Because ECC contains fine aggregate (such as quartz sand) and a moderate amount of fiber (such as PVA fiber), without large coarse aggregate, its surface damage is uniform, with some fibers exposed, and the matrix material spalling is small and less, so it is relatively intact.

[0003] Current methods for concrete surface damage identification are not applicable to ECC for the following reasons: 1. ECC damage is primarily characterized by a multi-layered, nonlinear network of small cracks, often accompanied by fiber exposure or pullout, resulting in complex three-dimensional interface damage. Single-angle imaging cannot fully capture these features, requiring multi-angle imaging and 3D structural identification to reconstruct the interaction between cracks and fibers. 2. Due to the superior material properties of ECC, the width and depth of cracks are relatively small. The surface damage depth and appearance of microcracks do not directly correlate with damage severity, as in conventional concrete. Furthermore, while the continuous exposure of fibers has a limited impact on performance, their complex manifestations complicate assessment, making traditional macroscopic geometric evaluation methods inapplicable. 3. ECC damage identification needs to be adapted to the requirements of 3D structural reconstruction, capable of capturing the intricate details of microcracks and fiber interfaces, and combining crack evolution with mechanical simulations for multi-stage assessment. Furthermore, ECC requires greater focus on crack propagation patterns and long-term performance degradation, rather than simply assessing a single damage state.

[0004] Therefore, conventional concrete testing methods (which primarily focus on crack detection and surface spalling) are not suitable for assessing the damage state of ECC due to these unique freeze-thaw damage characteristics. Understanding and accurately identifying these specific damage characteristics is crucial for the accurate prediction and assessment of ECC freeze-thaw damage. Summary of the Invention

[0005] In light of this, the present invention aims to provide a freeze-thaw damage prediction method and system for high-ductility cementitious composites that integrates multi-scale surface structure recognition. This method, which analyzes surface damage photographs to estimate material damage intensity, leverages image feature extraction and machine learning algorithms to directly correlate image information such as fiber exposure and surface spalling with damage intensity, enabling efficient and accurate prediction of ECC material properties after freeze-thaw damage.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for predicting freeze-thaw damage of high-ductility cement-based composite materials integrating surface multi-scale structure recognition, comprising the following steps:

[0007] Step S1, obtaining surface damage image information of the ECC material under freeze-thaw cycles, including surface peeling, fiber exposure, microcracks, and pores, and photographing the material surface using a high-resolution camera or microscope to capture the microscopic characteristics of the surface damage after freeze-thaw cycles;

[0008] In step S2, based on the surface damage image information obtained in step S1, the image is preprocessed. In combination with the multi-level and multi-scale characteristic representations of the ECC surface under freeze-thaw damage, a deep convolutional neural network (ResNet), U-Net semantic segmentation, and an XGBoost classifier are used to accurately extract key damage features. These extracted features are then combined with known information such as the number of freeze-thaw cycles and material ratios for annotation to construct a high-quality dataset for subsequent model training and evaluation.

[0009] Step S3: Training the damage intensity prediction model and defining the damage coefficient. Based on the dataset annotated in step S2, a machine learning algorithm is used to train the ECC freeze-thaw damage intensity prediction model. Through training, the image features are associated with the actual tested strength data, thereby establishing a mapping relationship between the image features and the damage intensity. At the same time, the surface damage coefficient is calculated through the model to quantify the degree of surface damage and reflect the degradation of the microstructure after freeze-thaw cycles.

[0010] Step S4: Based on the surface damage coefficient, a prediction model is used to predict the strength of the ECC after freeze-thaw damage, and a damage strength index is obtained to further evaluate the durability of the ECC material after freeze-thaw cycles.

[0011] In a preferred embodiment, in step S1, after obtaining surface damage information of the ECC material under freeze-thaw cycles, a database containing high-quality surface damage images is constructed; the process of establishing this database includes the following steps:

[0012] Step S11: Sample collection and pretreatment: The ECC material is subjected to different freeze-thaw cycles and its surface images are collected; the images are acquired by scanning electron microscopy (SEM) or high-resolution camera, involving the use of optical microscope or camera, high-resolution scanning, electron microscopy or computed tomography, to ensure that microscopic cracks, spalling, fiber exposure and pore expansion damage characteristics are captured;

[0013] Step S12: Data labeling: Classify and label the collected image data, including the number of freeze-thaw cycles, material ratio, and environmental parameters; ensure that the label information of each image in the database is accurate to facilitate subsequent model training;

[0014] Step S13: Image archiving and storage: The images are classified and stored according to the number of freeze-thaw cycles and damage type to facilitate subsequent retrieval.

[0015] In a preferred embodiment, step S2 specifically includes the following steps:

[0016] Step S21: converting the collected color image into a grayscale image using linear weighted RGB;

[0017] Step S22: Image denoising and enhancement: Use algorithms such as Gaussian filtering or median filtering to remove noise from the image; at the same time, use the CLAHE method in OpenCV to perform contrast-limited adaptive histogram equalization to further enhance the contrast of the local damage area;

[0018] Step S23: Use the deep convolutional neural network ResNet to extract features from the preprocessed grayscale image, providing deep semantic feature extraction capabilities to capture local and global features of the damage. At the same time, the hybrid attention mechanism, namely the SE module + TransformerAttention, enables the model to automatically focus on areas with significant damage features.

[0019] Step S24: Use U-Net to perform semantic segmentation of the damaged area and extract areas such as microcracks, pores, and fiber exposure; use Open3D tools to reconstruct the three-dimensional point cloud of the damaged area and analyze the multi-scale relationship between microscopic and macroscopic features; predict the damage type through a classifier and evaluate the progression of each damage during the freeze-thaw cycle; input the features extracted by U-Net into the XGBoost classifier to classify the damage features in the image.

[0020] In a preferred embodiment, classifying damage features in an image specifically includes:

[0021] 1) Fiber exposure: Fiber regions are extracted by grayscale difference, and the area and height of the fiber exposure region are identified as the first damage parameter;

[0022] 2) Cement-based spalling: The area and depth of surface spalling are extracted based on grayscale differentiation and defined as the second damage parameter;

[0023] 3) Porosity and microcracks: The feature maps generated by ResNet and U-net can capture the basic locations of cracks.

[0024] In a preferred embodiment, the pores and microcracks are further processed and verified by the following methods;

[0025] (1) Morphological operations: Morphological operations are applied to highlight crack edges and remove falsely detected noise points;

[0026] (2) Edge detection algorithm: Use Canny edge detection or Sobel operator on the feature map to further extract the clear edges of the cracks and identify the accurate location and shape of pores and cracks;

[0027] (3) Generative Adversarial Network (GAN) correction: GAN is used to generate the real crack distribution, which is compared with the pore and crack features extracted from the feature map to correct noise or artifacts and ensure the accuracy of damaged area identification.

[0028] In a preferred embodiment, step S3 specifically includes the following steps:

[0029] Step S31: Model training: The feature data extracted in step S2 is trained with the actual strength test data, and a mapping relationship between damage features and actual strength is established through a machine learning algorithm CNN to construct a strength prediction model for ECC after freeze-thaw damage;

[0030] Step S32: Calculation of surface damage coefficient: Based on the model output, determine a surface damage coefficient D s , is used to quantify the degree of surface damage of ECC materials; the surface damage coefficient D s The damage intensity coefficient is expanded from a single indicator to a multidimensional parameter matrix to establish a comprehensive index describing the degree of material degradation:

[0031]

[0032] Where: i represents the weight of the i-th micro-damage parameter, which is set according to its influence on the damage degree; P i represents the i-th micro damage parameter obtained in step S24.

[0033] In a preferred embodiment, in step S4, the strength of the ECC after freeze-thaw damage is predicted based on the surface damage coefficient to obtain a damage strength index, thereby evaluating the durability of the ECC material after freeze-thaw cycles;

[0034] f after =f before ·(1-k·D s )

[0035] Where: f after Indicates the strength of the material after freeze-thaw cycles; f before represents the strength of the material before freeze-thaw cycles; k represents an empirical constant used to adjust the influence of surface damage coefficient on strength prediction.

[0036] In a preferred embodiment, step S4 specifically includes:

[0037] Step S41: Strength prediction verification and error analysis: Use the model to predict new data, use cross-validation to compare the error between the predicted value and the actual test value, and verify the model accuracy;

[0038] Step S42: Damage strength trend analysis: Based on the predicted damage strength value, analyze the strength variation trend with the number of freeze-thaw cycles to quantify the durability degradation; construct time series data by sequentially collecting surface damage images after multiple freeze-thaw cycles, and introduce a recurrent neural network (RNN) to analyze the dynamic evolution of damage characteristics with the number of freeze-thaw cycles;

[0039] Step S43: Durability and remaining service life assessment: Determine the durability level of the material by analyzing the strength drop, and use a regression model to predict the remaining service life; combine historical inspection data of buildings or bridges, and provide intelligent decision support for material replacement or maintenance through a cloud-based big data analysis platform.

[0040] The present invention also provides a freeze-thaw damage prediction system for high-ductility cement-based composite materials that integrates surface multi-scale structure recognition, which runs the above-mentioned freeze-thaw damage prediction method for high-ductility cement-based composite materials that integrates surface multi-scale structure recognition, including an ECC surface damage information acquisition module, an ECC image feature extraction module, a damage strength prediction model training and surface damage coefficient determination module, and a strength prediction module.

[0041] The present invention also provides a computer-readable storage medium, comprising: a computer program stored on the computer-readable storage medium; when the computer program is run by a processor, the method for predicting freeze-thaw damage of high-ductility cement-based composite materials integrating surface multi-scale structure identification is executed.

[0042] Compared with existing technologies, the present invention offers the following advantages: The surface recognition-based ECC freeze-thaw damage prediction model provided by the present invention allows for non-destructive material testing, avoiding the limitations of destructive testing and enabling efficient prediction of material strength after freeze-thaw damage. Furthermore, by combining machine learning algorithms with surface image feature extraction, prediction accuracy is improved, providing effective technical support for the long-term service life of ECC materials in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the process of step S1 of the preferred embodiment of the present invention;

[0044] Figure 2 Schematic diagram of the process of step S2 of the preferred embodiment of the present invention;

[0045] Figure 3 Schematic diagram of the process of step S3 of the preferred embodiment of the present invention;

[0046] Figure 4 Schematic diagram of the process of step S4 of the preferred embodiment of the present invention;

[0047] Figure 5 This is a surface damage image of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0050] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form, and it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0051] A freeze-thaw damage prediction method for high-ductility cementitious composites integrating surface multi-scale structure identification, reference Figure 1-5 , specifically including the following steps:

[0052] Step S1, obtaining surface damage information of the ECC material under freeze-thaw cycles, including surface peeling, fiber exposure, microcracks, pores, etc., and photographing the material surface with a high-resolution camera or microscope to capture the microscopic characteristics of the surface damage after freeze-thaw;

[0053] In step S2, based on the surface damage image information obtained in step S1, it is preprocessed. Combined with the multi-level and multi-scale characteristics of the ECC surface under freeze-thaw damage, a deep convolutional neural network (ResNet), U-Net semantic segmentation, and XGBoost classifier algorithm are used to accurately extract key damage features (such as surface spalling, fiber exposure, porosity, and microcracks). These extracted features are then combined with known information such as the number of freeze-thaw cycles and material ratios for annotation to construct a high-quality dataset for subsequent model training and evaluation.

[0054] Step S3: Train the damage intensity prediction model and define the damage coefficient. Based on the dataset annotated in step S2, a machine learning algorithm is used to train the ECC freeze-thaw damage intensity prediction model. Through training, image features are correlated with actual test intensity data, establishing a mapping relationship between image features and damage intensity. Simultaneously, the model calculates a surface damage coefficient to quantify the extent of surface damage and reflect microstructural degradation after freeze-thaw cycles.

[0055] Step S4: Based on the surface damage coefficient, a prediction model is used to predict the strength of the ECC after freeze-thaw damage, and a damage strength index is obtained to further evaluate the durability of the ECC material after freeze-thaw cycles.

[0056] In step S1, after obtaining the surface damage information of the ECC material under the action of freeze-thaw cycles, a database containing high-quality surface damage images is constructed. The process of establishing this database includes the following steps:

[0057] Step S11: Sample Collection and Pretreatment: Surface images of the ECC material are collected after being subjected to different freeze-thaw cycles. Images can be acquired using a scanning electron microscope (SEM) or a high-resolution camera, including optical microscopy or camera, high-resolution scanning, electron microscopy, or computed tomography, to ensure that damage features such as microcracks, spalling, fiber exposure, and pore expansion are captured.

[0058] Step S12: Data Labeling: Classify and label the collected image data. Specific labels include freeze-thaw cycle count, material ratio (e.g., fiber content, auxiliary cementitious material ratio), and environmental parameters (e.g., temperature, saline solution concentration). This ensures that each image in the database is accurately labeled for subsequent model training.

[0059] Step S13: Image archiving and storage: The images are classified and stored according to the number of freeze-thaw cycles and damage type to facilitate subsequent retrieval.

[0060] In step S2, in order to extract the characteristic information of the ECC material after freeze-thaw damage, the image must first be grayscaled to make it more suitable for the processing flow of the computer vision algorithm.

[0061] Step S21: Image grayscale conversion: The captured color image is converted to a grayscale image using linearly weighted RGB to highlight the texture details of the surface damage area. Grayscale conversion removes color information, making microcracks and pore structures in the image more apparent through grayscale differences, facilitating subsequent algorithm detection of damage features.

[0062] Step 22, Image Denoising and Enhancement: Use algorithms such as Gaussian filtering or median filtering to remove noise from the image and reduce errors caused by equipment or environment. Contrast-limited adaptive histogram equalization (CLAHE) methods in OpenCV (e.g., cv2.createCLAHE()) can also be used to further enhance the contrast of localized damaged areas, making details more visible and facilitating more accurate feature recognition and extraction.

[0063] In step S2, in order to effectively identify and quantify the characteristics of ECC materials after freeze-thaw damage, a combination of deep convolutional neural network (ResNet), U-Net semantic segmentation and XGBoost classifier is used.

[0064] In step S23, a deep convolutional neural network (ResNet) is used to extract features from the preprocessed grayscale image. This provides deep semantic feature extraction capabilities and captures both local and global damage characteristics. Combined with a hybrid attention mechanism (SE module + TransformerAttention), the model automatically focuses on areas with significant damage characteristics (such as exposed fibers and crack propagation areas) while minimizing interference with non-damaged areas.

[0065] In step S24, U-Net is used to perform semantic segmentation of the damaged area and extract areas such as microcracks, pores, and fiber exposure. The Open3D tool is used to reconstruct the three-dimensional point cloud of the damaged area and analyze the multi-scale relationship between microscopic features (such as crack density and pore distribution) and macroscopic features (fiber exposure area and peeling area). The damage type is predicted by the classifier, and the progress of each damage during the freeze-thaw cycle is evaluated. This is a process included in the classification and recognition process. The features extracted by U-Net are input into the XGBoost classifier to classify the damage features in the image. The specific recognition classification includes:

[0066] Fiber exposure: Fiber regions are extracted by grayscale difference, and the area and height of fiber exposure regions are identified as damage parameters 1.

[0067] Cement-based spalling: The area and depth of surface spalling are extracted based on grayscale differentiation and defined as damage parameter 2.

[0068] Porosity and microcracks: The feature maps generated by ResNet and U-net can capture the basic location of cracks, but due to the small and irregular microcracks, there may still be noise and misjudgment. Further processing and verification are carried out through the following methods:

[0069] Morphological operations: Apply morphological operations (such as dilation and erosion operations) to highlight crack edges and remove false positives.

[0070] Especially for discontinuous cracks, morphological operations can connect them into more complete lines.

[0071] Edge detection algorithm: Use Canny edge detection or Sobel operator on the feature map to further extract cracks

[0072] The clear edges of the surface can identify the exact location and shape of pores and cracks.

[0073] Generative Adversarial Network (GAN) Correction: GAN is used to generate a realistic crack distribution, which is then compared with the pore and crack features (including area and penetration) extracted from the feature map to correct for noise or artifacts and ensure accurate identification of damaged areas.

[0074] In step S3, the damage intensity prediction model is trained and the surface damage coefficient is determined. Based on the damage features extracted in S2 and the annotated damage parameters, a damage intensity prediction model is constructed and trained, and the surface damage coefficient is calculated. Specifically, the following steps are included:

[0075] Step S31: Model training: The feature data extracted in S2 are trained with the actual strength test data, and a mapping relationship between damage features and actual strength is established through a machine learning algorithm (CNN) to construct a strength prediction model for ECC after freeze-thaw damage.

[0076] Step S32: Calculation of surface damage coefficient: Based on the model output, determine a surface damage coefficient D s, which is used to quantify the degree of surface damage of ECC materials. This coefficient reflects the deterioration of the material's microstructure after freeze-thaw cycles and can be used as an important parameter for subsequent strength prediction. A dynamic weight distribution algorithm is introduced for damage characteristics (such as fiber exposure area, crack density, pore size, etc.), and the contribution weight of each characteristic to damage strength prediction is adjusted according to the number of freeze-thaw cycles and environmental factors (such as temperature fluctuation amplitude, salt solution concentration, etc.). In addition, the damage intensity coefficient is expanded from a single indicator to a multidimensional parameter matrix to establish a comprehensive index describing the degree of material degradation.

[0077]

[0078] Where: i --The weight of the i-th microscopic parameter is set according to its impact on the damage degree;

[0079] P i —The i-th damage parameter obtained in S24. i is used to represent the index of the damage parameter, corresponding to the weight ω i and damage parameter P i The i-th item of .

[0080] The index i refers to the i-th microscopic damage parameter.

[0081] It is also used to express the association between the corresponding weight and the damage parameter, that is, ω i and P i There is a one-to-one correspondence.

[0082] In step S4, the strength of the ECC after freeze-thaw damage is predicted based on the surface damage coefficient to obtain a damage strength index, thereby evaluating the durability of the ECC material after freeze-thaw cycles.

[0083] f after =f before ·(1-k·D s )

[0084] Where: f after --The strength of the material after freeze-thaw cycles;

[0085] f before --The strength of the material before freeze-thaw cycles;

[0086] k - an empirical constant used to adjust the effect of surface damage coefficient on strength prediction.

[0087] Step S41: Strength prediction verification and error analysis: Use the model to predict new data, use cross-validation to compare the error between the predicted value and the actual test value, and verify the model accuracy.

[0088] Step S42: Damage Strength Trend Analysis: Based on the predicted damage strength values, analyze the strength variation trend with the number of freeze-thaw cycles to quantify durability degradation. By sequentially capturing surface damage images after multiple freeze-thaw cycles, time series data is constructed and a recurrent neural network (RNN) is introduced to analyze the dynamic evolution of damage characteristics with the number of freeze-thaw cycles.

[0089] Step S43: Durability and Remaining Life Assessment: By analyzing the strength drop, the material's durability level is determined, and a regression model is used to predict the remaining service life. Combined with historical inspection data for the building or bridge, a cloud-based big data analysis platform provides intelligent decision support for material replacement or repair.

[0090] Based on the above-mentioned freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification, the present invention also provides a freeze-thaw damage prediction system for high-ductility cement-based composite materials integrating surface multi-scale structure identification, which can directly obtain the surface damage characteristics of ECC materials on site and evaluate their strength and durability in real time.

[0091] Table 1: Extracted post-damage feature information

[0092]

Claims

1. A freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification, characterized in that: The following steps are involved: Step S1, obtaining surface damage image information of the ECC material under freeze-thaw cycles, including surface peeling, fiber exposure, microcracks, and pores, and photographing the material surface using a high-resolution camera or microscope to capture the microscopic characteristics of the surface damage after freeze-thaw cycles; Step S2: Based on the surface damage image information obtained in step S1, the image is preprocessed. In combination with the multi-level and multi-scale characteristics of the ECC surface under freeze-thaw damage, a deep convolutional neural network (ResNet), U-Net semantic segmentation, and XGBoost classifier are used to accurately extract key damage features. Subsequently, the key damage features are combined with known information such as the number of freeze-thaw cycles and material ratios for annotation to construct a high-quality dataset for subsequent model training and evaluation. Step S3: Train the damage strength prediction model and define the damage coefficient. Based on the data set annotated in step S2, use a machine learning algorithm to train the ECC freeze-thaw damage strength prediction model. Through training, the image features are associated with the actual test strength data, thereby establishing a mapping relationship between the image features and the damage strength. At the same time, the surface damage coefficient is calculated through the model to quantify the degree of surface damage and reflect the degradation of the microstructure after freeze-thaw cycles. A dynamic weight allocation algorithm is introduced for the damage features to adjust the contribution weight of each feature to the damage strength prediction according to the number of freeze-thaw cycles and environmental factors. Step S4: Based on the surface damage coefficient, a prediction model is used to predict the strength of the ECC after freeze-thaw damage, and a damage strength index is obtained to further evaluate the durability of the ECC material after freeze-thaw cycles.

2. The freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification according to claim 1, characterized in that: In step S1, after obtaining the surface damage information of the ECC material under the action of freeze-thaw cycles, a database containing high-quality surface damage images is constructed. The process of establishing this database includes the following steps: Step S11: Sample collection and pretreatment: The ECC material is subjected to different freeze-thaw cycles and its surface images are collected; the images are acquired by scanning electron microscopy (SEM) or high-resolution camera, involving the use of optical microscope or camera, high-resolution scanning, electron microscopy or computed tomography, to ensure that microscopic cracks, spalling, fiber exposure and pore expansion damage characteristics are captured; Step S12: Data labeling: Classify and label the collected image data, including the number of freeze-thaw cycles, material ratio, and environmental parameters; ensure that the label information of each image in the database is accurate to facilitate subsequent model training; Step S13: Image archiving and storage: The images are classified and stored according to the number of freeze-thaw cycles and damage type to facilitate subsequent retrieval.

3. The freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification according to claim 1, characterized in that: Step S2 specifically includes the following steps: Step S21: converting the collected color image into a grayscale image using linear weighted RGB; Step S22: Image denoising and enhancement: Use Gaussian filtering or median filtering algorithm to remove noise in the image; at the same time, use the CLAHE method in OpenCV to perform contrast-limited adaptive histogram equalization to further enhance the contrast of the local damage area; Step S23: Use the deep convolutional neural network ResNet to extract features from the preprocessed grayscale image, providing deep semantic feature extraction capabilities to capture local and global features of the damage. At the same time, combined with the hybrid attention mechanism, the model can automatically focus on areas with significant damage features. Step S24: Use U-Net to perform semantic segmentation of the damaged area and extract microcracks, pores, and fiber exposure areas; use Open3D tools to reconstruct the three-dimensional point cloud of the damaged area and analyze the multi-scale relationship between microscopic and macroscopic features; predict the damage type through a classifier and evaluate the progression of each damage during the freeze-thaw cycle; input the features extracted by U-Net into the XGBoost classifier to classify the damage features in the image.

4. The freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification according to claim 3 is characterized in that: Classifying damage features in images specifically includes: 1) Fiber exposure: Fiber regions are extracted by grayscale difference, and the area and height of the fiber exposure region are identified as the first damage parameter; 2) Cement-based spalling: The area and depth of surface spalling are extracted based on grayscale differentiation and defined as the second damage parameter; 3) Porosity and microcracks: The feature maps generated by ResNet and U-net can capture the basic locations of cracks.

5. The freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification according to claim 4, characterized in that: The pores and micro cracks were further processed and verified by; (1) Morphological operations: Morphological operations are applied to highlight crack edges and remove falsely detected noise points; (2) Edge detection algorithm: Use Canny edge detection or Sobel operator on the feature map to further extract the clear edges of the cracks and identify the accurate location and shape of pores and cracks; (3) Generative Adversarial Network (GAN) correction: GAN is used to generate the real crack distribution, which is compared with the pore and crack features extracted from the feature map to correct noise or artifacts and ensure the accuracy of damaged area identification.

6. The freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification according to claim 1, characterized in that: Step S3 specifically includes the following steps: Step S31: Model training: The feature data extracted in step S2 is trained with the actual strength test data, and a mapping relationship between damage features and actual strength is established through a machine learning algorithm CNN to construct a strength prediction model for ECC after freeze-thaw damage; Step S32: Calculation of surface damage coefficient: Based on the model output, a surface damage coefficient is determined. , used to quantify the degree of surface damage of ECC materials; the surface damage coefficient Reflects the degradation of the material's microstructure after freeze-thaw cycles, serving as an important parameter for subsequent strength prediction. The damage coefficient is expanded from a single indicator to a multidimensional parameter matrix, establishing a comprehensive index that describes the degree of material degradation: ,in: represents the weight of the i-th micro-damage parameter, which is set according to its impact on the damage degree; represents the i-th micro damage parameter obtained in step S24.

7. The freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification according to claim 1, characterized in that: In step S4, the strength of the ECC after freeze-thaw damage is predicted based on the surface damage coefficient to obtain a damage strength index, thereby evaluating the durability of the ECC material after freeze-thaw cycles; ,in: Indicates the strength of the material after freeze-thaw cycles; represents the strength of the material before freeze-thaw cycles; k represents an empirical constant used to adjust the influence of surface damage coefficient on strength prediction.

8. The freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure identification according to claim 7, characterized in that: Step 4 specifically includes: Step 41: Strength prediction verification and error analysis: Use the model to predict new data, use cross-validation to compare the error between the predicted value and the actual test value, and verify the accuracy of the model; Step 42: Damage Strength Trend Analysis: Based on the predicted damage strength values, analyze the strength variation trend with the number of freeze-thaw cycles to quantify durability degradation. By sequentially collecting surface damage images after multiple freeze-thaw cycles, construct time series data, and introduce a recurrent neural network (RNN) to analyze the dynamic evolution of damage characteristics with the number of freeze-thaw cycles. Step 43: Durability and Remaining Life Assessment: Determine the material's durability level by analyzing the strength drop and use a regression model to predict the remaining service life. Combined with historical inspection data of buildings or bridges, a cloud-based big data analysis platform provides intelligent decision-making support for material replacement or repair.

9. A freeze-thaw damage prediction system for high-ductility cement-based composite materials that integrates multi-scale surface structure recognition, characterized by A freeze-thaw damage prediction method for high-ductility cement-based composite materials integrating surface multi-scale structure recognition as described in any one of claims 1 to 8 is run, including an ECC surface damage information acquisition module, an ECC image feature extraction module, a damage strength prediction model training and surface damage coefficient determination module, and a strength prediction module.

10. A computer-readable storage medium, characterized in that include: The computer-readable storage medium stores a computer program; when the computer program is run by a processor, the method for predicting freeze-thaw damage of high-ductility cement-based composite materials integrating surface multi-scale structure identification as described in any one of claims 1 to 8 is executed.

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