Method for evaluating salt-freezing-resistant cycle performance of concrete

Abstract

The invention relates to the technical field of concrete durability, and discloses a method for evaluating salt-freezing-resistant cycle performance of concrete. The method comprises the following steps: acquiring salt solution concentration, temperature cycle and humidity cycle monitoring data of an environment where a target concrete test piece is located in real time, and synchronously acquiring surface deformation and internal micro-current response monitoring data of the test piece; generating environment coupling time sequence characteristics according to the environment monitoring data, and performing time-frequency domain fusion processing on the surface deformation and the internal micro-current response data based on a dynamic window function to obtain a concrete response characteristic tensor; performing space-time alignment on the environment coupling time sequence characteristics and the concrete response characteristic tensor, and constructing a three-dimensional degradation characteristic matrix; and performing multi-scale gradient analysis on the matrix to generate a surface degradation gradient field and an internal pore degradation gradient field. The method can comprehensively and accurately evaluate the salt-freezing cycle resistance of the concrete, fits the actual environment, captures degradation information in multiple dimensions, and deeply analyzes the coupling relationship between the environment and degradation.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete durability, in particular to a method for evaluating the salt-freeze cycle resistance of concrete. Background Art

[0002] In cold areas with saline-alkali environments, concrete structures are subjected to long-term salt-freeze cycles, and their performance will gradually deteriorate, which in turn affects the safety and service life of the structure. Currently, there are many evaluation methods for the resistance of concrete to salt-freeze cycles, but they have obvious limitations. Traditional evaluations mostly use static testing methods. For example, fixed salt solution concentration, temperature and humidity conditions are set in the laboratory, and after periodic freeze-thaw tests on concrete specimens, the degree of performance degradation is judged by measuring indicators such as mass loss and strength change. However, in actual projects, the environment in which concrete is located is dynamically changing. The salt solution concentration will fluctuate with factors such as precipitation and evaporation, and the temperature and humidity also show non-periodic alternating changes. Static testing is difficult to truly simulate the complex natural environment, resulting in a large deviation between the evaluation results and the actual situation. Existing methods for monitoring concrete degradation mostly focus on surface phenomena, such as spalling and cracking, or indirectly reflect internal damage through changes in a single physical quantity. These methods lack direct monitoring of changes in the concrete's internal microstructure. Under salt-freeze cycles, concrete undergoes a series of changes in internal pores due to salt crystal expansion and frost heave pressure. These microscopic changes are the root cause of macroscopic performance degradation. Relying solely on surface monitoring or single physical quantity measurements cannot fully capture the degradation process, making it difficult to accurately assess concrete's salt-freeze resistance. Existing assessment methods are relatively simplistic in data processing, typically analyzing environmental parameters and concrete performance parameters separately, ignoring the dynamic coupling between the two. Changes in environmental factors can affect the degradation process of concrete in real time, while concrete degradation can in turn alter its interaction with the environment. This lack of dynamic coupling reduces the accuracy and reliability of assessment models, making them ineffective in providing effective guidance for the maintenance and repair of concrete structures. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for evaluating the salt-freeze cycle resistance of concrete to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides a method for evaluating the salt-freeze cycle resistance of concrete, the method comprising: Real-time acquisition of salt solution concentration monitoring data, temperature cycle monitoring data, and humidity cycle monitoring data of the target concrete specimen's environment; simultaneous acquisition of surface deformation monitoring data and internal microcurrent response monitoring data of the target concrete specimen; Generate environmental coupling time series features based on the salt solution concentration monitoring data, temperature cycle monitoring data, and humidity cycle monitoring data; perform time-frequency domain fusion processing on the surface deformation monitoring data and the internal microcurrent response monitoring data based on a dynamic window function to obtain a concrete response feature tensor; Performing a spatiotemporal alignment operation on the environmental coupling time series feature and the concrete response feature tensor to construct a three-dimensional degradation feature matrix; performing multi-scale gradient analysis on the three-dimensional degradation feature matrix to generate a surface degradation gradient field and an internal pore degradation gradient field of the target concrete specimen; Preferably, the environmental coupling time series characteristics are generated according to the salt solution concentration monitoring data, the temperature cycle monitoring data and the humidity cycle monitoring data, specifically: The salt solution concentration monitoring data, the temperature cycle monitoring data, and the humidity cycle monitoring data are respectively standardized to obtain a standardized salt concentration sequence, a standardized temperature sequence, and a standardized humidity sequence; Adopting an adaptive weighted fusion algorithm to perform feature superposition on the standardized salt concentration sequence, the standardized temperature sequence, and the standardized humidity sequence to generate an initial environmental coupling sequence; A variational decomposition model is introduced to perform modal separation on the initial environmental coupling sequence and extract dominant modal components; interaction intensity coefficients of environmental parameters are calculated according to the dominant modal components, and the environmental coupling time series characteristics are reconstructed based on the interaction intensity coefficients; Preferably, the surface deformation monitoring data and the internal microcurrent response monitoring data are subjected to time-frequency domain fusion processing based on the dynamic window function to obtain the concrete response characteristic tensor, specifically: Performing a curvature feature extraction operation on the surface deformation monitoring data to obtain a surface curvature change sequence; performing an impedance feature analysis operation on the internal microcurrent response monitoring data to obtain a pore impedance change sequence; Setting an initial window length and a sliding step length of a dynamic window function, and dynamically adjusting the initial window length and the sliding step length based on the frequency domain energy distribution of the surface curvature change sequence and the pore impedance change sequence; Performing a joint time-frequency transformation on the surface curvature change sequence and the pore impedance change sequence using the adjusted dynamic window function to construct a joint time-frequency distribution graph; extracting the amplitude peak, energy accumulation, and frequency band concentration index from the joint time-frequency distribution graph and combining them to form the concrete response characteristic tensor; Preferably, the three-dimensional degradation characteristic matrix is ​​subjected to multi-scale gradient analysis to generate the surface degradation gradient field and the internal pore degradation gradient field of the target concrete specimen, specifically: The three-dimensional degradation characteristic matrix is ​​divided into a plurality of cubic units according to spatial coordinates; and a directional derivative operator is used to calculate the degradation change rate of each cubic unit in the normal direction, the tangential direction, and the radial direction respectively; Constructing a spatial gradient vector field according to the degradation change rate; performing divergence analysis and curl analysis on the spatial gradient vector field, and outputting a surface layer divergence distribution and an internal layer curl distribution, respectively; fusing the surface layer divergence distribution and the internal layer rotation distribution to generate the surface degradation gradient field and the internal pore degradation gradient field; Preferably, the method further comprises: dividing the target concrete specimen into key evaluation areas according to the surface degradation gradient field and the internal pore degradation gradient field; Calculate the degradation accumulation, gradient change rate and spatial correlation of each key assessment area to generate a comprehensive regional degradation index; Sort key assessment areas based on the comprehensive regional degradation index and determine regional inspection priorities; Preferably, the method further comprises: Generate a high-resolution detection point set according to the regional detection priority; Acquiring microscopic damage image data of the high-resolution detection point set based on ultrasonic flaw detection equipment; performing crack morphology segmentation and pore topology analysis on the microscopic damage image data to obtain a microscopic damage feature set; Preferably, the method further comprises: Mapping the microscopic damage feature set to the surface degradation gradient field and the internal pore degradation gradient field, and updating the degradation accumulation and gradient change rate of the key assessment area; Reconstruct the regional degradation comprehensive index based on the updated degradation accumulation and gradient change rate; Modifying the regional detection priority based on the reconstructed regional degradation comprehensive index; Preferably, the method further comprises: Constructing a time-varying stress distribution model based on the surface degradation gradient field and the internal pore degradation gradient field; An incremental iterative algorithm is used to calculate the equivalent fatigue load spectrum of the time-varying stress distribution model; Analyzing the stress amplitude variation cycle of the critical damage path based on the equivalent fatigue load spectrum; Preferably, the method further comprises: Calculating the cumulative damage degree of the critical damage path according to the stress amplitude variation cycle; Combined with the standard damage threshold curve of concrete materials, the remaining service cycles of the critical damage path are predicted; Based on the remaining service cycles of all critical damage paths, the salt-freeze cycle resistance performance evaluation results of the target concrete specimens are generated; Preferably, the method further comprises: Encapsulating the salt-freeze resistance cycle performance evaluation result into a performance evaluation data packet according to a preset communication protocol; Transmitting the performance evaluation data packet to a remote monitoring center via a wireless sensor network; The performance evaluation data packet is decoded and verified at the remote monitoring center, and a final performance evaluation report is output.

[0005] Compared with the prior art, the present invention has the following beneficial effects: By acquiring real-time monitoring data on the salt solution concentration, temperature cycle, and humidity cycle of the target concrete specimen's environment, the system accurately captures dynamically changing environmental factors, overcoming the limitations of traditional static testing, which often lacks simulation capabilities for complex natural environments, and making the assessment process more closely aligned with actual project conditions. Simultaneously collecting monitoring data on the concrete specimen's surface deformation and internal microcurrent response enables multi-dimensional monitoring of concrete degradation, focusing on both macroscopic surface changes and in-depth capture of internal microstructural changes. This avoids the incompleteness of information often associated with a single monitoring method and comprehensively reflects the degradation of concrete under salt-freeze cycles. Generating environmental coupling time series features based on environmental monitoring data clearly demonstrates the dynamic correlations and variations among salt solution concentration, temperature, and humidity, providing a foundation for analyzing the combined impact of environmental factors on concrete degradation. Using a dynamic window function to fuse surface deformation and internal microcurrent response monitoring data in the time-frequency domain, the resulting concrete response feature tensor effectively integrates monitoring information across different time and frequency ranges, revealing the dynamic response patterns of concrete degradation and enhancing the richness and accuracy of degradation feature descriptions. The three-dimensional degradation characteristic matrix is ​​constructed by temporally and spatially aligning the environmental coupling time series characteristics with the concrete response characteristic tensor. This fully considers the dynamic coupling relationship between environmental factors and concrete degradation response, achieving precise matching between the two in time and space. This facilitates in-depth analysis of the intrinsic connection between environmental changes and concrete degradation, overcoming the drawback of traditional methods that separate environmental parameters from performance parameters. Multi-scale gradient analysis of the three-dimensional degradation characteristic matrix generates surface degradation gradient fields and internal pore degradation gradient fields, which can intuitively demonstrate the distribution and development trends of concrete degradation at different scales. It can present both the spatial variation gradient of surface degradation and the microscopic gradient of internal pore degradation, providing a comprehensive perspective for understanding the mechanisms of concrete degradation. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1This is a working principle diagram of the method for evaluating the salt-freeze cycle performance of concrete according to the present invention; Figure 2 Flowchart for generating environmental coupling timing characteristics; Figure 3 Flowchart for generating degraded gradient fields for multi-scale gradient resolution; Figure 4 A flowchart for updating degradation indicators and revising priorities; Figure 5 Flowchart for building a time-varying stress distribution model and analyzing stress amplitude changes. DETAILED DESCRIPTION

[0007] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0008] See also Figure 1 The present invention provides a method for evaluating the salt-freeze cycle resistance of concrete, the method comprising: The target concrete specimen's environmental salt solution concentration, temperature cycle, and humidity cycle monitoring data are acquired in real time via an embedded sensor network. Simultaneously, a laser displacement sensor collects specimen surface deformation data, and an embedded microelectrode array collects internal microcurrent response data. The salt solution concentration, temperature cycle, and humidity cycle monitoring data are fused to generate environmental coupling time series features. The surface deformation and internal microcurrent response data are fused in the time-frequency domain using a dynamic window function to output a concrete response feature tensor. The environmental coupling time series features and the concrete response feature tensor are then spatially and temporally aligned to construct a three-dimensional degradation feature matrix. This matrix is ​​then subjected to multi-scale gradient analysis to generate surface degradation gradient fields and internal pore degradation gradient fields, completing the quantitative characterization of the salt-freeze cycle resistance performance.

[0009] Example 1: See Figure 2The salt solution concentration monitoring data, temperature cycle monitoring data, and humidity cycle monitoring data are input into the data processing module. The salt solution concentration monitoring data is collected by a conductivity sensor, the temperature cycle monitoring data is collected by a platinum resistance temperature sensor, and the humidity cycle monitoring data is collected by a capacitive humidity sensor. Each monitoring data set is processed using the z-score method to eliminate dimensional differences and generate standardized salt concentration series, standardized temperature series, and standardized humidity series. The standardized salt concentration series is calculated by subtracting the mean from the original series and dividing it by the standard deviation. The standardized temperature series and the standardized humidity series use the same processing logic.

[0010] An adaptive weighted fusion algorithm processes the standardized sequences. The algorithm first calculates the variance of each sequence and uses the inverse of the variance as the basis for assigning weight coefficients. The ratio of the inverse variance of the standardized salt concentration sequence to the sum of the inverse variances of the total sequence is used as the salt concentration weight coefficient. Weight coefficients for the standardized temperature and humidity sequences are generated using the same rules. The salt concentration weight coefficient, temperature weight coefficient, and humidity weight coefficient are multiplied by the corresponding standardized sequence, and the three products are superimposed to generate the initial environmental coupling sequence. The initial environmental coupling sequence is a one-dimensional array in the time dimension, with the same sampling frequency as the original monitoring data.

[0011] The variational decomposition model loads the initial environmental coupling sequence and performs modal decomposition. The model presets the number of eigenmodes to five, constrains the bandwidth parameter using a penalty factor, and outputs five modal components after iterative convergence. The energy contribution of each modal component is calculated, and the modal component with an energy contribution exceeding 70% is selected as the dominant modal component. The interaction strength coefficient is calculated by extracting the amplitude extreme point sequence of salt concentration, temperature, and humidity in the dominant modal component, calculating the mutual information entropy of these three sequences, and generating a 3×3 interaction strength coefficient matrix. This matrix is ​​then tensor-producted with the dominant modal component to reconstruct the environmental coupling time series feature. The environmental coupling time series feature contains a timestamp, coupling strength value, and three-dimensional data encoding the interaction mode.

[0012] Surface deformation monitoring data is collected by a laser displacement sensor at 0.1-second intervals. The raw point cloud data is fitted into a continuous surface using cubic spline interpolation. The Gaussian curvature change is calculated at each node of the surface, and a surface curvature change sequence is output. The sequence value represents the rate of change of curvature per unit area, with negative values ​​representing concave deformation and positive values ​​representing bulge deformation. Internal microcurrent response monitoring data is collected using a pre-buried four-probe electrode array with a current excitation frequency of 1 kHz. Based on Ohm's law, the current-voltage monitoring values ​​are converted into impedance modulus to generate a pore impedance change sequence. The sequence sampling interval is synchronized with the surface curvature change sequence.

[0013] The initial parameters of the dynamic window function are set to a window length of 10 seconds and a sliding step length of 2 seconds. The frequency-domain energy distribution analysis module applies a fast Fourier transform to the surface curvature change sequence, detecting the energy peak position in the 0.5-5 Hz frequency band. Simultaneously, it applies a wavelet packet decomposition to the pore impedance change sequence, calculating the energy entropy values ​​for subbands with scales 2-4. If the energy peak exceeds the threshold and the energy entropy falls below the set value, the window length is shortened to 5 seconds. If the energy peak is dispersed and the energy entropy increases, the window length is extended to 15 seconds. The sliding step length is dynamically adjusted to 1-3 seconds based on the energy concentration of the frequency band.

[0014] The adjusted window function processes the surface curvature and pore impedance change sequences. The window function slides along the time axis, performing a short-time Fourier transform on the data within the window at each step. The transformed results generate a time-frequency spectrum (A) for the surface curvature change sequence, and a time-frequency spectrum (B) for the pore impedance change sequence. The two spectra are aligned and overlaid along the time-frequency coordinate to construct a joint time-frequency distribution plot. The horizontal axis of the distribution plot is time (in seconds), and the vertical axis is frequency (in Hz). The chromaticity represents the amplitude intensity.

[0015] The logic for extracting the peak amplitude index is to scan the distribution map's amplitude matrix and record the coordinates of local maxima exceeding 20 dB. The energy accumulation index is calculated by integrating the amplitude intensity values ​​at each frequency point along the time axis within the 0.1-10 Hz frequency band. The frequency band concentration index is calculated by extracting the primary energy band (a continuous band with a total amplitude of at least 80%) and calculating the proportion of its half-power bandwidth to the total frequency band. Each of the three indicators is converted into a 64-bit floating-point array, aligned by timestamp, and combined into a 6×N-dimensional concrete response feature tensor, where N is the total number of time points.

[0016] The sensor acquisition module and processing module are connected via a real-time data bus. The analog signals from the conductivity sensor, temperature sensor, and humidity sensor are converted via a 24-bit ADC and then input into the standardization processing unit. The digital signal from the laser displacement sensor is transmitted via Gigabit Ethernet to the curvature calculation unit. The monitoring data from the microelectrode array is processed by an isolation amplifier and then input into the impedance conversion unit. The dynamic window function adjustment instructions are transmitted by the frequency domain analysis unit via shared memory. The construction of the joint time-frequency distribution map is performed on the GPU parallel computing unit, and amplitude peak detection is implemented using the CUDA parallel reduction algorithm.

[0017] The environmental coupling time series characteristics and concrete response characteristic tensors are temporarily stored in a distributed time series database. The database stores data blocks by time slice. Each data block contains a millisecond timestamp, a data checksum, and a compressed feature vector. The feature vector is compressed using both Delta and Huffman coding. The compressed data block is then transmitted to the next processing node via a message queue.

[0018] Example 2: See Figure 3The analysis of the three-dimensional degradation feature matrix is ​​performed in the spatial calculation engine. The matrix coordinate dimensions are X×Y×Z, where the X-axis represents the specimen length (0-100mm), the Y-axis represents the width (0-100mm), and the Z-axis represents the depth (0-50mm). The spatial partitioning module decomposes the matrix into 8,000 cubic cells with a 5mm step size, each containing eight vertex data values. The coordinates of the cell center are calculated by arithmetic averaging the vertex coordinates, with a spatial resolution error controlled within ±0.1mm.

[0019] Multi-directional derivative calculations are performed at the center of the cube element. Normal derivatives are processed using the Sobel operator: three adjacent layers of data are selected along the Z axis, and a three-dimensional convolution is performed using the convolution kernels [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]. Tangential derivatives are processed using the Prewitt operator: a two-dimensional convolution is performed in the XY plane using the convolution kernels [[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]. Radial derivatives are calculated using the central difference method: starting from the current center point, 12 adjacent points are selected at equal angles along the radial direction, and the weighted difference of their data values ​​is calculated. The derivative calculation results for the three directions are stored as floating-point arrays of normal rate of change, tangential rate of change, and radial rate of change, respectively.

[0020] The construction of the spatial gradient vector field is based on the rate of change array. The normal rate of change array is bound to the Z coordinate to generate the surface layer data set, and the tangential and radial rate of change arrays are bound to the XYZ coordinates to generate the internal layer data set. The surface layer data set inputs the divergence calculation module: a 0.1mm grid is established in the XY plane, and the divergence value of each grid point is calculated using the Gaussian divergence theorem: a 5mm radius circular domain is constructed with the point as the center, and the surface integral of the normal rate of change flux at the boundary of the circular domain is calculated. The internal layer data set inputs the curl calculation module: a 0.2mm grid is established in three-dimensional space, and the curl operator ▽× is used at each grid point to process the tangential and radial rate of change components.

[0021] The divergence distribution map is generated by smoothing the divergence values ​​of all grid points using bilinear interpolation and mapping them to a 256-level grayscale. The curl distribution map is generated by extracting the modulus of the curl vector at each grid point, applying a median filter, and mapping it to the RGB color spectrum. Regions in the divergence distribution map exceeding a threshold of ±0.05 are highlighted in red, while regions with a modulus greater than 0.1 are highlighted in blue. The surface degradation gradient field output is a 3D point cloud containing divergence values ​​and color coding, while the internal pore degradation gradient field output is a 4D tensor containing the curl vector and modulus.

[0022] Key assessment areas are defined based on the locations of extreme divergence and curl points. The 50 points with the largest positive divergence and the 50 points with the largest negative divergence are selected from the divergence distribution graph, while the 100 points with the largest curl modulus are selected from the curl distribution graph. Spherical spatial regions with a radius of 10 mm are generated, centered around each extreme point. Overlapping regions are merged, resulting in a total of no more than 200 key assessment areas. Each region is assigned a unique region identifier, and its spatial boundary coordinates and the numbers of the contained cube cells are recorded.

[0023] The degradation accumulation calculation is performed over time. For each key assessment area, the rate of change data for all contained cubic elements during 200 salt-freeze cycles is extracted. The surface area cumulative divergence calculation logic performs a surface integral of the normal rate of change for each element within the area at the end of each cycle. The interior area cumulative curl calculation logic performs a volume integral of the curl modulus for each element within the area at the end of each cycle. The integration results are converted to scalar values ​​as degradation accumulation.

[0024] Gradient change rate analysis uses time series derivatives. For each key assessment area, the change in cumulative degradation over 10 consecutive cycles is recorded, and the mean change per unit time interval is calculated. The time interval is normalized to one salt freezing cycle, and the output value is a floating-point number representing the rate of change. Spatial correlation is calculated using the Pearson correlation coefficient: the cumulative degradation series of the target area and eight adjacent areas (with spacing no greater than 15 mm) are selected, and the ratio of their covariance to standard deviation is calculated.

[0025] The regional degradation composite index is constructed based on three parameters: the cumulative degradation value, normalized to the range [0, 1] after logarithmic transformation; the gradient rate of change, normalized after hyperbolic tangent compression; and the spatial correlation, normalized directly by taking its absolute value. These three normalized values ​​are linearly superimposed with weights of 0.5:0.3:0.2 to generate the regional degradation composite index. The index values ​​for all key assessment areas are stored as floating-point arrays, with the array index bound to the area identifier.

[0026] Prioritizing regions for detection is achieved using a heap sort algorithm. A maximum heap data structure is constructed, with the region's comprehensive degradation index as the sorting key. During heap initialization, all key assessment region data is inserted. After performing a sinking operation, a prioritized list is output in descending order. Each entry in the list contains a region identifier, spatial center coordinates, and a comprehensive degradation index value. The list data is encapsulated in JSON format and stored in non-volatile memory.

[0027] The spatial computing engine's hardware platform is equipped with an FPGA accelerator. Three-dimensional convolution operations are parallelized using a programmable logic gate array: the X, Y, and Z convolutions of the Sobel operator are executed synchronously, reducing the single computation cycle to 5μs. Gaussian divergence calculations utilize multi-core DSP collaborative processing: each computing core is responsible for a 10×10 grid block, and boundary data is exchanged via a ring bus. Pearson correlation coefficient calculations are performed by a coprocessor with a built-in 128-bit floating-point unit, and a single correlation coefficient calculation takes 0.8ms. The heap sort algorithm is executed on the GPU: utilizing 1024 CUDA cores to compare node data in parallel, sorting millions of data points can be completed in 50ms.

[0028] Data storage uses a hierarchical structure. The original three-dimensional degradation feature matrix is ​​stored in a DDR4 memory pool, and the spatial gradient vector field is transferred to an NVMe solid-state drive. The spatial boundary information of the key assessment areas is encoded in an octree structure, and the degradation accumulation time series is compressed and stored using a Delta-of-Delta algorithm. The final output of the surface degradation gradient field data volume is 2.5MB / cycle, the internal pore degradation gradient field data volume is 3.7MB / cycle, and the regional detection priority list data volume is 150KB.

[0029] Example 3: See Figure 4 , the regional detection priority list is loaded into the spatial positioning system. The system parses the top 10% of the high-priority areas in the list, and each area corresponds to a spherical range in three-dimensional space. Taking the coordinates of the spherical center as the reference, a local coordinate system is established in the XY plane. A grid point array is generated at intervals of 0.5mm in the coordinate system, and the array range covers 2cm beyond the boundary of the spherical projection. The grid point height value is automatically compensated according to the curvature of the specimen surface, and finally a high-resolution detection point set containing 800-1200 detection points is formed. Each point records the absolute coordinates (X, Y, Z), area affiliation identifier, and priority weight coefficient.

[0030] The ultrasonic flaw detection equipment uses a UT-9000 phased array system with a probe center frequency of 50 MHz. The equipment's motion control module receives a collection of test points and automatically plans the optimal scanning path: points are grouped into regions, and points within the same region are scanned continuously in a zigzag pattern. The probe contact pressure is maintained at a constant 0.5 N, and a standard glycerin solution is used as the coupling agent. During the scanning process, ultrasonic pulses with a pulse width of 100 ns are emitted at each point, and the received echo signal sampling rate is 500 MHz. After time-domain gain compensation, the raw echo data is output as a 128×128 pixel microscopic damage image, with a single image resolution of 10 μm.

[0031] The micro-damage image processing pipeline is divided into two parallel channels. The crack morphology segmentation channel utilizes a U-Net network architecture: the input image is downsampled through five layers of convolution to extract features, and upsampled through four layers of deconvolution to restore resolution. The output layer uses a sigmoid activation function to generate a binary mask, and a connected component analysis algorithm is used to extract connected domains within the mask. For each connected domain, the crack length (main axis extension distance), crack width (average distance in orthogonal directions), and fractal dimension (box counting method to calculate boundary complexity) are calculated. The pore topology analysis channel utilizes morphological processing: the input image is median filtered, and then Otsu threshold segmentation is used to isolate pore regions. A skeletonization algorithm is used to extract the pore network and calculate the pore equivalent diameter (area equivalent circular diameter), connectivity (number of adjacent pore connections), and tortuosity (ratio of skeleton path length to straight-line distance). All parameters are stored by inspection point coordinates to form a micro-damage feature set.

[0032] Feature map updates are performed on the 3D spatial database. Crack length data is associated with the surface degradation gradient field: the current divergence value is read at the corresponding spatial coordinate, and a divergence-crack length linear regression model is established. The model slope parameter is dynamically adjusted based on the newly added data points, and the updated slope value is used to recalculate the divergence coefficient matrix. Pore tortuosity data is associated with the internal pore degradation gradient field: the curl modulus is read at the corresponding spatial coordinate, and a curl-tortuosity gradient descent optimization model is constructed. The model uses mean squared error as the loss function, with a learning rate set to 0.01, and outputs the updated curl coefficient matrix after 50 iterations.

[0033] Degradation Cumulative Value Recalculation Process: Within each key assessment area, a surface integral is performed on the normal rate of change of all grid points within the area based on the updated divergence coefficient matrix. The integration is performed using the Gaussian quadrature method, and 20 integration points are selected to calculate the weighted sum. Gradient Rate of Change Recalculation Process: The degradation cumulative value series for the most recent 20 salt-freeze cycles are extracted, first-order differencing is performed, and a sliding average is calculated (with a window width of 5 cycles). Spatial Correlation Recalculation Process: In spherical coordinate space, all areas within 15 mm of the center of the target area are searched and the eigenvalues ​​of the covariance matrix of the updated degradation cumulative value series are calculated.

[0034] The reconstruction process for the regional degradation comprehensive index is as follows: the updated cumulative degradation value is normalized using a double logarithmic transformation; the gradient change rate is compressed to the interval [0, 1] using a sigmoid function; and the spatial correlation is normalized using the maximum eigenvalue of the covariance matrix. The three parameters are weighted and summed using the original weight coefficients of 0.5:0.3:0.2 to generate a new regional degradation comprehensive index. The new index value replaces the corresponding value in the original priority list, forming an unsorted intermediate dataset.

[0035] Region detection priority revisions are implemented through a sorting algorithm. A bubble sort optimization strategy is employed: The first pass compares the index values ​​of all adjacent regions, swapping pairs in reverse order. The second pass only compares regions where the historical priority difference exceeds 30%. The sorting process preserves the spatial topology of the regions: when the index difference between adjacent regions is less than 0.05, the original relative order is maintained. Finally, a new region detection priority list is output, and a change log is recorded for regions where the difference between the old and new priorities exceeds 10%.

[0036] The hardware system consists of a motion control platform, an ultrasonic acquisition unit, and a data processing server. A six-axis robotic arm equipped with an ultrasonic probe performs scanning, with a repeatability accuracy of ±2μm. A laser interferometer is used for real-time monitoring of the motion trajectory, and path replanning is triggered when the position deviation exceeds 5μm. The ultrasonic acquisition unit is equipped with a 1GB cache and transmits image data via a PCIe3.0 interface. The data processing server is equipped with dual Xeon processors, with crack segmentation tasks assigned to four Tesla T4 GPUs, and topology analysis tasks handled by CPU multithreading. The feature map update module uses an in-memory computing architecture, with a dedicated buffer in the 128GB DDR4 memory to store gradient field update data.

[0037] A timing synchronization mechanism ensures data consistency. Ultrasonic scanning trigger signals, robotic arm position feedback signals, and salt-freeze cycle status signals are synchronized at the microsecond level via the PTP protocol. The microscopic damage image at each inspection point is timestamped (in the format of Unix timestamp + millisecond offset) and temporally correlated with the degradation gradient field data for the corresponding cycle number. Feature updates are performed during the intervals between salt-freeze cycles, and the system automatically detects when environmental parameters are stable and initiates computational tasks.

[0038] Data storage utilizes a hierarchical indexing structure. Raw micro-damage images are stored in directories by region number, with a single region data volume of approximately 1.2TB. A four-dimensional index is established for the micro-damage feature set: spatial coordinates (X, Y, Z) + timestamp, with feature values ​​stored as floating-point arrays. An update log records the coordinate range, old coefficient values, and new coefficient values ​​for each feature map modification. The log file is stored using binary differential compression. The final output of the new priority list includes a version identifier, establishing an incremental association with previous versions.

[0039] Example 4: See Figure 5The time-varying stress distribution model is constructed based on the spatial fusion of the surface degradation gradient field and the internal pore degradation gradient field. The divergence distribution data of the surface degradation gradient field (dimensions X×Y×1) and the curl modulus data of the internal pore degradation gradient field (dimensions X×Y×Z) are subjected to a tensor expansion operation: the surface data are replicated to 50 layers along the Z axis to form a pseudo-3D tensor; the internal data retain their original dimensions. After element-wise multiplication by the spatial coupling coefficient matrix, the two datasets are concatenated along the depth direction to form a four-dimensional stress feature volume (dimensions X×Y×Z×2), where the fourth dimension contains two channels: the divergence value and the curl modulus. This feature volume is converted to a continuous field using cubic B-spline interpolation and defined as the time-varying stress distribution model.

[0040] The incremental iterative algorithm is used to process the time-varying stress distribution model in stages. In the initialization stage, the concrete material parameters are loaded: the initial value of the elastic modulus is 30 GPa, the Poisson's ratio is 0.2, and the damage factor is 0.01. The first round of iterative calculation of the equivalent stress distribution: at the spatial grid point (i, j, k), according to the divergence value σ s and curl modulus σ r , the principal stresses are calculated using the plane strain assumption:

[0041] Where: Represents the equivalent stress value of the first round of iteration, in MPa (megapascal); σ s Represents the surface divergence value, dimensionless, characterizing the deformation gradient of the concrete surface; σ r represents the internal curl modulus, dimensionless, characterizing the directionality of the change in the pore structure of concrete; K represents the form-stress conversion coefficient, in MPa (megapascals), which is used to convert the dimensionless σ s , σ r The calculated results were converted into dimensioned equivalent stress values, and the K value was calibrated through a standard salt-freezing cycle test under the same conditions.

[0042] The calibration steps of the morphology-stress conversion coefficient K are as follows: (1) specimen preparation: select three groups of standard cubic specimens (size 100mm×100mm×100mm) with the same strength grade, aggregate type and mix ratio as the target concrete specimen, with three parallel specimens in each group; (2) salt-freeze cycle test: the three groups of specimens are subjected to 20, 50 and 100 salt-freeze cycle treatments respectively. The test conditions are: salt solution concentration 5% (mass fraction), freeze-thaw temperature range -20℃-20℃, and single cycle freeze-thaw time of 4h; (3) data collection: after each cycle, the surface divergence value σ of the specimen is collected according to the method mentioned above in this manual. s , internal curl modulus σ r At the same time, the embedded stress sensor is used to directly measure the equivalent stress σ inside the specimen eq,实测(Unit: MPa); (4) Fitting calculation: For each group of specimens, is the independent variable x (dimensionless), with σ eq,实测 is the dependent variable y (unit: MPa), and y = Kx is obtained through linear regression fitting, and the slope is the K value of this type of concrete; (5) Value selection principle: The K value of the target concrete specimen is selected from the above-mentioned calibrated K values ​​according to its actual strength grade and aggregate type. If there are multiple sets of calibration results, the arithmetic mean is taken as the final K value.

[0043] σ s , σ r The physical relationship with the equivalent stress is that during the salt-freeze cycle, the internal pore water freezes and expands to produce frost heave pressure, and the external salt solution penetration intensifies the destruction of the pore structure. The two together cause surface deformation and internal stress concentration: the surface divergence value σ s The larger the absolute value, the more significant the gradient of local depression or uplift on the surface, the more concentrated the internal pore frost heave pressure, and the greater the stress on the specimen; the internal curl modulus σ r The larger the value, the stronger the squeezing effect caused by the directional expansion of salt crystals, and the higher the degree of local stress concentration. The physical meaning of the morphology-stress conversion coefficient K is "the stress response value corresponding to the unit morphology change". Its calibration process is based on the test data under the same conditions to ensure that the dimensionless morphology / structural indicators are quantitatively associated with the dimensional mechanical stress indicators. The calculation results are consistent with the actual mechanical laws of salt-freeze degradation of concrete.

[0044] K value calibration example: The K values ​​of different types of concrete calibrated through the above steps are shown in the following table. The fluctuation range of the calibration results is ≤0.1MPa, and the stability meets the requirements of engineering calculations.

[0045] Table 1: K value calibration results of salt-freeze cycle test of concrete with different strength grades and aggregate types

[0046] The second round of iterative update of material parameters: When the stress exceeds 20 MPa, the elastic modulus is reduced according to an exponential decay model. When the local stress gradient exceeds 1 MPa / mm, the damage factor is linearly increased by 0.005. The third to fifth iterations repeat the stress redistribution calculation: based on the updated material parameters, the equilibrium equations are resolved, with an iterative step size of 0.1 salt-freeze cycles. Convergence is determined when the maximum relative error between successive iterations is less than 0.1%.

[0047] Equivalent fatigue load spectrum generation process: Extracting the final equivalent stress distribution after convergence , take the extreme stress value of the cross section every 5mm along the depth direction. Define the load spectrum entry as a three-dimensional tuple (position coordinate, stress amplitude , mean stress σ m ): Position coordinates are three-dimensional vectors (X, Y, Z); stress amplitude In cross section The difference between the maximum and minimum values; the mean stress σ m In cross section All cross-section entries are sorted by spatial position to form a load spectrum sequence, and a single salt-freeze cycle corresponds to a load spectrum instance.

[0048] The identification criterion for the critical damage path is that the stress amplitude fluctuation rate at the same spatial location exceeds 30% of its mean value during 10 consecutive salt-freeze cycles. The fluctuation rate is calculated as:

[0049] Where: δ represents the volatility (dimensionless), represents the stress amplitude of the t-th cycle (unit: MPa), t is the salt freezing cycle number, is the maximum stress amplitude in 10 consecutive cycles, The minimum stress amplitude in 10 consecutive cycles. The path that satisfies δ>0.3 is marked as the critical damage path, and its starting position coordinates, fluctuation cycle number T (i.e., the number of cycles that continuously meet the conditions), amplitude range [A min ,A max ] (unit: MPa).

[0050] The cumulative damage calculation is performed independently for each critical damage path. A piecewise linear accumulation model is used: the stress amplitude in the load spectrum is converted to Convert to the permissible number of cycles N according to the GB / T50082-2009 standard damage threshold curve p The current actual number of cycles n p Count from the start time of the path mark. Accumulated damage degree D p Defined as:

[0051] Where: M represents the number of load spectrum segments contained in the path, is the number of cycles actually experienced by the mth segment, The number of cycles allowed for the mth segment is obtained by interpolation of the standard curve: When taking N p =10 6 ;when When taking N p =10 3 ; Intermediate values ​​are interpolated using log-linear interpolation.

[0052] The prediction of the remaining service cycles is based on the damage evolution trend. The cumulative damage sequence of the last five salt-freezing cycles is extracted, and the damage increment per unit cycle is calculated:

[0053] Where: Indicates the current accumulated damage. Represents the cumulative damage degree before 5 cycles (all dimensionless).

[0054] The remaining service cycles of path p are calculated by the following model:

[0055] in: is the predicted value of the remaining number of cycles (unit: times), The cumulative damage degree of path p at the current moment, The damage increment of path p in the most recent unit time, represents the data sampling interval (fixed to 1 cycle), and the denominator represents the damage degree change rate. The minimum value is taken as the final predicted life value of the target concrete specimen.

[0056] The system hardware consists of a multi-core processor, a distributed storage array, and a real-time clock module. The time-varying stress model calculation is distributed to four physical cores: Core 1 processes surface field data expansion, Core 2 processes internal field data extraction, Core 3 performs tensor multiplication, and Core 4 is responsible for interpolation conversion. Incremental iteration tasks are parallelized on 8 logical cores: each core is responsible for The calculation of the spatial region and the iterative intermediate results are exchanged through shared memory. The cumulative damage degree is updated using a pipeline architecture: the first pipeline retrieves the standard threshold curve, and the second pipeline calculates Ratio, the third pipeline performs cumulative summation.

[0057] Data storage utilizes a three-tiered cache structure: raw gradient field data is stored in a DDR4 memory pool (128GB capacity); equivalent fatigue load spectra are stored in an NVMe solid-state drive (NVMe SSD) with a read / write bandwidth of 3GB / s; and critical path information is stored in non-volatile memory (with an access latency of 200ns). Timing control is synchronized via the PTP protocol: stress calculation cycles are tied to salt-freeze cycle events, and calculations are initiated within the temperature stabilization window after the cycle ends. Life prediction results are output as a structured array containing fields such as path coordinates, remaining life value, and damage change rate.

[0058] Example 5: The salt-freeze cycle performance evaluation result data packet is packaged at the edge computing node. The node receives the structured array output by the life prediction module. The array contains three data areas: a path coordinate area records the three-dimensional position information of the critical damage path in the form of floating-point triples; a remaining life value area stores the integer value of the remaining service cycles for each path; and a damage trend area contains the damage degree change rate and the last detection timestamp. Before packaging, data validation is performed to ensure that the path coordinate range is within the concrete specimen bounding box, the remaining life value is greater than zero, and the timestamp complies with the ISO8601 standard.

[0059] The MQTT protocol encapsulation process configures a fixed topic path: the first-level topic is the project number, and the second-level topic is the test piece identification code. The data packet structure strictly adheres to a predefined format: the first four bytes are fixed to the hexadecimal value A5A5, which is the packet header identifier; the 16-byte device ID is generated by concatenating the device MAC address and the device type code; the 32-byte timestamp contains the date, time zone, and milliseconds; the 128-byte evaluation result body is encoded in TLV format, with the path coordinates occupying 60 bytes, the remaining life value occupying 40 bytes, and auxiliary parameters occupying 28 bytes; the final 4-byte CRC checksum is calculated according to the IEEE standard. The complete data packet size is fixed at 184 bytes, and dynamic memory allocation is avoided by pre-allocating a memory buffer.

[0060] The LoRa wireless transmission module uses the E32-433T30D model, operating in the 433MHz frequency band. Channel detection is performed before transmission: it scans eight preset channels and selects a channel with a background noise level below -120dBm to establish a connection. The packet segmentation transmission strategy uses a split transmission strategy: each packet is treated as an independent transmission unit, and the transmission rate is adaptively adjusted. The initial rate is set at 5kbps, which is reduced to 1.2kbps after three consecutive transmission failures. A 2-byte sequence number is appended to each packet, and the receiving end reassembles the data stream based on the sequence number. The transmission interval is fixed at 2 seconds. The module has a built-in retransmission counter that triggers retransmission if no ACK signal is received after the timeout, with a maximum of three retries.

[0061] The remote monitoring center deploys a message broker server. The server is configured in a dual-node hot standby architecture, with the primary node processing real-time data streams and the backup node synchronizing memory status. The message broker implements MQTT version 3.1.1 and has three subscription queues: a high-priority queue processes packets with header identifiers matching A5A5, a medium-priority queue processes device status messages, and a low-priority queue processes log information. Upon arrival, a CRC check is performed immediately: the last four bytes are compared with a calculated value. If the check fails, the packet is discarded and a NAK signal is sent through the management channel.

[0062] Valid data packets enter the parsing pipeline. The MAC address and device type code are separated from the device ID field and routed to the corresponding project database. The timestamp field is converted to a standard time object in the UTC+8 time zone. The main body of the assessment result parsing adopts a state machine model: the type tag of the TLV structure is identified, the path coordinate type tag is parsed into a three-dimensional floating-point array, the remaining lifetime tag is parsed into an integer array, and the auxiliary parameter tag is parsed into a key-value dictionary. During the parsing process, anomalies such as array length out-of-bounds and value overflow are automatically detected, and abnormal data is transferred to an isolated storage area.

[0063] The report generation engine loads the parsed data. The 3D degradation cloud rendering module reads the path coordinate array and generates Voronoi primitives using the coordinate points as seeds. Each primitive's color mapping rule is: remaining life greater than 1000 cycles is displayed green, 500-1000 cycles is displayed yellow, and less than 500 cycles is displayed red. The life prediction curve module extracts time series data, with the horizontal axis representing the detection time point and the vertical axis representing the remaining life value. Adjacent data points are connected using cubic Bezier curves. Chart elements are described in SVG vector format, with a resolution set to 300dpi.

[0064] The template engine is used during the PDF document synthesis phase. The master template contains fixed areas such as the company logo, test date, and specimen information. Dynamically inserted areas reserve placeholders: the 3D cloud image placeholder is replaced with a rendered PNG raster image, and the life curve placeholder is replaced with a vector graphic. Document metadata settings include author information, creation tool version number, and security encryption fingerprint. The final report document is digitally signed: a digest is generated using the SHA-256 algorithm, and a 256-byte signature block is encrypted with an RSA private key and appended to the end of the file.

[0065] The disaster recovery mechanism implements dual-path protection. Redundant channels are established at the network transport layer: if the primary channel is interrupted, it automatically switches to a 4G backup link with a transmission rate of 1Mbps. The data center also implements local caching: after sending data packets, edge nodes retain a 24-hour local copy of the data. When the monitoring center sends a data retransmission request, the original data packet is retrieved from the cache and resent. Report storage utilizes dual off-site backup: the primary data center stores the original report, while the backup center stores the encrypted, compressed archive file.

[0066] An example of device linkage occurs at the end of a test cycle. When the minimum remaining life value of a critical path on a test specimen falls below 50 cycles, the system automatically triggers an alarm protocol: a command code 0x0F is sent to the field device via an MQTT management topic. Upon receiving the command, the field device controls the robotic arm to spray a red marker ring on the specimen surface, with the center of the marker ring representing the surface projection point of the minimum life path. Simultaneously, a device response code is returned to the monitoring center, containing information such as the operation time, location coordinates, and execution status.

[0067] Data center physical configuration example: The main server uses a Dell PowerEdge R750 with dual Xeon Gold 6338 processors. 3D rendering tasks are assigned to four NVIDIA A100 graphics cards, each with 80GB of video memory. The report storage array uses an all-flash architecture, with 24 3.84TB SSDs arranged in a RAID 60 array. The network interface is configured with dual 10GbE Fibre Channel, with multipath load balancing achieved through BGP. The air conditioning system is set up with temperature and humidity linkage: when the server cabinet temperature exceeds 35°C, the ambient humidity setpoint is automatically lowered.

[0068] Example transmission process record: At 09:23:17 on August 19, 2025, device D-CT-2107 transmitted the 382nd cycle assessment packet. The packet sequence number was 0x1A8F, and transmission took 2.8 seconds via channel 3. The monitoring center completed parsing at 09:23:21 and detected that the remaining life of path 3 had dropped to 48 cycles. An alarm command was issued at 09:23:25, and the field device sprayed a 20mm diameter marker ring at the coordinates at 09:23:31, simultaneously returning a successful response code. The complete report file was generated and stored at 09:24:03, with a recorded file size of 8.7MB.

[0069] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0070] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the performance of concrete against salt-freeze cycles, characterized in that: The following steps are involved: Real-time acquisition of salt solution concentration monitoring data, temperature cycle monitoring data, and humidity cycle monitoring data of the target concrete specimen's environment; simultaneous acquisition of surface deformation monitoring data and internal microcurrent response monitoring data of the target concrete specimen; generating an environmental coupling time series feature based on the salt solution concentration monitoring data, the temperature cycle monitoring data, and the humidity cycle monitoring data; Performing time-frequency domain fusion processing on the surface deformation monitoring data and the internal microcurrent response monitoring data based on a dynamic window function to obtain a concrete response characteristic tensor; Performing a spatiotemporal alignment operation on the environmental coupling time series feature and the concrete response feature tensor to construct a three-dimensional degradation feature matrix; Multi-scale gradient analysis is performed on the three-dimensional degradation characteristic matrix to generate a surface degradation gradient field and an internal pore degradation gradient field of the target concrete specimen.

2. The method for evaluating the salt-freeze cycle resistance of concrete according to claim 1, wherein: The environmental coupling time series characteristics are generated according to the salt solution concentration monitoring data, the temperature cycle monitoring data and the humidity cycle monitoring data, specifically: The salt solution concentration monitoring data, the temperature cycle monitoring data, and the humidity cycle monitoring data are respectively standardized to obtain a standardized salt concentration sequence, a standardized temperature sequence, and a standardized humidity sequence; Adopting an adaptive weighted fusion algorithm to perform feature superposition on the standardized salt concentration sequence, the standardized temperature sequence, and the standardized humidity sequence to generate an initial environmental coupling sequence; A variational decomposition model is introduced to perform modal separation on the initial environmental coupling sequence and extract dominant modal components; the interaction intensity coefficients of environmental parameters are calculated according to the dominant modal components, and the environmental coupling time series characteristics are reconstructed based on the interaction intensity coefficients.

3. The method for evaluating the salt-freeze cycle resistance of concrete according to claim 1, wherein: The surface deformation monitoring data and the internal microcurrent response monitoring data are subjected to time-frequency domain fusion processing based on the dynamic window function to obtain the concrete response characteristic tensor, which is specifically: Performing a curvature feature extraction operation on the surface deformation monitoring data to obtain a surface curvature change sequence; performing an impedance feature analysis operation on the internal microcurrent response monitoring data to obtain a pore impedance change sequence; Setting an initial window length and a sliding step length of a dynamic window function, and dynamically adjusting the initial window length and the sliding step length based on the frequency domain energy distribution of the surface curvature change sequence and the pore impedance change sequence; The surface curvature change sequence and the pore impedance change sequence are jointly transformed in time-frequency mode using the adjusted dynamic window function to construct a joint time-frequency distribution map; the amplitude peak, energy accumulation and frequency band concentration index in the joint time-frequency distribution map are extracted and combined to form the concrete response characteristic tensor.

4. The method for evaluating the salt-freeze cycle resistance of concrete according to claim 1, wherein: The multi-scale gradient analysis is performed on the three-dimensional degradation characteristic matrix to generate the surface degradation gradient field and the internal pore degradation gradient field of the target concrete specimen, specifically: The three-dimensional degradation characteristic matrix is ​​divided into a plurality of cubic units according to spatial coordinates; and a directional derivative operator is used to calculate the degradation change rate of each cubic unit in the normal direction, the tangential direction, and the radial direction respectively; Constructing a spatial gradient vector field according to the degradation change rate; performing divergence analysis and curl analysis on the spatial gradient vector field, and outputting a surface layer divergence distribution and an internal layer curl distribution, respectively; The surface layer divergence distribution and the internal layer rotation distribution are fused to generate the surface degradation gradient field and the internal pore degradation gradient field.

5. The method for evaluating the concrete salt-freeze cycle resistance according to claim 4, wherein: Also includes: dividing the target concrete specimen into key evaluation areas according to the surface degradation gradient field and the internal pore degradation gradient field; Calculate the degradation accumulation, gradient change rate and spatial correlation of each key assessment area to generate a comprehensive regional degradation index; Key assessment areas are ranked based on the comprehensive regional degradation index to determine regional detection priorities.

6. The method for evaluating the salt-freeze cycle resistance of concrete according to claim 5, characterized in that: Also includes: Generate a high-resolution detection point set according to the regional detection priority; Acquiring microscopic damage image data of the high-resolution detection point set based on ultrasonic flaw detection equipment; Crack morphology segmentation and pore topology analysis are performed on the microscopic damage image data to obtain a microscopic damage feature set.

7. The method for evaluating the salt-freeze cycle resistance of concrete according to claim 6, wherein: Also includes: Mapping the microscopic damage feature set to the surface degradation gradient field and the internal pore degradation gradient field, and updating the degradation accumulation and gradient change rate of the key assessment area; Reconstruct the regional degradation comprehensive index based on the updated degradation accumulation and gradient change rate; The region detection priority is modified based on the reconstructed region degradation comprehensive index.

8. The method for evaluating the salt-freeze cycle resistance of concrete according to claim 1, wherein: Also includes: Constructing a time-varying stress distribution model based on the surface degradation gradient field and the internal pore degradation gradient field; An incremental iterative algorithm is used to calculate the equivalent fatigue load spectrum of the time-varying stress distribution model; The stress amplitude variation period of the critical damage path is analyzed based on the equivalent fatigue load spectrum.

9. The method for evaluating the salt-freeze cycle resistance of concrete according to claim 8, wherein: Also includes: Calculating the cumulative damage degree of the critical damage path according to the stress amplitude variation cycle; Combined with the standard damage threshold curve of concrete materials, the remaining service cycles of the critical damage path are predicted; Based on the remaining service cycles of all critical damage paths, the salt-freeze cycle resistance performance evaluation results of the target concrete specimens are generated.

10. The method for evaluating the salt-freeze cycle resistance of concrete according to claim 9, characterized in that: Also includes: Encapsulating the salt-freeze resistance cycle performance evaluation result into a performance evaluation data packet according to a preset communication protocol; Transmitting the performance evaluation data packet to a remote monitoring center via a wireless sensor network; The performance evaluation data packet is decoded and verified at the remote monitoring center, and a final performance evaluation report is output.

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