A method and system for predicting the in-situ compressive strength of UHPC

Abstract

The application belongs to the technical field of concrete structure detection, and relates to a UHPC in-situ compressive strength prediction method and system. The method comprises the following steps: rebound testing, compression testing, headless bolt and headed bolt pull-out testing are performed on UHPC samples of different strength grades to obtain the rebound representative value, the compressive strength representative value, the headless bolt and headed bolt ultimate pull-out force representative value of each measurement area; the headless bolt nominal bond stress and the headed bolt nominal cone tensile stress are constructed; the response surface model is established and calibrated by taking the response variable and the and as the independent variables; the rebound testing, the headless bolt and headed bolt pull-out testing are performed on the UHPC member to be measured, and the measured and are substituted into the response surface model to obtain the UHPC member compressive strength prediction value. The application predicts the UHPC member compressive strength by using the difference between the double-mode pull-out failure modes and combining the rebound value, and improves the prediction accuracy.

Description

Technical Field

[0001] This invention belongs to the field of concrete structure testing technology, specifically relating to a method and system for predicting the in-situ compressive strength of UHPC. Background Technology

[0002] Ultra-high performance concrete (Ultra High Performance Concrete (UHPC) is increasingly widely used in bridge engineering, marine engineering, and protective structures due to its excellent mechanical properties and durability. In-situ rapid estimation of compressive strength is of great significance for the quality assessment of UHPC structures. However, there is currently no mature technology for in-situ compressive strength testing of UHPC.

[0003] Currently, the strength testing of ordinary strength grade concrete structures is usually carried out using the traditional pull-out method, which estimates the strength based on empirical formulas established from the ultimate pull-out force and compressive strength of a single bolt (headed bolt or headless bolt). However, this traditional pull-out method has the following shortcomings: 1) Usually, either headed bolts or headless bolts are used, but the difference in the pull-out failure modes of the two types of bolts is not distinguished. The pull-out of headed bolts causes the concrete structure to exhibit a cone tensile failure mode, while the pull-out of headless bolts causes the concrete structure to exhibit a bond-slip failure mode. 2) The coupling model was not effectively integrated with the pull-out factors. Traditional pull-out methods usually carry out pull-out tests with different diameters and anchoring depths. However, when establishing the model, a univariate regression model is established according to the working conditions of different anchoring depths and different diameters. The formula is generally a linear equation, and the model has poor adaptability. 3) It is not effectively integrated with other testing methods; for example, the rebound method is closely related to compressive strength, but the traditional pull-out method usually does not model the rebound value and pull-out force together.

[0004] Furthermore, a review of existing research findings, industry standards, and publicly available patents reveals that current UHPC in-situ strength measurement technology still faces numerous research gaps and technical shortcomings, as explained below: 1) Patent CN110553901B provides a set of anchor devices for testing the strength of reactive powder concrete by the post-installation pull-out method, which is characterized by high strength, full glue injection, simple structure and convenient operation. The structure is simple, and the anchoring end of the anchor adopts a pointed shape instead of the previous flat head type; however, this method mainly focuses on the post-installation method device and operation level. 2) The Technical Specification for Testing Concrete Strength by Pull-out Method (CECS 69:2011) provides the pull-out method, the pre-embedded pull-out method, and the corresponding unified strength measurement curve, expanding the application scope to compressive strength not exceeding 80MPa. When performing strength conversion, this method uses the least squares method for regression analysis, establishing linear relationship models between pull-out force and concrete compressive strength for both the pull-out method and the pre-embedded pull-out method. It also stipulates that the strength measurement curve is limited to the strength range of the regression data and cannot be extrapolated. Therefore, on the one hand, it does not mean that this linear model is applicable to UHPC, and on the other hand, the prediction method is relatively simple. 3) Patent CN103398899A discloses a post-installed pull-out method for testing the compressive strength of mountain sand concrete. It provides the test method and obtains a linear strength test curve, but specifies that the range is only applicable to C7.5 to C65. Although it provides a special strength test curve for mountain sand concrete, it is essentially the same as the method in the current standard. 4) The master's thesis from Hunan University, "Experimental Study on the Test of Compressive Strength of Ultra-High Performance Concrete by Pull-out Method", presents the strength measurement curves of UHPC under the pre-installed pull-out method (i.e., pre-embedded pull-out method) and the post-installed pull-out method. The study covers different pull-out methods, different strength grades, and different anchoring depths. However, the strength measurement curves proposed by this method still use the least squares method for regression analysis to establish a linear model between pull-out force and concrete compressive strength, which is essentially the same as the method in the current specifications. Summary of the Invention

[0005] To address the shortcomings of related technologies, this invention provides a method and system for predicting the in-situ compressive strength of UHPC, which aims to predict the compressive strength of UHPC components by utilizing the difference in pull-out failure modes of headless bolts and headed bolts and combining it with springback values, thereby improving the prediction accuracy of the in-situ compressive strength of UHPC.

[0006] This invention provides a method for predicting the in-situ compressive strength of UHPC, comprising the following steps: S1. Obtain the calibration sample dataset, including: performing rebound and compression tests on multiple UHPC specimens of different strength grades to obtain the representative rebound value for each test area. and representative value of compressive strength For each UHPC specimen, pull-out tests were conducted with headless bolts and headed bolts of different diameters at different anchorage depths to obtain representative values ​​of the ultimate pull-out force of the headless bolts. Representative value of ultimate pull-out force of headed bolt ; S2, structural stress characteristic value, including: nominal bond stress of headless bolts according to equation (4). According to equation (5), the nominal cone tensile stress of the headed bolt is constructed. ;in, The diameter of the bolt. For bolt anchorage depth, Shape parameters characterizing the lateral expansion of the fracture surface of a cone; (4); (5); S3. Establish and calibrate the response surface model, including: by For response variables, with , , Using the independent variable, we establish the power-law response surface model shown in equation (6), and after taking its natural logarithm, we obtain the log-linear response surface model shown in equation (7); where, , , , These are the parameters to be calibrated; (6); (7); A two-layer nested optimization strategy is adopted, based on the calibration sample dataset, to search for shape parameters through the outer loop. The inner layer uses the least squares method to fit a log-linear response surface model, and leaves-one-out cross-validation is used to determine the coefficients of determination. Determine the optimal shape parameters based on the criterion of maximization. and the corresponding parameters to be calibrated , , , This completes the calibration of the response surface model; S4. On-site in-situ compressive strength prediction, including: conducting springback tests, pull-out tests of headless bolts and headed bolts on the UHPC component to be tested, and obtaining representative springback values. The representative value of the ultimate pull-out force of headless bolts Representative value of ultimate pull-out force of headed bolt Substitute this value into the calibrated response surface model to obtain the predicted compressive strength of the UHPC component to be tested.

[0007] In some embodiments, the installation method of the headed bolts and headless bolts on the UHPC sample in step S1 is the same as the installation method of the headed bolts and headless bolts on the UHPC component to be tested in step S4; the installation method is one of the bolt pre-embedding method or the bolt post-installation method.

[0008] In some embodiments, in step S1, when performing the pull-out test, at least two different diameter headless bolts and headed bolts are used, and the anchoring depth is at least three different.

[0009] In some embodiments, in step S1, when performing a compressive strength test on the UHPC specimen, there are no fewer than four repeated compressive strength values ​​for each test area; a two-sided Grubbs test is performed on the repeated compressive strength values ​​for each test area, outliers are removed, and the arithmetic mean of the remaining compressive strength values ​​is taken as the representative compressive strength value for each test area. .

[0010] In some embodiments, during steps S1 and S4, when conducting the pull-out test, there are at least four repeated values ​​of the ultimate pull-out force for both the headless bolt and the headed bolt. A two-sided Grubbs test is performed once on the repeated values ​​of the ultimate pull-out force for both the headless bolt and the headed bolt in each test area. The arithmetic mean of the remaining data after removing outliers is taken as the representative value of the ultimate pull-out force for the headless bolt in each test area. Representative value of ultimate pull-out force of headed bolt .

[0011] In some embodiments, during steps S1 and S4, at least 16 rebound values ​​are measured for each test area during the rebound test. The three largest and three smallest rebound values ​​are discarded, and the arithmetic mean of the remaining rebound values ​​is taken as the representative rebound value for each test area. .

[0012] In some embodiments, in step S3, the shape parameters are searched through an outer loop. When, set shape parameters Located in the candidate interval Inside, and These are the half-apex angles of the cone's failure surface. The minimum and maximum values, with a preset step size. Iterate through the data to generate multiple candidate values ​​for shape parameters. , When fitting a log-linear response surface model using the least squares method in the inner layer, for each Perform the following operations: Calculate all according to equation (5) Nominal conical tensile stress of the headed bolt of a calibration sample ; Based on equation (7), construct the design matrix. and response vector , Including independent variables , , and intercept term The regression coefficients are solved using the least squares method. ,in, ; Calculate the leave-one-out cross-validation coefficient of determination according to equation (10). ,in, For the first The logarithmic value of the representative compressive strength of a calibrated sample. To remove the first After a set of calibrated samples, the response surface model... The predicted value, This is the mean of the representative compressive strength values ​​of all calibrated samples; (10); Then, select The largest As the optimal shape parameter, and at the same time obtain the corresponding regression coefficients. Thus, the optimal shape parameters are determined. and parameters to be calibrated , , , .

[0013] In some embodiments, a response surface methodology (RSM) and diagnostic steps are also included. This step is performed after step S3 and before step S4. Specifically, based on the calibrated response surface model, the coefficient of determination is calculated. The calculation and comparative analysis of the predicted and measured values ​​of compressive strength were conducted to verify whether the calibrated response surface model met the preset prediction accuracy requirements.

[0014] This invention also provides a UHPC in-situ compressive strength prediction system for performing the aforementioned UHPC in-situ compressive strength prediction method; the UHPC in-situ compressive strength prediction system includes: The test module is used to perform compressive strength tests on all UHPC specimens to obtain representative values ​​of compressive strength for each test area; it is also used to perform springback tests, pull-out tests on headless bolts and headed bolts on all UHPC specimens and UHPC components to be tested to obtain representative values ​​of springback, ultimate pull-out force of headless bolts, and ultimate pull-out force of headed bolts for each test area. The model building module is used to construct the stress characteristic values ​​of the nominal bond stress of the headless bolt and the nominal conical tensile stress of the headed bolt. The nominal bond stress of the headless bolt, the nominal conical tensile stress of the headed bolt, and the springback representative value are used as independent variables, and the compressive strength representative value is used as the response variable to establish a response surface model. The model calibration module is used to calibrate the response surface model based on the calibration sample dataset by searching for shape parameters in the outer loop, fitting a log-linear response surface model using the least squares method in the inner loop, and using the maximum coefficient of determination in leave-one-out cross-validation as the criterion. The prediction module is used to substitute the representative values ​​of springback, ultimate pull-out force of headless bolts, and ultimate pull-out force of headed bolts of the UHPC component to be tested in the field into the calibrated response surface model in order to obtain the predicted value of the compressive strength of the UHPC component to be tested.

[0015] Based on the above technical solution, the UHPC in-situ compressive strength prediction method and system in this embodiment of the invention utilizes the difference in pull-out failure modes of headless bolts and headed bolts, combined with rebound values, to predict the compressive strength of UHPC components. A response surface model is constructed and calibrated, thereby determining the prediction formula for the in-situ compressive strength of UHPC. During on-site testing, only rebound tests, headless bolt and headed bolt pull-out tests on the UHPC component to be tested are required. The obtained representative values ​​of rebound, headless bolt ultimate pull-out force, and headed bolt ultimate pull-out force are substituted into the calibrated response surface model to calculate the predicted compressive strength of the UHPC component to be tested. This improves the prediction accuracy of the in-situ compressive strength of UHPC, thus solving the technical defects of existing UHPC in-situ compressive strength testing technologies such as immaturity, poor applicability of traditional concrete pull-out testing methods, low prediction accuracy, weak model generalization ability, and single detection dimension. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart of the UHPC in-situ compressive strength prediction method of the present invention; Figure 2 This is a comparison chart of the measured and predicted values ​​of the compressive strength of the UHPC sample during the calibration stage of this invention. Detailed Implementation

[0017] The technical solutions in 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 a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] In the description of this invention, it should be understood that the terms "center", "lateral", "longitudinal", "upper", "lower", "top", "bottom", "inner", "outer", "left", "right", "front", "rear", "vertical", "horizontal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0019] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0020] refer to Figure 1 As shown, the present invention provides a method for predicting the in-situ compressive strength of UHPC, which includes the following steps S1 to S4.

[0021] Step S1: Obtain the calibration sample dataset, which includes: performing rebound and compression tests on multiple UHPC specimens of different strength grades to obtain the representative rebound value for each test area. and representative value of compressive strength For each UHPC specimen of different strength grades, different diameters were used. Headless bolts and headed bolts with different anchoring depths The pull-out test was conducted to obtain the representative value of the ultimate pull-out force of the headless bolt. Representative value of ultimate pull-out force of headed bolt Specifically, during the pull-out test, at least two different diameters of headless bolts and headed bolts are used, and the anchorage depth is at least three different types; the strength grade range in this step should cover the strength grades that may be involved in subsequent predictions. This yields the results obtained from... The calibration sample dataset consists of several calibration sample data points, each including the bolt diameter. Anchoring depth The representative value of the ultimate pull-out force of headless bolts Representative value of ultimate pull-out force of headed bolt Rebound value and representative value of compressive strength .

[0022] To further clarify, when conducting rebound tests, the methods specified in the national standard "Technical Standard for Field Testing of Concrete Structures" (GB / T 50784-2013) should be followed. At least 16 rebound values ​​should be measured for each test area. The three highest and three lowest rebound values ​​should be discarded, and the arithmetic mean of the remaining rebound values ​​should be calculated as the representative rebound value for each test area. .

[0023] To further clarify, when conducting compressive strength tests on UHPC specimens, at least four repeatable values ​​of compressive strength were obtained for each test area. Those skilled in the art will understand that at least four standard specimens (cubic or cylindrical) under identical conditions were prepared for each test area, and the compressive strength of these standard specimens is the compressive strength of the UHPC specimen's test area. When conducting pull-out tests, at least four repeatable values ​​of the ultimate pull-out force for both headless bolts and headed bolts were obtained. The repeatable values ​​of compressive strength, ultimate pull-out force for headless bolts, and ultimate pull-out force for headed bolts for each test area were subjected to a two-sided Grubbs test. After removing outliers, the arithmetic mean of the remaining data was taken as the representative compressive strength value for each test area. The representative value of the ultimate pull-out force of headless bolts Representative value of ultimate pull-out force of headed bolt .

[0024] The Grubbs two-tailed test is known to those skilled in the art, and its steps are briefly described below: Record a set of repeated values ​​(compressive strength, ultimate pull-out force of headless bolt or ultimate pull-out force of headed bolt) for each test area as follows: , , , Calculate its mean according to equation (1) Then, the standard deviation is calculated according to equation (2). ; (1); (2); Calculate the Grubbs statistic according to equation (3). ; (3); Based on sample size and significance level Consult the standard table to obtain the critical value for the Grubbs two-sided test. ;Will and If a comparison is made, Then it will be compared with the mean. The measurement with the largest absolute deviation is considered an outlier and discarded. If all measured values ​​are not found, the arithmetic mean of the remaining data after removing outliers is taken as the representative value for that test area (representative value of compressive strength, representative value of pull-out force of headless bolt, or representative value of pull-out force of headed bolt). This eliminates outliers caused by random experimental errors or operational mistakes, ensuring the reliability of the sample data.

[0025] It should also be noted that the installation method of headed bolts and headless bolts on the UHPC sample in step S1 must be consistent with the installation method of headed bolts and headless bolts on the UHPC component to be tested in subsequent step S4; the installation method is either the bolt pre-embedding method or the bolt post-installation method.

[0026] Step S2, constructing stress characteristic values, which include: The nominal bond stress of the headless bolt is constructed according to equation (4). It corresponds to the adhesive slip failure mode when the headless bolt is pulled out; according to formula (5), the nominal cone tensile stress of the headed bolt is constructed. This corresponds to the conical tensile failure mode when the headed bolt is pulled out; and The construction is designed to... and Converted into stress eigenvalues ​​with physical meaning; where, The diameter of the bolt. For bolt anchorage depth, A dimensionless shape parameter characterizing the lateral expansion of the cone's failure surface; specifically, , The half-apex angle of the cone-shaped failure surface of the concrete structure after the headed bolt is pulled out; (4); (5).

[0027] Step S3: Establish and calibrate the response surface model, which includes the following sub-steps S31 to S32.

[0028] Sub-step S31: Using compressive strength as a representative value For response variables, with , , Using the independent variable, a power-law response surface model as shown in equation (6) is established. Taking the natural logarithm of both sides of the equation of the power-law response surface model, a log-linear response surface model as shown in equation (7) is obtained; where, , , , These are the parameters to be calibrated; (6); (7).

[0029] Sub-step S32: Employing a double-nested optimization strategy, based on the calibrated sample dataset, the shape parameters are searched through the outer loop. The inner layer uses the least squares method to fit a log-linear response surface model, and the shape parameters are... and parameters to be calibrated , , , Joint calibration was performed, and the coefficient of determination was determined using leave-one-out cross-validation. Maximum as the criterion ( The larger the value, the stronger the model's generalization and prediction ability; this determines the optimal shape parameters. and the corresponding parameters to be calibrated , , , This completes the calibration of the response surface model.

[0030] To further explain, the shape parameters are searched through the outer loop. When, set shape parameters Located in the candidate interval Inside, and These are the half-apex angles of the cone's failure surface. The minimum and maximum values, with a preset step size. Traverse the candidate interval and generate multiple candidate values ​​for shape parameters. , When fitting a log-linear response surface model using the least squares method in the inner layer, for each candidate value of the shape parameter... Perform the following operations: Calculate all according to equation (5) Nominal conical tensile stress of the headed bolt of a calibration sample ,in, The numbering of the calibration sample, ; Based on equation (7), construct the design matrix. and response vector , Including independent variables , , and intercept term , It can be expressed as equation (8), The first column is a vector of all 1s (corresponding to the intercept term). (which is a constant term). It can be expressed as equation (9), and thus equation (7) can be expressed in matrix form. ,in, The regression coefficients were solved using the least squares method. ; (8); (9); Calculate the leave-one-out cross-validation coefficient of determination according to equation (10). ,in, For the first The logarithmic value of the representative compressive strength of a calibrated sample. To remove the first After a set of calibrated samples, the response surface model... The predicted value, This is the mean of the representative compressive strength values ​​of all calibrated samples; (10); Then, select The largest As the optimal shape parameter, and at the same time obtain the corresponding regression coefficients. Thus, the optimal shape parameters are determined. and parameters to be calibrated , , , This completes the calibration of the response surface model, thus ultimately determining the prediction formula for the in-situ compressive strength of UHPC.

[0031] It should be noted that the shape parameters characterizing the lateral expansion of the cone's failure surface are... The introduction of [the technology / method] and the use of data-driven methods for shape parameter [analysis / parameter analysis] The optimization avoids the subjective setting of the half-apex angle of the cone's failure surface. The resulting systematic errors improve the scientific rigor and adaptability of the model.

[0032] Step S4, in-situ compressive strength prediction, includes: performing rebound tests, pull-out tests of headless bolts and headed bolts on the test area of ​​the UHPC component to be tested, and obtaining representative rebound values. The representative value of the ultimate pull-out force of headless bolts Representative value of ultimate pull-out force of headed bolt Substitute this value into the calibrated response surface model to obtain the predicted compressive strength of the UHPC component to be tested.

[0033] It should be noted that the methods for obtaining the representative values ​​of springback in the test area of ​​the UHPC component under test, the representative values ​​of the ultimate pull-out force of the headless bolt, and the representative values ​​of the ultimate pull-out force of the headed bolt in step S4 are the same as those for obtaining the representative values ​​of springback in each test area of ​​the UHPC sample, the representative values ​​of the ultimate pull-out force of the headless bolt, and the representative values ​​of the ultimate pull-out force of the headed bolt in step S1.

[0034] The above illustrative embodiments propose a method for predicting the in-situ compressive strength of UHPC by fully utilizing the differences in dual-mode pull-out failure modes, combining springback values, and having clear physical meaning. It pioneers a dual-mode pull-out detection method combining headless bolts and headed bolts, constructing stress characteristic values ​​corresponding to the conical tensile failure mode of headed bolts and the bond-slip failure mode of headless bolts, respectively, and incorporating them as independent variables into the response surface model. Simultaneously, the representative springback value is also incorporated as an independent variable into the response surface model, achieving an organic integration of pull-out detection and springback non-destructive testing. By utilizing pull-out and rebound data of headless and headed bolts with different diameters, anchoring depths, and strength grades, the response surface model is calibrated, thereby determining the prediction formula for the in-situ compressive strength of UHPC. During field testing, only rebound tests and pull-out tests of headless and headed bolts are required on the UHPC component to be tested to obtain the predicted compressive strength value. Therefore, through collaborative modeling of multi-source test data, the reliability and accuracy of the prediction results are effectively improved, making up for the current lack of mature in-situ UHPC strength measurement technology in the industry.

[0035] In some embodiments, the UHPC in-situ compressive strength prediction method further includes a response surface analysis and diagnostic step, which is performed after step S3 and before step S4. Specifically, based on the calibrated response surface model, the coefficient of determination is calculated. The calculation and comparative analysis of the predicted and measured values ​​of compressive strength are used to verify whether the calibrated response surface model meets the preset prediction accuracy requirements, thereby verifying the sufficiency and robustness of the model.

[0036] The present invention also provides a UHPC in-situ compressive strength prediction system for performing the aforementioned UHPC in-situ compressive strength prediction method; the UHPC in-situ compressive strength prediction system includes an experimental module, a model construction module, a model calibration module, and a prediction module.

[0037] The testing module is used to perform compressive strength tests on all UHPC specimens to obtain representative values ​​of compressive strength for each test area. It is also used to perform springback tests, headless bolt and headed bolt pull-out tests on all UHPC specimens and the UHPC components under test to obtain representative values ​​of springback, ultimate pull-out force of headless bolts, and ultimate pull-out force of headed bolts for each test area. The model building module is used to construct stress characteristic values ​​of the nominal bond stress of headless bolts and the nominal conical tensile stress of headed bolts. Using the nominal bond stress of headless bolts, the nominal conical tensile stress of headed bolts, and the representative springback value as independent variables, and the representative compressive strength value as the response variable, a response surface model is established. The model calibration module is used to calibrate the response surface model based on the calibration sample dataset. This is achieved through an outer loop to search for shape parameters, an inner least squares method to fit a log-linear response surface model, and the maximum coefficient of determination in leave-one-out cross-validation. The prediction module is used to substitute the representative values ​​of springback, ultimate pull-out force of headless bolts, and ultimate pull-out force of headed bolts of the UHPC component to be tested in the field into the calibrated response surface model to obtain the predicted value of the compressive strength of the UHPC component to be tested.

[0038] The following describes the main process and technical effects of using the present invention for in-situ compressive strength prediction of UHPC based on specific embodiments.

[0039] Step S1: Obtain the calibration sample dataset: The relevant test parameters were set as follows: strength grades included C100, C120, C140, C160, and C180; UHPC specimen curing periods were 28 days and 56 days; bolt types included headed bolts (head embedded) and headless bolts (plain round); bolt diameter... Available in 12mm and 14mm diameters; bolt anchorage depth Three sizes are available: 20mm, 25mm, and 30mm; the headless bolts and headed bolts are installed on the UHPC specimens using a post-installation method. Pull-out tests were conducted on headless bolts and headed bolts in each test area, repeated 6 times each. The ultimate pull-out force of the headless bolt and the ultimate pull-out force of the headed bolt were recorded each time. A two-sided Grubbs test was performed on the 6 repeated values ​​of the ultimate pull-out force of the headless bolt and the 6 repeated values ​​of the ultimate pull-out force of the headed bolt. The arithmetic mean of the remaining data after removing outliers was taken to obtain the representative value of the ultimate pull-out force of the headless bolt. Representative value of ultimate pull-out force of headed bolt ; Rebound tests were conducted in each test area to obtain 16 rebound values. The three highest and three lowest rebound values ​​were removed, and the arithmetic mean of the remaining rebound values ​​was taken to obtain the representative rebound value for each test area. ; Four standard specimens under identical conditions were prepared for each test area and subjected to compressive strength tests to obtain four raw compressive strength values. A two-tailed Grubbs' test was performed on the four replicate compressive strength values, and the arithmetic mean of the remaining data after removing outliers was taken to obtain the representative compressive strength value. .

[0040] Step S2: Construct the nominal bond stress of the headless bolt. and nominal cone tensile stress of headed bolt .

[0041] Step S3: Using compressive strength as a representative value For response variables, with , , A response surface model is established with the independent variable as the variable; a two-layer nested optimization strategy is adopted, based on the calibration sample dataset, and the shape parameter is searched through the outer loop. The inner layer uses the least squares method to fit the response surface model, and leaves-one-out cross-validation is used to verify the coefficient of determination. Determine the optimal shape parameters based on the criterion of maximization. and the corresponding parameters to be calibrated , , , This completes the calibration of the response surface model. The calibration results of the parameters to be calibrated in the response surface model are shown in Table 1.

[0042] Table 1. Calibration results of parameters to be calibrated in the response surface model

[0043] After the response surface model was calibrated, response surface analysis and diagnostic steps were performed: Based on the calibrated response surface model, the predicted and measured values ​​of compressive strength for each test area were compared and analyzed, and the results are shown in Table 2. It can be seen that the deviation between the predicted and measured compressive strength values ​​for each test area during the calibration stage was between 9.25% and -9.55%, meeting the general requirement of ±10% prediction deviation. It can also be seen that age does not affect prediction accuracy. It is understandable that if the number of calibration samples increases, the prediction deviation will further decrease. (Reference) Figure 2 As shown in the figure, the purple line represents the case where the predicted compressive strength equals the measured compressive strength. The horizontal axis of each red dot represents the measured compressive strength, and the vertical axis represents the predicted compressive strength. The closer the red dot is to the purple line, the closer the predicted compressive strength is to the measured value, and the higher the accuracy of the prediction. Additionally, the coefficient of determination... The calculated result is 0.926, which indicates that the model fits the data very well; this verifies that the calibrated response surface model is robust and reliable.

[0044] Table 2. Measured and predicted compressive strength values ​​for each test area during the calibration phase.

[0045] Step S4: Prediction of in-situ compressive strength: Mark the test area on the surface of the UHPC component to be tested, grind it smooth, and remove the laitance. Sixteen testing points were selected within the test area to conduct rebound tests and obtain 16 rebound values. After removing the three maximum and three minimum values, the arithmetic mean of the remaining rebound values ​​was taken to obtain the representative rebound value. Pull-out tests of headless bolts and headed bolts were conducted within the test area, with each test repeated 6 times. The headless bolts and headed bolts were installed on the UHPC component under test using a post-installation method, consistent with the installation method in step S1. A two-sided Grubbs test was performed on the six repeated values ​​of the ultimate pull-out force of the headless bolts and the six repeated values ​​of the ultimate pull-out force of the headed bolts. The arithmetic mean of the remaining data after removing outliers was taken to obtain the representative value of the ultimate pull-out force of the headless bolts. Representative value of ultimate pull-out force of headed bolt ; Based on the optimal shape parameters determined in step S3 Using equations (4) and (5), calculate the nominal bond stress of the headless bolt. and nominal cone tensile stress of headed bolt ;Will , , Substituting the values ​​into the calibrated response surface model, the predicted compressive strength of the UHPC component under test is obtained.

[0046] To illustrate the technical effects of this invention, in-situ compressive strength prediction was performed on multiple UHPC components of the same age to obtain predicted compressive strength values. Standard specimens were then prepared under the same conditions for compressive strength testing to obtain measured compressive strength values. The predicted and measured compressive strength values ​​of the UHPC components were then compared and analyzed, as shown in Table 3. It can be seen that the deviation between the predicted and measured compressive strength values ​​of each UHPC component during the prediction stage is between 8.97% and -6.63%, meeting the general requirement of ±10% prediction deviation. Furthermore, the UHPC components used in Group 1 of Table 3 and the UHPC specimens used in Group 16 of Table 2 are UHPC molded parts produced in the same batch (including the same mix ratio, molding conditions, mixing process, and curing age). The standard specimens prepared by their retention are from the same batch, therefore the measured compressive strength values ​​of group 1 in Table 3 and group 16 in Table 2 are the same; similarly, the measured compressive strength values ​​of groups 2-5 in Table 3 and groups 17-20 in Table 2 are also the same; however, groups 6-15 in Table 3 use UHPC specimens prepared from different batches, therefore the standard specimens prepared by their retention also belong to different batches, thus the measured compressive strength values ​​of groups 6-15 in Table 3 are different from those of groups 1-5; the deviation between the predicted and measured compressive strength values ​​in Table 3 shows that, based on the method of this application, the prediction deviations of UHPC specimens prepared in the same batch (groups 1-5) or different batches (groups 6-15) as in the calibration stage meet the general requirements; thus, this invention can be applied to the prediction of in-situ compressive strength of UHPC, and the prediction accuracy is high.

[0047] Table 3. Measured and predicted compressive strength values ​​of the UHPC components under test during the prediction stage.

[0048] Through the description of several embodiments of the UHPC in-situ compressive strength prediction method and system of the present invention, it can be seen that the present invention has at least one or more of the following advantages: 1) This invention pioneers a dual-mode pull-out detection method combining headless bolts and headed bolts, fully distinguishing the differences between the two bolt pull-out failure modes. It constructs stress characteristic values ​​corresponding to the conical tensile failure mode of headed bolts and the bond-slip failure mode of headless bolts, respectively, and incorporates these values ​​as independent variables into the response surface model. This solves the problems of insufficient representativeness and poor strength prediction accuracy of traditional pull-out methods with single-mode detection. Furthermore, this invention overcomes the technical shortcomings of traditional pull-out methods with their single detection dimension by incorporating the representative value of springback as an independent variable into the response surface model, achieving an organic integration of pull-out detection and springback non-destructive testing. Through collaborative modeling of multi-source detection data, it effectively improves the reliability and accuracy of prediction results, compensating for the lack of mature UHPC in-situ strength measurement technology in the current industry, and significantly weakens the impact of UHPC curing age on prediction accuracy. It is suitable for situations in actual engineering where the on-site curing age may exceed the calibration range. 2) This invention integrates pull-out data and springback data of headless bolts and bolts with different diameters, anchoring depths and strength grades to construct a coupled response surface model. It fully explores and integrates the effective information of multi-dimensional detection data, which can effectively broaden the applicability of the model and significantly improve the generalization ability and engineering applicability of the model. 3) When conducting on-site compressive strength testing of UHPC components, this invention only requires performing springback tests, pull-out tests of headless bolts and headed bolts on the UHPC component to be tested. The obtained representative values ​​of springback, headless bolt ultimate pull-out force, and headed bolt ultimate pull-out force are substituted into the calibrated response surface model to calculate the predicted value of the compressive strength of the UHPC component to be tested. Therefore, the on-site testing process is simple, the calculation is convenient, and the application is highly practical, without the need for complex iterative calculations.

[0049] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0050] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for predicting the in-situ compressive strength of UHPC, characterized in that, Includes the following steps: S1. Obtain the calibration sample dataset, including: performing rebound and compression tests on multiple UHPC specimens of different strength grades to obtain the representative rebound value for each test area. and representative value of compressive strength For each UHPC specimen, pull-out tests were conducted with headless bolts and headed bolts of different diameters at different anchorage depths to obtain representative values ​​of the ultimate pull-out force of the headless bolts. Representative value of ultimate pull-out force of headed bolt ; S2, structural stress characteristic value, including: nominal bond stress of headless bolts according to equation (4). According to equation (5), the nominal cone tensile stress of the headed bolt is constructed. ;in, The diameter of the bolt. For bolt anchorage depth, Shape parameters characterizing the lateral expansion of the fracture surface of a cone; (4)； (5)； S3. Establish and calibrate the response surface model, including: by For response variables, with , , Using the independent variable, we establish the power-law response surface model shown in equation (6), and after taking its natural logarithm, we obtain the log-linear response surface model shown in equation (7); where, , , , These are the parameters to be calibrated; (6)； (7)； A two-layer nested optimization strategy is adopted, based on the calibration sample dataset, to search for shape parameters through the outer loop. The inner layer uses the least squares method to fit a log-linear response surface model, and leaves-one-out cross-validation is used to determine the coefficients of determination. Determine the optimal shape parameters based on the criterion of maximization. and the corresponding parameters to be calibrated , , , This completes the calibration of the response surface model; S4. On-site in-situ compressive strength prediction, including: conducting springback tests, pull-out tests of headless bolts and headed bolts on the UHPC component to be tested, and obtaining representative springback values. The representative value of the ultimate pull-out force of headless bolts Representative value of ultimate pull-out force of headed bolt Substitute this value into the calibrated response surface model to obtain the predicted compressive strength of the UHPC component to be tested.

2. The method for predicting the in-situ compressive strength of UHPC according to claim 1, characterized in that, The installation method of headed bolts and headless bolts on the UHPC sample in step S1 is the same as the installation method of headed bolts and headless bolts on the UHPC component to be tested in step S4; the installation method is one of the bolt pre-embedding method or the bolt post-installation method.

3. The method for predicting the in-situ compressive strength of UHPC according to claim 1, characterized in that, In step S1, when performing the pull-out test, at least two different diameters of headless bolts and headed bolts are used, and the anchoring depth is at least three different types.

4. The method for predicting the in-situ compressive strength of UHPC according to claim 1, characterized in that, In step S1, when performing compressive strength tests on the UHPC specimens, there are no fewer than four repeat values ​​of compressive strength for each test area; a two-sided Grubbs test is performed on the repeat values ​​of compressive strength for each test area, outliers are removed, and the arithmetic mean of the remaining compressive strength values ​​is taken as the representative value of compressive strength for each test area. .

5. The method for predicting the in-situ compressive strength of UHPC according to claim 1, characterized in that, In steps S1 and S4, during the pull-out test, there are at least four repeated values ​​of the ultimate pull-out force for both the headless bolt and the headed bolt. For each test area, a two-sided Grubbs test is performed on the repeated values ​​of the ultimate pull-out force for both the headless bolt and the headed bolt. The arithmetic mean of the remaining data after removing outliers is taken as the representative value of the ultimate pull-out force for the headless bolt in each test area. Representative value of ultimate pull-out force of headed bolt .

6. The method for predicting the in-situ compressive strength of UHPC according to claim 1, characterized in that, In steps S1 and S4, during the rebound test, at least 16 rebound values ​​are measured for each test area. The three largest and three smallest rebound values ​​are discarded, and the arithmetic mean of the remaining rebound values ​​is taken as the representative rebound value for each test area. .

7. The method for predicting the in-situ compressive strength of UHPC according to claim 1, characterized in that, In step S3, the shape parameters are searched through the outer loop. When, set shape parameters Located in the candidate interval Inside, and These are the half-apex angles of the cone's failure surface. The minimum and maximum values, with a preset step size. Iterate through the data to generate multiple candidate values ​​for shape parameters. , When fitting a log-linear response surface model using the least squares method in the inner layer, for each Perform the following operations: Calculate all according to equation (5) Nominal conical tensile stress of the headed bolt of a calibration sample ; Based on equation (7), construct the design matrix. and response vector , Including independent variables , , and intercept term The regression coefficients are solved using the least squares method. ,in, ; Calculate the leave-one-out cross-validation coefficient of determination according to equation (10). ,in, For the first The logarithmic value of the representative compressive strength of a calibrated sample. To remove the first After a set of calibrated samples, the response surface model... The predicted value, This is the mean of the representative compressive strength values ​​of all calibrated samples; (10)； Then, select The largest As the optimal shape parameter, and at the same time obtain the corresponding regression coefficients. Thus, the optimal shape parameters are determined. and parameters to be calibrated , , , .

8. The method for predicting the in-situ compressive strength of UHPC according to claim 1, characterized in that, It also includes a response surface analysis and diagnostic step, which is performed after step S3 and before step S4. Specifically, it involves calculating the coefficient of determination based on the calibrated response surface model. The calculation and comparative analysis of the predicted and measured values ​​of compressive strength were conducted to verify whether the calibrated response surface model met the preset prediction accuracy requirements.

9. A UHPC in-situ compressive strength prediction system, characterized in that, The method for predicting the in-situ compressive strength of UHPC as described in any one of claims 1 to 8; the UHPC in-situ compressive strength prediction system comprises: The test module is used to perform compressive strength tests on all UHPC specimens to obtain representative values ​​of compressive strength for each test area; it is also used to perform springback tests, pull-out tests on headless bolts and headed bolts on all UHPC specimens and UHPC components to be tested to obtain representative values ​​of springback, ultimate pull-out force of headless bolts, and ultimate pull-out force of headed bolts for each test area. The model building module is used to construct the stress characteristic values ​​of the nominal bond stress of the headless bolt and the nominal conical tensile stress of the headed bolt. The nominal bond stress of the headless bolt, the nominal conical tensile stress of the headed bolt, and the springback representative value are used as independent variables, and the compressive strength representative value is used as the response variable to establish a response surface model. The model calibration module is used to calibrate the response surface model based on the calibration sample dataset by searching for shape parameters in the outer loop, fitting a log-linear response surface model using the least squares method in the inner loop, and using the maximum coefficient of determination in leave-one-out cross-validation as the criterion. The prediction module is used to substitute the representative values ​​of springback, ultimate pull-out force of headless bolts, and ultimate pull-out force of headed bolts of the UHPC component to be tested in the field into the calibrated response surface model in order to obtain the predicted value of the compressive strength of the UHPC component to be tested.

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