A method for detecting strength of reinforced concrete in marine environment
By acquiring test data of reinforced concrete in a marine environment and using various models for analysis and prediction, the problem of performance degradation of reinforced concrete structures caused by corrosion has been solved, achieving scientific and accurate strength testing and improving the reliability and service life of the structure.
Patent Information
- Application Number
- CN202510000108.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-02
- Publication Date
- 2025-12-26
- Estimated Expiration
- 2045-01-02
AI Technical Summary
In marine environments, the performance degradation and reliability reduction of reinforced concrete structures due to corrosion affect structural safety and service life. Existing testing methods lack scientific rigor and accuracy.
By acquiring test data of reinforced concrete in a marine environment, and using performance analysis models, reinforced concrete flexural bearing capacity simulation test models, and reliability change prediction models, the performance of concrete and the mechanical properties of steel bars are analyzed and predicted. The strength test results are determined by combining the analysis results and prediction results.
It has achieved scientific, comprehensive and accurate strength testing of reinforced concrete in marine environments, ensuring the safety and service life of the structure.
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Figure CN119827318B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of reinforced concrete strength detection, and particularly relates to a reinforced concrete strength detection method in a marine environment. BACKGROUND
[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 continuously erode the reinforced concrete structure, leading to gradual degradation of material properties and damage to the structure. The occurrence of this durability problem not only affects the safety and service life of the structure, but also brings a heavy burden to the society and economy; repairing and reinforcing damaged structures requires huge capital investment, and frequent maintenance also interferes with the normal use of the structure, affecting the normal operation of marine engineering.
[0003] Research on 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 advancing science and technology.
[0004] Therefore, it is necessary to provide a reinforced concrete strength detection method in a marine environment. SUMMARY
[0005] The present application provides a reinforced concrete strength detection method in a marine environment, which uses performance analysis models, reinforced concrete flexural bearing capacity simulation test models, and reliability change prediction models to analyze and test the data of reinforced concrete based on test data of reinforced concrete in a marine environment, and determines the strength detection results of reinforced concrete according to the test data, analysis results, simulation test results, and reliability change prediction results, which can ensure the scientificity, comprehensiveness, and accuracy of the strength detection of reinforced concrete.
[0006] The present application provides a reinforced concrete strength detection method in a marine environment, which includes:
[0007] Obtaining test data of reinforced concrete in a marine environment;
[0008] Based on the test data, the corrosion influence degree of concrete performance and steel mechanical properties is analyzed using a set performance analysis model to obtain analysis results;
[0009] Based on the test data, the flexural bearing capacity of reinforced concrete is simulated and tested using a set reinforced concrete flexural bearing capacity simulation test model to obtain 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 the reliability change prediction result is obtained.
[0011] Based on the test data, the strength detection result of the reinforced concrete is determined by combining the analysis result, the simulation test result and the reliability change prediction result.
[0012] Further, the test data of the reinforced concrete in the marine environment is obtained, including:
[0013] The test instrument for testing the reinforced concrete is configured;
[0014] The internal structure change data, the steel corrosion degree data, the concrete performance change data and the steel real-time strength change data generated by the reinforced concrete in the marine environment are obtained by using the test instrument;
[0015] The steel corrosion degree data, the concrete performance change data and the steel real-time strength change data are used as test data.
[0016] Further, based on the test data, the performance analysis model is used to analyze the corrosion influence degree of the concrete performance and the steel mechanical performance, and the analysis result is obtained, including:
[0017] The performance analysis model is set; 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 evaluation sub-model is used to evaluate the corrosion influence degree of the concrete performance, and the evaluation result is obtained;
[0019] Based on the steel corrosion degree data and the steel real-time strength change data in the test data, the prediction sub-model is used to predict the corrosion influence degree of the steel mechanical performance, and the prediction result is obtained;
[0020] The evaluation result and the prediction result are combined to obtain the analysis result.
[0021] Further, based on the concrete performance change data in the test data, the evaluation sub-model is used to evaluate the corrosion influence degree of the concrete performance, and the evaluation result is obtained, including:
[0022] Based on the concrete performance change data, the typical feature data affecting the concrete compressive strength is obtained; the typical features include but are not limited to cement type, mix proportion, chloride ion concentration;
[0023] Based on the constructed concrete compressive strength analysis model, the initial analysis of the concrete compressive strength is carried out according to the typical characteristic data, and the initial analysis result of the concrete compressive strength is obtained.
[0024] According to the initial analysis result, the corrosion influence degree on the performance of the concrete is evaluated, and an evaluation result is obtained; the evaluation includes but is not limited to the methods of cross-validation and simulation experiment.
[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 a support vector regression algorithm in a machine learning algorithm, and is used for initial analysis of the compressive strength of the concrete in the first influence stage reflected by the typical characteristic data;
[0027] The second analysis sub-model is constructed based on a long short-term memory model, and is used for initial analysis of the compressive strength of the concrete in the second influence stage reflected by the typical characteristic data; wherein the division of the first influence stage and the second influence stage is determined based on the period of the concrete in the marine environment.
[0028] Further, based on the steel corrosion degree data and the real-time strength change data of the steel in the test data, the influence degree of corrosion on the mechanical properties of the steel is predicted by using a set of prediction sub-models, and a prediction result is obtained, including:
[0029] Based on the steel corrosion degree data and the real-time strength change data of the steel in the test data, target prediction data for prediction is obtained;
[0030] A multilayer perception model is used to construct a prediction sub-model;
[0031] According to the prediction sub-model, the influence degree of corrosion on the mechanical properties of the steel is predicted by combining the target prediction data, and a prediction result is obtained.
[0032] Further, based on the steel corrosion degree data and the real-time strength change data of the steel in the test data, the target prediction data for prediction is obtained, including:
[0033] Based on the steel corrosion degree data in the test data, rust product accumulation volume data is obtained;
[0034] According to the rust product accumulation volume data, the steel mechanical performance degradation degree data is matched and obtained based on a set of matching corresponding relationship database of rust product accumulation volume data and steel mechanical performance degradation degree data;
[0035] Based on the real-time strength change data of the steel bar, a set of steel bar corrosion numerical simulation model is used to output the corrosion product distribution data and corrosion depth data of the steel bar surface at several selected target period points; the numerical simulation model includes but is not limited to finite element analysis method and boundary element analysis method;
[0036] The steel bar mechanical property degradation degree data, the steel bar surface corrosion product distribution data and the corrosion depth data are summarized to obtain the target prediction data for prediction.
[0037] Further, based on the test data, a set of reinforced concrete flexural bearing capacity simulation test model is used to simulate the test of the reinforced concrete flexural bearing capacity, and the simulation test result is obtained, including:
[0038] Based on the test data, a set of reinforced concrete flexural bearing capacity simulation test model is used to simulate the test of the reinforced concrete flexural bearing capacity by using different loads at different simulation test positions, and the simulation test data is obtained; the simulation test data includes the bending moment of the reinforced concrete stress section and the stress distribution of the reinforced concrete;
[0039] Based on the simulation test data, the bending moment curvature change distribution trend line and the stress distribution graph are drawn;
[0040] According to the bending moment curvature change distribution trend line and the stress distribution graph, the change of the reinforced concrete flexural bearing capacity is obtained;
[0041] The change of the reinforced concrete flexural bearing capacity is taken as the simulation test result.
[0042] Further, based on the internal structure change data in the test data, a set of reliability change prediction model is used to predict the reliability change of the reinforced concrete structure, and the reliability change prediction result is obtained, including:
[0043] Based on the random process degradation model, the 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 the prediction result is obtained;
[0045] Based on the prediction result, the reliability change of the internal structure is analyzed according to the set of reliability change analysis indexes, and the reliability change prediction result is obtained.
[0046] Further, based on the test data, the analysis result, the simulation test result and the reliability change prediction result are combined to determine the strength detection result of the reinforced concrete, including:
[0047] Determine the initial test strength of the reinforced concrete based on the real-time strength change data of the steel bars in the test data;
[0048] Determine several strength change prediction values of the reinforced concrete according to the analysis result, the simulation test result and the reliability change prediction result;
[0049] Cluster and divide the several strength change prediction values to obtain several clustering intervals;
[0050] If the floating value of the strength change prediction values in the clustering interval is less than the set floating threshold value, then take the average value of the strength change prediction values in the clustering interval as the first typical strength change prediction value; if the floating value of the strength change prediction values in the clustering interval is greater than the set floating threshold value, then take the strength change prediction values in the clustering interval as the second typical strength 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, form a strength test data group, and take the strength test data group as the strength detection result of the reinforced concrete.
[0052] Compared with the prior art, the present application has the following advantages and beneficial effects: through the test data of the reinforced concrete under the marine environment, the performance analysis model, the reinforced concrete bending resistance capacity simulation test model and the reliability change prediction model are used to respectively analyze and test the data of the reinforced concrete and make prediction, and according to the test data, the analysis result, the simulation test result and the reliability change prediction result, the strength detection result of the reinforced concrete is determined, so that the scientificity, comprehensiveness and accuracy of the strength detection of the reinforced concrete can be ensured.
[0053] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and the appended drawings.
[0054] The technical solutions of the present application will be further described in detail below with the help of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS
[0055] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application and explain the present application together with the embodiments of the present application, and do not constitute a limitation to the present application. In the drawings:
[0056] Figure 1 It is a kind of marine environment under the reinforced concrete strength detection method step schematic diagram;
[0057] Figure 2A method for obtaining test data of reinforced concrete in a marine environment is shown in the flowchart.
[0058] Figure 3 A method for obtaining a prediction result of the degree of influence of corrosion on the mechanical properties of steel bars is shown in the flowchart. DETAILED DESCRIPTION
[0059] The preferred embodiments of the present application are described below with reference to the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.
[0060] The present application provides a method for detecting the strength of reinforced concrete in a marine environment, as shown in the flowchart. Figure 1 The method comprises the following steps:
[0061] Obtaining test data of reinforced concrete in a marine environment.
[0062] Based on the test data, the degree of influence of corrosion on the mechanical properties of steel bars and the performance of concrete are analyzed using a set performance analysis model, and an analysis result is obtained.
[0063] Based on the test data, a simulation test of the flexural bearing capacity of reinforced concrete is performed using a set simulation test model of the flexural bearing capacity of reinforced concrete, and a simulation test result is obtained.
[0064] Based on the test data, a prediction of the reliability change of the reinforced concrete structure is performed using a set reliability change prediction model, and a reliability change prediction result is obtained.
[0065] Based on the test data, the analysis result, the simulation test result, and the reliability change prediction result are combined to determine the strength detection result of the reinforced concrete.
[0066] The working principle of the above technical solution is as follows: in order to realize the method for detecting the strength of reinforced concrete in a marine environment, the present application first obtains test data of reinforced concrete in a marine environment; then, based on the test data, the degree of influence of corrosion on the mechanical properties of steel bars and the performance of concrete are analyzed using a set performance analysis model, and an analysis result is obtained; then, based on the test data, a simulation test of the flexural bearing capacity of reinforced concrete is performed using a set simulation test model of the flexural bearing capacity of reinforced concrete, and a simulation test result is obtained; then, based on the test data, a prediction of the reliability change of the reinforced concrete structure is performed using a set reliability change prediction model, and a reliability change prediction result is obtained; finally, based on the test data, the analysis result, the simulation test result, and the reliability change prediction result are combined to determine the strength detection result of the reinforced concrete.
[0067] The beneficial effects of the above technical solutions are: by using the scheme provided in the embodiment, the performance analysis model, the reinforced concrete bending resistance capacity simulation test model, and the reliability change prediction model are used to respectively analyze and test and predict the reinforced concrete based on the test data of the reinforced concrete in the marine environment, and the strength detection result of the reinforced concrete is determined according to the test data, the analysis result, the simulation test result, and the reliability change prediction result, so that the scientificity, comprehensiveness, and accuracy of the strength detection of the reinforced concrete can be ensured.
[0068] In one embodiment, as shown in Figure 2 test data of reinforced concrete in a marine environment are acquired, including:
[0069] A test instrument for testing the reinforced concrete is configured.
[0070] The test instrument is used to test internal structure change data, reinforced corrosion degree data, concrete performance change data, and real-time strength change data of the reinforced concrete in the marine environment caused by the influence of seawater environment.
[0071] The reinforced corrosion degree data, the concrete performance change data, and the real-time strength change data of the reinforced concrete are taken as the test data.
[0072] The working principle of the above technical solutions is: in order to acquire the test data of the reinforced concrete in the marine environment, the test instrument for testing the reinforced concrete is first configured, then the test instrument is used to test the internal structure change data, the reinforced corrosion degree data, the concrete performance change data, and the real-time strength change data of the reinforced concrete in the marine environment caused by the influence of seawater environment, and finally the reinforced corrosion degree data, the concrete performance change data, and the real-time strength change data of the reinforced concrete are taken as the test data.
[0073] The beneficial effects of the above technical solutions are: by using the scheme provided in the embodiment, the test instrument is used to test the internal structure change data, the reinforced corrosion degree data, the concrete performance change data, and the real-time strength change data of the reinforced concrete in the marine environment caused by the influence of seawater environment, so that the accuracy of the test data acquisition can be ensured.
[0074] In one embodiment, based on the test data, the corrosion influence degree on the concrete performance and the reinforced mechanical performance is analyzed by using a set performance analysis model to obtain an analysis result, including:
[0075] The performance analysis model is set; 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 influence of corrosion on the concrete performance is evaluated by using the evaluation sub-model, and evaluation results are obtained.
[0077] Based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the influence of corrosion on the steel bar mechanical performance is predicted by using the prediction sub-model, and prediction results are obtained.
[0078] The evaluation results and the prediction results are comprehensively evaluated, and analysis results are obtained.
[0079] The working principle of the above technical solution is as follows: in order to analyze the influence of corrosion on the concrete performance and the steel bar mechanical performance and obtain analysis results, the performance analysis model is set; 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 influence of corrosion on the concrete performance is evaluated by using the evaluation sub-model, and evaluation results are obtained; finally, based on the steel bar corrosion degree data and the steel bar real-time strength change data in the test data, the influence of corrosion on the steel bar mechanical performance is predicted by using the prediction sub-model, and prediction results are obtained; the evaluation results and the prediction results are comprehensively evaluated, and analysis results are obtained.
[0080] The beneficial effects of the above technical solution are as follows: by using the evaluation sub-model and the prediction sub-model, the influence of corrosion on the steel bar mechanical performance is analyzed and predicted, and accurate analysis results can be obtained.
[0081] In one embodiment, based on the concrete performance change data in the test data, the influence of corrosion on the concrete performance is evaluated by using the evaluation sub-model, and evaluation results are obtained, including:
[0082] Based on the concrete performance change data, typical feature data affecting the concrete compressive strength are obtained; the typical features include but are not limited to cement types, mix proportions, and chloride ion concentrations.
[0083] Based on the constructed concrete compressive strength analysis model, the concrete compressive strength is initially analyzed according to the typical feature data, and initial analysis results of the concrete compressive strength are obtained.
[0084] According to the initial analysis results, the influence of corrosion on the concrete performance is evaluated, and evaluation results are obtained; the evaluation includes but is not limited to the methods of cross-validation and simulation experiment.
[0085] The working principle of the technical solution is as follows: in order to realize the evaluation of the influence degree of corrosion on the performance of concrete by using the set evaluation sub-model, and obtain the evaluation result, the application obtains the typical characteristic data of the influence on the compressive strength of concrete based on the performance change data of concrete; the typical characteristics include but are not limited to cement type, mix ratio, and chloride ion concentration; then, based on the constructed concrete compressive strength analysis model, the initial analysis of the compressive strength of concrete is performed according to the typical characteristic data, and the initial analysis result of the compressive strength of concrete is obtained; finally, the influence degree of corrosion on the performance of concrete is evaluated according to the initial analysis result, and the evaluation result is obtained; the evaluation includes but is not limited to the methods of cross-validation and simulation experiment.
[0086] The beneficial effect of the technical solution is that by using the scheme provided in the embodiment, the initial analysis of the compressive strength of concrete is performed by using the concrete compressive strength analysis model, and the influence degree of corrosion on the performance of concrete is evaluated, so that 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 a support vector regression algorithm in a machine learning algorithm, and is used to perform the initial analysis of the compressive strength of concrete for the first influence stage reflected by the typical characteristic data.
[0089] The second analysis sub-model is constructed based on a long short-term memory model, and is used to perform the initial analysis of the compressive strength of concrete for the second influence stage reflected by the typical characteristic data; wherein the division of the first influence stage and the second influence stage is determined based on the period of concrete in the marine environment.
[0090] The working principle of the technical solution is as follows: the concrete compressive strength analysis model of the application includes a first analysis sub-model and a second analysis sub-model; the first analysis sub-model is constructed based on a support vector regression algorithm in a machine learning algorithm, and is used to perform the initial analysis of the compressive strength of concrete for the first influence stage reflected by the typical characteristic data; the second analysis sub-model is constructed based on a long short-term memory model, and is used to perform the initial analysis of the compressive strength of concrete for the second influence stage reflected by the typical characteristic data; wherein the division of the first influence stage and the second influence stage is determined based on the period of concrete in the marine environment.
[0091] The beneficial effect of the technical solution is that by using the scheme provided in the embodiment, the concrete compressive strength analysis model is divided into two analysis sub-models, and targeted analysis is performed for different influence stages, so that the pertinence and effectiveness of the analysis can be ensured.
[0092] In one embodiment, as shown in Figure 3 Based on the reinforcement corrosion degree data and the reinforcement real-time strength change data in the test data, the influence of corrosion on the mechanical properties of the reinforcement is predicted by using a set of prediction sub-models, and a prediction result is obtained, including:
[0093] Based on the reinforcement corrosion degree data and the reinforcement real-time strength change data in the test data, target prediction data for prediction is obtained;
[0094] A multilayer perception model is used to construct a prediction sub-model;
[0095] According to the prediction sub-model, the influence of corrosion on the mechanical properties of the reinforcement is predicted by combining the target prediction data, and a prediction result is obtained.
[0096] The working principle of the above technical solution is as follows: in order to predict the influence of corrosion on the mechanical properties of the reinforcement, the target prediction data for prediction is obtained based on the reinforcement corrosion degree data and the reinforcement real-time strength change data in the test data; then a multilayer perception model is used to construct a prediction sub-model; finally, the influence of corrosion on the mechanical properties of the reinforcement is predicted by combining the target prediction data according to the prediction sub-model, and a prediction result is obtained.
[0097] The beneficial effects of the above technical solution are as follows: by using the scheme provided in this embodiment, a multilayer perception model is used to construct a prediction sub-model; finally, the influence of corrosion on the mechanical properties of the reinforcement is predicted by using the prediction sub-model, and an accurate prediction result can be obtained.
[0098] In one embodiment, based on the reinforcement corrosion degree data and the reinforcement real-time strength change data in the test data, target prediction data for prediction is obtained, including:
[0099] Based on the reinforcement corrosion degree data in the test data, rust product accumulation volume data is obtained;
[0100] Based on the rust product accumulation volume data, a matching relationship database between the rust product accumulation volume data and the reinforcement mechanical property degradation degree data is set, and the reinforcement mechanical property degradation degree data is matched and obtained according to the matching relationship database;
[0101] Based on the reinforcement real-time strength change data, a set of rust product distribution data and corrosion depth data of the reinforcement surface at a selected target period point are output by using a set of reinforcement corrosion numerical simulation models; the numerical simulation models include but are not limited to finite element analysis methods and boundary element analysis methods;
[0102] The steel reinforcement mechanical property degradation degree data, the steel reinforcement surface corrosion product distribution data and the corrosion depth data are summarized to obtain the target prediction data for prediction.
[0103] The working principle of the technical solution is as follows: in order to obtain the target prediction data for prediction according to the steel reinforcement corrosion degree data and the steel reinforcement real-time strength change data in the test data, the present application first obtains the rust product accumulation volume data based on the steel reinforcement corrosion degree data in the test data; then, based on the rust product accumulation volume data, the steel reinforcement mechanical property degradation degree data is matched and obtained based on the matching corresponding relationship library of the set rust product accumulation volume data and the steel reinforcement mechanical property degradation degree data; then, based on the steel reinforcement real-time strength change data, the steel reinforcement surface corrosion product distribution data and the corrosion depth data of a plurality of selected target period points are output by using the set steel reinforcement corrosion numerical simulation model; the numerical simulation model includes but is not limited to the finite element analysis method and the boundary element analysis method; and the steel reinforcement mechanical property degradation degree data, the steel reinforcement surface corrosion product distribution data and the corrosion depth data are summarized to obtain the target prediction data for prediction.
[0104] The beneficial effects of the technical solution are as follows: by using the scheme provided in the embodiment, the accuracy of the target prediction data can be ensured by summarizing the steel reinforcement mechanical property degradation degree data, the steel reinforcement surface corrosion product distribution data and the corrosion depth data.
[0105] In one embodiment, based on the test data, the steel reinforced concrete flexural bearing capacity is simulated and tested by using the set steel reinforced concrete flexural bearing capacity simulation test model, and simulation test results are obtained, including:
[0106] Based on the test data, the steel reinforced concrete flexural bearing capacity is simulated and tested by using the set steel reinforced concrete flexural bearing capacity simulation test model, and simulation test data are obtained by using different loads at different simulation test positions; the simulation test data include the bending moment of the steel reinforced concrete stress section and the stress distribution of the steel reinforced concrete;
[0107] Based on the simulation test data, a bending moment curvature change distribution trend line and a stress distribution graph are generated;
[0108] According to the bending moment curvature change distribution trend line and the stress distribution graph, the change of the steel reinforced concrete flexural bearing capacity is obtained;
[0109] The change of the steel reinforced concrete flexural bearing capacity is taken as the simulation test result.
[0110] The working principle of the technical solution is that in order to simulate the test of the steel reinforced concrete bending resistance, obtain the simulation test result, the simulation test data of the steel reinforced concrete bending resistance is obtained by using the set steel reinforced concrete bending resistance simulation test model, adopting different loads and different simulation test positions; the simulation test data includes the bending moment of the steel reinforced concrete stress section and the stress distribution of the steel reinforced concrete; the bending moment curvature change distribution trend line and the stress distribution diagram are generated based on the simulation test data; the change of the steel reinforced concrete bending resistance is obtained according to the bending moment curvature change distribution trend line and the stress distribution diagram; and finally, the change of the steel reinforced concrete bending resistance is taken as the simulation test result.
[0111] The beneficial effect of the technical solution is that the simulation test of the steel reinforced concrete bending resistance is performed by using the set steel reinforced concrete bending resistance simulation test model, so that accurate simulation test data can be obtained.
[0112] In one embodiment, the reliability change of the steel reinforced concrete structure is predicted based on the internal structure change data in the test data by using the set reliability change prediction model to obtain the reliability change prediction result, including:
[0113] The reliability change prediction model is constructed based on the random process degradation model;
[0114] The internal structure change of the steel reinforced concrete structure is predicted based on the internal structure change data in the test data by using the reliability change prediction model to obtain the prediction result;
[0115] The reliability change of the internal structure is analyzed based on the prediction result according to the set reliability change analysis index to obtain the reliability change prediction result.
[0116] The working principle of the technical solution is that in order to simulate the test of the steel reinforced concrete bending resistance, obtain the simulation test result, the simulation test data of the steel reinforced concrete bending resistance is obtained by using the set steel reinforced concrete bending resistance simulation test model, adopting different loads and different simulation test positions; the simulation test data includes the bending moment of the steel reinforced concrete stress section and the stress distribution of the steel reinforced concrete; the bending moment curvature change distribution trend line and the stress distribution diagram are generated based on the simulation test data; the change of the steel reinforced concrete bending resistance is obtained according to the bending moment curvature change distribution trend line and the stress distribution diagram; and finally, the change of the steel reinforced concrete bending resistance is taken as the simulation test result.
[0117] The beneficial effects of the above technical solution are: by using the scheme provided in the embodiment, the accurate reliability change prediction result can be obtained by predicting the reliability change of the reinforced concrete structure.
[0118] In one embodiment, based on the test data, the analysis result, the simulation test result and the reliability change prediction result, the strength detection result of the reinforced concrete is determined, including:
[0119] Based on the real-time strength change data of the steel bar in the test data, the initial test strength of the reinforced concrete is determined;
[0120] According to the analysis result, the simulation test result and the reliability change prediction result, a plurality of strength change prediction values of the reinforced concrete are determined;
[0121] The plurality of strength change prediction values are clustered and divided to obtain a plurality of clustering intervals;
[0122] If the floating value of the strength change prediction value in the clustering interval is less than the set floating threshold value, the strength change prediction value in the clustering interval is averaged to be a first typical strength change prediction value; if the floating value of the strength change prediction value in the clustering interval is greater than the set floating threshold value, the strength change prediction value in the clustering interval is a second typical strength 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 formed, and the strength test data group is taken as the strength detection result of the reinforced concrete.
[0124] The working principle of the above technical solution is: in order to determine the strength detection result of the reinforced concrete, the initial test strength of the reinforced concrete is first determined based on the real-time strength change data of the steel bar in the test data; then, according to the analysis result, the simulation test result and the reliability change prediction result, a plurality of strength change prediction values of the reinforced concrete are determined; then, the plurality of strength change prediction values are clustered and divided to obtain a plurality of clustering intervals; if the floating value of the strength change prediction value in the clustering interval is less than the set floating threshold value, the strength change prediction value in the clustering interval is averaged to be a first typical strength change prediction value; if the floating value of the strength change prediction value in the clustering interval is greater than the set floating threshold value, the strength change prediction value in the clustering interval is a 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 taken as the strength detection result of the reinforced concrete.
[0125] The beneficial effects of the above technical solution are: by using the scheme provided in the embodiment, the real-time strength change data of the steel bars, the analysis results, the simulation test results and the reliability change prediction results can ensure that comprehensive and accurate strength detection results are obtained.
[0126] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the present application. Thus, if these modifications and changes are within the scope of the claims and their equivalents, the present application is also intended to include these modifications and changes.
Claims
1. A method for testing the strength of reinforced concrete in a marine environment, characterized in that, include: Obtain test data on reinforced concrete in a marine environment; including: Using the configured testing instruments for testing reinforced concrete, data on internal structural changes, steel corrosion degree, concrete performance changes, and real-time steel strength changes of reinforced concrete under the influence of seawater environment were obtained as test data. Based on test data and using a defined performance analysis model, the degree of corrosion impact on concrete performance and steel reinforcement mechanical properties is analyzed, and the analysis results are obtained, including: Based on concrete performance variation data, typical characteristic data affecting concrete compressive strength are obtained; typical characteristics include, but are not limited to, cement type, mix proportion, and chloride ion concentration. Based on the constructed concrete compressive strength analysis model, an initial analysis of the concrete compressive strength is performed according to typical characteristic data to obtain initial analysis results. The concrete compressive strength analysis model includes a first analysis sub-model constructed based on the support vector regression algorithm in machine learning algorithms and a second analysis sub-model constructed based on the long short-term memory model. The first analysis sub-model performs an initial analysis of the concrete compressive strength for the first influence stage reflected by the typical characteristic data; the second analysis sub-model performs an initial analysis of the concrete compressive strength for the second influence stage reflected by the typical characteristic data. The division between the first and second influence stages is determined based on the period of the concrete in the marine environment. Based on the initial analysis results, the degree of impact of corrosion on concrete performance is assessed, and the assessment results are obtained; the assessment includes, but is not limited to, using cross-validation and simulation experiments. Based on the data on the degree of steel corrosion and the real-time strength change of steel in the test data, the degree of influence of corrosion on the mechanical properties of steel is predicted by using the set prediction sub-model, and the prediction results are obtained. The analysis results are obtained by combining the comprehensive evaluation results and the prediction results; Based on the test data, the flexural bearing capacity of reinforced concrete was simulated and tested using a pre-defined simulation test model, and the simulation test results were obtained. Based on test data, the reliability change prediction model is used to predict the reliability change of reinforced concrete structures and obtain the reliability change prediction results. Based on test data, combined with analysis results, simulation test results, and reliability change prediction results, the strength test results of reinforced concrete are determined; including: The initial test strength of reinforced concrete is determined based on the real-time strength change data of the reinforcing bars in the test data. Based on the analysis results, simulation test results, and reliability change prediction results, several predicted values for the strength change of reinforced concrete are determined. Several predicted intensity change values are clustered to obtain several clustering intervals; If the fluctuation value of the intensity change prediction value in the cluster interval is less than the set fluctuation threshold, the average value of the intensity change prediction value in the cluster interval is taken as the first typical intensity change prediction value; if the fluctuation value of the intensity change prediction value in the cluster interval is greater than the set fluctuation threshold, the intensity change prediction value in the cluster interval is taken 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 set is formed, and the strength test data set is used as the strength test result of reinforced concrete.
2. The method for testing the strength of reinforced concrete in a marine environment according to claim 1, characterized in that, Based on the data on the degree of steel corrosion and the real-time strength changes of the steel bars in the test data, a predefined prediction sub-model is used to predict the degree of impact of corrosion on the mechanical properties of the steel bars, and the prediction results are obtained, including: Based on the data on the degree of steel corrosion and the real-time strength change of steel in the test data, target prediction data for prediction is obtained. A generative prediction sub-model is constructed using a multilayer perceptron model; Based on the prediction sub-model and combined with the target prediction data, the degree of corrosion impact on the mechanical properties of steel bars is predicted, and the prediction results are obtained.
3. The method for testing the strength of reinforced concrete in a marine environment according to claim 2, characterized in that, Based on the data on the degree of steel corrosion and the real-time strength change of the steel bars in the test data, the target prediction data is obtained, including: Based on the steel bar corrosion degree data in the test data, obtain the rust product accumulation volume data; Based on the rust product accumulation volume data, and using a set matching and correspondence database between the rust product accumulation volume data and the steel bar mechanical property degradation degree data, the steel bar mechanical property degradation degree data is obtained. Based on real-time strength variation data of reinforcing bars, and using a set numerical simulation model for reinforcing bar corrosion, the distribution data of corrosion products and corrosion depth data of reinforcing bar surfaces at several selected target period points are output; the numerical simulation model includes, but is not limited to, finite element analysis method and boundary element analysis method; By summarizing data on the degree of degradation of the mechanical properties of steel bars, the distribution data of rust products on the surface of steel bars, and the rust depth data, target prediction data for prediction is obtained.
4. The method for testing the strength of reinforced concrete in a marine environment according to claim 1, characterized in that, Based on test data, the flexural bearing capacity of reinforced concrete was simulated and tested using a pre-defined simulation model. The simulation results were obtained, including: Based on the test data, using the established simulation test model for the flexural bearing capacity of reinforced concrete, the flexural bearing capacity of reinforced concrete was simulated and tested at different locations with different loads to obtain simulation test data; the simulation test data includes the bending moment of the reinforced concrete stress section and the stress distribution of the reinforced concrete. Based on the simulated test data, a trend line for the distribution of bending moment curvature and a stress distribution map are generated. Based on the trend line of bending moment curvature change and the stress distribution diagram, the change of the flexural bearing capacity of reinforced concrete can be obtained; The variation in the flexural bearing capacity of reinforced concrete is used as the result of the simulation test.
5. The method for testing the strength of reinforced concrete in a marine environment according to claim 1, characterized in that, Based on the internal structural change data in the test data, the reliability change of the reinforced concrete structure is predicted using a pre-defined reliability change prediction model. The reliability change prediction results are obtained, including: A reliability change prediction model is constructed based on a stochastic process degradation model. Based on the internal structural change data in the test data, the reliability change prediction model is used to predict the internal structural changes of the reinforced concrete structure and obtain the prediction results. Based on the prediction results, the reliability changes of the internal structure are analyzed according to the set reliability change analysis indicators to obtain the reliability change prediction results.
Citation Information
Patent Citations
Concrete material durability prediction method
CN118818021A