A systematic error tracing method for drop hammer deflection apparatus calibration

Abstract

The present application relates to a kind of systematic error tracing method for drop hammer deflection meter calibration, the method first constructs a simulation database covering multiple working conditions, the database includes the simulation results of deflection value under different working conditions and related influencing factor data, by analyzing the data obtained in actual measurement of drop hammer deflection meter, in combination with the law in simulation database, accurately identify the error source existing in measured data, and quantitatively evaluate the influence of these error sources on deflection value measurement result.Finally, using Monte Carlo simulation method, simulate the deflection value distribution under the influence of different error sources, further reveal the error size and uncertainty of different error sources on measurement result.The error tracing method proposed in the present application has the advantages of system, accuracy and repeatability, can effectively provide optimization suggestions for calibration and use of drop hammer deflection meter, improve measurement accuracy and reliability.

Description

Technical Field

[0001] This invention relates to error tracing of a falling weight deflectometer, specifically a systematic error tracing method for the calibration of a falling weight deflectometer. Background Technology

[0002] Falling weight deflectometers (FWD) play a crucial role in road engineering, assessing the modulus and load-bearing capacity of pavement structural layers by simulating the impact of traffic loads on the pavement and recording its deformation. However, during calibration, the presence of systematic errors has become a major factor limiting its measurement accuracy.

[0003] Currently, the calibration of falling weight deflectometers, both domestically and internationally, mainly focuses on the technical and procedural aspects, ensuring equipment qualification through a series of standard operations. However, this calibration method often only focuses on whether the equipment is qualified, lacking in-depth analysis and tracing of potential errors. During the calibration process, various factors such as ambient temperature, humidity, sensor performance, and the accuracy of the calibration equipment can all affect the calibration results, challenging the stability and accuracy of the measurement results.

[0004] Although some studies have explored the calibration error of falling weight deflectometers, existing error tracing methods often analyze only a specific factor, lacking comprehensiveness and systematicity. This singular, fragmented analytical approach fails to fully reveal the source and magnitude of the error, resulting in inaccurate and incomplete error tracing results. Furthermore, in terms of error correction, existing techniques mostly remain at the level of simple parameter adjustments, lacking in-depth theoretical analysis and model building. Such correction methods often only reduce errors to a certain extent, failing to fundamentally solve the problem.

[0005] Therefore, in order to address the above problems, it is necessary to develop a systematic error tracing method for the calibration of falling weight deflectometers. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a systematic error tracing method for the calibration of falling weight deflectometers. This method should be able to comprehensively analyze various influencing factors and their interrelationships during the calibration process, achieve accurate error tracing, and provide effective error correction suggestions. Simultaneously, this method should also be able to implement a real-time monitoring and feedback mechanism to ensure the accuracy and reliability of the measurement results, providing strong support for the development of the road engineering field.

[0007] The technical solution adopted to achieve the purpose of this invention is a systematic error tracing method for the calibration of a falling weight deflectometer, which includes:

[0008] S1. Using finite element simulation software, different parameters were set to simulate the drop hammer deflection test under various working conditions.

[0009] S2. Establish a simulation database to record the simulation results of deflection values ​​under different working conditions and related influencing factors.

[0010] S3. Analyze the results of the simulation database and use sensitivity analysis to obtain the influence of a single influencing factor on the deflection value, as well as the influence of the interaction effect of different combinations of influencing factors on the deflection value.

[0011] S4. Compare the simulation results with the measured results, and use residual analysis to analyze the magnitude and distribution of the error;

[0012] S5. Analyze the correlation between error and various influencing factors to preliminarily identify the error sources;

[0013] S6. Under actual working conditions, Monte Carlo simulation technology is used to simulate the distribution of deflection values ​​when the error source changes randomly.

[0014] S7. Observe the distribution of deflection values, obtain the error magnitude and range corresponding to each error source, and consider those exceeding the threshold as major error sources, and those not exceeding the threshold as minor error sources.

[0015] Compared with the prior art, the present invention has the following advantages:

[0016] 1. This invention employs a standardized finite element simulation process to ensure the consistency and repeatability of simulation results.

[0017] 2. This invention can quickly identify single or multiple factors that have a significant impact on simulation results through sensitivity analysis, thus significantly improving analysis efficiency.

[0018] 3. This invention employs residual analysis to effectively reveal data anomalies and trends, providing key clues for error tracing and improving reliability.

[0019] 4. This invention utilizes Monte Carlo simulation technology to quantitatively evaluate error sources in the calibration process, accurately presenting the specific impact of each error source.

[0020] 5. The systematic error tracing method proposed in this invention has wide applicability and can be applied to various types of falling weight deflectometers. Attached Figure Description

[0021] Figure 1 This is a flowchart of the systematic error tracing method for the calibration of a falling weight deflectometer according to the present invention.

[0022] Figure 2 As an example, the ABAQUS finite element simulation software was used to create a model diagram for the drop weight deflection test.

[0023] Figure 3This is a model diagram of the drop weight deflectometer in ABAQUS finite element simulation software, used for detailed modeling in this embodiment. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0025] like Figure 1 As shown, the systematic error tracing method for the calibration of falling weight deflectometers according to the present invention includes the following steps:

[0026] S1. Using finite element simulation software, set different parameters to conduct drop hammer deflection test simulations under various working conditions. The specific operation is as follows:

[0027] This embodiment uses ABAQUS finite element simulation software to establish a model for the drop weight deflection test, such as... Figure 2 As shown, by adjusting various parameters of the model, the working conditions are expanded, and a deflection database is constructed using simulation results. In this embodiment, the falling weight deflectometer is finely modeled in the simulation software. In the finite element simulation model, 1 is the contact plate C3D8R element, 2 is the bearing support S4R element, 3 is the deflectometer falling weight C3D8R element, 4 is the fixed support B31 element, and 5 is the vehicle connector B31 element, as shown. Figure 3 As shown. The above functions can also be achieved using the finite element simulation software ANSYS, which will not be elaborated here.

[0028] After modeling the falling weight deflectometer and test plate, multiple loop statements were written in the initial modeling code to modify the model parameters for multiple working conditions in batches, thus realizing the batch creation of finite element models. Windows batch processing commands were used, with .bat script files edited in Python, and DOS commands were used to implement subroutine calls to the model and automatic calculation submission.

[0029] S2. Establish a simulation database to record the simulation results of deflection values ​​under different working conditions and their related influencing factors. The specific steps are as follows:

[0030] After the ABAQUS finite element simulation model is completed, the macro recording function of ABAQUS is used, combined with the loop statement written in Python, to batch obtain the deflection time history curves and peak values ​​of target points under various working conditions, thereby establishing a pavement deflection response database under multiple working conditions.

[0031] S3. Analyze the results of the simulation database, and use sensitivity analysis techniques to quantitatively or qualitatively reveal the influence of a single influencing factor on the deflection value, as well as the interaction effect of different combinations of influencing factors on the deflection value. The specific operation is as follows:

[0032] Using single-factor sensitivity analysis, the changes in deflection value are compared one by one with each influencing factor (such as temperature, humidity, etc.) to identify which factors have the greatest impact on the deflection value and quantitatively reveal the impact of a single influencing factor on the deflection value.

[0033] Multifactor sensitivity analysis was used to compare the changes in deflection values ​​when multiple influencing factors changed simultaneously, qualitatively analyzing the interaction effects of different combinations of influencing factors on the deflection values. When quantitatively analyzing the effects of different influencing factors on the deflection values, the Boussinesq point load equation was referenced: δ=P(1-η 2 Let P be the load, δ be the deflection value, E be the modulus, η be Poisson's ratio, and r be the radial distance from the load center. The deflection value is directly proportional to the load and inversely proportional to the modulus. Therefore, if the load is 1% higher, the deflection value will be similarly 1% higher. Because the same load relates to all FWD deflection values, the modulus will be either equally high or equally low, consistent with the error in measuring the load. The variation of deflection value with time and distance makes a 1% load measurement accuracy sufficient.

[0034] S4. Use the FWD to be calibrated to conduct an on-site drop weight deflection test to obtain measured data. The specific operation is as follows:

[0035] In this embodiment, a drop hammer deflection test was conducted using a FWD to be calibrated. The test was conducted at two drop hammer points: the center of the site and the edge of the site. Drop hammer deflection tests were performed using FWD at load levels of 100kN, 120kN, 140kN, and 160kN, for a total of 8 drop hammer deflection tests. For each drop hammer load level, 2 pre-hammer tests and 5 drop hammer tests were performed. The drop hammer deflection test data and the data of each influencing factor were recorded.

[0036] S5. Compare the simulation results with the measured results, and use residual analysis to analyze the magnitude and distribution of the error, and obtain the degree of correlation between the error and various influencing factors, based on the preliminary determination of the error source. The specific operation is as follows:

[0037] Using residual analysis, operating conditions with the same influencing factors are selected. The measured data obtained in S4 and the simulation data obtained in S3 are compared to calculate the residuals (i.e., errors). The magnitude and distribution of the residuals are analyzed, as well as the correlation between the residuals and each influencing factor, to identify the error sources in the measured data. In the calibration equation of the best fit, whether the regression coefficients corresponding to each influencing factor are zero is analyzed to determine whether they produce random errors in the test deflection value. If the regression coefficient of a certain influencing factor is significantly non-zero, it indicates that the influencing factor has a significant impact on the test deflection value, that is, the influencing factor is the error source.

[0038] The calibration equation for the best fit used in this embodiment is based on the measured deflection value X from the FWD. FWD (mm), Simulated deflection value Y FWD (mm), pavement thickness h (cm), drop weight load P (kN), cumulative load application times, load application location (center of slab, edge of slab, corner of slab), temperature gradient ΔT (℃ / m), etc., to form a multiple regression equation Y FWD =k0*X FWD +k1 / h+k2 / h 2 +k3 / h 3 +k4*P+k5*P 2 +k6*P 3 +k7*ΔT+ε, where k1~k7 are the regression coefficients of the calibration equation system (if there is no relevant error source, the corresponding regression coefficient is close to 0), and ε is the residual term.

[0039] S6. Under actual working conditions, Monte Carlo simulation technology is used to simulate the distribution of deflection values ​​when the error source changes randomly. The specific operation is as follows:

[0040] Based on the identified error sources, the Monte Carlo simulation method is used to simulate the magnitude and distribution of deflection values ​​when the influencing factors corresponding to the error sources (such as temperatures within the daily range of 5-35 degrees Celsius) change randomly. This method accurately identifies and quantifies the specific impact of each factor on the deflection value, so as to assess the magnitude and range of errors generated by different error sources.

[0041] S7. Observe the distribution of deflection values, obtain the error magnitude and range corresponding to each error source. If the value exceeds the threshold, it is considered a major error source. If the value does not exceed the threshold, it is considered a minor error source. The threshold can be obtained through the requirements in the equipment calibration procedure.

[0042] This invention provides a systematic error tracing method for the calibration of falling weight deflectometers. First, a simulation database covering various working conditions is constructed. This database contains simulation results of deflection values ​​and related influencing factor data under different working conditions (such as temperature, falling weight load, and material modulus). Based on this, the database is used to quantitatively or qualitatively reveal the influence of single influencing factors on deflection values, as well as the interaction effects of different combinations of influencing factors. By analyzing the data obtained from actual measurements using the falling weight deflectometer (FWD) and combining the patterns in the simulation database, error sources in the measured data are accurately identified, and the magnitude of these error sources' influence on the deflection measurement results is quantitatively assessed. The key to this step is to compare the simulation results with the measured results, using methods such as residual analysis to find the differences between the two, thereby initially determining the source of error. Finally, Monte Carlo simulation is used to simulate the distribution of deflection values ​​under the influence of different error sources, further revealing the magnitude and uncertainty of the errors and uncertainties caused by different error sources on the measurement results. Compared with existing technologies, the error tracing method proposed in this invention has the advantages of systematicness, accuracy and repeatability, and can effectively provide optimization suggestions for the calibration and use of falling weight deflectometers (FWD), thereby improving measurement accuracy and reliability.

Claims

1. A systematic error tracing method for the calibration of a falling weight deflectometer, characterized in that, include: S1. Using finite element simulation software, different parameters were set to simulate the drop hammer deflection test under various working conditions. S2. Establish a simulation database to record the simulation results of deflection values ​​under different working conditions and related influencing factors. S3. Analyze the results of the simulation database and use sensitivity analysis to obtain the influence of a single influencing factor on the deflection value, as well as the influence of the interaction effect of different combinations of influencing factors on the deflection value. S4. Compare the simulation results with the measured results, and use residual analysis to analyze the magnitude and distribution of the error; S5. Analyze the correlation between error and various influencing factors to preliminarily identify the error sources; Specifically, this includes: analyzing the regression coefficients of each influencing factor through the best-fit calibration equation to determine whether they introduce random errors into the measured deflection value; if the regression coefficient of a certain influencing factor is significantly non-zero, that influencing factor is considered an error source; the best-fit calibration equation is based on the measured deflection value X from the FWD. FWD Simulated deflection value Y FWD The following factors were considered: deck thickness h, drop weight load P, cumulative load application times, load application location, and temperature gradient ΔT. These factors were used to form a multiple regression equation: In the formula, k1~k7 are the regression coefficients of the calibration equation system, and ε is the residual term; S6. Under actual working conditions, Monte Carlo simulation technology is used to simulate the distribution of deflection values ​​when the error source changes randomly. S7. Observe the distribution of deflection values, obtain the error magnitude and range corresponding to each error source, and consider those exceeding the threshold as major error sources, and those not exceeding the threshold as minor error sources.

2. The systematic error tracing method for the calibration of a falling weight deflectometer according to claim 1, characterized in that: The finite element simulation software includes, but is not limited to, ANSYS or ABAQUS.

3. The systematic error tracing method for the calibration of a falling weight deflectometer according to claim 1, characterized in that: The sensitivity analysis techniques in step S3 include, but are not limited to, single-factor sensitivity analysis or multi-factor sensitivity analysis.

4. The systematic error tracing method for the calibration of a falling weight deflectometer according to claim 1, characterized in that: The residual analysis techniques in step S4 include, but are not limited to, absolute residual analysis, relative residual analysis, or standardized residual analysis.

5. The systematic error tracing method for the calibration of a falling weight deflectometer according to claim 1, characterized in that: The Monte Carlo simulation technique in step S6 includes random sampling, probability distribution fitting, and statistical analysis steps to simulate the distribution of deflection values ​​under different conditions.

6. The systematic error tracing method for the calibration of a falling weight deflectometer according to claim 1, characterized in that: The threshold value in step S7 is determined according to the calibration procedure of the falling weight deflectometer.

7. The systematic error tracing method for the calibration of a falling weight deflectometer according to any one of claims 1-6, characterized in that: The falling weight deflectometer includes vehicle-mounted or trailer-mounted falling weight deflectometers.

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