Performance detection method and system for ultra-high performance concrete

Abstract

The invention discloses a performance detection method and system for ultra-high performance concrete, particularly relates to the technical field of building material performance detection, and aims to solve the problem of strength test discreteness caused by neglecting of an aging effect after high-temperature maintenance in an existing detection method. The method comprises the following steps: recording a first time point when a test piece is cooled at room temperature and a second time point when a press machine is loaded, and generating a comprehensive aging factor in combination with double quantitative analysis of a fiber pull-out fragment proportion and a crack fractal dimension in a damage process image; matching a strength correction coefficient from a preset table according to the aging factor, and dynamically calibrating a first compressive strength test result; accurate association between the aging damage mechanism and the macroscopic strength is achieved, and the reliability evaluation precision of the ultra-high performance concrete in major engineering is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of building material performance testing technology, and more specifically, to a method and system for testing the performance of ultra-high performance concrete. Background Technology

[0002] Ultra-high performance concrete (UHPC), due to its significantly higher compressive strength than conventional concrete, has been widely used in the quality control of major projects such as nuclear power plant containment structures and long-span bridges. Currently, the industry generally adopts high-temperature curing processes to improve the strength performance of UHPC. Its compressive strength testing is mainly carried out according to general concrete standards (such as ASTM C39), and data is obtained by destructive testing of cubic or cylindrical specimens using a pressure machine.

[0003] Existing testing methods do not consider the continuous evolution of the hydration reaction within UHPC materials after high-temperature curing, leading to significant strength differences in the same specimen at different resting times. This dispersion in test results introduced by the aging variable makes it impossible to establish unified criteria for engineering acceptance, seriously affecting the reliability evaluation of this new material in critical structures. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a performance testing method and system for ultra-high performance concrete to solve the problems mentioned in the background art.

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

[0006] The performance testing method for ultra-high performance concrete includes the following steps:

[0007] S1. Record the first time point when the ultra-high performance concrete specimens after high-temperature curing complete room temperature cooling.

[0008] S2. Place the specimen in the press for the first compressive strength test, simultaneously collect image data of the specimen failure process, and record the second time point at the start of the test;

[0009] S3. Analyze the fragment morphology characteristics in the image data, calculate the proportion of fiber-pulled fragments to the total fragment area, and extract the damage crack network and calculate its fractal dimension.

[0010] S4. Calculate the time interval between the second time point and the first time point, and generate a comprehensive aging factor by combining the proportion of fiber-pulled fragments to the total fragment area and the fractal dimension of the damaged crack network.

[0011] S5. Select the corresponding intensity correction coefficient from the preset timeliness correction coefficient table based on the comprehensive timeliness factor;

[0012] S6. Multiply the first compressive strength test result by the strength correction factor and output the calibrated compressive strength value.

[0013] Furthermore, the first time point at which the ultra-high performance concrete specimens, after high-temperature curing, completed room temperature cooling was recorded, including:

[0014] Temperature sensors were placed on the surface of ultra-high performance concrete specimens;

[0015] The surface temperature of ultra-high performance concrete specimens was continuously monitored using temperature sensors.

[0016] Determine whether the ultra-high performance concrete specimens have completed room temperature cooling;

[0017] Record the moment when the room temperature cooling is completed as the first time point.

[0018] Furthermore, when the surface temperature reaches the ambient room temperature and remains stable, the ultra-high performance concrete specimen is considered to have completed room temperature cooling.

[0019] Furthermore, the specimen was placed in a press for the first compressive strength test, and video data of the specimen failure process was simultaneously acquired. The second time point at the start of the test was also recorded, including:

[0020] Industrial cameras were placed in the loading area of ​​the press.

[0021] The press is started to apply a load to the ultra-high performance concrete specimen until the specimen fails.

[0022] The surface changes of ultra-high performance concrete specimens under load were continuously captured using industrial cameras.

[0023] Record the moment when the press starts loading as the second time point;

[0024] Save continuous images of the entire failure process of ultra-high performance concrete specimens as image data.

[0025] Furthermore, the morphological characteristics of fragments in the image data are analyzed, the proportion of fiber-pulled fragments to the total fragment area is calculated, and the fracture crack network is extracted and its fractal dimension is calculated, including:

[0026] Extracting fragment images of ultra-high performance concrete specimens after failure from image data;

[0027] Based on the difference in gray values ​​of exposed fiber areas in fragment images, the regions where fibers have been pulled out of fragments are segmented and identified.

[0028] Calculate the ratio of the area of ​​the fiber-pulled-out fragment region to the total area of ​​the fragment image, and use this ratio as the proportion of the fiber-pulled-out fragment to the total fragment area.

[0029] Extracting crack images of ultra-high performance concrete specimens after failure from image data;

[0030] Binarization of the crack image yields a binary crack network map;

[0031] The fractal dimension of the broken crack network is obtained by calculating the fractal dimension of the binary image of the crack network using the box counting method.

[0032] Furthermore, the fractal dimension of the binary image of the crack network is calculated using the box counting method, and the fractal dimension of the damaged crack network is obtained as follows:

[0033] The binary image of the crack network is divided into square grids of different side lengths; the number of grids covering crack pixels in each grid of side length is counted; a linear relationship is fitted between the logarithm of the grid side length and the logarithm of the number of covering grids; the absolute value of the slope of the linear relationship is used as the fractal dimension of the crack network.

[0034] Furthermore, the time interval between the second time point and the first time point is calculated, and a comprehensive aging factor is generated by combining the proportion of fiber-pulled fragments to the total fragment area and the fractal dimension of the damaged crack network, including:

[0035] The time interval is obtained by subtracting the time value of the first time point from the time value of the second time point.

[0036] Obtain the proportion of fiber-pulled fragments to the total fragment area calculated by analyzing image data;

[0037] Obtain the fractal dimension of the damaged crack network calculated using the box counting method;

[0038] Input the time interval, the proportion of fiber-pulled fragments to the total fragment area, and the fractal dimension of the broken crack network into the weighted summation formula;

[0039] Perform the calculation operation of the weighted summation formula and output the calculation result as the comprehensive timeliness factor.

[0040] Furthermore, based on the comprehensive timeliness factor, the corresponding intensity correction coefficient is selected from the preset timeliness correction coefficient table, including:

[0041] Obtain the comprehensive timeliness factor generated by the weighted summation formula;

[0042] Locate the mapping relationship between the timeliness correction coefficient table and the comprehensive timeliness factor;

[0043] Determine the intensity correction coefficient value corresponding to the comprehensive timeliness factor based on the mapping relationship;

[0044] Select the corresponding strength correction factor value as the output result.

[0045] Furthermore, the first compressive strength test result is multiplied by a strength correction factor to output the calibrated compressive strength value, including:

[0046] Obtain the first compressive strength test result obtained through the press test;

[0047] Obtain the strength correction factor value selected through the aging correction factor table;

[0048] The value of the first compressive strength test result is multiplied by the value of the strength correction factor;

[0049] The result of the multiplication operation is output as the calibrated compressive strength value.

[0050] On the other hand, the present invention provides a performance testing system for ultra-high performance concrete, comprising the following modules:

[0051] The cooling monitoring module is used to record the first time point when the ultra-high performance concrete specimens completed room temperature cooling after high-temperature curing.

[0052] The failure analysis module is used to place the specimen in the press for the first compressive strength test, simultaneously acquire image data of the specimen failure process, and record the second time point at the start of the test;

[0053] The damage quantification module is used to analyze the fragment morphology characteristics in image data, calculate the proportion of fiber pull-out fragments to the total fragment area, and extract the damage crack network and calculate its fractal dimension.

[0054] The time fusion module is used to calculate the time interval between the second time point and the first time point, and to generate a comprehensive time aging factor by combining the proportion of fiber pull-out fragments to the total fragment area and the fractal dimension of the damaged crack network.

[0055] The coefficient mapping module is used to select the corresponding intensity correction coefficient from the preset timeliness correction coefficient table based on the comprehensive timeliness factor;

[0056] The strength output module is used to multiply the first compressive strength test result by a strength correction factor and output the calibrated compressive strength value.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. By employing a time-dependent damage synergistic calibration mechanism, the engineering reliability of ultra-high performance concrete strength testing is effectively improved. The internal aging evolution process of the material is dynamically linked to macroscopic damage characteristics: based on the precise recording of the completion point of room temperature cooling after high-temperature curing, the starting benchmark for the aging effect is established; combined with the synchronous acquisition of damage images during press loading, the entire process of material damage evolution is tracked; the quantitative analysis of the fiber pull-out fragment ratio directly reflects the degree of debonding failure of the fiber-matrix interface over time; the accurate calculation of the fractal dimension of the crack network objectively characterizes the time-dependent cumulative effect of microcrack propagation; both serve as key damage indicators, jointly constructing a dynamic evaluation model for material performance degradation.

[0059] 2. By integrating time intervals and dual damage parameters, a comprehensive aging factor is generated, establishing a quantitative mapping relationship between microscopic damage mechanisms and macroscopic strength decay. Based on the aging correction coefficient table, the comprehensive aging factor is transformed into a strength calibration basis, ultimately outputting a compressive strength value that eliminates aging deviations. This achieves a closed-loop correlation from the continuous evolution of the material's internal hydration reaction to engineering acceptance criteria, providing a consistent strength evaluation benchmark for major projects and solving the problem of test result dispersion caused by neglecting post-curing aging variables in traditional methods. Attached Figure Description

[0060] Figure 1 This is a flowchart of the performance testing method for ultra-high performance concrete of the present invention;

[0061] Figure 2 This is a schematic diagram of the performance testing system for ultra-high performance concrete of the present invention. Detailed Implementation

[0062] 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.

[0063] Example 1: Figure 1 The present invention provides a performance testing method for ultra-high performance concrete, which includes the following steps:

[0064] S1. Record the first time point when the ultra-high performance concrete specimens after high-temperature curing complete room temperature cooling.

[0065] S2. Place the specimen in the press for the first compressive strength test, simultaneously collect image data of the specimen failure process, and record the second time point at the start of the test;

[0066] S3. Analyze the fragment morphology characteristics in the image data, calculate the proportion of fiber-pulled fragments to the total fragment area, and extract the damage crack network and calculate its fractal dimension.

[0067] S4. Calculate the time interval between the second time point and the first time point, and generate a comprehensive aging factor by combining the proportion of fiber-pulled fragments to the total fragment area and the fractal dimension of the damaged crack network.

[0068] S5. Select the corresponding intensity correction coefficient from the preset timeliness correction coefficient table based on the comprehensive timeliness factor;

[0069] S6. Multiply the first compressive strength test result by the strength correction factor and output the calibrated compressive strength value.

[0070] S1. Record the first time point at which the ultra-high performance concrete specimens, after high-temperature curing, complete room temperature cooling. The specific implementation is as follows:

[0071] After the ultra-high performance concrete (UHVPC) specimens underwent high-temperature curing, the room temperature cooling process was monitored and recorded at the first time point. A metal thermocouple temperature sensor was tightly attached to the central surface of the UHVPC specimen. A thermally conductive medium was used to fill the contact gap between the temperature sensor and the UHVPC surface, ensuring airless physical contact between the sensor's measuring end and the specimen surface. The temperature sensor was connected to a data acquisition device via wires. The data acquisition device was set to collect temperature data once per second, with a temperature range covering 0°C to 200°C and a measurement accuracy controlled within ±0.5°C.

[0072] The surface temperature of the ultra-high performance concrete specimen is continuously acquired using a temperature sensor, and the data acquisition equipment records the temperature values ​​and corresponding acquisition times in a time-series format. The baseline value of the ambient room temperature is synchronously acquired using an independent temperature sensor positioned 1 meter away from the ultra-high performance concrete specimen in an area free from heat sources and with good air circulation. When the surface temperature of the ultra-high performance concrete specimen first enters the positive or negative fluctuation range of the ambient room temperature baseline value, a stable state determination process is initiated. The threshold of this fluctuation range is dynamically determined based on the ambient room temperature; for example, a threshold of ±1.0 degrees Celsius is used when the ambient room temperature is below 20 degrees Celsius, and a threshold of ±0.5 degrees Celsius is used when the ambient room temperature is above 20 degrees Celsius.

[0073] Determining a stable state requires continuous monitoring of temperature data over multiple consecutive sampling periods, specifically 10 sampling periods. If the deviation between the 10 consecutive sample values ​​and the ambient room temperature reference value does not exceed a set threshold, the temperature is considered to have reached a stable state. For example, when the ambient room temperature reference value is 25.0 degrees Celsius, and the fluctuation threshold is set to ±0.5 degrees Celsius, if the surface temperature of the ultra-high performance concrete specimen reaches 25.3 degrees Celsius at 14:35:16.203, and the subsequent 10 consecutive sample values ​​up to 14:35:26.203 remain within the range of 24.8–25.4 degrees Celsius, then the stability condition is met.

[0074] After determining the stable state, the data acquisition device automatically marks the current moment as the confirmation node for the ultra-high performance concrete specimen to have completed room temperature cooling. Precise recording of this moment is achieved through the built-in clock module of the data acquisition device, which has been pre-synchronized with a standard time source. The generated time data contains complete time information and is written to the test record file as the first time point. The temperature sensors must be placed away from visible defect areas on the surface of the ultra-high performance concrete specimen. Each ultra-high performance concrete specimen should have at least three temperature sensors, and the last recorded moment of reaching a stable state from each sensor is taken as the first time point for that specimen.

[0075] When discrepancies exist in the stabilization times recorded by different temperature sensors, an anomaly handling mechanism is implemented: if the difference in stabilization times recorded by multiple sensors exceeds 5 minutes, the cooling process is deemed abnormal, and the test is repeated. Temperature data is stored using a segmented recording mechanism. Raw temperature data is saved as a structured data file, containing data items such as time point, specimen identifier, sensor identifier, and temperature value. The judgment result is recorded in a separate file, including the ambient room temperature baseline, the stabilization judgment start time, the judgment pass time, and the final determined first time point.

[0076] After the data acquisition equipment completes the judgment, it issues a prompt signal. The operator verifies the temperature change curve on-site and, once confirmed, manually enters the first time point into the detection database to establish a data association with subsequent testing steps. During temperature monitoring, the ambient room temperature baseline is recalibrated every 30 minutes by reading the average of the latest 10 samples from the independent temperature sensor. In the stable state judgment process, if data exceeding the threshold occurs during the 8th sample, the judgment process automatically resets and recounts.

[0077] The starting time for surface temperature monitoring of ultra-high performance concrete specimens is 5 minutes after the specimens are removed from the high-temperature curing equipment to avoid sensor measurement deviations caused by sudden temperature changes. Silicon-based thermal conductive materials with a thermal conductivity of not less than 3.0 W / m·K are selected as the heat transfer medium. The clock synchronization of the data acquisition equipment adopts a network time protocol, with a time synchronization error controlled within 50 milliseconds. The minimum spacing between temperature sensor placement points is one-third of the specimen surface length to ensure spatial coverage of temperature monitoring.

[0078] The anomaly handling mechanism includes an automatic alarm function, triggering an equipment check command when temperature data is missing for more than three consecutive sampling periods. The original temperature data file and the judgment result file are indexed and linked through the specimen number to ensure data traceability. During operator review, it is necessary to confirm that the temperature change curve shows a stable and converging pattern; if there is an increasing trend in the fluctuation amplitude, cooling is considered incomplete. The first time point is recorded with an accuracy of 1 / 100th of a second, in a 24-hour continuous timing format.

[0079] S2. Place the specimen in the press for the first compressive strength test, simultaneously acquire image data of the specimen failure process, and record the second time point at the start of the test. The specific implementation is as follows:

[0080] After the ultra-high performance concrete (UHVPC) specimens were cooled to room temperature, compressive strength testing and image acquisition were performed. The UHVPC specimen was placed centered on the press platform, and the distance between the press platen and the upper surface of the specimen was adjusted to within 1 mm. Two industrial cameras were symmetrically positioned on both sides of the press loading area, with the camera lens axis at a 45-degree angle to the central axis of the UHVPC specimen, and the camera height aligned with the center of the specimen. The industrial cameras used global shutter CMOS sensors with a resolution of 1920×1080 pixels and a frame rate of 60 frames per second, and were equipped with ring LED lights to ensure an illumination of no less than 1000 lux.

[0081] The press control system is activated, and the load application rate is set to 0.5 MPa per second. This rate is achieved through closed-loop control of the press's hydraulic servo system. When the press starts loading, the control system synchronously triggers the industrial camera to begin recording; the two systems achieve millisecond-level synchronous response through opto-isolation circuitry. The press continuously applies axial load to the ultra-high performance concrete specimen, with the load value monitored in real time by a built-in pressure sensor at a sampling frequency of 1000 times per second. The industrial camera continuously records the surface changes of the ultra-high performance concrete specimen under load, and the video stream is temporarily stored in the camera buffer in uncompressed format.

[0082] The precise moment the press starts loading is recorded as the second time point. This moment is determined as follows: when the pressure sensor detects that the load value first exceeds 1000 Newtons, the control system generates a time stamp signal. The time stamp signal is transmitted to the time recorder via a coaxial cable. The time recorder has been pre-synchronized with a standard clock source, with the synchronization error controlled within 10 milliseconds. The second time point data includes year, month, day, hour, minute, second, and millisecond information, for example, recorded as 2023-08-15 14:35:30.452. This time point data is stored in the test database in association with the press number and the ultra-high performance concrete specimen number.

[0083] The industrial camera recording process continued until the ultra-high performance concrete specimen completely failed. Failure was determined by any of the following criteria: the load value decreased by 80% from its peak value or the ultra-high performance concrete specimen broke into three or more independent fragments. After recording, the raw video data in the industrial camera's cache was transmitted to the storage server via gigabit Ethernet. The video data was named according to the following rule: ultra-high performance concrete specimen number_test date_camera number.MOV. The storage server automatically generated a video index file, recording the video start time, frame rate, resolution, and the frame number at the corresponding second time point.

[0084] The industrial camera performs real-time focus and exposure adjustments during shooting. The specific control logic is as follows: a sharpness assessment is performed every 10 frames; autofocus is triggered when the edge sharpness value falls below a set threshold. The exposure time is dynamically adjusted every second based on the image histogram to ensure the median grayscale value remains within the range of 128±20. If a single camera malfunctions (e.g., 5 consecutive frames are lost), the system automatically switches to a backup viewpoint to continue shooting and generates an equipment alarm log.

[0085] The video data storage employs a dual-backup mechanism: the original video files are stored on the primary storage array, while a copy transcoded to H.264 format is stored on the backup server. Transcoding parameters are set as follows: a constant frame rate of 60fps, a resolution reduced to 1280×720 pixels, and a bitrate of no less than 20Mbps. All video files are tagged with metadata including the ultra-high performance concrete specimen number, the second time point, the press number, and camera parameters. Test personnel verify the video playability on-site; if keyframes are missing (e.g., blurred image at the moment of destruction), the test must be repeated.

[0086] Safety devices are installed in the loading area of ​​the press to trigger an emergency stop procedure when the load exceeds 120% of the estimated strength of the ultra-high performance concrete specimen. A polycarbonate protective cover is installed in front of the industrial camera lens to prevent damage to the optical components from flying debris. After the test, residual debris on the load-bearing platform is cleaned, and the zero-point drift of the press sensor is checked. If the drift exceeds 0.1% of full scale, sensor calibration is performed. A draft test report is generated for each test, including data items such as the second time point, timestamps of key points on the load curve, and video storage path, for subsequent analysis.

[0087] S3. Analyze the fragment morphology characteristics in the image data, calculate the proportion of fiber-pulled fragments to the total fragment area, and simultaneously extract the fracture crack network and calculate its fractal dimension. The specific implementation is as follows:

[0088] Continuous video data of the entire failure process of ultra-high performance concrete (UHVPC) specimens was retrieved from the storage server. This video data was captured by an industrial camera during the loading process of the press. Keyframe images from 3 seconds after the complete failure of the UHVPC specimen were extracted as source data for fragment analysis. The criteria for determining a keyframe were the moment when the load value dropped to 10% of the peak load and no fragments obscured the specimen body. After fragment image extraction, preprocessing was performed: first, Gaussian filtering was applied to eliminate noise, with the filter kernel size set to 5×5 pixels and a standard deviation of 1.5; then, histogram equalization was used to enhance image contrast, expanding the grayscale value range to the full 0-255 range.

[0089] The method for identifying fiber-exposed fragment regions based on grayscale features of fragment images involves the following segmentation approach: In the preprocessed fragment image, the exposed fiber areas exhibit high grayscale values ​​due to the reflective properties of the metal. A dynamic grayscale threshold is set for binarization segmentation. The threshold is calculated by subtracting 30 from the average grayscale value of the top 10% of pixels in the fragment image to obtain the lower limit of the segmentation threshold. For example, when the average grayscale value of the top 10% of pixels is 220, the segmentation threshold is set to 190. After threshold segmentation, a binary mask image is generated, where white areas (grayscale values ​​≥ the threshold) are marked as fiber-exposed fragment regions.

[0090] The ratio of the area of ​​the fiber-pulled-out fragment region to the total area of ​​the fragment image is calculated using a pixel counting method. The area of ​​the fiber-pulled-out fragment region is the total number of white pixels in the binary mask image, and the total area of ​​the fragment image is the product of the image width and height pixel values. The final ratio is calculated as follows: the area of ​​the fiber-pulled-out fragment region divided by the total area of ​​the fragment image, then multiplied by 100%. This ratio is recorded to three decimal places in the analysis report, and the binary mask image is saved as evidence of the process.

[0091] Crack analysis-specific images were extracted from the same continuous image data, with frames at the moment when the load reached 90% of the peak load selected as the source data for crack images. Crack image preprocessing included: converting to grayscale and performing median filtering with a 3×3 pixel filter window; edge enhancement using the Laplacian operator with an enhancement coefficient of 2.0. The enhanced crack images were then binarized, with the threshold set as follows: the median of the overall image grayscale was calculated, and 70% of this median was used as the binarization threshold. After binarization, a crack network binary map was generated, where crack pixels were marked as black (grayscale value 0) and the background as white (grayscale value 255).

[0092] The fractal dimension of the crack network binary image is calculated using box counting. The specific steps are as follows: Prepare a sequence of square grid side lengths, starting with 5 pixels and increasing by 5 pixels in increments until the side length exceeds half the length of the shorter side of the image. For example, for a 1920×1080 pixel image, the side length sequence would be [5, 10, 15, ..., 540]. Overlay each square grid of each side length onto the crack network binary image, with the grid starting at the top left corner of the image. Count the number of grids that cover at least one crack pixel, recording this as the number of covered grids for the current side length.

[0093] A dataset was created using the logarithm of the grid side length and the logarithm of the number of covering grids. The logarithm of both side length and covering grids was taken to base 10. A linear regression model was fitted to this dataset using the least squares method. The fitting formula was: the logarithm of the covering grids equals the slope multiplied by the logarithm of the side length plus the intercept. The slope of the linear regression model was calculated, and its absolute value was taken as the fractal dimension of the fractured crack network. The fractal dimension value was recorded to two decimal places, and the fitted curve was saved for verification.

[0094] A verification mechanism is implemented during fiber region segmentation: when the area of ​​fiber pull-out fragments exceeds 60%, a manual review process is automatically triggered. During crack binarization, if the proportion of crack pixels in the image is less than 5%, it is considered invalid data and a new frame is selected. In box counting calculations, the determination coefficient of the fitted linear regression must be no less than 0.98; otherwise, the side length sequence range is expanded and recalculated.

[0095] All intermediate processing data is stored according to the following rules: the original fragment image is saved in PNG format, the binary mask image is saved in BMP format, the crack binary image is saved in TIFF format, and the fractal dimension calculation data is saved as a CSV table, including fields such as side length value, number of covered grids, and logarithmic value. The proportion of fiber pull-out fragments to the total fragment area and the fractal dimension of the broken crack network are linked by an index established between the specimen number and the previous test data.

[0096] The image analysis process is executed on a dedicated computing device equipped with a dedicated graphics card to accelerate image processing. Fiber segmentation and crack recognition are implemented using an open-source computer vision library, version 4.5.0. Detailed logs are maintained throughout the process, including the time consumed at each step, parameter values, and any anomalies. Operators must verify key intermediate results: confirming that the fiber region segmentation does not include high-grayscale aggregates, that crack binarization does not break the main crack path, and that the box-counting mesh coverage has no offset error.

[0097] Fragments from ultra-high performance concrete specimens after failure were physically collected, and the actual percentage of fiber-extracted fragment area was used as a verification benchmark. When the deviation between the image analysis results and the physical measurements exceeded 10%, the grayscale segmentation threshold was adjusted and the data reprocessed. The fractal dimension calculation was set to a reference range of 1.2-1.8; values ​​outside this range required checking the accuracy of the crack image selection time. The final analysis report included a summary table of processing parameters explaining the basis for each key step's settings.

[0098] S4. Calculate the time interval between the second time point and the first time point, and generate a comprehensive aging factor by combining the proportion of fiber-pulled fragments to the total fragment area and the fractal dimension of the damaged crack network. The specific implementation is as follows:

[0099] The system retrieves a second time point recorded by the pressure machine test and a first time point recorded by temperature monitoring from the testing database. Both time points are stored in year-month-day-hour-minute-second-millisecond format. When calculating the time interval between the second and first time points, both time points are converted to Unix timestamp format, accurate to the millisecond level. The time interval is calculated as follows: the timestamp value of the second time point minus the timestamp value of the first time point. The result is stored as a floating-point number in seconds. For example, if the timestamp of the first time point is 1692000000.000 and the timestamp of the second time point is 1692003600.000, the time interval is 3600.000 seconds. During the timestamp conversion process, the time zone is uniformly set to UTC+8 to avoid time zone conversion errors.

[0100] The percentage of fiber-pulled fragments relative to the total fragment area, calculated through fragment image analysis, is obtained from the image analysis report. This percentage is stored as a percentage and ranges from 0 to 100. Simultaneously, the fractal dimension of the damaged crack network, calculated using the crack image box counting method, is obtained. The fractal dimension value is stored as a double-precision floating-point number, retaining three decimal places. Both parameters are linked to the corresponding ultra-high performance concrete specimen number through database indexing to ensure data traceability. Data validity is verified before the acquisition process: the percentage value is checked to ensure it falls within the 0-100 range, and the fractal dimension is checked to ensure it falls within the 1.0-2.0 range. If these values ​​are outside this range, a data review process is triggered.

[0101] The three parameters—time interval, the proportion of fiber-pulled-out fragments to the total fragment area, and the fractal dimension of the damaged crack network—are input into a weighted summation formula. The structure of the weighted summation formula is: the comprehensive aging factor equals the time interval multiplied by the first weighting coefficient, plus the proportion of fiber-pulled-out fragments to the total fragment area multiplied by the second weighting coefficient, plus the fractal dimension of the damaged crack network multiplied by the third weighting coefficient. The weighting coefficients are determined by measuring the correlation coefficients between each parameter and the strength decay rate through 300 sets of destructive tests on ultra-high performance concrete specimens with different mix proportions, and normalizing these correlation coefficients into weight values. Specifically, the weighting ranges are set as follows: the first weighting coefficient is between 0.4 and 0.6, the second weighting coefficient is between 0.2 and 0.3, and the third weighting coefficient is between 0.2 and 0.3.

[0102] When performing the weighted summation calculation, the input parameters are first standardized: the time interval is divided by 86400 to convert to days; the proportion of fiber-pulled fragments to the total fragment area is divided by 100 to convert to decimal form; the fractal dimension of the broken crack network remains unchanged. The standardized parameters are then substituted into the weighted summation formula, which is expressed as: standardized time interval multiplied by the first weighting coefficient, plus the standardized fiber proportion multiplied by the second weighting coefficient, plus the fractal dimension multiplied by the third weighting coefficient. The calculation process uses double-precision floating-point arithmetic, and intermediate results are retained to six decimal places.

[0103] The output calculation result is used as a comprehensive timeliness factor, which is a dimensionless numerical value stored in double-precision floating-point format. The numerical range of the comprehensive timeliness factor is controlled between 0.5 and 3.0 through experimental data calibration. Detailed logs are recorded during the calculation process, including the original values ​​of input parameters, standardized values, weight coefficient values, intermediate calculation results, and the final comprehensive timeliness factor.

[0104] For example, when the standardization time interval is 1.5 days, the standardization fiber ratio is 0.25, the fractal dimension is 1.65, and the weighting coefficients are 0.5, 0.25, and 0.25 respectively, the calculation process is 1.5×0.5+0.25×0.25+1.65×0.25=0.75+0.0625+0.4125=1.225.

[0105] The dynamic adjustment mechanism for the weighting coefficients is as follows: when the proportion of fiber-pulled debris to the total debris area is less than 10%, the second weighting coefficient automatically increases by 0.05; when the time interval exceeds 30 days, the first weighting coefficient decreases by 0.1. The upper limit of the adjustment range is ±20% of the original weights to ensure the stability of the calculation results. Anomaly detection is included in the calculation process: if the comprehensive efficiency factor exceeds the range of 0.5-3.0, the system automatically checks whether the input parameters are abnormal and re-acquires the data.

[0106] During parameter standardization, boundary conditions are set for time interval conversion: time intervals less than 3600 seconds are calculated as 1 hour, and time intervals greater than 2592000 seconds are calculated as 30 days. Fiber ratio conversion retains four decimal places, and fractal dimension uses the original value directly without conversion. Weighted summation calculations are performed in a dedicated computing unit, which undergoes floating-point precision calibration every 6 months to ensure calculation errors are less than 0.001.

[0107] A mapping table is established between the comprehensive aging factor calculation results and the input parameters for storage. The table includes the original values ​​of the time interval, fiber ratio, fractal dimension, weighting coefficients, and the final comprehensive aging factor value. Each calculation generates a unique identifier, which is associated with the ultra-high performance concrete specimen number and test date. When operators review the calculation log, they must verify whether the weighting coefficient values ​​meet the preset range, whether the standardization process is accurate, and whether the calculation results are within the expected range.

[0108] A quality control process for calculating the comprehensive timeliness factor is established: One calculation is randomly selected every 10 calculations for manual recalculation. If the recalculated result deviates from the system result by more than 5%, a calculation logic check is triggered. A retry mechanism is set for input parameter acquisition: if a database query fails, it automatically waits 5 seconds and retryes, with a maximum of 3 retries. The final output of the comprehensive timeliness factor is written to the inspection report in JSON format, including the numerical value, calculation timestamp, and data source index.

[0109] Experimental verification of the weighting coefficient settings was conducted: Typical ultra-high performance concrete specimens were selected, and the fiber ratio was varied at fixed time intervals and fractal dimension to observe the trend of the comprehensive aging factor. When the fiber ratio increased from 15% to 35%, the comprehensive aging factor should exhibit a monotonically increasing characteristic, with an increase ranging from 0.1 to 0.3. If this pattern is not observed, the weighting coefficients should be recalibrated. All calculation parameters are stored in plaintext in the system configuration file, allowing adjustment of the baseline value of the weighting coefficients based on the material formulation.

[0110] S5. Select the corresponding intensity correction coefficient from the preset timeliness correction coefficient table based on the comprehensive timeliness factor. The specific implementation is as follows:

[0111] The comprehensive aging factor, generated through a weighted summation formula, is obtained from the output of the comprehensive aging factor calculation. This factor is stored in the testing database in double-precision floating-point format, with a value ranging from 0.5 to 3.0. Before the acquisition operation, data validity is verified: the comprehensive aging factor is checked to ensure it is within the preset value range. If it exceeds the range, an exception handling process is triggered, automatically notifying technical personnel to review the preceding calculation steps. The comprehensive aging factor data record includes a generation timestamp, a calculation process log index, and the associated ultra-high performance concrete specimen number.

[0112] The mapping relationship between the pre-defined timeliness correction coefficient table and the comprehensive timeliness factor is located. The timeliness correction coefficient table has a two-dimensional relational table structure and is stored in an encrypted configuration file. The relational table contains three columns of data: the lower limit of the comprehensive timeliness factor, the upper limit of the comprehensive timeliness factor, and the corresponding intensity correction coefficient value. The location rule for the mapping relationship is: when the comprehensive timeliness factor is greater than or equal to the lower limit of a row and less than the upper limit of that row, it is considered a successful match. For example, if the relational table sets the interval [1.0, 1.5) to correspond to an intensity correction coefficient of 1.05, then the comprehensive timeliness factor of 1.25 matches this interval.

[0113] The method for constructing the aging correction factor table is as follows: Through 200 sets of destructive tests on ultra-high performance concrete specimens with different curing periods, the ratio of actual strength to initial test strength was measured. This ratio was then grouped according to the comprehensive aging factor interval and the arithmetic mean was taken to generate the table. The table update mechanism involves recalculating the average value every 50 new sets of experimental data, while historical data is retained for version tracking. The table contains 8 non-overlapping intervals, with an interval width set to 0.25 and boundary values ​​accurate to three decimal places.

[0114] The intensity correction coefficient is determined based on the mapping relationship, using an exact matching method: when the comprehensive timeliness factor falls within a preset range, the intensity correction coefficient value corresponding to that range is directly read. If the comprehensive timeliness factor is exactly equal to the range boundary value, it is assigned to the range with the higher upper limit. The intensity correction coefficient value is stored in double-precision floating-point format, retaining three decimal places, with a typical value range between 0.90 and 1.15. The determination process records a detailed matching log, including the input comprehensive timeliness factor value, the matched range, and the corresponding intensity correction coefficient value.

[0115] Post-processing of selected operations: When multiple candidate intervals are matched, the earliest created interval record is selected; if no interval is matched, the temporary intensity correction coefficient is calculated using linear interpolation. The interpolation method is as follows: Select the two closest interval boundary points and calculate the coefficient value according to the distance ratio. For example, if the comprehensive timeliness factor 1.65 is not in the table, take 1.08 corresponding to 1.60 and 1.12 corresponding to 1.70, and calculate 1.08 + 0.5 × (1.12 - 1.08) = 1.10 according to the ratio (1.65 - 1.60) / (1.70 - 1.60) = 0.5.

[0116] The strength correction factor value is output to the calibration system and simultaneously written to the test report results column. The output data includes the strength correction factor value, matching method marker, and calculation timestamp. A dual-channel verification mechanism is established: after the main system outputs, the backup system synchronously performs the same table lookup operation; if the deviation between the two results exceeds 0.01, manual verification is triggered. A bidirectional index is established between the output results and the ultra-high performance concrete specimen number and the comprehensive aging factor value.

[0117] The timeliness correction coefficient table is equipped with a dynamic protection mechanism: each access requires digital certificate authentication, and operation records are encrypted and stored. The table is stored in a memory-resident database, with a response time controlled within 50 milliseconds. The exception handling process includes: when three consecutive queries miss the range, the boundary range is automatically expanded by 10%; when the interpolation calculation result exceeds the range of 0.85-1.20, the default value of 1.00 is forcibly adopted.

[0118] The table lookup operator needs to perform data verification monthly: randomly select 5 comprehensive timeliness factor values, manually calculate the intensity correction coefficients that should be matched, and compare the error with the system output. If the verification error exceeds 0.5%, the table calibration procedure is initiated. Historical version data is retained during table version upgrades, supporting backward compatibility queries. Confidence level labels are added to the output results: interval matching results are labeled as high confidence, and interpolation results are labeled as medium confidence.

[0119] The application range of the strength correction factor is controlled: when the factor value is below 0.95, a material durability warning is automatically triggered; when the factor value is above 1.10, a note on the material's superior performance is generated. Each output generates a unique transaction number, including the date, equipment number, and operator code. The data traceability system supports reverse lookup of related original test data through comprehensive timeliness factors, forming a complete chain of evidence.

[0120] An offline backup mechanism for the timeliness correction coefficient table is established: All data is exported to read-only storage every morning at midnight. The backup file includes metadata such as table creation date, last update time, and number of data records. The table access interface is configured with flow control: the maximum number of queries per second is capped at 100; queries exceeding this limit are automatically queued. Cyclic redundancy check codes are used during transmission to ensure data integrity.

[0121] Boundary tests are performed on the mapping relationship: Feature points such as the minimum value of the comprehensive aging factor (0.50), the maximum value (3.00), and boundary values ​​(1.00, 2.00) are selected to verify whether the output strength correction coefficient conforms to the material strength attenuation law. When the boundary test results deviate from the expected value by more than 1%, the aging correction coefficient table is reconstructed. All matching operations are performed in an independent and secure environment, physically isolated from the network.

[0122] Output formatting: Strength correction coefficient values ​​are converted to string format, retaining three decimal places, and prefixed with "K=". Result files are stored in directories based on the hash value of the ultra-high performance concrete specimen number. Each file contains three data items: comprehensive aging factor, strength correction coefficient, and matching status code. The user interface displays an animated diagram of the matching process in real time to assist technicians in understanding the mapping logic.

[0123] S6. Multiply the first compressive strength test result by the strength correction factor, and output the calibrated compressive strength value. The specific implementation is as follows:

[0124] The first compressive strength test result obtained through the compression test is retrieved from the compression test data storage. This result is in double-precision floating-point format, in megapascals (MPa), with a value range between 150.0 and 250.0 MPa. Before retrieval, a data validity check is performed: the test result is checked to ensure it falls within the preset material strength range. If it is below 100.0 MPa or above 300.0 MPa, a data review process is triggered. The data record of the first compressive strength test result includes the test timestamp, load-displacement curve feature points, and the associated ultra-high performance concrete specimen number. Data retrieval is performed by searching the latest test record by specimen number through a database query interface, with a query frequency limited to no more than 10 requests per second.

[0125] The intensity correction coefficient value selected from the timeliness correction coefficient table is retrieved from the output of the timeliness correction coefficient table. This value is stored in double-precision floating-point format, retaining three decimal places. The retrieval operation requires version consistency verification: check if the generation time of the intensity correction coefficient value is later than the calculation time of the associated comprehensive timeliness factor. If the time order is reversed, the table lookup process is retried. The intensity correction coefficient value includes a confidence level marker and matching method record, with a typical value range between 0.90 and 1.15. The data acquisition channel adopts a dual-link redundancy design; in the event of a primary link failure, it automatically switches to the backup link, with a switching latency controlled within 200 milliseconds.

[0126] The first compressive strength test result is multiplied by the strength correction factor. The specific calculation process is as follows: two double-precision floating-point numbers are input into the multiplier, which executes the IEEE 754 floating-point multiplication standard. Unit consistency is confirmed before calculation: the compressive strength unit is megapascals (MPa), and the strength correction factor is a dimensionless value, ensuring the multiplication result remains in MPa. The calculation process retains six intermediate precision decimal places, and the final result is rounded to three decimal places. For example, when the first compressive strength test result is 185.6 MPa and the strength correction factor is 1.072, the multiplication process is 185.6 × 1.072 = 198.9632, which, after rounding, yields 198.963 MPa.

[0127] The multiplication operation is equipped with an anomaly protection mechanism: when the intensity correction coefficient is less than 0.85 or greater than 1.25, the calculation is automatically frozen and a parameter out-of-bounds alarm is issued; when the product of input values ​​exceeds 300.0 MPa, a high-precision calculation mode (extended to eight decimal places) is activated. The hardware temperature is monitored in real time during the calculation process; if the arithmetic unit temperature exceeds 85 degrees Celsius, the calculation is paused until it cools down to below 65 degrees Celsius. Each calculation generates a unique transaction ID, which is bound to the operator's identification code and recorded in the audit log.

[0128] The calculated result obtained from the multiplication operation is output as the calibrated compressive strength value. The output format is a structured data object, containing four elements: numerical value, unit, confidence level, and timestamp. The output channel adopts a dual-buffering mechanism: the main buffer immediately outputs to the display terminal, while the backup buffer writes to non-volatile memory. The calibrated compressive strength value is automatically associated with the ultra-high performance concrete specimen file, overwriting the fields of the original test results. Before output, a data rationality check is performed: it checks whether the calibrated value is within 90% to 115% of the original value; if it exceeds this range, an anomaly flag is added.

[0129] The calibrated compressive strength values ​​are stored using version control, retaining historical versions with each update. The storage format is a JSON document, including the calibration calculation time, a snapshot of the input parameters, and a summary of the calculation process. When output to the test report, it is converted to a table format: the first column is the ultra-high performance concrete specimen number, the second column is the original compressive strength value, the third column is the strength correction factor, and the fourth column is the calibrated compressive strength value. Footnotes are added below the table explaining the calibration basis and confidence level.

[0130] Establish an output verification mechanism: One result is randomly selected from every 10 calibrations for manual recalculation using an independent calculation tool. System calibration is triggered when the result deviates from the system output by more than 0.5%. The verification process records the difference value, recalculation time, and verification personnel information. When the calibrated compressive strength value is transmitted to the enterprise resource planning system, a digital signature is added to ensure data integrity.

[0131] After data output, the monitoring process is activated: when the compressive strength values ​​of five consecutive calibrated specimens fluctuate by more than 15%, a material homogeneity analysis report is automatically generated. The output interface dynamically displays visualization charts of the calibration process: the left-hand bar chart compares the original strength values ​​with the calibrated strength values, and the right-hand trend chart shows the change history of the strength correction coefficient. Before confirming the output, the operator must perform a final review, and the review action is recorded along with the operation time and the operator's employee number.

[0132] The application range of the calibrated compressive strength value is controlled as follows: If the value is lower than 90% of the design strength, it is automatically marked as a non-conforming product; if the value is higher than 120% of the design strength, material optimization analysis is triggered. The output file naming convention is: Specimen Number_Calibration Strength_Date.TXT. File storage directories are established by month and year, with a retention period of no less than 10 years. The file header information includes the calibration standard, equipment number, and environmental temperature and humidity records.

[0133] Hardware-level protection is implemented for multiplication operations: the arithmetic unit performs self-calibration every 24 hours, using a standard resistor network for testing. If the self-calibration error exceeds 0.01%, it automatically switches to a backup arithmetic unit. A cyclic redundancy check (CRC) code is added during the calculation process; if the check fails, the calculation is re-executed. A time watermark is added to the output data packets to prevent data tampering.

[0134] Before publishing calibrated compressive strength values, compliance checks are performed: the displayed value range is controlled between 0.0 and 999.9 MPa; values ​​outside this range are automatically converted to scientific notation; negative values ​​are automatically converted to zero and marked as abnormal. The publishing system has an approval process: after confirmation by a junior engineer, it is submitted to a senior engineer for secondary review, and both signatures are required for validity. Finally, the data is synchronized to a cloud storage platform for off-site disaster recovery backup.

[0135] Establish an output data traceability chain: The calibrated compressive strength value allows for reverse lookup to original temperature records, image analysis data, comprehensive timeliness factors, and other parameters throughout the entire process. The traceability query response time is designed to be no more than 3 seconds, and batch data export is supported. Each data release generates a quality report, including calculated stability indicators, data timeliness scores, and system availability statistics.

[0136] In the ultra-high performance concrete strength test of this embodiment, steps S1-S6 collaboratively construct a strength calibration system based on the time-varying properties of the material. Compared with conventional methods that simply record test time or a single failure mode, this scheme establishes a dynamic correlation model between multi-dimensional aging influence mechanisms and material damage characteristics. Specifically, by accurately capturing the time interval from the end of high-temperature curing to the strength test (S1-S2), and combining the quantitative characteristics of fiber-matrix interface failure during the failure process (S3) and the fractal analysis of crack network complexity (S3), a comprehensive aging factor (S4) is constructed to characterize the time-varying evolution law of material performance. Furthermore, through the aging correction coefficient table (S5), a mapping is established between the microscopic damage mechanism and the macroscopic strength decay, ultimately achieving dynamic calibration of compressive strength (S6).

[0137] This solution addresses the problem of strength testing bias caused by neglecting the accumulation of internal material damage and time-varying behavior in existing technologies.

[0138] Quantification of the impact of aging: By linking time intervals with damage characteristics, this method overcomes the shortcomings of traditional methods in assessing the effects of aging after maintenance. For example, the fiber pull-out ratio directly reflects the degree of decay of fiber reinforcement over time, while the fractal dimension quantifies the aging sensitivity of crack propagation.

[0139] Improved calibration accuracy: The strength correction coefficient is derived from the damage-strength attenuation mapping relationship (S5) established by a large number of experiments, which more objectively reflects the true performance of the material compared with empirical correction formulas.

[0140] Process traceability: A closed-loop data chain is formed from temperature monitoring (S1), damage imaging (S2-S3) to the calculation process (S4-S6) to ensure the verifiability of calibration results.

[0141] This scheme achieves a complete technical path from microscopic damage evolution to macroscopic strength calibration through multi-step collaboration, providing a more reliable evaluation basis for predicting the service life of ultra-high performance concrete in engineering.

[0142] Example 2: Figure 2 A schematic diagram of the performance testing system for ultra-high performance concrete of the present invention is provided. The performance testing system for ultra-high performance concrete includes the following modules:

[0143] The cooling monitoring module is used to record the first time point when the ultra-high performance concrete specimens completed room temperature cooling after high-temperature curing.

[0144] The failure analysis module is used to place the specimen in the press for the first compressive strength test, simultaneously acquire image data of the specimen failure process, and record the second time point at the start of the test;

[0145] The damage quantification module is used to analyze the fragment morphology characteristics in image data, calculate the proportion of fiber pull-out fragments to the total fragment area, and extract the damage crack network and calculate its fractal dimension.

[0146] The time fusion module is used to calculate the time interval between the second time point and the first time point, and to generate a comprehensive time aging factor by combining the proportion of fiber pull-out fragments to the total fragment area and the fractal dimension of the damaged crack network.

[0147] The coefficient mapping module is used to select the corresponding intensity correction coefficient from the preset timeliness correction coefficient table based on the comprehensive timeliness factor;

[0148] The strength output module is used to multiply the first compressive strength test result by a strength correction factor and output the calibrated compressive strength value.

[0149] All calculations involved in the embodiments are 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 performance testing method for ultra-high performance concrete, characterized in that, Includes the following steps: S1. Record the first time point when the ultra-high performance concrete specimens after high-temperature curing complete room temperature cooling. S2. Place the specimen in the press for the first compressive strength test, simultaneously collect image data of the specimen failure process, and record the second time point at the start of the test; S3. Analyze the fragment morphology characteristics in the image data, calculate the proportion of fiber-pulled fragments to the total fragment area, and extract the damage crack network and calculate its fractal dimension. S4. Calculate the time interval between the second time point and the first time point, and generate a comprehensive aging factor by combining the proportion of fiber-pulled fragments to the total fragment area and the fractal dimension of the damaged crack network. S5. Select the corresponding intensity correction coefficient from the preset timeliness correction coefficient table based on the comprehensive timeliness factor; S6. Multiply the first compressive strength test result by the strength correction factor and output the calibrated compressive strength value.

2. The performance testing method for ultra-high performance concrete according to claim 1, characterized in that, Record the first time point at which the ultra-high performance concrete specimens completed room temperature cooling after high-temperature curing, including: Temperature sensors were placed on the surface of ultra-high performance concrete specimens; The surface temperature of ultra-high performance concrete specimens was continuously monitored using temperature sensors. Determine whether the ultra-high performance concrete specimens have completed room temperature cooling; Record the moment when the room temperature cooling is completed as the first time point.

3. The performance testing method for ultra-high performance concrete according to claim 2, characterized in that, When the surface temperature reaches the ambient room temperature and remains stable, the ultra-high performance concrete specimen is considered to have completed room temperature cooling.

4. The performance testing method for ultra-high performance concrete according to claim 2, characterized in that, The specimen was placed in a press for the first compressive strength test. Simultaneously, video data of the specimen's failure process was acquired, and the second time point at the start of the test was recorded, including: Industrial cameras were placed in the loading area of ​​the press. The press is started to apply a load to the ultra-high performance concrete specimen until the specimen fails. The surface changes of ultra-high performance concrete specimens under load were continuously captured using industrial cameras. Record the moment when the press starts loading as the second time point; Save continuous images of the entire failure process of ultra-high performance concrete specimens as image data.

5. The performance testing method for ultra-high performance concrete according to claim 4, characterized in that, Analyze the morphological characteristics of fragments in the image data, calculate the proportion of fiber-pulled fragments to the total fragment area, and simultaneously extract the fracture crack network and calculate its fractal dimension, including: Extracting fragment images of ultra-high performance concrete specimens after failure from image data; Based on the difference in gray values ​​of exposed fiber areas in fragment images, the regions where fibers have been pulled out of fragments are segmented and identified. Calculate the ratio of the area of ​​the fiber-pulled-out fragment region to the total area of ​​the fragment image, and use this ratio as the proportion of the fiber-pulled-out fragment to the total fragment area. Extracting crack images of ultra-high performance concrete specimens after failure from image data; Binarization of the crack image yields a binary crack network map; The fractal dimension of the broken crack network is obtained by calculating the fractal dimension of the binary image of the crack network using the box counting method.

6. The performance testing method for ultra-high performance concrete according to claim 5, characterized in that, The fractal dimension of the crack network binary image was calculated using the box counting method, and the fractal dimension of the damaged crack network was obtained as follows: The binary image of the crack network is divided into square grids of different side lengths; the number of grids covering crack pixels in each grid of side length is counted; a linear relationship is fitted between the logarithm of the grid side length and the logarithm of the number of covering grids; the absolute value of the slope of the linear relationship is used as the fractal dimension of the crack network.

7. The performance testing method for ultra-high performance concrete according to claim 5, characterized in that, The time interval between the second time point and the first time point is calculated. A comprehensive aging factor is generated by combining the proportion of fiber-pulled fragments to the total fragment area and the fractal dimension of the damaged crack network, including: The time interval is obtained by subtracting the time value of the first time point from the time value of the second time point. Obtain the proportion of fiber-pulled fragments to the total fragment area calculated by analyzing image data; Obtain the fractal dimension of the damaged crack network calculated using the box counting method; Input the time interval, the proportion of fiber-pulled fragments to the total fragment area, and the fractal dimension of the broken crack network into the weighted summation formula; Perform the calculation operation of the weighted summation formula and output the calculation result as the comprehensive timeliness factor.

8. The performance testing method for ultra-high performance concrete according to claim 7, characterized in that, Based on the comprehensive timeliness factor, select the corresponding intensity correction coefficient from the preset timeliness correction coefficient table, including: Obtain the comprehensive timeliness factor generated by the weighted summation formula; Locate the mapping relationship between the timeliness correction coefficient table and the comprehensive timeliness factor; Determine the intensity correction coefficient value corresponding to the comprehensive timeliness factor based on the mapping relationship; Select the corresponding strength correction factor value as the output result.

9. The performance testing method for ultra-high performance concrete according to claim 8, characterized in that, Multiply the first compressive strength test result by the strength correction factor to output the calibrated compressive strength value, including: Obtain the first compressive strength test result obtained through the press test; Obtain the strength correction factor value selected through the aging correction factor table; The value of the first compressive strength test result is multiplied by the value of the strength correction factor; The result of the multiplication operation is output as the calibrated compressive strength value.

10. A performance testing system for ultra-high performance concrete, used to implement the performance testing method for ultra-high performance concrete according to any one of claims 1-9, characterized in that, Includes the following modules: The cooling monitoring module is used to record the first time point when the ultra-high performance concrete specimens completed room temperature cooling after high-temperature curing. The failure analysis module is used to place the specimen in the press for the first compressive strength test, simultaneously acquire image data of the specimen failure process, and record the second time point at the start of the test; The damage quantification module is used to analyze the fragment morphology characteristics in image data, calculate the proportion of fiber pull-out fragments to the total fragment area, and extract the damage crack network and calculate its fractal dimension. The time fusion module is used to calculate the time interval between the second time point and the first time point, and to generate a comprehensive time aging factor by combining the proportion of fiber pull-out fragments to the total fragment area and the fractal dimension of the damaged crack network. The coefficient mapping module is used to select the corresponding intensity correction coefficient from the preset timeliness correction coefficient table based on the comprehensive timeliness factor; The strength output module is used to multiply the first compressive strength test result by a strength correction factor and output the calibrated compressive strength value.

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