Method and system for evaluating the interfacial crack resistance of a uhcp wet joint

Abstract

The application discloses a kind of UHPC wet joint interface crack resistance performance evaluation method and system, specifically relates to material mechanics performance test technical field, for solving the problem that existing evaluation method cannot reveal interface bonding failure and UHPC internal cracking competition mechanism;Real-time strain data is collected by strain sensor arranged in interface and internal area, corresponding strain evolution curve is generated, feature point of strain change rate sudden increase is extracted therefrom, strain evolution sequence is intercepted based on feature point and dynamic time warping distance is calculated to quantify evolution asynchrony, according to which the dominant mode of competition relationship is determined, and finally a quantitative evaluation index is output;Macroscopic appearance to micro-damage mechanism is changed, which provides a scientific basis for optimizing interface design and construction technology.

Description

Technical Field

[0001] This invention relates to the field of material mechanical property testing technology, and more specifically, to a method and system for evaluating the crack resistance performance of UHPC wet joint interfaces. Background Technology

[0002] In bridge engineering, ultra-high performance concrete (UHPC) is widely used in wet joint connections of composite beam structures due to its superior mechanical properties and durability. The interfacial crack resistance at this location directly determines the structural integrity and long-term service life. Currently, for those skilled in the art, the common method for evaluating the interfacial crack resistance of UHPC wet joints mainly relies on conducting direct mechanical property tests on molded specimens, such as axial tensile or bending tests, and judging its performance by determining its ultimate bearing capacity or observing its macroscopic failure mode.

[0003] However, these assessment methods essentially focus on a final mechanical state, making it difficult to reveal the potential, competing damage evolution mechanisms within wet joints under load and constraints. Specifically, under complex multidimensional constraints, interface failure may stem from two distinct but competing mechanisms: one is debonding due to interfacial adhesion failure, and the other is cracking within the UHPC body caused by stress concentration. Existing technologies cannot effectively distinguish and quantify the dominant role and evolution of these two failure modes in the damage process, resulting in assessment conclusions that remain at a macroscopic level and failing to provide criteria reflecting the underlying mechanisms for targeted optimization of interface treatment processes or material design. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for evaluating the crack resistance performance of UHPC wet joint interface to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for evaluating the crack resistance performance of wet joint interfaces in UHPC includes the following steps:

[0007] S1. Real-time strain data of the interface bonding area and the internal area of ​​the UHPC body are collected by strain sensors arranged on the UHPC wet joint specimen.

[0008] S2. Generate the interfacial bonding strain evolution curve and the UHPC internal strain evolution curve based on real-time strain data, respectively.

[0009] S3. Extract the first feature point from the interfacial bonding strain evolution curve and extract the second feature point from the strain evolution curve inside the UHPC. The first feature point and the second feature point correspond to the positions where the strain rate of change suddenly increases.

[0010] S4. Based on the first feature point and the second feature point, extract the strain evolution feature sequences of the interface and the internal region respectively. Quantify the asynchronous nature of the evolution by calculating the dynamic time regularization distance of the two strain evolution feature sequences, and determine the competitive relationship between interface bonding failure and UHPC internal cracking.

[0011] S5. Quantitative evaluation index of crack resistance performance of UHPC wet joint interface based on competitive relationship.

[0012] Furthermore, real-time strain data of the interfacial bonding area and the internal region of the UHPC body were collected using strain sensors arranged on the UHPC wet joint specimen, including:

[0013] A first set of strain sensors is arranged parallel to the interface direction in the bonding area, and a second set of strain sensors is arranged perpendicular to the potential cracking direction in the internal area of ​​the UHPC body. The first set of strain sensors and the second set of strain sensors synchronously collect real-time strain data.

[0014] Furthermore, based on real-time strain data, interfacial bonding strain evolution curves and UHPC internal strain evolution curves are generated, including:

[0015] The real-time strain data were aligned according to time series, and the moving average filtering method was used to eliminate high-frequency noise interference.

[0016] The filtered real-time strain data of the interfacial bonding region is plotted as an interfacial bonding strain evolution curve.

[0017] Simultaneously, the real-time strain data of the internal region of the filtered UHPC body are plotted as the strain evolution curve inside the UHPC.

[0018] Furthermore, a first feature point is extracted from the interfacial bonding strain evolution curve, and a second feature point is extracted from the strain evolution curve inside the UHPC. The first and second feature points correspond to the locations of sudden increases in the strain rate of change, including:

[0019] The first derivatives of the interfacial bonding strain evolution curve and the strain evolution curve inside the UHPC were calculated to obtain the corresponding strain rate curves.

[0020] Identify the first and second peak points exceeding a preset threshold in the strain rate curve;

[0021] The position of the first peak point in the interfacial bonding strain evolution curve is marked as the first feature point, and the position of the second peak point in the strain evolution curve inside the UHPC is marked as the second feature point.

[0022] Furthermore, strain evolution feature sequences of the interface and internal region are extracted based on the first and second feature points, respectively. The asynchronous nature of their evolution is quantified by calculating the dynamic time warp distance between the two strain evolution feature sequences, thus determining the competitive relationship between interface bonding failure and internal cracking of the UHPC, including:

[0023] A set time window centered on the first feature point is used to extract the interfacial bonding strain evolution curve as the interfacial strain evolution feature sequence.

[0024] The same time window of the internal strain evolution curve of UHPC is extracted with the second feature point as the center and taken as the internal strain evolution feature sequence.

[0025] The asynchronicity of evolution is quantified by calculating the dynamic time warp distance of the two-strain evolution characteristic sequences;

[0026] The competitive relationship between interfacial bonding failure and internal cracking in UHPC was determined, including both a dominant competitive relationship and a dominant cooperative relationship.

[0027] Furthermore, the quantification of evolution asynchronicity includes: using a dynamic time warping algorithm to calculate the minimum warping path distance between the interface strain evolution feature sequence and the internal strain evolution feature sequence.

[0028] Furthermore, when the dynamic time warp distance is greater than a set threshold, it is determined that the competitive relationship is dominant; when the dynamic time warp distance is less than or equal to the set threshold, it is determined that the cooperative relationship is dominant.

[0029] Furthermore, based on the competitive relationship, quantitative evaluation indicators for the crack resistance performance of the UHPC wet joint interface are output, including:

[0030] The values ​​of the dynamic time-normalized distance are mapped to the preset crack resistance performance level division range, and the corresponding crack resistance performance level index is generated based on the mapping results.

[0031] The first-level index is output when the dynamic time warp distance is in the first numerical range, the second-level index is output when the dynamic time warp distance is in the second numerical range, and the third-level index is output when the dynamic time warp distance is in the third numerical range.

[0032] Furthermore, mapping the dynamic time-regulating distance value to the preset crack resistance performance level division range includes: establishing a correspondence between the dynamic time-regulating distance value and the crack resistance performance level, wherein a smaller dynamic time-regulating distance corresponds to a higher crack resistance performance level, and a larger dynamic time-regulating distance corresponds to a lower crack resistance performance level; and converting the calculated dynamic time-regulating distance value into the corresponding crack resistance performance level index by querying the established correspondence.

[0033] On the other hand, the present invention provides a UHPC wet joint interface crack resistance evaluation system, comprising the following modules:

[0034] The strain acquisition module is used to acquire real-time strain data of the interface bonding area and the internal area of ​​the UHPC body through strain sensors arranged on the UHPC wet joint specimen.

[0035] The curve generation module is used to generate interface bonding strain evolution curves and UHPC internal strain evolution curves based on real-time strain data.

[0036] The feature extraction module is used to extract a first feature point from the interfacial bonding strain evolution curve and a second feature point from the strain evolution curve inside the UHPC. The first and second feature points correspond to the locations where the strain rate of change suddenly increases.

[0037] The competition determination module is used to extract strain evolution feature sequences of the interface and internal region based on the first feature point and the second feature point, respectively. By calculating the dynamic time regularization distance of the two strain evolution feature sequences, the asynchronous nature of their evolution is quantified, and the competitive relationship between interface bonding failure and UHPC internal cracking is determined.

[0038] The index evaluation module is used to output quantitative evaluation indexes of the crack resistance performance of the UHPC wet joint interface based on the competitive relationship.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. By acquiring strain data from the interfacial bonding area and the internal region of the UHPC body in real time, and based on feature point extraction and dynamic time warping analysis of the strain evolution curve, a quantitative assessment of the crack resistance performance of the wet joint interface of UHPC was achieved. This method can dynamically capture the entire strain response process of the interface and internal region under load, revealing the inherent competitive mechanism of damage evolution. By identifying feature points of sudden increases in strain rate of change, the starting moment of interface debonding or internal cracking can be accurately located, avoiding the limitations of relying solely on the final failure mode. At the same time, by using the dynamic time warping distance to quantify the asynchronicity of the strain evolution sequence, the complex competitive relationship is transformed into a calculable index, thus providing an objective and quantitative basis for the assessment. This allows the assessment results to not only reflect macroscopic performance but also to deeply reveal the microscopic damage evolution law of the material during the stress process, providing direct technical support for optimizing interface treatment processes and material design.

[0041] 2. By extracting the strain evolution characteristic sequence of the interface and internal region and calculating its dynamic time regularization distance, the dominant roles of interfacial bonding failure and internal cracking in the failure process of UHPC are effectively distinguished. This overcomes the shortcomings of existing technologies that only focus on the final mechanical state, enabling the evaluation to shift from static results to dynamic process analysis. After outputting quantitative evaluation indicators, engineers can adjust the material ratio or construction process according to the dominant competitive relationship. For example, they can strengthen interfacial bonding measures when the asynchronicity is high, or optimize the internal fiber distribution when the synergy is dominant. The entire scheme is based on real-time monitoring and data processing of strain sensors, realizing non-destructive and continuous evaluation of the crack resistance performance of wet joints. This significantly improves the accuracy of testing and engineering applicability, providing a reliable technical means for the prediction and maintenance of the long-term service performance of bridge structures. Attached Figure Description

[0042] Figure 1 This is a flowchart of a method for evaluating the crack resistance performance of a UHPC wet joint interface according to the present invention;

[0043] Figure 2 This is a schematic diagram of the structure of a UHPC wet joint interface crack resistance evaluation system according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1: Figure 1 This invention provides a method for evaluating the crack resistance performance of wet joint interfaces in UHPC, which includes the following steps:

[0046] S1. Real-time strain data of the interface bonding area and the internal area of ​​the UHPC body are collected by strain sensors arranged on the UHPC wet joint specimen.

[0047] S2. Generate the interfacial bonding strain evolution curve and the UHPC internal strain evolution curve based on real-time strain data, respectively.

[0048] S3. Extract the first feature point from the interfacial bonding strain evolution curve and extract the second feature point from the strain evolution curve inside the UHPC. The first feature point and the second feature point correspond to the positions where the strain rate of change suddenly increases.

[0049] S4. Based on the first feature point and the second feature point, extract the strain evolution feature sequences of the interface and the internal region respectively. Quantify the asynchronous nature of the evolution by calculating the dynamic time regularization distance of the two strain evolution feature sequences, and determine the competitive relationship between interface bonding failure and UHPC internal cracking.

[0050] S5. Quantitative evaluation index of crack resistance performance of UHPC wet joint interface based on competitive relationship.

[0051] S1. Real-time strain data of the interfacial bonding area and the internal area of ​​the UHPC body are collected by strain sensors arranged on the UHPC wet joint specimen. Specific implementation includes:

[0052] During implementation, strain sensors were first arranged in the interface bonding area of ​​the UHPC wet joint specimen. This interface bonding area specifically refers to the bonding interface between the UHPC and the existing concrete structure. During arrangement, it was ensured that the measurement direction of the first set of strain sensors was parallel to the interface direction. Parallel to the interface direction means that the sensor's sensitive axis is aligned with the tangent direction of the interface. This arrangement effectively captures the shear strain signal generated during the interface debonding process. To determine the interface direction, the geometric contour and orientation of the interface need to be accurately calibrated during specimen preparation using optical measurement or template positioning. In practice, resistance strain gauges were used as the first set of strain sensors; conventional foil strain gauges could be selected. During installation, the strain gauges were firmly adhered to the centerline of the interface bonding area using epoxy resin adhesive and connected to the data acquisition equipment using wires. After installation, zero-point calibration and sensitivity coefficient verification were performed to ensure the accuracy of the measurement data.

[0053] Next, a second set of strain sensors is placed inside the UHPC body. The inside of the UHPC body refers to the main part of the UHPC material in the wet joint, away from the interface bonding area. When placing the sensors, ensure that the measurement direction of the second set of strain sensors is perpendicular to the potential crack direction. The potential crack direction refers to the crack propagation direction predicted based on the material mechanical properties and load conditions. It can be determined through pre-conducted finite element stress analysis or empirical models. For example, under axial tensile load, the direction of the maximum principal stress is usually perpendicular to the potential crack surface. Therefore, the sensor placement direction should be parallel to this direction of the maximum principal stress to achieve effective monitoring. In actual operation, resistance strain gauges are also used as the second set of strain sensors. When installing, select a location with a high stress concentration coefficient in the inside of the UHPC body, such as the section change or near the loading point. Fix the strain gauges by drilling or surface bonding, and use a protective coating to prevent environmental interference. After installation, linearity testing and temperature compensation adjustment are required to eliminate measurement errors.

[0054] Finally, synchronous acquisition of real-time strain data from the first and second sets of strain sensors was achieved. Synchronous acquisition means that all sensors start recording data simultaneously under the same time reference, ensuring the temporal consistency of the acquired strain data. To achieve synchronization, a multi-channel data acquisition system was adopted. This system integrates a timing trigger module and an analog-to-digital converter. All strain sensors are connected to different channels of the acquisition system through shielded cables. The system is set with a uniform sampling frequency, such as 1000 sample points per second, and a hardware trigger mode is enabled, so that all channels start acquiring data simultaneously under the action of an external trigger signal. During the acquisition process, real-time strain data is output in the form of voltage signals, which are converted into strain values ​​through a calibration formula. The calibration formula is calculated based on the sensitivity coefficient of the strain gauge and the bridge circuit configuration. The data acquisition software monitors the signal quality in real time and uses an anti-aliasing filter to eliminate high-frequency noise, ensuring that the acquired strain data can accurately reflect the dynamic response of the interface bonding area and the internal area of ​​the UHPC body. After acquisition, the data is stored in a time series format for easy subsequent processing and analysis.

[0055] S2. Based on real-time strain data, generate interfacial bonding strain evolution curves and UHPC internal strain evolution curves respectively. Specific implementation includes:

[0056] During implementation, the real-time strain data collected from the first and second groups of strain sensors are first time-series aligned. Time-series alignment means adjusting the strain data collected by different sensors to a unified time reference to ensure that the timestamps of the data points are completely consistent, thereby eliminating the data asynchrony problem caused by differences in sampling start time or clock drift. In specific implementation, a linear interpolation method is used to resample the original strain data onto a common time axis. The common time axis is referenced to the system clock and a fixed time interval is set, such as 1000 points per second. Linear interpolation estimates the strain value at the intermediate point by calculating the linear relationship between adjacent data points, ensuring that the data from all sensors have corresponding strain readings at the same time point. During the processing, it is necessary to check the integrity of the timestamps of the data and remove abnormal timestamps to ensure that the aligned data sequence can accurately reflect the change law of strain over time.

[0057] Next, a moving average filtering method is used to eliminate high-frequency noise interference. Moving average filtering is a time-domain filtering technique that smooths the signal by averaging multiple consecutive data points in a data sequence, thereby suppressing high-frequency noise components. High-frequency noise mainly originates from electronic equipment interference, environmental vibration, or random fluctuations during signal transmission. The window size of the moving average filter needs to be set according to the sampling frequency and noise characteristics. For example, when the sampling frequency is 1000 points per second, the window size can be set to 10 points. That is, each filtered output value is calculated from the strain data of the current point and the previous 9 points. During the calculation, the strain values ​​of all data points within the window are summed and then divided by the window size to obtain the smoothed strain value. During the filtering process, it is necessary to ensure that the window size is appropriate. Too small a window will result in incomplete noise filtering, while too large a window may mask the true strain change trend. Therefore, the setting of the window size needs to be verified through experiments. For example, the filtering effect of different window sizes can be tested on pre-acquired data, and the parameter with the highest signal-to-noise ratio can be selected. The data sequence after moving average filtering retains the low-frequency components of strain evolution, providing a clean data foundation for subsequent curve plotting.

[0058] Then, the real-time strain data of the filtered interfacial bonding region is plotted as an interfacial bonding strain evolution curve. The real-time strain data of the interfacial bonding region refers to the data collected by the first set of strain sensors after time series alignment and moving average filtering. When plotting the curve, time is used as the horizontal axis and strain value is used as the vertical axis. The plotting function in the data processing software is used to connect the data points into a continuous curve. The unit of the time axis is usually seconds, and the unit of the strain axis is microstrain. During the curve plotting process, appropriate coordinate ranges and legends need to be set to ensure that the curve is clear and readable. For example, the time axis range covers the entire loading process, and the strain axis range is dynamically adjusted according to the minimum and maximum values ​​of the measured data. Common scientific computing tools can be selected in the plotting software to realize data visualization by calling built-in functions. The plotted interfacial bonding strain evolution curve can intuitively show the strain response process of the interfacial bonding region under load, including the characteristics of the elastic stage, plastic stage and failure stage.

[0059] Simultaneously, the real-time strain data of the internal region of the filtered UHPC body is plotted as the internal strain evolution curve of the UHPC. The real-time strain data of the internal region of the UHPC body refers to the data collected by the second set of strain sensors after the same preprocessing steps. The plotting method is consistent with the interfacial bonding strain evolution curve, using the same time axis and strain axis settings to ensure the comparability of the two curves. When plotting, attention should be paid to the continuity of the data sequence to avoid curve breakage due to missing data or outliers. Missing points can be handled by interpolation or data filling methods, such as using the average value of the preceding and following data points for replacement. The plotted internal strain evolution curve of the UHPC reflects the strain development law inside the UHPC material under load, and together with the interfacial bonding strain evolution curve, it forms the basis of the analysis.

[0060] Throughout the implementation process, all data processing and plotting steps are executed in batches using programming scripts or software tools to ensure consistency and efficiency. For example, Python scripts are used to call the NumPy library for data alignment and filtering, and the Matplotlib library for curve plotting. The scripts must define clear input parameters and output formats. Input parameters include the path to the original strain data file, time interval, and filter window size. The output is two generated strain evolution curve image files. During implementation, the correctness of the data processing logic must be verified. For example, the noise reduction effect can be confirmed by comparing the spectrum analysis of the data before and after filtering, or the smoothness of the curve can be checked by visualization to ensure that the final generated interface bonding strain evolution curve and UHPC internal strain evolution curve can be accurately used for subsequent feature extraction and competition relationship determination.

[0061] S3. Extract the first feature point from the interfacial bonding strain evolution curve and the second feature point from the strain evolution curve inside the UHPC. The first and second feature points correspond to the locations where the strain rate of change suddenly increases. Specific implementation includes:

[0062] In the implementation process, the first derivatives of the interfacial bonding strain evolution curve and the UHPC internal strain evolution curve are first calculated to obtain the corresponding strain rate curves. These curves are continuous curves generated in the previous steps through data processing, with time as the horizontal axis and strain value as the vertical axis. The first derivative represents the instantaneous rate of change of strain over time and can reflect the acceleration or deceleration trend of strain evolution. The first derivative is calculated using a numerical differentiation method, specifically the central difference algorithm. This algorithm approximates the derivative by calculating the ratio of the strain difference between adjacent data points to the time interval. For the interfacial bonding strain evolution curve, the strain value at each time point and its adjacent time points is taken, and the strain rate at the current time point is calculated. The strain rate of change is calculated by subtracting the strain value from the previous time point from the strain value at the next time point and dividing by twice the time interval. The time interval is determined by the sampling frequency of the data acquisition. For example, when the sampling frequency is 1000 points per second, the time interval is 0.001 seconds to ensure the numerical stability of the calculation results. The same method is applied to the strain evolution curve inside the UHPC to obtain the corresponding strain rate of change curve. The horizontal axis of the strain rate of change curve is time, and the vertical axis is the strain rate of change. The unit is usually microstrain per second. During the calculation, the data boundaries need to be checked to avoid calculation errors at the endpoints. Boundary points can be processed by mirror expansion or truncation. The final strain rate of change curve can clearly show the fluctuation characteristics of the strain rate of change, providing a basis for subsequent peak point identification.

[0063] Next, the first and second peak points exceeding a preset threshold are identified in the strain rate curve. The strain rate curve is the data sequence calculated in the previous step. A peak point refers to a local maximum point on the curve, meaning the strain rate at that point is greater than the value of its adjacent data points. The preset threshold is used to distinguish between significant strain rate spikes and random fluctuations. The preset threshold is set based on the statistical characteristics of the strain rate data. Specifically, by calculating the mean and standard deviation of the entire strain rate sequence, the threshold is set to the mean plus twice the standard deviation. For example, in the preliminary test, multiple sets of data are collected to calculate the distribution of the strain rate and determine the threshold range. A sliding window method is used when identifying peak points, and the window size is determined based on the data. According to density adjustment, for example, if a window contains 5 data points, check whether the center point of the window is larger than other points in the window and exceeds a preset threshold at the same time. If the condition is met, it is marked as a peak point. For the strain rate curve corresponding to the interface bonding strain evolution curve, the identified peak point is called the first peak point. For the strain rate curve corresponding to the strain evolution curve inside the UHPC, the identified peak point is called the second peak point. In the process of identification, it is necessary to handle the case of multiple peak points. Select the point with the largest strain rate as the representative peak point, or select the first peak point that exceeds the threshold according to the time sequence to ensure the uniqueness and representativeness of the peak points. After the identification is completed, record the time coordinate and strain rate value corresponding to the peak point.

[0064] Then, the position of the first peak point on the interfacial bonding strain evolution curve is marked as the first feature point, and the position of the second peak point on the strain evolution curve inside the UHPC is marked as the second feature point. The first peak point is the peak point identified in the strain rate curve corresponding to the interfacial bonding strain evolution curve, and its position is determined by the time coordinate. The corresponding strain value point is found on the interfacial bonding strain evolution curve using this time coordinate, and this point is marked as the first feature point. Similarly, the time coordinate corresponding to the second peak point is located on the strain evolution curve inside the UHPC and marked as the second feature point. The marking process is implemented through data indexing, for example, matching corresponding points in the stored data sequence according to the timestamp and adding labels to indicate the feature point attributes. The points represent the locations where the strain rate of change suddenly increases, i.e., the moments when the strain response changes significantly. In the interfacial bonding strain evolution curve, the first feature point may correspond to the onset of interfacial debonding, while in the strain evolution curve inside the UHPC, the second feature point may correspond to the initiation of internal cracking. After marking, the coordinate information of the feature points, including time and strain values, is saved for subsequent analysis. The entire implementation process is automated through programming scripts, such as using Python to write algorithms and calling numerical differentiation and peak detection functions in the SciPy library to ensure computational accuracy and efficiency. During implementation, the correctness of the feature point marking needs to be verified, for example, by visually checking whether the feature points are located in the abrupt change region of the curve, or by comparing the marking results under different thresholds to select the optimal parameters.

[0065] S4. Based on the first and second feature points, extract strain evolution feature sequences of the interface and internal regions respectively. Quantify the asynchronous nature of their evolution by calculating the dynamic time regularization distance between the two strain evolution feature sequences, and determine the competitive relationship between interface bonding failure and internal cracking of the UHPC. Specific implementation includes:

[0066] During implementation, a set time window is first extracted from the interfacial bond strain evolution curve centered on the first feature point as the interfacial strain evolution feature sequence. The first feature point is the marker point extracted from the interfacial bond strain evolution curve in the previous step, corresponding to the location of the sudden increase in strain rate. The set time window refers to a data interval of a fixed time length extended forward and backward from the time coordinate of the first feature point. The length of the time window needs to be determined according to the load application rate and material response characteristics. For example, by analyzing the typical duration of strain change through pre-tests, the window length is set to cover the entire process from the initial change of strain to stabilization. Specifically, the window length can be set to 0.1 seconds, based on the sampling frequency and strain evolution. Rate calculations ensure that the window contains sufficient strain data points to reflect the evolution pattern. During the truncation process, all data points within the range of time coordinates from the first feature point minus half the window to the first feature point plus half the window are selected from the stored interfacial bonding strain evolution curve data. This forms an interfacial strain evolution characteristic sequence, which is a set of time-strain data pairs. The time axis unit is consistent with the original curve, and the strain axis unit is microstrain. During the truncation process, data integrity must be checked to avoid sequence breakage due to missing data. Missing points can be supplemented by linear interpolation. The final interfacial strain evolution characteristic sequence is used to characterize the strain response process of the interfacial bonding region near the feature point.

[0067] Next, the same time window of the strain evolution curve inside the UHPC is extracted with the second feature point as the center as the internal strain evolution feature sequence. The second feature point is the marker point extracted from the strain evolution curve inside the UHPC in the previous step, which also corresponds to the position of the sudden increase in strain rate. The same time window means that it is exactly the same as the window length used when extracting the interface strain evolution feature sequence, for example, 0.1 seconds, to ensure that the two sequences are comparable. The extraction method is the same. Data points with time coordinates in the range of the second feature point time minus half of the window to the second feature point time plus half of the window are selected from the UHPC internal strain evolution curve data to form the internal strain evolution feature sequence. This sequence is also a time-strain value data pair. The time unit and strain unit are consistent with the interface strain evolution feature sequence. When extracting, it is necessary to ensure that the time base of the two feature points is consistent. If there is a time offset of the feature point, it is necessary to ensure that the window start point is synchronized by time axis alignment. After extraction, it is verified whether the sequence length is equal. If they are not equal, they can be adjusted by zero padding or truncation. The final internal strain evolution feature sequence is used to characterize the strain response process of the internal region of the UHPC body near the feature point.

[0068] Then, the evolutionary asynchrony is quantified by calculating the dynamic time warping distance between the two strain evolution characteristic sequences. The dynamic time warping distance is an index that measures the similarity between two time series and can handle the stretching and distortion of the sequences along the time axis. Quantifying evolutionary asynchrony refers to assessing the degree of asynchronous relationship between the interface strain evolution characteristic sequence and the internal strain evolution characteristic sequence in the time dimension. The calculation employs the dynamic time warping algorithm, which constructs a distance matrix between the two sequences. Each element in the distance matrix represents the Euclidean distance between corresponding data points of the two sequences. The Euclidean distance is calculated as the absolute value of the difference between the two strain values. The core steps of the algorithm include initializing the distance matrix, starting from the left side of the matrix... Starting from the top corner, the minimum cumulative distance for each position is calculated. The minimum cumulative distance is obtained by summing the current point distance and the minimum cumulative distance of the adjacent points. The entire matrix is ​​iterated until the bottom right corner, and finally the minimum regularized path distance is obtained. The minimum regularized path distance is the dynamic time regularization distance. The larger the value, the higher the asynchrony of the evolution of the two sequences. Constraints need to be set during the calculation, such as the slope of the regularized path and boundary restrictions, to avoid excessive distortion. The algorithm is implemented by executing a programming script, such as using Python to call a dedicated time series analysis library. The input parameters are the interface strain evolution feature sequence and the internal strain evolution feature sequence, and the output result is the value of the dynamic time regularization distance.

[0069] Finally, the competitive relationship between interface bonding failure and internal cracking of UHPC is determined. This competitive relationship includes both dominant competitive and cooperative relationships. The determination is based on the comparison between the dynamic time-warped distance and a set threshold. The set threshold is used to distinguish the level of asynchrony, and its value is determined through statistical analysis of historical data. For example, multiple sets of specimen data with known competitive and cooperative relationships are collected, their dynamic time-warped distance distributions are calculated, and the threshold is set as the median or a specific quantile of the distance distribution. The specific value needs to be adjusted according to the actual application scenario. For example, the impact of different thresholds on the determination accuracy is tested in pre-tests, and the value with the smallest classification error is selected. The determination rule is that when the dynamic time-warped distance is greater than the set threshold... When the dynamic time warp distance is less than or equal to a set threshold, it indicates that the evolution of the two sequences is highly asynchronous, and the competitive relationship between interface bonding failure and UHPC internal cracking is dominant, meaning that one failure mode occurs before the other. When the dynamic time warp distance is less than or equal to a set threshold, it indicates that the evolution of the two sequences is low asynchronous, and the synergistic relationship between interface bonding failure and UHPC internal cracking is dominant, meaning that the two failure modes occur almost simultaneously. The judgment process is implemented through conditional judgment statements, such as comparing the dynamic time warp distance with the set threshold in the program and outputting labels for competitive or synergistic relationships. During implementation, the reliability of the judgment needs to be verified, for example, by comparing the consistency between the actual failure mode and the judgment result, optimizing the threshold parameters, and ensuring the accuracy of the evaluation conclusion.

[0070] S5. Quantitative evaluation indicators of the crack resistance performance of UHPC wet joint interfaces are output based on competitive relationships. Specific implementation includes:

[0071] In the implementation process, the dynamic time-gain distance (VTD) is first mapped to a preset crack resistance performance level classification interval. The VTD is a quantified value calculated in the previous step using the VTD algorithm, representing the asynchronicity between the interfacial strain evolution characteristic sequence and the internal strain evolution characteristic sequence. Its magnitude reflects the degree of competition between interfacial bonding failure and internal cracking in the UHPC. The preset crack resistance performance level classification interval refers to a predefined set of numerical ranges, each corresponding to a specific crack resistance performance level. This is used to discretize the continuous VTD values ​​into classification indicators. The preset crack resistance performance level classification interval is based on statistical analysis of historical test data, specifically by collecting the failure test results of multiple sets of UHPC wet joint specimens, including the actual crack resistance performance of the specimens and... The corresponding dynamic time-warped distance values ​​are evaluated based on indicators such as crack width and load-bearing capacity, and are divided into three levels: high, medium, and low. Then, the distribution characteristics of the dynamic time-warped distance values ​​are analyzed, and clustering algorithms such as K-means are used to divide the distance values ​​into three clusters. The boundaries of the clusters are the dividing points of the numerical intervals. For example, the first numerical interval corresponds to the cluster with smaller dynamic time-warped distances, the second numerical interval corresponds to the cluster with intermediate values, and the third numerical interval corresponds to the cluster with larger values. The specific values ​​of the interval boundaries are determined through iterative optimization to ensure that the data points within each interval have high homogeneity and the distinction between intervals. The mapping process is implemented through programming. For example, the interval boundary values ​​are defined in the data processing script to check which interval the input dynamic time-warped distance value falls into, thereby triggering the corresponding output logic.

[0072] The corresponding crack resistance performance index is generated based on the mapping results. The crack resistance performance index is a discrete numerical label used to intuitively represent the crack resistance performance level of the UHPC wet joint interface. The generation process is based on preset mapping rules. When the dynamic time-regulated distance is in the first numerical range, the first-level index is output, representing a high crack resistance performance level. When the dynamic time-regulated distance is in the second numerical range, the second-level index is output, representing a medium crack resistance performance level. When the dynamic time-regulated distance is in the third numerical range, the third-level index is output, representing a low crack resistance performance level. The specific numerical form of the level index can be set according to application requirements, such as using numbers 1, 2, 3 or letters A, B, C, but it must be clearly defined and kept consistent before implementation. The level index is generated through conditional judgment statements. For example, the dynamic time-regulated distance is compared with the interval boundary in the program. If the distance is less than the first boundary, the first-level index is output. If the distance is between the first and second boundaries, the second-level index is output. If the distance is greater than the second boundary, the third-level index is output. The output results are saved in the form of data files or visualization reports for easy use and analysis later.

[0073] Mapping the values ​​of dynamic time-regulated distances to preset crack resistance performance level ranges involves establishing a correspondence between dynamic time-regulated distance values ​​and crack resistance performance levels. This correspondence is a mapping table or function that maps input distance values ​​to output crack resistance performance levels. When establishing this correspondence, it's necessary to define the association rules between the range of dynamic time-regulated distance values ​​and crack resistance performance levels. Smaller dynamic time-regulated distances correspond to higher crack resistance performance levels, while larger dynamic time-regulated distances correspond to lower crack resistance performance levels. This rule is based on the principles of materials mechanics. Smaller dynamic time-regulated distances indicate high synchronicity between interface and internal strain evolution, meaning a dominant synergistic relationship, typically corresponding to better interface adhesion and crack resistance. Larger distances indicate high asynchronicity, meaning a dominant competitive relationship, potentially indicating a higher risk of interface debonding or internal cracking. The specific content of the correspondence is determined through experimental calibration. For example, in pre-experiments, the actual crack resistance performance corresponding to different distance values ​​is tested, and the mapping rules are adjusted until the level output matches the actual failure mode. After establishment, the correspondence is stored in the system in the form of a lookup table or mathematical function. For example, an array structure can be used to store range boundaries and level labels, or a piecewise linear function can be used to describe the mapping relationship.

[0074] By querying the established correspondence, the calculated dynamic time-regulated distance values ​​are converted into corresponding crack resistance performance indexes. The query process is a data retrieval operation; the input is the dynamic time-regulated distance value, and the output is the crack resistance performance index. During implementation, the established correspondence data is first loaded, for example, by reading the interval boundaries and grade definitions from a configuration file or database. Then, the correspondence is traversed according to the input value to determine the numerical interval to which it belongs. For example, a binary search algorithm is used to quickly locate the position of the value in the interval. Finally, the corresponding grade index is returned. The query process is executed by an automated script to ensure real-time performance and accuracy. For example, a query module can be integrated into the data acquisition system. Once the dynamic time-regulated distance is calculated, the conversion logic is triggered immediately, and the grade index is output. During implementation, the correctness of the query needs to be verified. For example, the mapping results of the boundary values ​​can be checked through test cases, or compared with actual experimental data to optimize the correspondence parameters and ensure the reliability of the evaluation indicators.

[0075] Example 2: Figure 2 A schematic diagram of a UHPC wet joint interface crack resistance evaluation system according to the present invention is provided. The UHPC wet joint interface crack resistance evaluation system includes the following modules:

[0076] The strain acquisition module is used to acquire real-time strain data of the interface bonding area and the internal area of ​​the UHPC body through strain sensors arranged on the UHPC wet joint specimen.

[0077] The curve generation module is used to generate interface bonding strain evolution curves and UHPC internal strain evolution curves based on real-time strain data.

[0078] The feature extraction module is used to extract a first feature point from the interfacial bonding strain evolution curve and a second feature point from the strain evolution curve inside the UHPC. The first and second feature points correspond to the locations where the strain rate of change suddenly increases.

[0079] The competition determination module is used to extract strain evolution feature sequences of the interface and internal region based on the first feature point and the second feature point, respectively. By calculating the dynamic time regularization distance of the two strain evolution feature sequences, the asynchronous nature of their evolution is quantified, and the competitive relationship between interface bonding failure and UHPC internal cracking is determined.

[0080] The index evaluation module is used to output quantitative evaluation indexes of the crack resistance performance of the UHPC wet joint interface based on the competitive relationship.

[0081] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0082] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0083] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0084] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0086] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0087] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the crack resistance performance of wet joint interfaces in UHPC, characterized in that, Includes the following steps: S1. Real-time strain data of the interface bonding area and the internal area of ​​the UHPC body are collected by strain sensors arranged on the UHPC wet joint specimen. S2. Generate the interfacial bonding strain evolution curve and the UHPC internal strain evolution curve based on real-time strain data, respectively. S3. Extract the first feature point from the interfacial bonding strain evolution curve and extract the second feature point from the strain evolution curve inside the UHPC. The first feature point and the second feature point correspond to the positions where the strain rate of change suddenly increases. S4. Based on the first and second feature points, extract strain evolution feature sequences of the interface and internal regions respectively. Quantify the asynchronous nature of their evolution by calculating the dynamic time regularization distance between the two strain evolution feature sequences, and determine the competitive relationship between interface bonding failure and internal cracking of the UHPC, including: A set time window centered on the first feature point is used to extract the interfacial bonding strain evolution curve as the interfacial strain evolution feature sequence. The same time window of the internal strain evolution curve of UHPC is extracted with the second feature point as the center and taken as the internal strain evolution feature sequence. The asynchronicity of evolution is quantified by calculating the dynamic time warp distance of the two-strain evolution characteristic sequences; The competitive relationship between interface bonding failure and UHPC internal cracking is determined. The competitive relationship includes competitive dominance and cooperative dominance. When the dynamic time warp distance is greater than a set threshold, it is determined that the competitive relationship is dominant. When the dynamic time warp distance is less than or equal to the set threshold, it is determined that the cooperative relationship is dominant. S5. Quantitative evaluation index of crack resistance performance of UHPC wet joint interface based on competitive relationship.

2. The method for evaluating the crack resistance performance of a UHPC wet joint interface according to claim 1, characterized in that, Real-time strain data of the interfacial bonding area and the internal area of ​​the UHPC body were collected by strain sensors deployed on the UHPC wet joint specimen, including: A first set of strain sensors is arranged parallel to the interface direction in the bonding area, and a second set of strain sensors is arranged perpendicular to the potential cracking direction in the internal area of ​​the UHPC body. The first set of strain sensors and the second set of strain sensors synchronously collect real-time strain data.

3. The method for evaluating the crack resistance performance of a UHPC wet joint interface according to claim 1, characterized in that, Based on real-time strain data, interfacial bonding strain evolution curves and UHPC internal strain evolution curves were generated, including: The real-time strain data were aligned according to time series, and the moving average filtering method was used to eliminate high-frequency noise interference. The filtered real-time strain data of the interfacial bonding region is plotted as an interfacial bonding strain evolution curve. Simultaneously, the real-time strain data of the internal region of the filtered UHPC body are plotted as the strain evolution curve inside the UHPC.

4. The method for evaluating the crack resistance performance of a UHPC wet joint interface according to claim 1, characterized in that, The first feature point is extracted from the interfacial bonding strain evolution curve, and the second feature point is extracted from the strain evolution curve inside the UHPC. The first and second feature points correspond to the locations of sudden increases in strain rate, including: The first derivatives of the interfacial bonding strain evolution curve and the strain evolution curve inside the UHPC were calculated to obtain the corresponding strain rate curves. Identify the first and second peak points exceeding a preset threshold in the strain rate curve; The position of the first peak point in the interfacial bonding strain evolution curve is marked as the first feature point, and the position of the second peak point in the strain evolution curve inside the UHPC is marked as the second feature point.

5. The method for evaluating the crack resistance performance of a UHPC wet joint interface according to claim 1, characterized in that, The quantification of evolution asynchronicity includes: using a dynamic time warping algorithm to calculate the minimum warping path distance between the interface strain evolution feature sequence and the internal strain evolution feature sequence.

6. The method for evaluating the crack resistance performance of a UHPC wet joint interface according to claim 1, characterized in that, Quantitative evaluation indicators of the crack resistance performance of UHPC wet joint interfaces based on competitive relationships include: The values ​​of the dynamic time-normalized distance are mapped to the preset crack resistance performance level division range, and the corresponding crack resistance performance level index is generated based on the mapping results. The first-level index is output when the dynamic time warp distance is in the first numerical range, the second-level index is output when the dynamic time warp distance is in the second numerical range, and the third-level index is output when the dynamic time warp distance is in the third numerical range.

7. The method for evaluating the crack resistance performance of a UHPC wet joint interface according to claim 6, characterized in that, Mapping the dynamic time regularization distance to a preset crack resistance performance level range includes: establishing a correspondence between the dynamic time regularization distance value and the crack resistance performance level, where a smaller dynamic time regularization distance corresponds to a higher crack resistance performance level, and a larger dynamic time regularization distance corresponds to a lower crack resistance performance level; and converting the calculated dynamic time regularization distance value into the corresponding crack resistance performance level index by querying the established correspondence.

8. A UHPC wet joint interface crack resistance evaluation system, used to implement the UHPC wet joint interface crack resistance evaluation method according to any one of claims 1-7, characterized in that, Includes the following modules: The strain acquisition module is used to acquire real-time strain data of the interface bonding area and the internal area of ​​the UHPC body through strain sensors arranged on the UHPC wet joint specimen. The curve generation module is used to generate interface bonding strain evolution curves and UHPC internal strain evolution curves based on real-time strain data. The feature extraction module is used to extract a first feature point from the interfacial bonding strain evolution curve and a second feature point from the strain evolution curve inside the UHPC. The first and second feature points correspond to the locations where the strain rate of change suddenly increases. The competition determination module is used to extract strain evolution feature sequences of the interface and internal region based on the first feature point and the second feature point, respectively. By calculating the dynamic time regularization distance of the two strain evolution feature sequences, the asynchronous nature of their evolution is quantified, and the competitive relationship between interface bonding failure and UHPC internal cracking is determined. The index evaluation module is used to output quantitative evaluation indexes of the crack resistance performance of the UHPC wet joint interface based on the competitive relationship.

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