Early warning and correction method of sample deviation in geotechnical test and test management system

Abstract

The application relates to a method for early warning and correction of deviation of sampling quantity in rock-soil testing and a testing management system, which comprises the following steps: generating a sampling management model based on sampling quantity information corresponding to different strata and testing indexes corresponding to different testing projects; obtaining sampling quantity confidence and testing index confidence based on preset confidence analysis rules and the sampling management model; generating a sampling quantity adjustment relationship formula according to the sampling quantity confidence and the testing index confidence; inputting the sampling quantity adjustment relationship formula into the sampling management model to generate an early warning correction model; and obtaining current testing results, inputting the current testing results into the early warning correction model to generate sampling quantity early warning information or sampling quantity correction information. The application has the effect of controlling the sampling quantity in the testing process and avoiding waste of sampling.

Description

Technical Field

[0001] This invention relates to the technical field of geotechnical testing, and in particular to a method for early warning and correction of deviations in sampling volume during geotechnical testing, as well as a test management system. Background Technology

[0002] Currently, geotechnical testing is an important laboratory testing method in geological exploration. Through geotechnical testing, the properties of the soil and rock layers at the construction site can be determined, providing fundamental geological information for subsequent project construction. Generally, before a geological exploration project begins, exploration technicians will make a preliminary plan for the sampling quantity and testing items for each stratum based on the collected geological data and exploration technical requirements. However, due to factors such as the uncertainty of the site's soil and rock layers, the long fieldwork period, the large number of project participants, and the possibility of losing a small number of samples, problems such as insufficient sampling quantity and inadequate statistical samples for testing items arise when compiling the exploration report.

[0003] In related technologies, it is generally only after the field work of the exploration project is completed that it can be determined whether the number of samples for the geotechnical test is reasonable and whether the samples for each test item are sufficient. During the test, it is impossible to provide guidance for the sampling work in the field construction process of the exploration. Summary of the Invention

[0004] In order to control the number of samples during the experiment, avoid waste of samples, and improve the rationality of the sampling quantity and the distribution of the experiment in various strata, this application provides a method for early warning and correction of deviation of sampling quantity in geotechnical experiments and an experiment management system.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution:

[0006] A method for early warning and correction of deviations in sampling quantity during geotechnical testing, comprising:

[0007] Based on the sampling quantity information corresponding to different strata, as well as the sampling quantity information and test indicators corresponding to different test projects, a sampling management model is generated;

[0008] Based on the preset confidence analysis rules and the sampling management model, the confidence scores of the sampling quantity and the test index are obtained.

[0009] Based on the confidence level of the sampling quantity and the confidence level of the test index, a formula for adjusting the sampling quantity is generated;

[0010] The sampling quantity adjustment formula is input into the sampling management model to generate an early warning and correction model.

[0011] Obtain the current test results, input the current test results into the early warning and correction model, and generate sampling quantity early warning information or sampling quantity correction information.

[0012] By adopting the above technical solution, before conducting geotechnical tests, professionals will conduct a site survey and provide preliminary information on the sampling quantity and test indicators. The sampling quantity refers to the number of soil and rock samples taken at the project site. Too many samples will result in wasted sampling time and an extended overall testing period, while too few samples will lead to inaccurate test results. Therefore, determining the sampling quantity is crucial. Test indicators refer to the acceptable values ​​for the test results. These indicators are determined based on engineering requirements and relevant engineering specifications, standards, or design requirements. To enable dynamic adjustment of the quantity and test parameters of soil and rock samples during the experiment, confidence level analysis rules are used to analyze the confidence levels of the quantity and test parameters of soil and rock samples. Then, the confidence levels are used to identify the sampling quantity and test parameters related to the test items that need adjustment. The correlation between the test parameters related to the test items that need adjustment and the sampling quantity is determined, and a relationship formula is generated to provide early warning and correction for the sampling quantity. That is, the relationship formula is used to determine whether the sampling quantity is accurate and appropriate based on the test results during the experiment. In this way, the sampling quantity can be dynamically adjusted during the experiment to avoid the waste of sampling.

[0013] In a preferred embodiment, this application can be further configured as follows: the generation of a sampling management model based on sampling quantity information corresponding to different strata, and sampling quantity information and test indicators corresponding to different test projects, specifically includes:

[0014] Based on the sampling quantity information of different strata, obtain the sampling stratum information and the corresponding sampling quantity;

[0015] Obtain preliminary exploration information corresponding to different strata, associate the sampling quantity information, the preliminary exploration information, the sampling strata information, the sampling quantity and the test indicators corresponding to different test projects, and generate a sampling management model.

[0016] By adopting the above technical solution, the sampling quantity information includes different stratigraphic information of the project site, as well as the sampling quantity values ​​corresponding to different stratigraphic information. Therefore, based on the stratigraphic order represented by the sampling stratigraphic information, a sampling management model consistent with the stratigraphic order of the project site is generated, and the acquired preliminary exploration information and test indicators are associated with this sampling management model. For example, in the sampling management model, the corresponding sampling stratigraphic information is a fill soil layer, the sampling quantity of the fill soil layer is 37, and the preliminary exploration information of the fill soil layer includes geological information, soil mechanics information, hydrological information, and underground facility information. Information, etc., the test indicators corresponding to the plain fill strata include moisture content index, limit moisture content index, and soil particle specific gravity index, etc. In addition, the sampling quantity refers to the number of samples used for a single test item. The same sample needs to be tested for different parameters. Therefore, the sampling management model is based on the actual geological structure sequence and links the sampling quantity and test indicators together, which facilitates the management of the test of each stratum, improves the management quality, facilitates the control of the sampling quantity during the test, and helps to avoid the sampling quantity being concentrated on a few test items, while some important test items are missed or the test quantity is insufficient.

[0017] In a preferred embodiment, this application can be further configured as follows: obtaining the confidence level of the sampling quantity and the confidence level of the experimental index based on the preset confidence level analysis rules and the sampling management model specifically includes:

[0018] Based on the preset confidence analysis rules and combined with the sampling stratum information, the sampling quantity in the sampling management model is analyzed to obtain the sampling quantity confidence.

[0019] Based on the preset confidence analysis rules and combined with the preliminary survey information, the test indicators in the sampling management model are analyzed to obtain the confidence level of the test indicators.

[0020] By adopting the above technical solution, the confidence analysis rules for corresponding sampling quantities and corresponding test indicators differ. For example, the confidence analysis rules for corresponding sampling quantities consider historical data, that is, judging whether the geological type and stratigraphic stratification of the stratum are accurate through historical geological data. For instance, if historical geological data indicates that a certain stratum in the project site changes frequently and is greatly affected by different hydrological and geological movements, then the confidence of the relevant sampling quantity for that stratum is low. The confidence analysis rules for corresponding test indicators need to consider the data from previous actual explorations and combine them with engineering requirements to judge the rationality of the indicators. For instance, if the difference in the exploration data corresponding to a certain test project is large, that is, the exploration project is unstable, then the confidence of the corresponding test indicator is low. Or, if the engineering requirements for the test project are high, but the test indicator is not much different from the average value of the test project, then the confidence of the corresponding test indicator is low. Therefore, by analyzing the sampling quantity and test indicators of each stratum in the sampling management model, the corresponding confidence can be determined, which is beneficial for targeted sampling quantity control during the experiment and ensuring the accuracy of the sampling quantity.

[0021] In a preferred embodiment, this application can be further configured such that: generating a sample size adjustment formula based on the sample size confidence level and the test index confidence level specifically includes:

[0022] Based on the confidence level of the sampling quantity, obtain the low-confidence sampling quantity; based on the confidence level of the test index, obtain the low-confidence test index.

[0023] Based on the low-confidence sampling quantity and the low-confidence test index, obtain the sampling quantity adjustment coefficient;

[0024] Based on the sampling quantity adjustment coefficient, a sampling quantity adjustment formula is generated.

[0025] By adopting the above technical solution, based on the confidence level of the sampling quantity, a sampling quantity with low confidence is extracted, and based on the confidence level of the experimental index, an experimental index with low confidence is extracted. The method for determining whether the confidence levels of the sampling quantity and experimental index are low is by comparing them with preset thresholds. These preset thresholds are set by experts based on preliminary survey data and statistical principles. To ensure that low-confidence sampling quantities and low-confidence experimental indices can cover the sampling quantities requiring adjustment, the corresponding thresholds are set relatively high. Furthermore, by analyzing the low-confidence sampling quantities and the low-confidence experimental indices, coefficients for adjusting the sampling quantities are obtained. For example, the correlation between low-confidence sampling quantities and the low-confidence experimental indices can be analyzed, or the accuracy of the experimental index can be determined by simulating experimental results with different sampling quantities, thereby obtaining the sampling quantity adjustment coefficient. Based on this, the sampling quantity adjustment formula facilitates adjustments to the sampling quantity during the experiment based on actual experimental results, thus ensuring the accuracy of the sampling quantity.

[0026] In a preferred embodiment, this application can be further configured such that: inputting the sampling quantity adjustment formula into the sampling management model to generate an early warning and correction model specifically includes:

[0027] Obtain geotechnical test plan information, and based on the geotechnical test plan information, obtain the sampling time adjustment coefficient;

[0028] The sampling quantity adjustment formula and the sampling time adjustment coefficient are input into the sampling management model to generate an early warning and correction model.

[0029] By adopting the above technical solution, the geotechnical test plan information represents the specific event information of sampling and testing in the overall geotechnical test project, including the time and details of sampling and testing. However, for strata with large or rapid underground variations, or due to unfavorable sampling locations, the test results using the sampled material may be inconsistent with the actual stratum conditions. Therefore, it is necessary to adjust the sampling and corresponding test times, or adjust the number of samples, and correct the final test results through testing multiple samples. Thus, based on the geotechnical plan information and preliminary exploration information, unreasonable sampling or testing items can be identified, for example... For example, by using preliminary exploration information to identify strata with relatively loose soil or active hydrological activity, and then using geotechnical planning information to determine whether the sampling and testing corresponding to these strata are reasonable, or by analyzing the meteorological conditions that will occur in the area to predict the geological conditions of different strata, and then using geotechnical planning information to determine whether the sampling and testing corresponding to these strata are reasonable, the actual testing can be conducted. In this way, when the actual test is conducted, the results of the test on the sampled material can be determined based on the preliminary test results to see if there is any inconsistency between the sampled material and the actual stratum conditions. If such a situation occurs, the sampling time adjustment coefficient is used to adjust the sampling quantity, thereby achieving the effect of dynamically adjusting the sampling quantity during the test.

[0030] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0031] A test management system, the test management system comprising:

[0032] The sampling management model generation module is used to generate sampling management models based on the sampling quantity information of different strata and the test indicators of different test projects.

[0033] The confidence level acquisition module is used to obtain the confidence level of the sampling quantity and the confidence level of the test index based on the preset confidence level analysis rules and the sampling management model.

[0034] The sampling quantity adjustment relationship generation module is used to generate a sampling quantity adjustment relationship based on the sampling quantity confidence level and the test index confidence level.

[0035] The early warning and correction model generation module is used to input the sampling quantity adjustment formula into the sampling management model to generate an early warning and correction model.

[0036] The warning or correction information generation module is used to obtain the current test results, input the current test results into the warning and correction model, and generate sampling quantity warning information or sampling quantity correction information.

[0037] Optionally, the sampling management model generation module includes:

[0038] The sampling quantity information extraction submodule is used to obtain the sampling stratum information and the corresponding sampling quantity based on the sampling quantity information of different strata.

[0039] The sampling management model generation submodule is used to obtain the preliminary exploration information corresponding to different strata, associate the preliminary exploration information, the sampling strata information, the sampling quantity and the test indicators corresponding to different test projects, and generate the sampling management model.

[0040] Optionally, the confidence level acquisition module includes:

[0041] The sampling quantity confidence acquisition submodule is used to analyze the sampling quantity in the sampling management model based on preset confidence analysis rules and combined with the sampling stratum information, and obtain the sampling quantity confidence.

[0042] The test index confidence level acquisition submodule is used to analyze the test index in the sampling management model based on preset confidence level analysis rules and combined with the preliminary survey information, and obtain the test index confidence level.

[0043] Thirdly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0044] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for early warning and correction of deviations in sampling quantities during geotechnical testing.

[0045] Fourthly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0046] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for early warning and correction of deviations in sampling quantities during geotechnical testing.

[0047] In summary, this application includes at least one of the following beneficial technical effects:

[0048] 1. Before conducting geotechnical tests, professionals conduct a site survey and provide preliminary information on the sampling quantity and test indicators. The sampling quantity refers to the number of geotechnical samples taken at the project site. Too many samples will result in wasted sampling time and an extended overall testing period, while too few samples will lead to inaccurate test results. Therefore, determining the sampling quantity is crucial. Test indicators refer to the acceptable values ​​for the test items. These indicators are determined based on engineering requirements and relevant engineering specifications, standards, or design requirements. Therefore, to dynamically adjust the sampling quantity and test indicators during the test, confidence level analysis is used to analyze the confidence levels of the sampling quantity and test indicators. The confidence levels are then used to identify the sampling quantity and test item-related indicators that require adjustment. The correlation between the indicators and the sampling quantity is determined, generating a relationship for early warning and correction of the sampling quantity. This relationship allows for dynamic adjustment of the sampling quantity during the test, avoiding wasted sampling.

[0049] 2. The confidence analysis rules for corresponding sampling quantities differ from those for corresponding test indicators. For example, the confidence analysis rules for corresponding sampling quantities consider historical data, i.e., judging whether the geological type and stratigraphic stratification of the stratum are accurate through historical geological data. For instance, if historical geological data indicates that a certain stratum at the project site changes frequently and is greatly affected by different hydrological and geological movements, then the confidence of the relevant sampling quantity for that stratum is low. The confidence analysis rules for corresponding test indicators need to consider the data from previous actual explorations and combine them with engineering requirements to judge the rationality of the indicators. For instance, if the difference in the exploration data corresponding to a certain test project is large, i.e., the exploration project is unstable, then the confidence of the corresponding test indicator is low. Or, if the engineering requirements for the test project are high, but the test indicator is not much different from the average value of the test project, then the confidence of the corresponding test indicator is low. Therefore, by analyzing the sampling quantity and test indicators of each stratum in the sampling management model to determine the corresponding confidence, it is beneficial to carry out targeted sampling quantity control during the test and ensure the accuracy of the sampling quantity.

[0050] 3. Based on the confidence level of the sampling quantity, extract the sampling quantity with lower confidence. Similarly, based on the confidence level of the experimental index, extract the experimental index with lower confidence. The method for determining whether the confidence levels of the sampling quantity and experimental index are low is by comparing them with preset thresholds. These preset thresholds are set by experts based on preliminary survey data and statistical principles. To ensure that low-confidence sampling quantities and low-confidence experimental indices can cover the sampling quantities requiring adjustment, the corresponding thresholds are set relatively high. Furthermore, by analyzing the low-confidence sampling quantities and the low-confidence experimental indices, coefficients for adjusting the sampling quantity are obtained. For example, the correlation between low-confidence sampling quantities and the low-confidence experimental indices can be analyzed, or the accuracy of the experimental index can be determined by simulating experimental results with different sampling quantities, thereby obtaining the sampling quantity adjustment coefficient. Based on this, the sampling quantity adjustment formula facilitates adjustments to the sampling quantity during the experiment based on actual experimental results, thus ensuring the accuracy of the sampling quantity. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating an implementation of the method for early warning and correction of deviations in sampling quantity during geotechnical testing in this application embodiment;

[0052] Figure 2 This is a flowchart illustrating the implementation of S10 of the deviation warning and correction method for sampling quantity in geotechnical testing in this application embodiment;

[0053] Figure 3 This is a flowchart illustrating the implementation of S20 of the deviation warning and correction method for sampling quantity in geotechnical testing in this application embodiment;

[0054] Figure 4 This is a flowchart illustrating the implementation of S30 of the deviation warning and correction method for sampling quantity in geotechnical testing in this application embodiment;

[0055] Figure 5 This is a flowchart illustrating the implementation of S40 of the deviation warning and correction method for sampling quantity in geotechnical testing in this application embodiment;

[0056] Figure 6 This is a principle block diagram of the test management system in an embodiment of this application;

[0057] Figure 7 This is an internal structural diagram of the computer device for early warning and correction of deviation in geotechnical testing in the embodiments of this application. Detailed Implementation

[0058] The following is in conjunction with the appendix Figure 1-7 This application will be described in further detail.

[0059] In one embodiment, such as Figure 1As shown, this application discloses a method for early warning and correction of deviations in sampling quantity during geotechnical testing, which specifically includes the following steps:

[0060] S10: Generate a sampling management model based on the sampling quantity information corresponding to different strata, as well as the sampling quantity information and test indicators corresponding to different test projects.

[0061] In this embodiment, sampling quantity information refers to the quantity of samples taken from different underground strata of the project. Test indicators refer to the indicators used to judge test results during specific tests. Sample quantity information refers to the quantity of samples used for a single test item. The sampling management model refers to the data model used to manage the corresponding sampling and test items.

[0062] Specifically, geotechnical testing refers to the testing and analysis of underground soil and rock. Geotechnical testing includes sampling and testing. Sampling involves obtaining samples of underground soil and rock layers using technical means, while testing involves analyzing the obtained samples to determine the geological conditions of the strata. Therefore, before conducting geotechnical testing, professional technicians will preliminarily determine the sampling location, quantity, and timing based on the geological data of the sampling site, as well as the actual needs (e.g., the geological parameter requirements of subway projects or underground tunnel projects). The system identifies the indicators used to determine whether the test results meet the actual requirements when conducting experiments on samples. Therefore, it obtains information such as the sampling location, sampling quantity, and sampling time of the sampling projects in different strata of the corresponding project location, as determined by professional technicians. This includes the sampling quantity information, the sample quantity information used for different test projects, and the indicators used to determine whether the test results meet the actual requirements. These include the sample quantity information and test indicators. The system then associates the information contained in the corresponding sampling quantity information, sample quantity information, and test indicators to generate a data model for managing the corresponding sampling projects and test projects. This model is the sampling management model.

[0063] It is important to note that a single sample can be used for multiple different experiments. Therefore, each sample quantity corresponds to an experimental indicator. That is, each sample experiment corresponds to an experimental indicator used to determine the experimental structure, and each individual experimental indicator also corresponds to a sample. The total number of samples is less than the number of experimental items.

[0064] S20: Based on the preset confidence analysis rules and sampling management model, obtain the confidence level of the sampling quantity and the confidence level of the test index.

[0065] In this embodiment, the confidence level of the sample quantity refers to the confidence level of the sample quantity information. The confidence level of the test index refers to the confidence level of the test index.

[0066] Specifically, since the obtained sampling quantity information and test indicators are inferred by professional technicians and have not been actually verified, the results obtained by actual operation based on the current sampling quantity information and test indicators may not necessarily reflect the actual geological conditions. Therefore, based on the preset confidence analysis rules, which include reading the confidence levels recorded by professional technicians (the confidence levels recorded by professional technicians themselves based on experience and the quantity of corresponding geological data when judging the sampling quantity information), and analyzing the historical geological data of the project site, the rules for comprehensively judging the confidence level of each sampling quantity information and test indicator are as follows: for example, using the confidence interval of historical geological data or historical test data, if the width of the confidence interval is large, the corresponding confidence level is low. Each sampling quantity information and test indicator is analyzed separately to obtain the confidence level of the sampling quantity information and test indicator, that is, the confidence level of the sampling quantity and the confidence level of the test indicator.

[0067] It is important to note that the confidence level of the sample quantity is the confidence level of the number of samples taken from each stratum, while the confidence level of the test index is the confidence level of the index of each individual test item.

[0068] S30: Generate the sample quantity adjustment formula based on the confidence level of the sample quantity and the confidence level of the test index.

[0069] In this embodiment, the sampling quantity adjustment formula refers to the formula used to adjust the sampling quantity.

[0070] Specifically, based on the confidence levels of the sampling quantity and the test index, which represent the degree of trustworthiness of the sampling quantity information and the test index, sampling quantity information and test indexes with lower confidence levels are extracted. The sampling quantity information corresponding to these lower confidence levels is used as the sampling quantity information that needs to be adjusted during the test. (Since one sample can be used for multiple different tests, the sampling quantity information corresponding to the lower confidence level is the sampling quantity with the lower confidence level among the sampling quantities of a parameter value in the sampling quantity information for each stratum. For example, the sampling quantity for detecting the moisture content of the fill soil stratum is 137, the sampling quantity for detecting density is 139, and the sampling quantity for detecting the limit moisture content is...) The sampling quantity confidence level is 134, and the sampling quantity for testing the consolidation coefficient is 102. The sampling quantity confidence level refers to the confidence level of the sampling quantity for testing the moisture content, density, limit moisture content, and consolidation coefficient of the above-mentioned plain fill soil layer. The sampling quantity information corresponding to the sampling quantity information with lower confidence level is the sampling quantity information corresponding to the lower confidence level of the sampling quantity for testing the moisture content, density, limit moisture content, and consolidation coefficient of the plain fill soil layer. The sampling quantity information corresponding to the test index with lower confidence level is the sampling quantity information corresponding to each test item with lower confidence level in all test items. For example, if the confidence level of the index for testing the moisture content of the sample of the plain fill soil layer is low, then the corresponding sampling quantity information is the sampling quantity information for testing the moisture content of the plain fill soil layer. The geological conditions of the project site are simulated, and random simulated values ​​are assigned to the test results of corresponding experimental items based on the required sampling quantity. These simulated values ​​are then analyzed to determine whether they accurately represent the simulated geological conditions. For example, the water content of soil and rock at different depths is simulated with different values, and corresponding 3D geological models are constructed. Simulated values ​​are assigned to the test results of the water content experimental items corresponding to the required sampling quantity and location. These simulated values ​​are then combined with the 3D geological model to determine whether they reflect the water content in the model. The sampling quantity and location are then adjusted accordingly. Finally, the adjusted sampling quantity and location are used to determine the water content. The test results of the experimental project are simulated and assigned values. Then, combined with the geological 3D model, it is determined whether the simulated values ​​can reflect the water content in the geological 3D model. Through multiple adjustments and simulations, the sampling quantity closest to the water content in the geological 3D model is determined. This sampling quantity is used as the standard sampling quantity. The simulated test results and adjusted sampling quantities are determined when the difference between the simulated test results and the water content in the geological 3D model is the largest and the smallest. These simulated test results and adjusted sampling quantities are used as correction limit values ​​and warning values. Combining the warning value, correction limit value, standard sampling quantity, and the sampling quantity that needs to be adjusted, a sampling quantity adjustment formula is generated.

[0071] Furthermore, the sampling quantity information with low confidence and the sample quantity information corresponding to the test index with low confidence can be extracted. This low-confidence sampling quantity information and the sample quantity information corresponding to the test index are used as the sampling quantity information and sample quantity information that need to be adjusted during the experiment. (Since one sample can be used for multiple different experiments, the sampling quantity information corresponding to the low-confidence sampling quantity information is the lowest-confidence sampling quantity among the parameter values ​​in the sampling quantity information for each stratum. For example, the sampling quantity for detecting the moisture content of the fill soil stratum is 137, the sampling quantity for detecting density is 139, and the sampling quantity for detecting the limit moisture content is 134.) The sampling quantity for testing the consolidation coefficient is 102. The sampling quantity confidence level refers to the confidence level of the sampling quantity for testing the moisture content, density, limit moisture content, and consolidation coefficient of the above-mentioned plain fill soil layer. The sampling quantity information corresponding to the sampling quantity information with lower confidence level is the sampling quantity information corresponding to the lower confidence level of the sampling quantity for testing the moisture content, density, limit moisture content, and consolidation coefficient of the plain fill soil layer. The sampling quantity information corresponding to the test index with lower confidence level is the sampling quantity information corresponding to each test item with lower confidence level in all test items. For example, if the confidence level of the index for testing the moisture content of the sample of the plain fill soil layer is lower, then the corresponding sampling quantity information is the sampling quantity information for testing the moisture content of the plain fill soil layer.The geological conditions of the project site are simulated, and based on the required adjustment of sampling and sampling quantities, random simulated values ​​are assigned to the test results of corresponding experimental items. These simulated values ​​are analyzed to determine whether they accurately represent the simulated geological conditions. For example, the water content of soil and rock at different depths is simulated with different values, and corresponding 3D geological models are constructed. Based on the required adjustment of sampling and sampling quantities, simulated values ​​are assigned to the test results of water content experimental items corresponding to the sampling quantities and locations. These simulated values ​​are then combined with the 3D geological model to determine whether they reflect the water content in the model. The sampling quantities and locations are then adjusted, and simulated values ​​are assigned based on the adjusted sampling quantities and locations. By combining the simulated values ​​with the 3D geological model, it is determined whether the simulated values ​​accurately reflect the water content in the model. Through multiple adjustments and simulations, the sampling and demeritization quantities closest to the water content in the 3D geological model are determined. These quantities are then used as standard values. The simulated test results, along with the adjusted sampling and demeritization quantities, are used as correction and warning values. Combining these warning values, correction limits, standard sampling and demeritization quantities, and the required adjustment quantities, formulas for adjusting the sampling and demeritization quantities are generated.

[0072] S40: Input the sampling quantity adjustment formula into the sampling management model to generate an early warning and correction model.

[0073] In this embodiment, the early warning correction model refers to a data model used to provide early warning correction for the sampling quantity during actual experiments.

[0074] Specifically, the sampling quantity adjustment formula is input into the sampling management model, and the corresponding sampling quantity adjustment formula, as well as the sampling quantity information and test indicators with low confidence, are associated to generate a data model that performs early warning correction on the sampling quantity based on the sampling quantity adjustment formula during the actual test, namely the early warning correction model.

[0075] S50: Obtain the current test results, input the current test results into the early warning and correction model, and generate sampling quantity early warning information or sampling quantity correction information.

[0076] In this embodiment, the current test result refers to the result of the actual test currently being conducted. The sampling quantity warning information refers to information that provides an early warning regarding the current sampling quantity. The quantity correction information refers to information that corrects the current sampling quantity.

[0077] Specifically, during actual testing, the test results and actual sampling quantity of each test item are input into the early warning and correction model. In the early warning and correction model, the current test results are compared in real time with the correction limit value and early warning value in the corresponding sampling quantity adjustment formula. If the early warning value is reached, an early warning message is generated to remind the staff at the sampling site that the current test results are incorrect, i.e., sampling quantity early warning information. If the correction limit value is reached, information to remind the staff at the sampling site to correct the sampling quantity is generated based on the corresponding standard sampling quantity, i.e., sampling quantity correction information.

[0078] In one embodiment, such as Figure 2 As shown, in step S10, a sampling management model is generated based on the sampling quantity information corresponding to different strata, as well as the sampling quantity information and test indicators corresponding to different test projects. Specifically, this includes:

[0079] S11: Based on the sampling quantity information of different strata, obtain the sampling stratum information and the corresponding sampling quantity.

[0080] In this embodiment, the sampling stratigraphic information refers to the geological information of the sampled strata. The sampling quantity refers to the specific number of samples taken.

[0081] Specifically, from the sampling quantity information corresponding to different strata, the geological information and geological number information corresponding to each stratum, as well as the sample quantity value of the specific sample taken in that stratum, are extracted to obtain the sampling stratum information and the corresponding sampling quantity.

[0082] S12: Obtain preliminary exploration information for different strata, associate it with sampling quantity information, preliminary exploration information, sampling strata information, sampling quantity and test indicators for different test projects, and generate a sampling management model.

[0083] In this embodiment, preliminary exploration information refers to the data and information collected during on-site exploration before conducting geotechnical tests.

[0084] Specifically, data and information from the field investigation prior to geotechnical testing are obtained, i.e., preliminary investigation information. Based on the numbering sequence of each stratum in the project location, represented by the sampling stratum information, the sampling quantity information, preliminary investigation information, sampling stratum information, sampling quantity, and test indicators of the samples from that stratum are correlated to obtain a sampling management model that includes sampling project information, test project information, and geological exploration information for each stratum in the project location.

[0085] In one embodiment, such as Figure 3As shown, in step S20, based on the preset confidence analysis rules and sampling management model, the confidence levels of the sampling quantity and the experimental indicators are obtained, specifically including:

[0086] S21: Based on the preset confidence analysis rules and combined with the sampling stratum information, analyze the sampling quantity in the sampling management model to obtain the confidence level of the sampling quantity.

[0087] Specifically, determining the confidence level of the sampling quantity means judging whether the test results of a certain test project can represent the actual geological conditions after using the sampling quantity of samples. For example, in the fill soil stratum at the project site, judging whether the test results of the water content test project using 37 samples (i.e., the sampling quantity) can represent the actual water content of the fill soil stratum. Therefore, the preset confidence level analysis rule is to combine the size of the project site and the geological data of each stratum represented by the sampling stratum information to calculate the confidence interval corresponding to each sampling quantity, and then judge the confidence level corresponding to each sampling quantity. In this way, the sampling quantity in the sampling management model is analyzed through the confidence level analysis rule to obtain the confidence level of the sampling quantity.

[0088] Furthermore, different confidence analysis methods can be used for different strata. For example, since plain fill and miscellaneous fill generally have relatively uniform geological characteristics, confidence interval calculation is used to provide the uncertainty interval for the estimate. Silty coarse sand and silty fine sand contain medium-coarse sand and fine silt with larger particles, and the arrangement and interaction of particles may affect the geological properties. Therefore, reliability analysis can better take into account the complexity between these particles.

[0089] S22: Based on the preset confidence analysis rules and combined with the preliminary survey information, analyze the test indicators in the sampling management model to obtain the confidence level of the test indicators.

[0090] Specifically, the confidence level of the test index is to determine whether the qualification index of a certain test project is accurate. Therefore, using the most recently acquired preliminary exploration information can make the judgment result more consistent with the current situation. Thus, based on the preset confidence level analysis rules, combined with the geological data of each stratum in the project location represented by the preliminary exploration information, the test index in the sampling management model is analyzed to obtain the confidence level of the test index.

[0091] In one embodiment, such as Figure 4 As shown, in step S30, a sample quantity adjustment formula is generated based on the sample quantity confidence level and the test index confidence level, specifically including:

[0092] S31: Based on the confidence level of the sampling quantity, obtain the low-confidence sampling quantity; based on the confidence level of the test index, obtain the low-confidence test index.

[0093] In this embodiment, the low-confidence sample size refers to the sample size with a relatively low confidence level. The low-confidence test index refers to the test index with a relatively low confidence level.

[0094] Specifically, confidence level represents the degree of trustworthiness. Therefore, the higher the confidence level of the sample size and test indicators, the less likely they are to need adjustment in the actual experiment. The purpose of adjustment is to ensure the accuracy of the final test results. Therefore, to ensure the accuracy of the final test results and avoid errors in sample size, a high confidence threshold is set, such as 80%, 85%, 90%, or 95%. This confidence threshold can be set arbitrarily, for example, based on the importance of the project and the geological complexity of the project location. Using this confidence threshold, the confidence level corresponding to each sample size and test indicator is compared with the confidence threshold to obtain low-confidence sample sizes and low-confidence test indicators that are below the confidence threshold.

[0095] S32: Obtain the sampling quantity adjustment coefficient based on the low-confidence sampling quantity and the low-confidence test index.

[0096] In this embodiment, the sampling quantity adjustment coefficient refers to a coefficient used to adjust the value of the sampling quantity.

[0097] Specifically, geotechnical tests are simulated using low-confidence sampling quantities and low-confidence test indicators. This involves simulating the geological strata at the project site using the sampling stratigraphic information corresponding to the low-confidence sampling quantities, establishing a three-dimensional geological model. This model represents geological processes, groundwater flow, and seismic activity. Simulated sampling is conducted using the sampling quantities and locations corresponding to the low-confidence sampling quantities, and tests are performed on the simulated samples corresponding to the low-confidence sampling quantities to obtain simulation test results. Furthermore, control simulation sampling is conducted by adjusting the sampling quantities and locations, and tests are performed on the simulated samples corresponding to the low-confidence sampling quantities to obtain control simulation test results. The simulation test results and control simulation test results are analyzed in conjunction with the actual situation of the three-dimensional geological model to determine the gap between the simulation test results and the actual situation of the three-dimensional geological model under different sampling quantities and locations. The result with the smallest gap is then determined. The ratio of the sample size corresponding to the simulated test result to the low-confidence sample size is used as the sampling size adjustment coefficient for the corresponding low-confidence sample size. The sample size corresponding to the simulated test result with the largest difference is used as the correction limit value (i.e., the sampling size needs to be corrected after reaching this value in the actual test). The sample size corresponding to the smallest difference (excluding the sampling size adjustment coefficient) is used as the warning value (i.e., the sampling size needs to be warned after reaching this value in the actual test). Similarly, the sampling size adjustment coefficient, correction limit value, and warning value for the corresponding low-confidence test index are obtained through the low-confidence test index. The sampling size adjustment coefficient for the corresponding low-confidence sample size is used to adjust the sampling size according to the actual sampling size in the actual test, and the sampling size adjustment coefficient for the corresponding low-confidence test index is used to adjust the sampling size according to the actual test result in the actual test.

[0098] S33: Generate the sampling quantity adjustment formula based on the sampling quantity adjustment coefficient.

[0099] Specifically, based on the corresponding low-confidence sampling quantity and the sampling quantity adjustment coefficient of the corresponding low-confidence test index, a sampling quantity adjustment formula is generated for adjusting the sampling quantity according to the actual sampling quantity in the actual test, and a sampling quantity adjustment formula is generated for adjusting the sampling quantity according to the actual test results in the actual test. The corresponding correction limit value and warning value are then associated with the corresponding sampling quantity adjustment formula.

[0100] In one embodiment, such as Figure 5 As shown, in step S40, the sampling quantity adjustment formula is input into the sampling management model to generate the early warning and correction model, specifically including:

[0101] S41: Obtain geotechnical test plan information, and obtain the sampling time adjustment coefficient based on the geotechnical test plan information.

[0102] In this embodiment, geotechnical test plan information refers to the specific event information regarding sampling and testing within the overall geotechnical test project. The sampling time adjustment factor is a factor used to adjust the sampling quantity based on the sampling time.

[0103] Specifically, this involves acquiring detailed event information regarding sampling and testing within the overall geotechnical testing project, i.e., geotechnical testing plan information. This plan includes the timing and details of sampling and testing. Based on the geological conditions of each stratum represented in the sampling management model, the intensity of change in each stratum is determined. For example, the rate of change in water content of each stratum is determined by its hydrological conditions, and the rate of change in sediment content is determined by its porosity and shear strength. Furthermore, the interval between each sampling and testing item for each stratum, as represented by the geotechnical testing project, is used to determine whether the interval exceeds the corresponding intensity of change. For instance, if the interval between sampling and testing for the water content test is 1 hour, and the corresponding rate of change in water content is an average of 1% every half hour, then the interval for the water content test is less than the corresponding intensity of change. The sampling quantity for the moisture content test needs to be adjusted. Therefore, it is necessary to determine whether adjusting only the sampling quantity can improve the accuracy of the final moisture content test results. For example, if the interval between sampling and testing for the moisture content test remains constant at 1 hour, the corresponding rate of change in moisture content is 1% on average every half hour. Simulate different sampling quantities and assign values ​​to them, and determine whether the moisture content test results are the same as the final moisture content. If it is determined that they are different under different sampling quantities, then obtain the coefficient for adjusting the sampling time based on the time difference between the interval of the moisture content test and the corresponding rate of change. For example, if the time difference is half an hour, then increase the interval of the sampling time for the three samplings by ten minutes. If it is determined that they are the same under a certain sampling quantity, then use that sampling quantity as the coefficient for adjusting the sampling quantity in the actual test, i.e., the sampling time adjustment coefficient.

[0104] Furthermore, in actual testing, based on preliminary tests, it is determined whether the interval between sampling and testing for the moisture content test is 1 hour, corresponding to a moisture content change rate of 1% on average every half hour. If so, the sampling quantity is adjusted using a sampling time adjustment coefficient.

[0105] S42: Input the sampling quantity adjustment formula and sampling time adjustment coefficient into the sampling management model to generate an early warning and correction model.

[0106] Specifically, the sampling quantity adjustment formula and sampling time adjustment coefficient are input into the sampling management model, and the corresponding sampling quantity adjustment formula and sampling time adjustment coefficient are associated to generate an early warning and correction model.

[0107] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0108] In one embodiment, a test management system is provided, which corresponds one-to-one with the deviation warning and correction methods for sampling quantities in the geotechnical tests described above. For example... Figure 6 As shown, the test management system includes a sampling management model generation module, a confidence level acquisition module, a sampling quantity adjustment formula generation module, an early warning and correction model generation module, and an early warning or correction information generation module. Detailed descriptions of each functional module are as follows:

[0109] The sampling management model generation module is used to generate a sampling management model based on the sampling quantity information of different strata, as well as the sampling quantity information and test indicators of different test projects.

[0110] The confidence level acquisition module is used to obtain the confidence level of the sampling quantity and the confidence level of the test index based on the preset confidence level analysis rules and sampling management model.

[0111] The sampling quantity adjustment formula generation module is used to generate the sampling quantity adjustment formula based on the sampling quantity confidence level and the test index confidence level.

[0112] The early warning and correction model generation module is used to input the sampling quantity adjustment formula into the sampling management model to generate the early warning and correction model;

[0113] The warning or correction information generation module is used to obtain the current test results, input the current test results into the warning and correction model, and generate sampling quantity warning information or sampling quantity correction information.

[0114] Optionally, the sampling management model generation module includes:

[0115] The sampling quantity information extraction submodule is used to obtain the sampling stratum information and the corresponding sampling quantity based on the sampling quantity information of different strata.

[0116] The sampling management model generation submodule is used to obtain the preliminary exploration information corresponding to different strata, associate the sampling quantity information, preliminary exploration information, sampling strata information, sampling quantity and test indicators corresponding to different test projects, and generate the sampling management model.

[0117] Optionally, the confidence level acquisition module includes:

[0118] The sampling quantity confidence acquisition submodule is used to analyze the sampling quantity in the sampling management model based on preset confidence analysis rules and combined with sampling stratum information, and obtain the sampling quantity confidence.

[0119] The test index confidence level acquisition submodule is used to analyze the test indexes in the sampling management model based on preset confidence level analysis rules and combined with previous survey information, and obtain the test index confidence level.

[0120] Optionally, the module for generating the sample quantity adjustment formula includes:

[0121] The low-confidence test index acquisition submodule is used to obtain the low-confidence sample quantity based on the sample quantity confidence level, and to obtain the low-confidence test index based on the test index confidence level.

[0122] The sampling quantity adjustment coefficient acquisition submodule is used to obtain the sampling quantity adjustment coefficient based on the low-confidence sampling quantity and the low-confidence test index.

[0123] The sampling quantity adjustment formula generation submodule is used to generate the sampling quantity adjustment formula based on the sampling quantity adjustment coefficient.

[0124] Optionally, the early warning and correction model generation module includes:

[0125] The sampling time adjustment factor acquisition submodule is used to obtain geotechnical test plan information and, based on the geotechnical test plan information, obtain the sampling time adjustment factor.

[0126] The early warning and correction model generation submodule is used to input the sampling quantity adjustment formula and the sampling time adjustment coefficient into the sampling management model to generate the early warning and correction model.

[0127] For specific limitations regarding the test management system, please refer to the limitations on deviation warning and correction methods for sampling quantities in geotechnical tests mentioned above, which will not be repeated here. Each module in the aforementioned test management system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0128] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores sampling management models, sampling quantity confidence levels, test index confidence levels, sampling quantity adjustment relationships, early warning and correction models, sampling quantity early warning information, and sampling quantity correction information. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a method for early warning and correction of sampling quantity deviations in geotechnical testing.

[0129] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0130] Based on the sampling quantity information corresponding to different strata, as well as the sampling quantity information and test indicators corresponding to different test projects, a sampling management model is generated;

[0131] Based on the preset confidence analysis rules and sampling management model, the confidence scores of the sampling quantity and the test index are obtained.

[0132] Based on the confidence levels of the sampling quantity and the test index, generate the formula for adjusting the sampling quantity;

[0133] Input the sampling quantity adjustment formula into the sampling management model to generate an early warning and correction model;

[0134] Obtain the current test results, input the current test results into the early warning and correction model, and generate early warning information or correction information for the sampling quantity.

[0135] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0136] Based on the sampling quantity information corresponding to different strata, as well as the sampling quantity information and test indicators corresponding to different test projects, a sampling management model is generated;

[0137] Based on the preset confidence analysis rules and sampling management model, the confidence scores of the sampling quantity and the test index are obtained.

[0138] Based on the confidence levels of the sampling quantity and the test index, generate the formula for adjusting the sampling quantity;

[0139] Input the sampling quantity adjustment formula into the sampling management model to generate an early warning and correction model;

[0140] Obtain the current test results, input the current test results into the early warning and correction model, and generate early warning information or correction information for the sampling quantity.

[0141] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0142] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0143] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for early warning and correction of deviation of sampling amount in geotechnical test, characterized in that, The deviation early warning and correction method of the sampling amount in the rock-soil test comprises: Based on the sampling amount information corresponding to different strata, the sampling amount information corresponding to different test items, and the test indexes, a sampling management model is generated; Based on the preset confidence analysis rule and the sampling management model, sampling amount confidence and test index confidence are obtained; According to the sampling amount confidence and the test index confidence, a sampling amount adjustment relationship is generated; The sampling amount adjustment relationship is input into the sampling management model to generate an early warning correction model; The current test result is obtained, and the current test result is input into the early warning correction model to generate sampling amount early warning information or sampling amount correction information.

2. The method of claim 1, wherein the method further comprises: Based on the sampling amount information corresponding to different strata, the sampling amount information corresponding to different test items, and the test indexes, a sampling management model is generated, specifically comprising: Based on the sampling amount information corresponding to different strata, the sampling stratum information and the corresponding sampling amount are obtained; The early investigation information corresponding to different strata is obtained, and the sampling management model is generated by associating the sampling amount information corresponding to different test items, the early investigation information, the sampling stratum information, the sampling amount, and the test indexes.

3. The method of claim 2, wherein the method further comprises: Based on the preset confidence analysis rule and the sampling management model, sampling amount confidence and test index confidence are obtained, specifically comprising: Based on the preset confidence analysis rule, the sampling amount in the sampling management model is analyzed in combination with the sampling stratum information to obtain sampling amount confidence; Based on the preset confidence analysis rule, the test indexes in the sampling management model are analyzed in combination with the early investigation information to obtain test index confidence.

4. The method for early warning and correction of deviation of sampling amount in geotechnical test according to claim 1, characterized in that, According to the sampling amount confidence and the test index confidence, a sampling amount adjustment relationship is generated, specifically comprising: According to the sampling amount confidence, a low-confidence sampling amount is obtained, and according to the test index confidence, a low-confidence test index is obtained; According to the low-confidence sampling amount and the low-confidence test index, a sampling amount adjustment coefficient is obtained; According to the sampling amount adjustment coefficient, a sampling amount adjustment relationship is generated.

5. The method for early warning and correction of deviation of sampling amount in geotechnical test according to claim 1, characterized in that, The sampling amount adjustment relationship is input into the sampling management model to generate an early warning correction model, specifically comprising: The sampling time adjustment coefficient is obtained according to the rock-soil test plan information; The sampling amount adjustment relationship and the sampling time adjustment coefficient are input into the sampling management model to generate an early warning correction model.

6. A test management system characterized by comprising: The test management system comprises: A sampling management model generation module is configured to generate a sampling management model based on the sampling amount information corresponding to different strata, the sampling amount information corresponding to different test items, and the test indexes; A confidence acquisition module is configured to obtain sampling amount confidence and test index confidence based on a preset confidence analysis rule and the sampling management model; A sampling amount adjustment relationship generation module is configured to generate a sampling amount adjustment relationship according to the sampling amount confidence and the test index confidence; The early warning and correction model generation module is configured to input the sampling quantity adjustment formula into the sampling management model to generate an early warning and correction model. The early warning or correction information generation module is configured to obtain a current test result, input the current test result into the early warning and correction model, and generate sampling quantity early warning information or sampling quantity correction information.

7. The test management system according to claim 6, characterized by The sampling management model generation module comprises: A sampling quantity information extraction submodule is configured to obtain sampling stratum information and corresponding sampling quantities based on sampling quantity information corresponding to different strata. A sampling management model generation submodule is configured to obtain early survey information corresponding to different strata, associate the sampling quantity information corresponding to different test items, the early survey information, the sampling stratum information, the sampling quantities, and the test indexes, and generate a sampling management model.

8. The test management system according to claim 7, characterized by The confidence level acquisition module comprises: A sampling quantity confidence level acquisition submodule is configured to analyze the sampling quantities in the sampling management model based on a preset confidence level analysis rule and in combination with the sampling stratum information to obtain a sampling quantity confidence level. A test index confidence level acquisition submodule is configured to analyze the test indexes in the sampling management model based on a preset confidence level analysis rule and in combination with the early survey information to obtain a test index confidence level.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The computer program is executed by the processor to implement the steps of the method for early warning and correction of deviation of a sampling quantity in a geotechnical test according to any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the method for early warning and correction of deviation of a sampling quantity in a geotechnical test according to any one of claims 1 to 5.

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