Reinforced concrete strength detection method in marine environment

Abstract

The invention belongs to the technical field of reinforced concrete strength detection, and provides a reinforced concrete strength detection method in a marine environment, which comprises the following steps: acquiring test data of reinforced concrete in the marine environment; based on the test data, utilizing a set performance analysis model to analyze the corrosion influence degree on the concrete performance and the mechanical property of the steel bar to obtain an analysis result; based on the test data, utilizing a set reinforced concrete flexural capacity simulation test model to perform simulation test on the flexural capacity of the reinforced concrete to obtain a simulation test result; based on the test data, utilizing a set reliability change prediction model to predict the reliability change condition of the reinforced concrete structure to obtain a reliability change prediction result; and determining a strength detection result of the reinforced concrete on the basis of test data in combination with an analysis result, a simulation test result and a reliability change prediction result. According to the invention, the comprehensiveness and accuracy of strength detection of reinforced concrete can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reinforced concrete strength detection, and in particular to a reinforced concrete strength detection method in a marine environment. Background Art

[0002] Reinforced concrete structures have been widely used in marine and coastal engineering due to their significant advantages in mechanical properties and economy; however, these structures exposed to harsh marine environments for a long time face many challenges. Environmental factors such as chloride ions, wave impact, salt spray, and temperature changes continue to corrode reinforced concrete structures, causing the material properties to gradually degrade and the structure to become damaged. The emergence of this durability problem not only affects the safety and service life of the structure, but also brings a heavy burden to the social economy; repairing and reinforcing damaged structures requires huge capital investment, and frequent maintenance will also interfere with the normal use of the structure and affect the normal operation of marine engineering.

[0003] Studying the strength and reliability of reinforced concrete structures in marine environments is of great significance for improving structural performance, ensuring public safety, promoting economic development, and promoting scientific and technological progress.

[0004] Therefore, it is necessary to provide a reinforced concrete strength testing method in a marine environment. Summary of the invention

[0005] The present invention provides a reinforced concrete strength detection method under a marine environment. Based on the test data of reinforced concrete under a marine environment, a performance analysis model, a reinforced concrete flexural bearing capacity simulation test model, and a reliability change prediction model are used to respectively perform data analysis testing and prediction on the reinforced concrete. The strength detection result of the reinforced concrete is determined according to the test data, analysis results, simulation test results, and reliability change prediction results, thereby ensuring the scientificity, comprehensiveness, and accuracy of the strength detection of the reinforced concrete.

[0006] The present invention provides a reinforced concrete strength detection method in a marine environment, comprising:

[0007] Obtain test data on reinforced concrete in marine environments;

[0008] Based on the test data, the set performance analysis model is used to analyze the degree of corrosion impact on the concrete performance and steel bar mechanical properties to obtain analysis results;

[0009] Based on the test data, the reinforced concrete flexural bearing capacity simulation test model is used to simulate the flexural bearing capacity of reinforced concrete and obtain the simulation test results;

[0010] Based on the test data, the reliability change prediction model is used to predict the reliability change of the reinforced concrete structure and obtain the reliability change prediction result;

[0011] Based on the test data, combined with the analysis results, simulation test results and reliability change prediction results, the strength test results of reinforced concrete are determined.

[0012] Furthermore, test data of reinforced concrete in marine environment is obtained, including:

[0013] Configure a test apparatus for testing reinforced concrete;

[0014] Use testing instruments to test and obtain data on internal structural changes of reinforced concrete under the influence of seawater environment, data on steel bar corrosion degree, data on concrete performance changes, and data on real-time steel bar strength changes;

[0015] The steel bar corrosion degree data, concrete performance change data and steel bar real-time strength change data are used as test data.

[0016] Furthermore, based on the test data, the influence of corrosion on concrete performance and steel bar mechanical properties is analyzed using the set performance analysis model, and the analysis results are obtained, including:

[0017] Setting a performance analysis model; the performance analysis model includes an evaluation sub-model and a prediction sub-model;

[0018] Based on the concrete performance change data in the test data, the set evaluation sub-model is used to evaluate the degree of corrosion impact on the concrete performance and obtain the evaluation results;

[0019] Based on the steel bar corrosion degree data and steel bar real-time strength change data in the test data, the set prediction sub-model is used to predict the degree of corrosion influence on the mechanical properties of the steel bar to obtain the prediction results;

[0020] The evaluation results and prediction results are integrated to obtain the analysis results.

[0021] Furthermore, based on the concrete performance change data in the test data, the set evaluation sub-model is used to evaluate the degree of corrosion impact on the concrete performance, and the evaluation results are obtained, including:

[0022] Based on the concrete performance change data, obtain typical characteristic data affecting the compressive strength of concrete; typical characteristics include but are not limited to cement type, mix ratio, and chloride ion concentration;

[0023] Based on the constructed concrete compressive strength analysis model and typical characteristic data, an initial analysis of the concrete compressive strength is performed to obtain the initial analysis results of the concrete compressive strength;

[0024] Based on the initial analysis results, the degree of influence of corrosion on the performance of concrete is evaluated to obtain evaluation results; the evaluation includes but is not limited to the use of cross-validation and simulation experiment methods.

[0025] Further, the concrete compressive strength analysis model includes a first analysis sub-model and a second analysis sub-model;

[0026] The first analysis sub-model is constructed based on the support vector regression algorithm in the machine learning algorithm, and is used to perform an initial analysis of the compressive strength of concrete in the first impact stage reflected by the typical characteristic data;

[0027] The second analysis sub-model is constructed based on the long short-term memory model, and is used to perform an initial analysis of the compressive strength of concrete for the second impact stage reflected by the typical characteristic data; wherein the division of the first impact stage and the second impact stage is determined based on the period of the concrete in the marine environment.

[0028] Furthermore, based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the set prediction sub-model is used to predict the degree of corrosion influence on the mechanical properties of the steel bar, and the prediction results are obtained, including:

[0029] Based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the target prediction data for prediction is obtained;

[0030] Using the multi-layer perceptron model, a generative prediction sub-model is constructed;

[0031] According to the prediction sub-model and combined with the target prediction data, the degree of influence of corrosion on the mechanical properties of steel bars is predicted to obtain the prediction results.

[0032] Furthermore, based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the predicted target prediction data is obtained, including:

[0033] Based on the steel bar corrosion degree data in the test data, the rust product accumulation volume data is obtained;

[0034] According to the rust product accumulation volume data, based on the set matching correspondence library of the rust product accumulation volume data and the steel bar mechanical property degradation degree data, the steel bar mechanical property degradation degree data is matched to obtain the steel bar mechanical property degradation degree data;

[0035] Based on the real-time strength change data of the steel bars, using the set steel bar corrosion numerical simulation model, output the corrosion product distribution data and corrosion depth data of the steel bar surface at several selected target periodic points; the numerical simulation model includes but is not limited to the finite element analysis method and the boundary element analysis method;

[0036] The data on the degree of degradation of the mechanical properties of steel bars, the distribution data of corrosion products on the surface of steel bars and the data on the corrosion depth are summarized to obtain the target prediction data for prediction.

[0037] Furthermore, based on the test data, a simulation test of the flexural bearing capacity of reinforced concrete is performed using a set simulation test model for the flexural bearing capacity of reinforced concrete to obtain simulation test results, including:

[0038] Based on the test data, the set reinforced concrete flexural bearing capacity simulation test model is used to simulate the flexural bearing capacity of reinforced concrete by adopting different loads at different simulation test positions to obtain simulation test data; the simulation test data includes the bending moment of the reinforced concrete load-bearing section and the stress distribution of the reinforced concrete;

[0039] Based on the simulation test data, draw and generate the bending moment curvature change distribution trend line and stress distribution diagram;

[0040] According to the distribution trend line of bending moment curvature and stress distribution diagram, the change of reinforced concrete bending bearing capacity is obtained;

[0041] The changes in the flexural bearing capacity of reinforced concrete are taken as simulation test results.

[0042] Furthermore, based on the internal structure change data in the test data, the reliability change prediction model is used to predict the reliability change of the reinforced concrete structure, and the reliability change prediction results are obtained, including:

[0043] Based on the random process degradation model, a reliability change prediction model is constructed;

[0044] Based on the internal structure change data in the test data, the reliability change prediction model is used to predict the internal structure change of the reinforced concrete structure and obtain the prediction result;

[0045] Based on the prediction results, the reliability change of the internal structure is analyzed according to the set reliability change analysis indicators to obtain the reliability change prediction results.

[0046] Furthermore, based on the test data, combined with the analysis results, simulation test results and reliability change prediction results, the strength test results of reinforced concrete are determined, including:

[0047] Determine the initial test strength of reinforced concrete based on the real-time strength change data of the steel bars in the test data;

[0048] Determine several predicted values ​​of strength changes of reinforced concrete based on analysis results, simulation test results and reliability change prediction results;

[0049] Clustering is performed on a number of intensity change prediction values ​​to obtain a number of clustering intervals;

[0050] If the floating value of the intensity change prediction value in the clustering interval is less than the set floating threshold, the intensity change prediction value in the clustering interval is averaged and used as the first typical intensity change prediction value; if the floating value of the intensity change prediction value in the clustering interval is greater than the set floating threshold, the intensity change prediction value in the clustering interval is used as the second typical intensity change prediction value;

[0051] Based on the initial test strength, the first typical strength change prediction value and the second typical strength change prediction value, a strength test data group is composed, and the strength test data group is used as the strength test result of the reinforced concrete.

[0052] Compared with the prior art, the present invention has the following advantages and beneficial effects: based on the test data of reinforced concrete in a marine environment, the performance analysis model, the reinforced concrete flexural bearing capacity simulation test model, and the reliability change prediction model are used to perform data analysis testing and prediction on the reinforced concrete respectively, and the strength test results of the reinforced concrete are determined according to the test data, analysis results, simulation test results and reliability change prediction results, thereby ensuring the scientificity, comprehensiveness and accuracy of the strength test of the reinforced concrete.

[0053] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0054] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 A schematic diagram of the steps of a reinforced concrete strength testing method in a marine environment;

[0057] Figure 2A schematic diagram of the method steps for obtaining test data of reinforced concrete in a marine environment;

[0058] Figure 3 Schematic diagram of the method steps for predicting the degree of influence of corrosion on the mechanical properties of steel bars and obtaining the prediction results. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] The present invention provides a method for testing the strength of reinforced concrete in a marine environment. Figure 1 As shown, including:

[0061] Obtain test data on reinforced concrete in marine environments;

[0062] Based on the test data, the set performance analysis model is used to analyze the degree of corrosion impact on the concrete performance and steel bar mechanical properties to obtain analysis results;

[0063] Based on the test data, the reinforced concrete flexural bearing capacity simulation test model is used to simulate the flexural bearing capacity of reinforced concrete and obtain the simulation test results;

[0064] Based on the test data, the reliability change prediction model is used to predict the reliability change of the reinforced concrete structure and obtain the reliability change prediction result;

[0065] Based on the test data, combined with the analysis results, simulation test results and reliability change prediction results, the strength test results of reinforced concrete are determined.

[0066] The working principle of the above technical solution is as follows: in order to realize the reinforced concrete strength detection method under the marine environment, the present invention first obtains the test data of reinforced concrete under the marine environment; then, based on the test data, the set performance analysis model is used to analyze the degree of influence of corrosion on the concrete performance and the mechanical properties of the steel bars, and the analysis results are obtained; then, based on the test data, the set reinforced concrete flexural bearing capacity simulation test model is used to simulate the flexural bearing capacity of the reinforced concrete and obtain the simulation test results; then, based on the test data, the set reliability change prediction model is used to predict the reliability change of the reinforced concrete structure and obtain the reliability change prediction results; finally, based on the test data, the strength detection results of the reinforced concrete are determined in combination with the analysis results, the simulation test results and the reliability change prediction results.

[0067] The beneficial effects of the above technical scheme are: by adopting the scheme provided in this embodiment, based on the test data of reinforced concrete in a marine environment, the performance analysis model, the reinforced concrete flexural bearing capacity simulation test model, and the reliability change prediction model are used to perform data analysis testing and prediction on the reinforced concrete respectively, and the strength test results of the reinforced concrete are determined based on the test data, analysis results, simulation test results and reliability change prediction results, which can ensure the scientificity, comprehensiveness and accuracy of the strength test of the reinforced concrete.

[0068] In one embodiment, Figure 2 As shown, the test data of reinforced concrete in marine environment is obtained, including:

[0069] Configure a test apparatus for testing reinforced concrete;

[0070] Use testing instruments to test and obtain data on internal structural changes of reinforced concrete under the influence of seawater environment, data on steel bar corrosion degree, data on concrete performance changes, and data on real-time steel bar strength changes;

[0071] The steel bar corrosion degree data, concrete performance change data and steel bar real-time strength change data are used as test data.

[0072] The working principle of the above technical solution is: in order to obtain the test data of reinforced concrete in a marine environment, the present invention first configures a test instrument for testing reinforced concrete; then uses the test instrument to test and obtain the internal structure change data, steel bar corrosion degree data, concrete performance change data and steel bar real-time strength change data of the reinforced concrete in the marine environment caused by the influence of the seawater environment; finally, the steel bar corrosion degree data, concrete performance change data and steel bar real-time strength change data are used as test data.

[0073] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, by using testing instruments, the internal structure change data, steel bar corrosion degree data, concrete performance change data and steel bar real-time strength change data caused by the influence of seawater environment on reinforced concrete in a marine environment can be tested and obtained, thereby ensuring the accuracy of test data acquisition.

[0074] In one embodiment, based on the test data, the influence of corrosion on the concrete performance and the steel bar mechanical properties is analyzed using a set performance analysis model to obtain analysis results, including:

[0075] Setting a performance analysis model; the performance analysis model includes an evaluation sub-model and a prediction sub-model;

[0076] Based on the concrete performance change data in the test data, the set evaluation sub-model is used to evaluate the degree of corrosion impact on the concrete performance and obtain the evaluation results;

[0077] Based on the steel bar corrosion degree data and steel bar real-time strength change data in the test data, the set prediction sub-model is used to predict the degree of corrosion influence on the mechanical properties of the steel bar to obtain the prediction results;

[0078] The evaluation results and prediction results are integrated to obtain the analysis results.

[0079] The working principle of the above technical solution is: in order to analyze the degree of influence of corrosion on concrete performance and steel bar mechanical properties and obtain analysis results, the present invention first sets a performance analysis model; the performance analysis model includes an evaluation sub-model and a prediction sub-model; then based on the concrete performance change data in the test data, the set evaluation sub-model is used to evaluate the degree of influence of corrosion on concrete performance and obtain evaluation results; finally, based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the set prediction sub-model is used to predict the degree of influence of corrosion on the steel bar mechanical properties and obtain prediction results; the evaluation results and the prediction results are combined to obtain analysis results.

[0080] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, by using the set evaluation sub-model and prediction sub-model, the degree of corrosion influence on the mechanical properties of steel bars is analyzed and predicted, which can ensure accurate analysis results.

[0081] In one embodiment, based on the concrete performance change data in the test data, the corrosion impact degree of the concrete performance is evaluated by using the set evaluation sub-model to obtain the evaluation results, including:

[0082] Based on the concrete performance change data, obtain typical characteristic data affecting the compressive strength of concrete; typical characteristics include but are not limited to cement type, mix ratio, and chloride ion concentration;

[0083] Based on the constructed concrete compressive strength analysis model and typical characteristic data, an initial analysis of the concrete compressive strength is performed to obtain the initial analysis results of the concrete compressive strength;

[0084] Based on the initial analysis results, the degree of influence of corrosion on the performance of concrete is evaluated to obtain evaluation results; the evaluation includes but is not limited to the use of cross-validation and simulation experiment methods.

[0085] The working principle of the above technical solution is: in order to evaluate the degree of influence of corrosion on concrete performance by using the set evaluation sub-model and obtain the evaluation result, the present invention first obtains the typical characteristic data affecting the compressive strength of concrete based on the concrete performance change data; the typical characteristics include but are not limited to cement type, mix ratio, chloride ion concentration; then based on the constructed concrete compressive strength analysis model, according to the typical characteristic data, the initial analysis of the concrete compressive strength is performed to obtain the initial analysis result of the concrete compressive strength; finally, according to the initial analysis result, the degree of influence of corrosion on the concrete performance is evaluated to obtain the evaluation result; the evaluation includes but is not limited to the use of cross-validation and simulation experiments.

[0086] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, by using the concrete compressive strength analysis model, an initial analysis of the concrete compressive strength is performed, and the degree of influence of corrosion on the concrete performance is evaluated, an accurate evaluation result can be obtained.

[0087] In one embodiment, the concrete compressive strength analysis model includes a first analysis sub-model and a second analysis sub-model;

[0088] The first analysis sub-model is constructed based on the support vector regression algorithm in the machine learning algorithm, and is used to perform an initial analysis of the compressive strength of concrete in the first impact stage reflected by the typical characteristic data;

[0089] The second analysis sub-model is constructed based on the long short-term memory model, and is used to perform an initial analysis of the compressive strength of concrete for the second impact stage reflected by the typical characteristic data; wherein the division of the first impact stage and the second impact stage is determined based on the period of the concrete in the marine environment.

[0090] The working principle of the above technical solution is: the concrete compressive strength analysis model of the present invention includes a first analysis sub-model and a second analysis sub-model; the first analysis sub-model is constructed based on the support vector regression algorithm in the machine learning algorithm, and is used to perform an initial analysis on the compressive strength of the concrete for the first impact stage reflected by the typical characteristic data; the second analysis sub-model is constructed based on the long short-term memory model, and is used to perform an initial analysis on the compressive strength of the concrete for the second impact stage reflected by the typical characteristic data; wherein, the division of the first impact stage and the second impact stage is determined based on the period in which the concrete is in the marine environment.

[0091] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, the concrete compressive strength analysis model is divided into two analysis sub-models, and targeted analysis is performed on different impact stages, thereby ensuring the pertinence and effectiveness of the analysis.

[0092] In one embodiment, Figure 3 As shown, based on the steel bar corrosion degree data and steel bar real-time strength change data in the test data, the set prediction sub-model is used to predict the degree of corrosion influence on the mechanical properties of the steel bar, and the prediction results are obtained, including:

[0093] Based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the target prediction data for prediction is obtained;

[0094] Using the multi-layer perceptron model, a generative prediction sub-model is constructed;

[0095] According to the prediction sub-model and combined with the target prediction data, the degree of influence of corrosion on the mechanical properties of steel bars is predicted to obtain the prediction results.

[0096] The working principle of the above technical solution is: in order to predict the degree of influence of corrosion on the mechanical properties of steel bars, the present invention first obtains target prediction data for prediction based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data; then uses the multi-layer perceptron model to construct a prediction sub-model; finally, according to the prediction sub-model, combined with the target prediction data, the degree of influence of corrosion on the mechanical properties of the steel bars is predicted to obtain a prediction result.

[0097] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, a prediction sub-model is constructed by utilizing a multi-layer perceptron model; finally, based on the prediction sub-model, the degree of influence of corrosion on the mechanical properties of steel bars is predicted, and accurate prediction results can be obtained.

[0098] In one embodiment, based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the predicted target prediction data is obtained, including:

[0099] Based on the steel bar corrosion degree data in the test data, the rust product accumulation volume data is obtained;

[0100] According to the rust product accumulation volume data, based on the set matching correspondence library of the rust product accumulation volume data and the steel bar mechanical property degradation degree data, the steel bar mechanical property degradation degree data is matched to obtain the steel bar mechanical property degradation degree data;

[0101] Based on the real-time strength change data of the steel bars, using the set steel bar corrosion numerical simulation model, output the corrosion product distribution data and corrosion depth data of the steel bar surface at several selected target periodic points; the numerical simulation model includes but is not limited to the finite element analysis method and the boundary element analysis method;

[0102] The data on the degree of degradation of the mechanical properties of steel bars, the distribution data of corrosion products on the surface of steel bars and the data on the corrosion depth are summarized to obtain the target prediction data for prediction.

[0103] The working principle of the above technical solution is: in order to obtain the predicted target prediction data according to the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the present invention first obtains the rust product accumulation volume data based on the steel bar corrosion degree data in the test data; then, according to the rust product accumulation volume data, based on the set matching correspondence library of the rust product accumulation volume data and the steel bar mechanical property degradation degree data, the steel bar mechanical property degradation degree data is matched to obtain; then, based on the steel bar real-time strength change data, using the set steel bar corrosion numerical simulation model, the rust product distribution data and the corrosion depth data on the steel bar surface of several selected target periodic points are output; the numerical simulation model includes but is not limited to the finite element analysis method and the boundary element analysis method; the steel bar mechanical property degradation degree data, the rust product distribution data and the corrosion depth data on the steel bar surface are summarized to obtain the target prediction data for prediction.

[0104] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, the accuracy of the target prediction data can be guaranteed by summarizing the data on the degree of degradation of the mechanical properties of the steel bars, the distribution data on the corrosion products on the surface of the steel bars and the corrosion depth data.

[0105] In one embodiment, based on the test data, a simulation test is performed on the bending bearing capacity of reinforced concrete using a set simulation test model for the bending bearing capacity of reinforced concrete to obtain simulation test results, including:

[0106] Based on the test data, the set reinforced concrete flexural bearing capacity simulation test model is used to simulate the flexural bearing capacity of reinforced concrete by adopting different loads at different simulation test positions to obtain simulation test data; the simulation test data includes the bending moment of the reinforced concrete load-bearing section and the stress distribution of the reinforced concrete;

[0107] Based on the simulation test data, draw and generate the bending moment curvature change distribution trend line and stress distribution diagram;

[0108] According to the distribution trend line of bending moment curvature and stress distribution diagram, the change of reinforced concrete bending bearing capacity is obtained;

[0109] The changes in the flexural bearing capacity of reinforced concrete are taken as simulation test results.

[0110] The working principle of the above technical scheme is: in order to use the set reinforced concrete flexural bearing capacity simulation test model to simulate the flexural bearing capacity of reinforced concrete and obtain the simulation test results, the present invention first uses the set reinforced concrete flexural bearing capacity simulation test model based on the test data to simulate the flexural bearing capacity of reinforced concrete at different simulation test positions by adopting different loads to obtain simulation test data; the simulation test data includes the bending moment of the reinforced concrete load-bearing section and the reinforced concrete stress distribution; based on the simulation test data, a bending moment curvature change distribution trend line and a stress distribution diagram are drawn and generated; according to the bending moment curvature change distribution trend line and the stress distribution diagram, the change of the flexural bearing capacity of reinforced concrete is obtained; finally, the change of the flexural bearing capacity of reinforced concrete is used as the simulation test result.

[0111] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, using the set reinforced concrete flexural bearing capacity simulation test model, a simulation test is performed on the flexural bearing capacity of reinforced concrete, which can ensure that accurate simulation test data is obtained.

[0112] In one embodiment, based on the internal structure change data in the test data, the reliability change of the reinforced concrete structure is predicted using a set reliability change prediction model to obtain a reliability change prediction result, including:

[0113] Based on the random process degradation model, a reliability change prediction model is constructed;

[0114] Based on the internal structure change data in the test data, the reliability change prediction model is used to predict the internal structure change of the reinforced concrete structure and obtain the prediction result;

[0115] Based on the prediction results, the reliability change of the internal structure is analyzed according to the set reliability change analysis indicators to obtain the reliability change prediction results.

[0116] The working principle of the above technical solution is: in order to use the set reliability change prediction model to predict the reliability change of the reinforced concrete structure and obtain the reliability change prediction result, the present invention first constructs a reliability change prediction model based on the random process degradation model; then based on the internal structure change data in the test data, the reliability change prediction model is used to predict the internal structure change of the reinforced concrete structure and obtain the prediction result; finally, based on the prediction result, according to the set reliability change analysis index, the reliability change of the internal structure is analyzed to obtain the reliability change prediction result.

[0117] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, by predicting the reliability change of the reinforced concrete structure, an accurate reliability change prediction result can be obtained.

[0118] In one embodiment, based on the test data, combined with the analysis results, simulation test results and reliability change prediction results, the strength test results of reinforced concrete are determined, including:

[0119] Determine the initial test strength of reinforced concrete based on the real-time strength change data of the steel bars in the test data;

[0120] Determine several predicted values ​​of strength changes of reinforced concrete based on analysis results, simulation test results and reliability change prediction results;

[0121] Clustering is performed on a number of intensity change prediction values ​​to obtain a number of clustering intervals;

[0122] If the floating value of the intensity change prediction value in the clustering interval is less than the set floating threshold, the intensity change prediction value in the clustering interval is averaged and used as the first typical intensity change prediction value; if the floating value of the intensity change prediction value in the clustering interval is greater than the set floating threshold, the intensity change prediction value in the clustering interval is used as the second typical intensity change prediction value;

[0123] Based on the initial test strength, the first typical strength change prediction value and the second typical strength change prediction value, a strength test data group is composed, and the strength test data group is used as the strength test result of the reinforced concrete.

[0124] The working principle of the above technical scheme is as follows: in order to determine the strength detection result of reinforced concrete, the present invention first determines the initial test strength of reinforced concrete based on the real-time strength change data of the steel bars in the test data; then, according to the analysis results, simulation test results and reliability change prediction results, several strength change prediction values ​​of reinforced concrete are determined; then, several strength change prediction values ​​are clustered to obtain several cluster intervals; if the floating value of the strength change prediction value in the cluster interval is less than the set floating threshold, the strength change prediction value in the cluster interval is averaged and used as the first typical strength change prediction value; if the floating value of the strength change prediction value in the cluster interval is greater than the set floating threshold, the strength change prediction value in the cluster interval is used as the second typical strength change prediction value; finally, based on the initial test strength, the first typical strength change prediction value and the second typical strength change prediction value, a strength test data group is formed, and the strength test data group is used as the strength detection result of reinforced concrete.

[0125] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, through the real-time strength change data of steel bars, as well as analysis results, simulation test results and reliability change prediction results, it is possible to ensure that comprehensive and accurate strength test results are obtained.

[0126] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A reinforced concrete strength detection method in a marine environment, characterized in that: include: Obtain test data on reinforced concrete in marine environments; Based on the test data, the set performance analysis model is used to analyze the degree of corrosion impact on the concrete performance and steel bar mechanical properties to obtain analysis results; Based on the test data, the reinforced concrete flexural bearing capacity simulation test model is used to simulate the flexural bearing capacity of reinforced concrete and obtain the simulation test results; Based on the test data, the reliability change prediction model is used to predict the reliability change of the reinforced concrete structure and obtain the reliability change prediction result; Based on the test data, combined with the analysis results, simulation test results and reliability change prediction results, the strength test results of reinforced concrete are determined.

2. The reinforced concrete strength detection method in a marine environment according to claim 1, characterized in that: Obtain test data on reinforced concrete in marine environments, including: Configure a test apparatus for testing reinforced concrete; Use testing instruments to test and obtain data on internal structural changes of reinforced concrete under the influence of seawater environment, data on steel bar corrosion degree, data on concrete performance changes, and data on real-time steel bar strength changes; The steel bar corrosion degree data, concrete performance change data and steel bar real-time strength change data are used as test data.

3. The reinforced concrete strength detection method in a marine environment according to claim 2, characterized in that: Based on the test data, the established performance analysis model is used to analyze the degree of corrosion impact on concrete performance and steel bar mechanical properties, and the analysis results are obtained, including: Setting a performance analysis model; the performance analysis model includes an evaluation sub-model and a prediction sub-model; Based on the concrete performance change data in the test data, the set evaluation sub-model is used to evaluate the degree of corrosion impact on the concrete performance and obtain the evaluation results; Based on the steel bar corrosion degree data and steel bar real-time strength change data in the test data, the set prediction sub-model is used to predict the degree of corrosion influence on the mechanical properties of the steel bar to obtain the prediction results; The evaluation results and prediction results are integrated to obtain the analysis results.

4. The reinforced concrete strength detection method in a marine environment according to claim 3, characterized in that: Based on the concrete performance change data in the test data, the set evaluation sub-model is used to evaluate the degree of corrosion impact on concrete performance and obtain evaluation results, including: Based on the concrete performance change data, obtain typical characteristic data affecting the compressive strength of concrete; typical characteristics include but are not limited to cement type, mix ratio, and chloride ion concentration; Based on the constructed concrete compressive strength analysis model and typical characteristic data, an initial analysis of the concrete compressive strength is performed to obtain the initial analysis results of the concrete compressive strength; Based on the initial analysis results, the degree of influence of corrosion on the performance of concrete is evaluated to obtain evaluation results; the evaluation includes but is not limited to the use of cross-validation and simulation experiment methods.

5. The reinforced concrete strength detection method in a marine environment according to claim 4, characterized in that: The concrete compressive strength analysis model includes a first analysis sub-model and a second analysis sub-model; The first analysis sub-model is constructed based on the support vector regression algorithm in the machine learning algorithm, and is used to perform an initial analysis of the compressive strength of concrete in the first impact stage reflected by the typical characteristic data; The second analysis sub-model is constructed based on the long short-term memory model, and is used to perform an initial analysis of the compressive strength of concrete for the second impact stage reflected by the typical characteristic data; wherein the division of the first impact stage and the second impact stage is determined based on the period of the concrete in the marine environment.

6. The reinforced concrete strength testing method in a marine environment according to claim 3, characterized in that: Based on the steel bar corrosion degree data and steel bar real-time strength change data in the test data, the set prediction sub-model is used to predict the degree of corrosion impact on the mechanical properties of the steel bar, and the prediction results are obtained, including: Based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the target prediction data for prediction is obtained; Using the multi-layer perceptron model, a generative prediction sub-model is constructed; According to the prediction sub-model and combined with the target prediction data, the degree of influence of corrosion on the mechanical properties of steel bars is predicted to obtain the prediction results.

7. The reinforced concrete strength testing method in a marine environment according to claim 6, characterized in that: Based on the steel bar corrosion degree data and steel bar real-time strength change data in the test data, the predicted target prediction data is obtained, including: Based on the steel bar corrosion degree data in the test data, the rust product accumulation volume data is obtained; According to the rust product accumulation volume data, based on the set matching correspondence library of the rust product accumulation volume data and the steel bar mechanical property degradation degree data, the steel bar mechanical property degradation degree data is matched to obtain the steel bar mechanical property degradation degree data; Based on the real-time strength change data of the steel bars, using the set steel bar corrosion numerical simulation model, output the corrosion product distribution data and corrosion depth data of the steel bar surface at several selected target periodic points; the numerical simulation model includes but is not limited to the finite element analysis method and the boundary element analysis method; The data on the degree of degradation of the mechanical properties of steel bars, the distribution data of corrosion products on the surface of steel bars and the data on the corrosion depth are summarized to obtain the target prediction data for prediction.

8. The reinforced concrete strength testing method in a marine environment according to claim 2, characterized in that: Based on the test data, the reinforced concrete flexural bearing capacity simulation test model is used to simulate the flexural bearing capacity of reinforced concrete and obtain the simulation test results, including: Based on the test data, the set reinforced concrete flexural bearing capacity simulation test model is used to simulate the flexural bearing capacity of reinforced concrete by adopting different loads at different simulation test positions to obtain simulation test data; the simulation test data includes the bending moment of the reinforced concrete load-bearing section and the stress distribution of the reinforced concrete; Based on the simulation test data, draw and generate the bending moment curvature change distribution trend line and stress distribution diagram; According to the distribution trend line of bending moment curvature and stress distribution diagram, the change of reinforced concrete bending bearing capacity is obtained; The changes in the flexural bearing capacity of reinforced concrete are taken as simulation test results.

9. The reinforced concrete strength testing method in a marine environment according to claim 2, characterized in that: Based on the internal structure change data in the test data, the reliability change prediction model is used to predict the reliability change of the reinforced concrete structure and obtain the reliability change prediction results, including: Based on the random process degradation model, a reliability change prediction model is constructed; Based on the internal structure change data in the test data, the reliability change prediction model is used to predict the internal structure change of the reinforced concrete structure and obtain the prediction result; Based on the prediction results, the reliability change of the internal structure is analyzed according to the set reliability change analysis indicators to obtain the reliability change prediction results.

10. The reinforced concrete strength testing method in a marine environment according to claim 2, characterized in that: Based on the test data, combined with the analysis results, simulation test results and reliability change prediction results, the strength test results of reinforced concrete are determined, including: Determine the initial test strength of reinforced concrete based on the real-time strength change data of the steel bars in the test data; Determine several predicted values ​​of strength changes of reinforced concrete based on analysis results, simulation test results and reliability change prediction results; Clustering is performed on a number of intensity change prediction values ​​to obtain a number of clustering intervals; If the floating value of the intensity change prediction value in the clustering interval is less than the set floating threshold, the intensity change prediction value in the clustering interval is averaged and used as the first typical intensity change prediction value; if the floating value of the intensity change prediction value in the clustering interval is greater than the set floating threshold, the intensity change prediction value in the clustering interval is used as the second typical intensity change prediction value; Based on the initial test strength, the first typical strength change prediction value and the second typical strength change prediction value, a strength test data group is composed, and the strength test data group is used as the strength test result of the reinforced concrete.

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