Systematic error tracing method for calibration of drop weight deflectometer

Abstract

The invention relates to a systematic error traceability method for calibration of a drop weight deflectometer, which comprises the following steps of: firstly, constructing a simulation database covering various working conditions, the database comprising simulation results of deflection values under different working conditions and related influence factor data; by analyzing data obtained by the drop weight deflectometer in actual measurement and combining rules in a simulation database, error sources existing in actual measurement data are accurately identified, and the influence of the error sources on a deflection value measurement result is quantitatively evaluated. And finally, adopting a Monte Carlo simulation method to simulate deflection value distribution under the influence of different error sources, and further revealing the error size and uncertainty of the measurement result caused by the different error sources. The error tracing method provided by the invention has the advantages of systematicness, accuracy and repeatability, optimization suggestions can be effectively provided for calibration and use of the drop weight deflectometer, and the measurement accuracy and reliability are improved.

Description

Technical Field

[0001] The invention relates to error tracing of a drop weight deflectometer, and in particular to a systematic error tracing method for calibration of a drop weight deflectometer. Background Art

[0002] The falling weight deflectometer (FWD) plays a key role in the field of road engineering. It evaluates the modulus and bearing capacity of the pavement structure layer by simulating the impact of vehicle load on the road surface and recording its deformation. However, during the calibration process, the existence of systematic errors has become the main factor restricting its measurement accuracy.

[0003] At present, the calibration of drop weight deflectometers at home and abroad is mainly focused on the technical and process levels, and a series of standard operations are used to ensure the qualification of the equipment. However, this calibration method often only focuses on whether the equipment is qualified, but lacks in-depth analysis and traceability of potential errors. During the calibration process, various factors such as ambient temperature, humidity, sensor performance, and calibration equipment accuracy may affect the calibration results, resulting in challenges to the stability and accuracy of the measurement results.

[0004] Although some studies have explored the calibration error of the drop weight deflectometer, the existing error tracing methods often only analyze a specific factor and lack comprehensiveness and systematicness. This single and fragmented analysis method is difficult to fully reveal the source and impact of the error, resulting in inaccurate and incomplete results of error tracing. At the same time, in terms of error correction, most of the existing technologies remain at the level of simple parameter adjustment, lacking in-depth theoretical analysis and model establishment. This correction method can often only reduce the error to a certain extent, but cannot fundamentally solve the problem.

[0005] Therefore, in view of the above problems, it is necessary to develop a systematic error tracing method for the calibration of the drop weight deflectometer. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a systematic error tracing method for the calibration of a drop weight deflectometer. The method should be able to comprehensively analyze the various influencing factors and their interrelationships in the calibration process, achieve accurate error tracing, and provide effective error correction suggestions. At the same time, the method should also be able to realize real-time monitoring and feedback mechanisms to ensure the accuracy and reliability of the measurement results, and provide strong support for the development of the road engineering field.

[0007] The technical solution adopted to achieve the purpose of the present invention is a systematic error tracing method for the calibration of a drop weight deflectometer, the method comprising:

[0008] S1. Use finite element simulation software to set different parameters to simulate the drop weight 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 their related influencing factors;

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

[0011] S4. Compare the simulation results with the measured results and use the residual analysis method to analyze the size and distribution of the errors;

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

[0013] S6. Under the 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 size and range corresponding to each error source, and if it exceeds the threshold, it is regarded as the main error source; if it does not exceed the threshold, it is regarded as a secondary error source.

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

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

[0017] 2. The present invention can quickly identify single or multiple factors that have a significant impact on simulation results through sensitivity analysis, which significantly improves analysis efficiency.

[0018] 3. The present invention adopts residual analysis to effectively reveal data anomalies and trends, provide key clues for error tracing, and improve reliability.

[0019] 4. The present invention uses Monte Carlo simulation technology to quantitatively evaluate the error sources in the calibration process and accurately present the specific impact of each error source.

[0020] 5. The systematic error tracing method proposed in the present invention has wide applicability and can be applied to various types of drop weight deflectometers. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of a method for tracing the systematic errors of the calibration of a drop weight deflectometer.

[0022] Figure 2 For the embodiment, ABAQUS finite element simulation software is used to establish a model diagram of the drop weight deflection test.

[0023] Figure 3This is a model diagram of the drop weight deflectometer in the embodiment of the fine modeling in the ABAQUS finite element simulation software. DETAILED DESCRIPTION

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

[0025] like Figure 1 As shown, the method for tracing the systematic error of the calibration of the drop weight deflectometer of the present invention comprises the following steps:

[0026] S1. Use finite element simulation software to set different parameters to simulate the drop weight deflection test under various working conditions. The specific operations are as follows:

[0027] This embodiment uses ABAQUS finite element simulation software to establish a model for the drop weight deflection test. Figure 2 As shown in FIG. 1 , by adjusting various parameters of the model, the working condition is expanded, and the deflection database is constructed using the simulation results. In this embodiment, the drop weight deflectometer is finely modeled in the simulation software. In the finite element simulation model, 1 is the contact plate C3D8R unit, 2 is the load-bearing bracket S4R unit, 3 is the deflectometer drop weight C3D8R unit, 4 is the fixed bracket B31 unit, and 5 is the vehicle connector B31 unit. Figure 3 The present invention can also realize the above functions through the finite element simulation software ANSYS, which will not be described in detail here.

[0028] After modeling the drop weight deflectometer and the test plate, multiple loop statements were written in the initial modeling code for the model parameters of multiple working conditions to batch modify the model parameters, thereby realizing the batch establishment of finite element models. Windows batch processing commands were used to edit the .bat script file through Python, and DOS commands were used to call the model's subroutine and automatically submit the calculation.

[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 operations are as follows:

[0030] After the finite element simulation model of ABAQUS is calculated, the macro recording function of ABAQUS is used, and the loop statement is written in Python program to batch obtain the deflection time history curves and peak values ​​of the target points under various working conditions, so as to establish 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 impact of a single influencing factor on the deflection value, as well as the impact of the interaction effect of different influencing factors on the deflection value. The specific operations are as follows:

[0032] Use single factor sensitivity analysis technology to compare the changes in deflection values ​​when each influencing factor (such as temperature, humidity, etc.) changes one by one, so as 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] The multi-factor sensitivity analysis technique is used to compare the changes in deflection values ​​when multiple influencing factors change at the same time, and to qualitatively analyze the impact of the interaction effect of different influencing factors on the deflection value; when conducting a quantitative analysis of the impact of different influencing factors on the deflection value, refer to the boussinesq point load equation: δ = P (1-η 2 ) / Eπr, where P is the load, δ is the deflection value, E is the modulus, η is the Poisson's ratio, and r is the radial distance from the center of the load. The deflection value is proportional to the load and inversely proportional to the modulus. Therefore, if the load is 1% too high, the deflection value will similarly be 1% too high. Because the same load is associated with all FWD deflection values, the modulus will be equally high or equally low by the same amount of error in the measured load. The variation of deflection values ​​over time and distance makes a load measurement accuracy of 1% sufficient.

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

[0035] The FWD to be calibrated is used to carry out on-site drop hammer deflection test experiments. In the on-site test of this embodiment, two drop hammer points, namely the center and edge of the site, are tested. The FWD is used to carry out drop hammer deflection tests at load levels of 100kN, 120kN, 140kN, and 160kN, respectively. A total of 8 drop hammer deflection tests are carried out, with 2 pre-hammers and 5 drop hammers carried out for each drop hammer load level. The drop hammer deflection test data and the data of various influencing factors are recorded.

[0036] S5. Compare the simulation results with the measured results, use the residual analysis method to analyze the size and distribution of the error, obtain the degree of correlation between the error and each influencing factor, and preliminarily determine the error source. The specific operations are as follows:

[0037] Using the residual analysis method, select the working conditions with the same influencing factors, compare the measured data obtained by S4 and the simulation data obtained by S3, and calculate the residual (i.e. error); analyze the size and distribution of the residual, and analyze the degree of correlation between the residual and each influencing factor to identify the error source in the measured data. In the best fit calibration equation, by analyzing whether the regression coefficient corresponding to each influencing factor is zero, it is determined whether they produce random errors in the test deflection value. If the regression coefficient of a certain influencing factor is significantly not zero, it indicates that the influencing factor has a significant impact on the test deflection value, that is, the influencing factor is an error source.

[0038] The best fitting calibration equation used in this embodiment is based on the FWD measured deflection value X FWD (mm), simulation deflection value Y FWD (mm), pavement panel thickness h (cm), drop weight load size P (kN), cumulative load action times, load action location (middle of the plate, edge of the plate, corner), temperature gradient ΔT (℃ / m), etc.), to form a multivariate 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 group (if there is no relevant error source, the corresponding regression coefficient is close to 0), and ε is the residual term.

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

[0040] Based on the identified error sources, the Monte Carlo simulation method is used to simulate the size and distribution of the deflection values ​​when the influencing factors corresponding to the error sources (such as temperature within the daily range of 5-35 degrees Celsius) change randomly. This method can accurately identify and quantify the specific impact of each factor on the deflection value to evaluate the error size and range caused by different error sources.

[0041] S7. Observe the distribution of deflection values, obtain the error size and range corresponding to each error source, and if it exceeds the threshold, it is considered as the main error source. If it does not exceed the threshold, it is considered as a secondary error source. The threshold can be obtained according to the requirements in the equipment calibration procedure.

[0042] The present invention is directed to a systematic error tracing method for calibration of a drop weight deflectometer. First, a simulation database covering a variety of working conditions is constructed. The database contains simulation results of deflection values ​​under different working conditions (such as temperature, drop weight load, material modulus, etc.) and related influencing factor data. On this basis, this database is used to quantitatively or qualitatively reveal the influence of a single influencing factor on the deflection value, as well as the influence of the interaction effect of different influencing factors on the deflection value. By analyzing the data obtained by the drop weight deflectometer (FWD) in actual measurement and combining the rules in the simulation database, the error sources in the measured data are accurately identified, and the influence of these error sources on the deflection value measurement results is quantitatively evaluated. The key to this step is to compare the simulation results with the measured results, and use residual analysis and other methods to find the difference between the two, so as to preliminarily determine the source of the error. Finally, the Monte Carlo simulation method is used to simulate the distribution of deflection values ​​under the influence of different error sources, and further reveal the error size and uncertainty caused by different error sources on the measurement results. Compared with the prior art, the error tracing method proposed in the present invention has the advantages of systematicity, accuracy and repeatability, and can effectively provide optimization suggestions for the calibration and use of the falling weight deflectometer (FWD) to improve the measurement accuracy and reliability.

Claims

1. A method for tracing the systematic error of the calibration of a drop weight deflectometer, characterized in that: include: S1. Use finite element simulation software to set different parameters to simulate the drop weight deflection test under various working conditions; S2. Establish a simulation database to record the simulation results of deflection values ​​under different working conditions and their related influencing factors; S3. Analyze the results of the simulation database and use sensitivity analysis techniques to obtain the influence of a single influencing factor on the deflection value, as well as the influence of the interaction effect of different influencing factors on the deflection value; S4. Compare the simulation results with the measured results and use the residual analysis method to analyze the size and distribution of the errors; S5. Analyze the correlation between the error and various influencing factors to preliminarily determine the error source; S6. Under the 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 size and range corresponding to each error source, and if it exceeds the threshold, it is regarded as the main error source; if it does not exceed the threshold, it is regarded as a secondary error source.

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

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

4. The method for tracing the systematic error of the calibration of the drop weight deflectometer according to claim 1 is characterized in that: The residual analysis technique in step S4 includes but is not limited to absolute residual analysis, relative residual analysis or standardized residual analysis.

5. The method for tracing the systematic error of the calibration of the drop weight deflectometer according to claim 1 is characterized by: The best fitting calibration equation is used to analyze whether the regression coefficients corresponding to each influencing factor are zero to determine whether they produce random errors in the test deflection value. If the regression coefficient of a certain influencing factor is significantly not zero, the influencing factor is the error source; the best fitting calibration equation is based on the FWD measured deflection value, simulated deflection value, pavement panel thickness h, drop weight load size P, cumulative load action times, load action position, temperature gradient ΔT, to form a multivariate regression equation y=k0*x+k1 / h+k2 / h 2 +k3 / h 3 +k4*P+k5*ΔT+b, where k1~k7 are the regression coefficients of the calibration equation group, and ε is the residual term.

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

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

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

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